WorldWideScience

Sample records for plausible spatio-temporal model

  1. Spatio-temporal chaos : A solvable model

    NARCIS (Netherlands)

    Diks, C; Takens, F; DeGoede, J

    1997-01-01

    A solvable coupled map lattice model exhibiting spatio-temporal chaos is studied. Exact expressions are obtained for the spectra of Lyapunov exponents as a function of the model parameters. Although the model has spatio-temporal structure, the time series measured at a single lattice site are shown

  2. Spatio-temporal Data Model Based on Relational Database System

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    In this paper,the entity-relation data model for integrating spatio-temporal data is designed.In the design,spatio-temporal data can be effectively stored and spatiao-temporal analysis can be easily realized.

  3. Spatio-temporal modeling of nonlinear distributed parameter systems

    CERN Document Server

    Li, Han-Xiong

    2011-01-01

    The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s

  4. Modelling spatio-temporal interactions within the cell

    Indian Academy of Sciences (India)

    Padmini Rangamani; Ravi Iyengar

    2007-01-01

    Biological phenomena at the cellular level can be represented by various types of mathematical formulations. Such representations allow us to carry out numerical simulations that provide mechanistic insights into complex behaviours of biological systems and also generate hypotheses that can be experimentally tested. Currently, we are particularly interested in spatio-temporal representations of dynamic cellular phenomena and how such models can be used to understand biological specificity in functional responses. This review describes the capability and limitations of the approaches used to study spatio-temporal dynamics of cell signalling components.

  5. Large scale stochastic spatio-temporal modelling with PCRaster

    NARCIS (Netherlands)

    Karssenberg, D.J.; Drost, N.; Schmitz, O.; Jong, K. de; Bierkens, M.F.P.

    2013-01-01

    PCRaster is a software framework for building spatio-temporal models of land surface processes (http://www.pcraster.eu). Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations are available to model

  6. Large scale stochastic spatio-temporal modelling with PCRaster

    NARCIS (Netherlands)

    Karssenberg, D.J.; Drost, N.; Schmitz, O.; Jong, K. de; Bierkens, M.F.P.

    2013-01-01

    PCRaster is a software framework for building spatio-temporal models of land surface processes (http://www.pcraster.eu). Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations are available to model buil

  7. Modeling spatio-temporal wildfire ignition point patterns

    Science.gov (United States)

    Amanda S. Hering; Cynthia L. Bell; Marc G. Genton

    2009-01-01

    We analyze and model the structure of spatio-temporal wildfire ignitions in the St. Johns River Water Management District in northeastern Florida. Previous studies, based on the K-function and an assumption of homogeneity, have shown that wildfire events occur in clusters. We revisit this analysis based on an inhomogeneous K-...

  8. Spatial and spatio-temporal bayesian models with R - INLA

    CERN Document Server

    Blangiardo, Marta

    2015-01-01

    Dedication iiiPreface ix1 Introduction 11.1 Why spatial and spatio-temporal statistics? 11.2 Why do we use Bayesian methods for modelling spatial and spatio-temporal structures? 21.3 Why INLA? 31.4 Datasets 32 Introduction to 212.1 The language 212.2 objects 222.3 Data and session management 342.4 Packages 352.5 Programming in 362.6 Basic statistical analysis with 393 Introduction to Bayesian Methods 533.1 Bayesian Philosophy 533.2 Basic Probability Elements 573.3 Bayes Theorem 623.4 Prior and Posterior Distributions 643.5 Working with the Posterior Distribution 663.6 Choosing the Prior Distr

  9. A Mixed Land Cover Spatio-temporal Data Model Based on Object-oriented and Snapshot

    Directory of Open Access Journals (Sweden)

    LI Yinchao

    2016-07-01

    Full Text Available Spatio-temporal data model (STDM is one of the hot topics in the domains of spatio-temporal database and data analysis. There is a common view that a universal STDM is always of high complexity due to the various situation of spatio-temporal data. In this article, a mixed STDM is proposed based on object-oriented and snapshot models for modelling and analyzing landcover change (LCC. This model uses the object-oriented STDM to describe the spatio-temporal processes of land cover patches and organize their spatial and attributive properties. In the meantime, it uses the snapshot STDM to present the spatio-temporal distribution of LCC on the whole via snapshot images. The two types of models are spatially and temporally combined into a mixed version. In addition to presenting the spatio-temporal events themselves, this model could express the transformation events between different classes of spatio-temporal objects. It can be used to create database for historical data of LCC, do spatio-temporal statistics, simulation and data mining with the data. In this article, the LCC data in Heilongjiang province is used for case study to validate spatio-temporal data management and analysis abilities of mixed STDM, including creating database, spatio-temporal query, global evolution analysis and patches spatio-temporal process expression.

  10. Inverse hydrological modelling of spatio-temporal rainfall patterns

    Science.gov (United States)

    Grundmann, Jens; Hörning, Sebastian; Bárdossy, András

    2016-04-01

    Distributed hydrological models are commonly used for simulating the non-linear response of a watershed to rainfall events for addressing different hydrological properties of the landscape. Such models are driven by spatial rainfall patterns for consecutive time steps, which are normally generated from point measurements using spatial interpolation methods. However, such methods fail in reproducing the true spatio-temporal rainfall patterns especially in data scarce regions with poorly gauged catchments or for highly dynamic, small scaled rainstorms which are not well recorded by existing monitoring networks. Consequently, uncertainties are associated with poorly identified spatio-temporal rainfall distribution in distributed rainfall-runoff-modelling since the amount of rainfall received by a catchment as well as the dynamics of the runoff generation of flood waves are underestimated. For addressing these challenges a novel methodology for inverse hydrological modelling is proposed using a Markov-Chain-Monte-Carlo framework. Thereby, potential candidates of spatio-temporal rainfall patterns are generated and selected according their ability to reproduce the observed surface runoff at the catchment outlet for a given transfer function in a best way. The Methodology combines the concept of random mixing of random spatial fields with a grid-based spatial distributed rainfall runoff model. The conditional target rainfall field is obtained as a linear combination of unconditional spatial random fields. The corresponding weights of the linear combination are selected such that the spatial variability of the rainfall amounts as well as the actual observed rainfall values are reproduced. The functionality of the methodology is demonstrated on a synthetic example. Thereby, the known spatio-temporal distribution of rainfall is reproduced for a given number of point observations of rainfall and the integral catchment response at the catchment outlet for a synthetic catchment

  11. Spatio-temporal Modeling of Mosquito Distribution

    Science.gov (United States)

    Dumont, Y.; Dufourd, C.

    2011-11-01

    We consider a quasilinear parabolic system to model mosquito displacement. In order to use efficiently vector control tools, like insecticides, and mechanical control, it is necessary to provide density estimates of mosquito populations, taking into account the environment and entomological knowledges. After a brief introduction to mosquito dispersal modeling, we present some theoretical results. Then, considering a compartmental approach, we get a quasilinear system of PDEs. Using the time splitting approach and appropriate numerical methods for each operator, we construct a reliable numerical scheme. Considering vector control scenarii, we show that the environment can have a strong influence on mosquito distribution and in the efficiency of vector control tools.

  12. Large scale stochastic spatio-temporal modelling with PCRaster

    Science.gov (United States)

    Karssenberg, Derek; Drost, Niels; Schmitz, Oliver; de Jong, Kor; Bierkens, Marc F. P.

    2013-04-01

    PCRaster is a software framework for building spatio-temporal models of land surface processes (http://www.pcraster.eu). Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations are available to model builders as Python functions. The software comes with Python framework classes providing control flow for spatio-temporal modelling, Monte Carlo simulation, and data assimilation (Ensemble Kalman Filter and Particle Filter). Models are built by combining the spatial operations in these framework classes. This approach enables modellers without specialist programming experience to construct large, rather complicated models, as many technical details of modelling (e.g., data storage, solving spatial operations, data assimilation algorithms) are taken care of by the PCRaster toolbox. Exploratory modelling is supported by routines for prompt, interactive visualisation of stochastic spatio-temporal data generated by the models. The high computational requirements for stochastic spatio-temporal modelling, and an increasing demand to run models over large areas at high resolution, e.g. in global hydrological modelling, require an optimal use of available, heterogeneous computing resources by the modelling framework. Current work in the context of the eWaterCycle project is on a parallel implementation of the modelling engine, capable of running on a high-performance computing infrastructure such as clusters and supercomputers. Model runs will be distributed over multiple compute nodes and multiple processors (GPUs and CPUs). Parallelization will be done by parallel execution of Monte Carlo realizations and sub regions of the modelling domain. In our approach we use multiple levels of parallelism, improving scalability considerably. On the node level we will use OpenCL, the industry standard for low-level high performance computing kernels. To combine multiple nodes we will use

  13. Stochastic spatio-temporal modelling with PCRaster Python

    Science.gov (United States)

    Karssenberg, D.; Schmitz, O.; de Jong, K.

    2012-04-01

    PCRaster Python is a software framework for building spatio-temporal models of land surface processes (Karssenberg, Schmitz, Salamon, De Jong, & Bierkens, 2010; PCRaster, 2012). Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations, developed in C++, are available to model builders as Python functions. Users create models by combining these functions in a Python script. As construction of large iterative models is often difficult and time consuming for non-specialists in programming, the software comes with a set of Python framework classes that provide control flow for static modelling, temporal modelling, stochastic modelling using Monte Carlo simulation, and data assimilation techniques including the Ensemble Kalman filter and the Particle Filter. A framework for integrating model components with different time steps and spatial discretization is currently available as a prototype (Schmitz, de Jong, & Karssenberg, in review). The software includes routines for visualisation of stochastic spatio-temporal data for prompt, interactive, visualisation of model inputs and outputs. Visualisation techniques include animated maps, time series, probability distributions, and animated maps with exceedance probabilities. The PCRaster Python software is used by researchers from a large range of disciplines, including hydrology, ecology, sedimentology, and land use change studies. Applications include global scale hydrological modelling and error propagation in large-scale land use change models. The software runs on MS Windows and Linux operating systems, and OS X (under development).

  14. Research on Process-oriented Spatio-temporal Data Model

    Directory of Open Access Journals (Sweden)

    XUE Cunjin

    2016-02-01

    Full Text Available According to the analysis of the present status and existing problems of spatio-temporal data models developed in last 20 years,this paper proposes a process-oriented spatio-temporal data model (POSTDM,aiming at representing,organizing and storing continuity and gradual geographical entities. The dynamic geographical entities are graded and abstracted into process objects series from their intrinsic characteristics,which are process objects,process stage objects,process sequence objects and process state objects. The logical relationships among process entities are further studied and the structure of UML models and storage are also designed. In addition,through the mechanisms of continuity and gradual changes impliedly recorded by process objects,and the modes of their procedure interfaces offered by the customized ObjcetStorageTable,the POSTDM can carry out process representation,storage and dynamic analysis of continuity and gradual geographic entities. Taking a process organization and storage of marine data as an example,a prototype system (consisting of an object-relational database and a functional analysis platform is developed for validating and evaluating the model's practicability.

  15. Spatio-temporal statistical models with applications to atmospheric processes

    Energy Technology Data Exchange (ETDEWEB)

    Wikle, C.K.

    1996-12-31

    This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model.

  16. Spatio-temporal statistical models with applications to atmospheric processes

    Energy Technology Data Exchange (ETDEWEB)

    Wikle, Christopher K. [Iowa State Univ., Ames, IA (United States)

    1996-01-01

    This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model.

  17. Spatio-temporal GIS Data Model Based on Event Semantics

    Institute of Scientific and Technical Information of China (English)

    XU Zhihong; BIAN Fuling

    2003-01-01

    There are mainly four kinds of models to record and deal with historical information. By taking them as reference, the spatio-temporal model based on event semantics is proposed. In this model, according to the way for describing an event, all the information are divided into five domains. This paper describes the model by using the land parcel change in the cadastral information system, and expounds the model by using five tables corresponding to the five domains.With the aid of this model, seven examples are given on historical query,trace back and recurrence. This model can be implemented either in the extended relational database or in the object-oriented database.

  18. A Spatio-temporal Model of African Animal Trypanosomosis Risk.

    Directory of Open Access Journals (Sweden)

    Ahmadou H Dicko

    Full Text Available African animal trypanosomosis (AAT is a major constraint to sustainable development of cattle farming in sub-Saharan Africa. The habitat of the tsetse fly vector is increasingly fragmented owing to demographic pressure and shifts in climate, which leads to heterogeneous risk of cyclical transmission both in space and time. In Burkina Faso and Ghana, the most important vectors are riverine species, namely Glossina palpalis gambiensis and G. tachinoides, which are more resilient to human-induced changes than the savannah and forest species. Although many authors studied the distribution of AAT risk both in space and time, spatio-temporal models allowing predictions of it are lacking.We used datasets generated by various projects, including two baseline surveys conducted in Burkina Faso and Ghana within PATTEC (Pan African Tsetse and Trypanosomosis Eradication Campaign national initiatives. We computed the entomological inoculation rate (EIR or tsetse challenge using a range of environmental data. The tsetse apparent density and their infection rate were separately estimated and subsequently combined to derive the EIR using a "one layer-one model" approach. The estimated EIR was then projected into suitable habitat. This risk index was finally validated against data on bovine trypanosomosis. It allowed a good prediction of the parasitological status (r2 = 67%, showed a positive correlation but less predictive power with serological status (r2 = 22% aggregated at the village level but was not related to the illness status (r2 = 2%.The presented spatio-temporal model provides a fine-scale picture of the dynamics of AAT risk in sub-humid areas of West Africa. The estimated EIR was high in the proximity of rivers during the dry season and more widespread during the rainy season. The present analysis is a first step in a broader framework for an efficient risk management of climate-sensitive vector-borne diseases.

  19. Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

    KAUST Repository

    Zhang, Bohai

    2014-01-01

    Various continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset.

  20. Spatio-Temporal Behavior Analysis and Pheromone-Based Fusion Model for Big Trace Data

    National Research Council Canada - National Science Library

    Luliang Tang; Qianqian Zou; Xia Zhang; Chang Ren; Qingquan Li

    2017-01-01

    ..., and overlooking the influence of previous activities on future behaviors. We propose a Pheromone-based Fusion Model, viewing human behaviors as similar to insect foraging behaviors to model spatio-temporal recreational activity patterns, on and offline...

  1. Hierarchical network model for the analysis of human spatio-temporal information processing

    Science.gov (United States)

    Schill, Kerstin; Baier, Volker; Roehrbein, Florian; Brauer, Wilfried

    2001-06-01

    The perception of spatio-temporal pattern is a fundamental part of visual cognition. In order to understand more about the principles behind these biological processes, we are analyzing and modeling the presentation of spatio-temporal structures on different levels of abstraction. For the low- level processing of motion information we have argued for the existence of a spatio-temporal memory in early vision. The basic properties of this structure are reflected in a neural network model which is currently developed. Here we discuss major architectural features of this network which is base don Kohonens SOMs. In order to enable the representation, processing and prediction of spatio-temporal pattern on different levels of granularity and abstraction the SOMs are organized in a hierarchical manner. The model has the advantage of a 'self-teaching' learning algorithm and stored temporal information try local feedback in each computational layer. The constraints for the neural modeling and data set for training the neural network are obtained by psychophysical experiments where human subjects' abilities for dealing with spatio-temporal information is investigated.

  2. Research of Cadastral Data Modelling and Database Updating Based on Spatio-temporal Process

    Directory of Open Access Journals (Sweden)

    ZHANG Feng

    2016-02-01

    Full Text Available The core of modern cadastre management is to renew the cadastre database and keep its currentness,topology consistency and integrity.This paper analyzed the changes and their linkage of various cadastral objects in the update process.Combined object-oriented modeling technique with spatio-temporal objects' evolution express,the paper proposed a cadastral data updating model based on the spatio-temporal process according to people's thought.Change rules based on the spatio-temporal topological relations of evolution cadastral spatio-temporal objects are drafted and further more cascade updating and history back trace of cadastral features,land use and buildings are realized.This model implemented in cadastral management system-ReGIS.Achieved cascade changes are triggered by the direct driving force or perceived external events.The system records spatio-temporal objects' evolution process to facilitate the reconstruction of history,change tracking,analysis and forecasting future changes.

  3. Probabilistic Algorithm for Electromagnetic Brain Imaging with Spatio-Temporal and Forward Model Priors

    DEFF Research Database (Denmark)

    Stahlhut, Carsten; Wipf, David; Attias, Hagai T.;

    2010-01-01

    In this paper we present a novel spatio-temporal inverse method for solving the inverse M/EEG problem. The contribution is two-folded; firstly, the proposed model allows for a sparse spatial and temporal source representation of the M/EEG by applying an automatic relevance determination type prior....... The utility of a sparse spatio-temporal representation is based on the assumption that the underlying source activity is indeed sparse and smooth in time. Secondly, we seek to reduce the influence of forward model errors on the source estimates, by applying a stochastic forward model. Applying a stochastic...... forward model is motivated by the random noise contributions such as the geometry of the cortical surface and the electrode positions. Simulated data provide evidence that the spatio-temporal model leads to improved source estimates, especially at low signal-to-noise ratios, which is often the case in M/EEG....

  4. Displaced calibration of PM10 measurements using spatio-temporal models

    Directory of Open Access Journals (Sweden)

    Daniela Cocchi

    2007-12-01

    Full Text Available PM10 monitoring networks are equipped with heterogeneous samplers. Some of these samplers are known to underestimate true levels of concentrations (non-reference samplers. In this paper we propose a hierarchical spatio-temporal Bayesian model for the calibration of measurements recorded using non-reference samplers, by borrowing strength from non co-located reference sampler measurements.

  5. Design and implementation of segment oriented spatio-temporal model in urban panoramic maps

    Science.gov (United States)

    Li, Haiting; Fei, Lifan; Peng, Qingshan; Li, Yanhong

    2009-10-01

    Object-oriented spatio-temporal model is directed by human cognition that each object has what/where/when attributes. The precise and flexible structure of such models supports multi-semantics of space and time. This paper reviews current research of spatio-temporal models using object-oriented approach and proposed a new spatio-temporal model based on segmentation in order to resolve the updating problem of some special GIS system by taking advantages of object-oriented spatio-temporal model and adopting category theory. Category theory can be used as a unifying framework for specifying complex systems and it provides rules on how objects may be joined. It characterizes the segments of object through mappings between them. The segment-oriented spatio-temporal model designed for urban panoramic maps is described and implemented. We take points and polylines as objects in this model in the management of panoramic map data. For the randomness of routes which transportation vehicle adopts each time, road objects in this model are split into some segments by crossing points. The segments still remains polyline type, but the splitting makes it easier to update the panoramic data when new photos are captured. This model is capable of eliminating redundant data and accelerating data access when panoramas are unchanged. For evaluation purpose, the data types and operations are designed and implemented in PostgreSQL and the results of experiments come out to prove that this model is efficient and expedient in the application of urban panoramic maps.

  6. Application of 3D Spatio-Temporal Data Modeling, Management, and Analysis in DB4GEO

    Science.gov (United States)

    Kuper, P. V.; Breunig, M.; Al-Doori, M.; Thomsen, A.

    2016-10-01

    Many of todaýs world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given.

  7. A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets

    KAUST Repository

    Xu, Ganggang

    2015-01-01

    When spatio-temporal datasets are large, the computational burden can lead to failures in the implementation of traditional geostatistical tools. In this paper, we propose a computationally efficient Bayesian hierarchical spatio-temporal model in which the spatial dependence is approximated by a Gaussian Markov random field (GMRF) while the temporal correlation is described using a vector autoregressive model. By introducing an auxiliary lattice on the spatial region of interest, the proposed method is not only able to handle irregularly spaced observations in the spatial domain, but it is also able to bypass the missing data problem in a spatio-temporal process. Because the computational complexity of the proposed Markov chain Monte Carlo algorithm is of the order O(n) with n the total number of observations in space and time, our method can be used to handle very large spatio-temporal datasets with reasonable CPU times. The performance of the proposed model is illustrated using simulation studies and a dataset of precipitation data from the coterminous United States.

  8. A Spatio-Temporal Enhanced Metadata Model for Interdisciplinary Instant Point Observations in Smart Cities

    Directory of Open Access Journals (Sweden)

    Nengcheng Chen

    2017-02-01

    Full Text Available Due to the incomprehensive and inconsistent description of spatial and temporal information for city data observed by sensors in various fields, it is a great challenge to share the massive, multi-source and heterogeneous interdisciplinary instant point observation data resources. In this paper, a spatio-temporal enhanced metadata model for point observation data sharing was proposed. The proposed Data Meta-Model (DMM focused on the spatio-temporal characteristics and formulated a ten-tuple information description structure to provide a unified and spatio-temporal enhanced description of the point observation data. To verify the feasibility of the point observation data sharing based on DMM, a prototype system was established, and the performance improvement of Sensor Observation Service (SOS for the instant access and insertion of point observation data was realized through the proposed MongoSOS, which is a Not Only SQL (NoSQL SOS based on the MongoDB database and has the capability of distributed storage. For example, the response time of the access and insertion for navigation and positioning data can be realized at the millisecond level. Case studies were conducted, including the gas concentrations monitoring for the gas leak emergency response and the smart city public vehicle monitoring based on BeiDou Navigation Satellite System (BDS used for recording the dynamic observation information. The results demonstrated the versatility and extensibility of the DMM, and the spatio-temporal enhanced sharing for interdisciplinary instant point observations in smart cities.

  9. Computational EEG modelling of decision making under ambiguity reveals spatio-temporal dynamics of outcome evaluation.

    Science.gov (United States)

    Jollans, Lee; Whelan, Robert; Venables, Louise; Turnbull, Oliver H; Cella, Matteo; Dymond, Simon

    2017-03-15

    Complex human cognition, such as decision-making under ambiguity, is reflected in dynamic spatio-temporal activity in the brain. Here, we combined event-related potentials with computational modelling of the time course of decision-making and outcome evaluation during the Iowa Gambling Task. Measures of choice probability generated using the Prospect Valence Learning Delta (PVL-Delta) model, in addition to objective trial outcomes (outcome magnitude and valence), were applied as regressors in a general linear model of the EEG signal. The resulting three-dimensional spatio-temporal characterization of task-related neural dynamics demonstrated that outcome valence, outcome magnitude, and PVL-Delta choice probability were expressed in distinctly separate event related potentials. Our findings showed that the P3 component was associated with an experience-based measure of outcome expectancy. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. A dynamic nonstationary spatio-temporal model for short term prediction of precipitation

    OpenAIRE

    Sigrist, Fabio; Künsch, Hans R.; Stahel, Werner A.

    2011-01-01

    Precipitation is a complex physical process that varies in space and time. Predictions and interpolations at unobserved times and/or locations help to solve important problems in many areas. In this paper, we present a hierarchical Bayesian model for spatio-temporal data and apply it to obtain short term predictions of rainfall. The model incorporates physical knowledge about the underlying processes that determine rainfall, such as advection, diffusion and convection. It...

  11. A hierarchical Bayesian spatio-temporal model for extreme precipitation events

    KAUST Repository

    Ghosh, Souparno

    2011-03-01

    We propose a new approach to model a sequence of spatially distributed time series of extreme values. Unlike common practice, we incorporate spatial dependence directly in the likelihood and allow the temporal component to be captured at the second level of hierarchy. Inferences about the parameters and spatio-temporal predictions are obtained via MCMC technique. The model is fitted to a gridded precipitation data set collected over 99 years across the continental U.S. © 2010 John Wiley & Sons, Ltd..

  12. Travel cost inference from sparse, spatio-temporally correlated time series using markov models

    DEFF Research Database (Denmark)

    Yang, B.; Guo, C.; Jensen, C.S.

    2013-01-01

    of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each...... road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending...... with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies...

  13. A model for optimizing file access patterns using spatio-temporal parallelism

    Energy Technology Data Exchange (ETDEWEB)

    Boonthanome, Nouanesengsy [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Patchett, John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Geveci, Berk [Kitware Inc., Clifton Park, NY (United States); Ahrens, James [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Bauer, Andy [Kitware Inc., Clifton Park, NY (United States); Chaudhary, Aashish [Kitware Inc., Clifton Park, NY (United States); Miller, Ross G. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Shipman, Galen M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2013-01-01

    For many years now, I/O read time has been recognized as the primary bottleneck for parallel visualization and analysis of large-scale data. In this paper, we introduce a model that can estimate the read time for a file stored in a parallel filesystem when given the file access pattern. Read times ultimately depend on how the file is stored and the access pattern used to read the file. The file access pattern will be dictated by the type of parallel decomposition used. We employ spatio-temporal parallelism, which combines both spatial and temporal parallelism, to provide greater flexibility to possible file access patterns. Using our model, we were able to configure the spatio-temporal parallelism to design optimized read access patterns that resulted in a speedup factor of approximately 400 over traditional file access patterns.

  14. TRAVELLING FRONT SOLUTIONS IN A DIFFUSIVE VECTOR DISEASE MODEL WITH SPATIO-TEMPORAL DELAY

    Institute of Scientific and Technical Information of China (English)

    2012-01-01

    This paper is concerned with travelling front solutions to a vector disease model with a spatio-temporal delay incorporated as an integral convolution over all the past time up to now and the whole one-dimensional spatial domain R.When the delay kernel is assumed to be the strong generic kernel,using the linear chain techniques and the geometric singular perturbation theory,the existence of travelling front solutions is shown for small delay.

  15. Spatio-Temporal Change Modeling of Lulc: a Semantic Kriging Approach

    Science.gov (United States)

    Bhattacharjee, S.; Ghosh, S. K.

    2015-07-01

    Spatio-temporal land-use/ land-cover (LULC) change modeling is important to forecast the future LULC distribution, which may facilitate natural resource management, urban planning, etc. The spatio-temporal change in LULC trend often exhibits non-linear behavior, due to various dynamic factors, such as, human intervention (e.g., urbanization), environmental factors, etc. Hence, proper forecasting of LULC distribution should involve the study and trend modeling of historical data. Existing literatures have reported that the meteorological attributes (e.g., NDVI, LST, MSI), are semantically related to the terrain. Being influenced by the terrestrial dynamics, the temporal changes of these attributes depend on the LULC properties. Hence, incorporating meteorological knowledge into the temporal prediction process may help in developing an accurate forecasting model. This work attempts to study the change in inter-annual LULC pattern and the distribution of different meteorological attributes of a region in Kolkata (a metropolitan city in India) during the years 2000-2010 and forecast the future spread of LULC using semantic kriging (SemK) approach. A new variant of time-series SemK is proposed, namely Rev-SemKts to capture the multivariate semantic associations between different attributes. From empirical analysis, it may be observed that the augmentation of semantic knowledge in spatio-temporal modeling of meteorological attributes facilitate more precise forecasting of LULC pattern.

  16. Constructing a raster-based spatio-temporal hierarchical data model for marine fisheries application

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Marine information has been increasing quickly. The traditional database technologies have disadvantages in manipulating large amounts of marine information which relates to the position in 3-D with the time. Recently, greater emphasis has been placed on GIS (geographical information system)to deal with the marine information. The GIS has shown great success for terrestrial applications in the last decades, but its use in marine fields has been far more restricted. One of the main reasons is that most of the GIS systems or their data models are designed for land applications. They cannot do well with the nature of the marine environment and for the marine information. And this becomes a fundamental challenge to the traditional GIS and its data structure. This work designed a data model,the raster-based spatio-temporal hierarchical data model (RSHDM), for the marine information system, or for the knowledge discovery from spatio-temporal data, which bases itself on the nature of the marine data and overcomes the shortages of the current spatio-temporal models when they are used in the field. As an experiment, the marine fishery data warehouse (FDW) for marine fishery management was set up, which was based on the RSHDM. The experiment proved that the RSHDM can do well with the data and can extract easily the aggregations that the management needs at different levels.

  17. Spatial and spatio-temporal models with R-INLA

    OpenAIRE

    Blangiardo, M; Cameletti, M.; Baio, G; H. Rue

    2013-01-01

    During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint.Recently the use of Gaussian random fields has become increasingly popular in epidemiology as ver...

  18. Spatio-temporal modeling for real-time ozone forecasting.

    Science.gov (United States)

    Paci, Lucia; Gelfand, Alan E; Holland, David M

    2013-05-01

    The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts.

  19. Spatio-temporal Background Models for Outdoor Surveillance

    Directory of Open Access Journals (Sweden)

    Pless Robert

    2005-01-01

    Full Text Available Video surveillance in outdoor areas is hampered by consistent background motion which defeats systems that use motion to identify intruders. While algorithms exist for masking out regions with motion, a better approach is to develop a statistical model of the typical dynamic video appearance. This allows the detection of potential intruders even in front of trees and grass waving in the wind, waves across a lake, or cars moving past. In this paper we present a general framework for the identification of anomalies in video, and a comparison of statistical models that characterize the local video dynamics at each pixel neighborhood. A real-time implementation of these algorithms runs on an 800 MHz laptop, and we present qualitative results in many application domains.

  20. spTimer: Spatio-Temporal Bayesian Modeling Using R

    Directory of Open Access Journals (Sweden)

    Khandoker Shuvo Bakar

    2015-02-01

    Full Text Available Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power. Implementation of these methods using the Markov chain Monte Carlo (MCMC computational techniques, however, requires development of problem-specific and user-written computer code, possibly in a low-level language. This programming requirement is hindering the widespread use of the Bayesian model-based methods among practitioners and, hence there is an urgent need to develop high-level software that can analyze large data sets rich in both space and time. This paper develops the package spTimer for hierarchical Bayesian modeling of stylized environmental space-time monitoring data as a contributed software package in the R language that is fast becoming a very popular statistical computing platform. The package is able to fit, spatially and temporally predict large amounts of space-time data using three recently developed Bayesian models. The user is given control over many options regarding covariance function selection, distance calculation, prior selection and tuning of the implemented MCMC algorithms, although suitable defaults are provided. The package has many other attractive features such as on the fly transformations and an ability to spatially predict temporally aggregated summaries on the original scale, which saves the problem of storage when using MCMC methods for large datasets. A simulation example, with more than a million observations, and a real life data example are used to validate the underlying code and to illustrate the software capabilities.

  1. Spatio-temporal Models of Lymphangiogenesis in Wound Healing.

    Science.gov (United States)

    Bianchi, Arianna; Painter, Kevin J; Sherratt, Jonathan A

    2016-09-01

    Several studies suggest that one possible cause of impaired wound healing is failed or insufficient lymphangiogenesis, that is the formation of new lymphatic capillaries. Although many mathematical models have been developed to describe the formation of blood capillaries (angiogenesis), very few have been proposed for the regeneration of the lymphatic network. Lymphangiogenesis is a markedly different process from angiogenesis, occurring at different times and in response to different chemical stimuli. Two main hypotheses have been proposed: (1) lymphatic capillaries sprout from existing interrupted ones at the edge of the wound in analogy to the blood angiogenesis case and (2) lymphatic endothelial cells first pool in the wound region following the lymph flow and then, once sufficiently populated, start to form a network. Here, we present two PDE models describing lymphangiogenesis according to these two different hypotheses. Further, we include the effect of advection due to interstitial flow and lymph flow coming from open capillaries. The variables represent different cell densities and growth factor concentrations, and where possible the parameters are estimated from biological data. The models are then solved numerically and the results are compared with the available biological literature.

  2. A general science-based framework for dynamical spatio-temporal models

    Science.gov (United States)

    Wikle, C.K.; Hooten, M.B.

    2010-01-01

    Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic

  3. [Application of Bayesian spatio-temporal modeling in describing the brucellosis infections].

    Science.gov (United States)

    Zheng, Yang; Feng, Zi-jian; Li, Xiao-song

    2011-01-01

    Based on the number of brucellosis cases reported from the national infectious diseases reporting system in Inner Mongolia from 2000 to 2007, a model was developed. Theories of spatial statistics were used, together with knowledge on infectious disease epidemiology and the frame of Bayesian statistics, before the Bayesian spatio-temporal models were respectively set. The effects of space, time, space-time and the relative covariates were also considered. These models were applied to analyze the brucellosis distribution and time trend in Inner Mongolia during 2000-2007. The results of Bayesian spatio-temporal models was expressed by mapping of the disease and compared to the conventional statistical methods. Results showed that the Bayesian models, under consideration of space-time effect and the relative covariates (deviance information criterion, DIC=2388.000), seemed to be the best way to serve the purpose. The county-level spatial correlation of brucellosis epidemics was positive and quite strong in Inner Mongolia. However, the spatial correlation varied with time and the coefficients ranged from 0.968 to 0.973, having a weakening trend during 2000-2007. Types of region and number of stock (cattle and sheep) might be related to the brucellosis epidemics, and the effect on the number of cattle and sheep changed by year. Compared to conventional statistical methods, Bayesian spatio-temporal modeling could precisely estimate the incidence relative risk and was an important tool to analyze the epidemic distribution patterns of infectious diseases and to estimate the incidence relative risk.

  4. Moving Object Trajectories Meta-Model And Spatio-Temporal Queries

    CERN Document Server

    Boulmakoul, Azedine; Lbath, Ahmed

    2012-01-01

    In this paper, a general moving object trajectories framework is put forward to allow independent applications processing trajectories data benefit from a high level of interoperability, information sharing as well as an efficient answer for a wide range of complex trajectory queries. Our proposed meta-model is based on ontology and event approach, incorporates existing presentations of trajectory and integrates new patterns like space-time path to describe activities in geographical space-time. We introduce recursive Region of Interest concepts and deal mobile objects trajectories with diverse spatio-temporal sampling protocols and different sensors available that traditional data model alone are incapable for this purpose.

  5. Spatio-temporal modelling for assessing air pollution in Santiago de Chile

    Science.gov (United States)

    Nicolis, Orietta; Camaño, Christian; Mařın, Julio C.; Sahu, Sujit K.

    2017-01-01

    In this work, we propose a space-time approach for studying the PM2.5 concentration in the city of Santiago de Chile. In particular, we apply the autoregressive hierarchical model proposed by [1] using the PM2.5 observations collected by a monitoring network as a response variable and numerical weather forecasts from the Weather Research and Forecasting (WRF) model as covariate together with spatial and temporal (periodic) components. The approach is able to provide short-term spatio-temporal predictions of PM2.5 concentrations on a fine spatial grid (at 1km × 1km horizontal resolution.)

  6. Bayesian spatio-temporal modelling of tobacco-related cancer mortality in Switzerland

    Directory of Open Access Journals (Sweden)

    Verena Jürgens

    2013-05-01

    Full Text Available Tobacco smoking is a main cause of disease in Switzerland; lung cancer being the most common cancer mortality in men and the second most common in women. Although disease-specific mortality is decreasing in men, it is steadily increasing in women. The four language regions in this country might play a role in this context as they are influenced in different ways by the cultural and social behaviour of neighbouring countries. Bayesian hierarchical spatio-temporal, negative binomial models were fitted on subgroup-specific death rates indirectly standardized by national references to explore age- and gender-specific spatio-temporal patterns of mortality due to lung cancer and other tobacco-related cancers in Switzerland for the time period 1969-2002. Differences influenced by linguistic region and life in rural or urban areas were also accounted for. Male lung cancer mortality was found to be rather homogeneous in space, whereas women were confirmed to be more affected in urban regions. Compared to the German-speaking part, female mortality was higher in the French-speaking part of the country, a result contradicting other reports of similar comparisons between France and Germany. The spatio-temporal patterns of mortality were similar for lung cancer and other tobacco-related cancers. The estimated mortality maps can support the planning in health care services and evaluation of a national tobacco control programme. Better understanding of spatial and temporal variation of cancer of the lung and other tobacco-related cancers may help in allocating resources for more effective screening, diagnosis and therapy. The methodology can be applied to similar studies in other settings.

  7. Spatio-temporal correlations in models of collective motion ruled by different dynamical laws

    Science.gov (United States)

    Cavagna, Andrea; Conti, Daniele; Giardina, Irene; Grigera, Tomas S.; Melillo, Stefania; Viale, Massimiliano

    2016-12-01

    Information transfer is an essential factor in determining the robustness of biological systems with distributed control. The most direct way to study the mechanisms ruling information transfer is to experimentally observe the propagation across the system of a signal triggered by some perturbation. However, this method may be inefficient for experiments in the field, as the possibilities to perturb the system are limited and empirical observations must rely on natural events. An alternative approach is to use spatio-temporal correlations to probe the information transfer mechanism directly from the spontaneous fluctuations of the system, without the need to have an actual propagating signal on record. Here we test this method on models of collective behaviour in their deeply ordered phase by using ground truth data provided by numerical simulations in three dimensions. We compare two models characterized by very different dynamical equations and information transfer mechanisms: the classic Vicsek model, describing an overdamped noninertial dynamics and the inertial spin model, characterized by an underdamped inertial dynamics. By using dynamic finite-size scaling, we show that spatio-temporal correlations are able to distinguish unambiguously the diffusive information transfer mechanism of the Vicsek model from the linear mechanism of the inertial spin model.

  8. Spatio-Temporal Risk Assessment Process Modeling for Urban Hazard Events in Sensor Web Environment

    Directory of Open Access Journals (Sweden)

    Wei Wang

    2016-11-01

    Full Text Available Immediate risk assessment and analysis are crucial in managing urban hazard events (UHEs. However, it is a challenge to develop an immediate risk assessment process (RAP that can integrate distributed sensors and data to determine the uncertain model parameters of facilities, environments, and populations. To solve this problem, this paper proposes a RAP modeling method within a unified spatio-temporal framework and forms a 10-tuple process information description structure based on a Meta-Object Facility (MOF. A RAP is designed as an abstract RAP chain that collects urban information resources and performs immediate risk assessments. In addition, we propose a prototype system known as Risk Assessment Process Management (RAPM to achieve the functions of RAP modeling, management, execution and visualization. An urban gas leakage event is simulated as an example in which individual risk and social risk are used to illustrate the applicability of the RAP modeling method based on the 10-tuple metadata framework. The experimental results show that the proposed RAP immediately assesses risk by the aggregation of urban sensors, data, and model resources. Moreover, an extension mechanism is introduced in the spatio-temporal RAP modeling method to assess risk and to provide decision-making support for different UHEs.

  9. Structured Spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires

    DEFF Research Database (Denmark)

    Møller, Jesper; Diaz-Avalos, Carlos

    2010-01-01

    Spatio-temporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shot-noise process, are considered. Such models are flexible and tractable...

  10. Structured spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires

    DEFF Research Database (Denmark)

    Møller, Jesper; Diaz-Avalos, Carlos

    Spatio-temporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shot-noise process, are considered. Such models are flexible and tractable...

  11. Un modelo multidimensional conceptual espacio-temporal A conceptual spatio-temporal multidimensional model

    Directory of Open Access Journals (Sweden)

    Francisco Moreno

    2010-07-01

    Full Text Available Hoy, gracias a los sistemas de posicionamiento global y dispositivos móviles equipados con sensores de rastreo, se puede recopilar una gran cantidad de datos sobre objetos móviles, es decir, datos espacio-temporales relacionados con el movimiento seguido por esos objetos. Por otro lado, las bodegas de datos, usualmente modeladas mediante una vista multidimensional de los datos, son bases de datos especializadas para soportar la toma de decisiones. Desafortunadamente, las bodegas de datos convencionales están principalmente orientadas al manejo de datos alfanuméricos. En este artículo, se incorporan elementos temporales a un modelo multidimensional conceptual espacial dando origen a un modelo multidimensional conceptual espacio-temporal. La propuesta se ilustra con un caso de estudio relacionado con la migración de animalesToday, thanks to global positioning systems technologies and mobile devices equipped with tracking sensors, and a lot of data about moving objects can be collected, e.g., spatio-temporal data related to the movement followed by objects. On the other hand, data warehouses, usually modeled using a multidimensional view of data, are specialized databases to support the decision-making process. Unfortunately, conventional data warehouses are mainly oriented to manage alphanumeric data. In this article, we incorporate temporal elements to a conceptual spatial multidimensional model resulting in a spatio-temporal multidimensional model. We illustrate our proposal with a case study related to animal migration.

  12. Robust seismicity forecasting based on Bayesian parameter estimation for epidemiological spatio-temporal aftershock clustering models.

    Science.gov (United States)

    Ebrahimian, Hossein; Jalayer, Fatemeh

    2017-08-29

    In the immediate aftermath of a strong earthquake and in the presence of an ongoing aftershock sequence, scientific advisories in terms of seismicity forecasts play quite a crucial role in emergency decision-making and risk mitigation. Epidemic Type Aftershock Sequence (ETAS) models are frequently used for forecasting the spatio-temporal evolution of seismicity in the short-term. We propose robust forecasting of seismicity based on ETAS model, by exploiting the link between Bayesian inference and Markov Chain Monte Carlo Simulation. The methodology considers the uncertainty not only in the model parameters, conditioned on the available catalogue of events occurred before the forecasting interval, but also the uncertainty in the sequence of events that are going to happen during the forecasting interval. We demonstrate the methodology by retrospective early forecasting of seismicity associated with the 2016 Amatrice seismic sequence activities in central Italy. We provide robust spatio-temporal short-term seismicity forecasts with various time intervals in the first few days elapsed after each of the three main events within the sequence, which can predict the seismicity within plus/minus two standard deviations from the mean estimate within the few hours elapsed after the main event.

  13. A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data

    Directory of Open Access Journals (Sweden)

    Lei Lin

    2016-05-01

    Full Text Available Fire detection based on multi-temporal remote sensing data is an active research field. However, multi-temporal detection processes are usually complicated because of the spatial and temporal variability of remote sensing imagery. This paper presents a spatio-temporal model (STM based forest fire detection method that uses multiple images of the inspected scene. In STM, the strong correlation between an inspected pixel and its neighboring pixels is considered, which can mitigate adverse impacts of spatial heterogeneity on background intensity predictions. The integration of spatial contextual information and temporal information makes it a more robust model for anomaly detection. The proposed algorithm was applied to a forest fire in 2009 in the Yinanhe forest, Heilongjiang province, China, using two-month HJ-1B infrared camera sensor (IRS images. A comparison of detection results demonstrate that the proposed algorithm described in this paper are useful to represent the spatio-temporal information contained in multi-temporal remotely sensed data, and the STM detection method can be used to obtain a higher detection accuracy than the optimized contextual algorithm.

  14. Challenges for modelling spatio-temporal variations of malaria risk in Malawi

    Science.gov (United States)

    Lowe, R.; Chirombo, J.; Tompkins, A. M.

    2012-04-01

    Malaria is the leading cause of morbidity and mortality in Malawi with more than 6 million episodes reported each year. Malaria poses a huge economic burden to Malawi in terms of the direct cost of treating malaria patients and also indirect costs resulting from workdays lost in agriculture and industry and absenteeism from school. Malawi implements malaria control activities within the Roll Back Malaria framework, with the objective to provide those most at risk (i.e. children under five years, pregnant woman and individuals with suppressed immune systems) access to personal and community protective measures. However, at present there is no mechanism by which to target the most 'at risk' populations ahead of an impending epidemic. Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the mosquito and the availability of breeding sites, but also socio-economic conditions such as levels of urbanisation, poverty and education, which influence human vulnerability and vector habitat. The many potential drivers of malaria, both extrinsic, such as climate, and intrinsic, such as population immunity are often difficult to disentangle. This presents a challenge for modelling of malaria risk in space and time. Using an age-stratified spatio-temporal dataset of malaria cases at the district level from July 2004 - June 2011, we use a spatio-temporal modelling framework to model variations in malaria risk in Malawi. Climatic and topographic variations are accounted for using an interpolation method to relate gridded products to administrative districts. District level data is tested in the model to account for confounding factors, including the proportion of the population living in urban areas; residing in traditional housing; with no toilet facilities; who do not attend school, etc, the number of health facilities per population and yearly estimates of insecticide-treated mosquito net distribution. In order to account for

  15. Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Tansu Filik

    2016-03-01

    Full Text Available In this paper, the spatio-temporal (multi-channel linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressive moving average (MARMA models are formulated in matrix form and efficient linear prediction coefficient estimation techniques are first used and revised. It is shown in detail how to apply these MARMA models to the spatially distributed wind speed measurements. The proposed MARMA models are tested using real wind speed measurements which are collected from the five stations around Canakkale region of Turkey. According to the test results, considerable improvements are observed over the well known persistence, autoregressive (AR and multi-channel/vector autoregressive (VAR models. It is also shown that the model can predict wind speed very fast (in milliseconds which is suitable for the immediate short-term forecasting.

  16. Modelling individual routines and spatio-temporal trajectories in human mobility

    CERN Document Server

    Pappalardo, Luca

    2016-01-01

    Human mobility modelling is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad hoc networks or for what-if analysis and simulation in urban ecosystems. Current generative models generally fail in accurately reproducing the individuals' recurrent daily schedules and at the same time in accounting for the possibility that individuals may break the routine and modify their habits during periods of unpredictability of variable duration. In this article we present DITRAS (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility in a realistic way. DITRAS operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. The mobility diary is constructed by a Markov model which captures the tendency of individuals to follow or break their routine. The mobility trajectory is produced by a model based on the concept of preferential exploration and pref...

  17. MODELLING AND SIMULATION OF A NEUROPHYSIOLOGICAL EXPERIMENT BY SPATIO-TEMPORAL POINT PROCESSES

    Directory of Open Access Journals (Sweden)

    Viktor Beneš

    2011-05-01

    Full Text Available We present a stochastic model of an experimentmonitoring the spiking activity of a place cell of hippocampus of an experimental animal moving in an arena. Doubly stochastic spatio-temporal point process is used to model and quantify overdispersion. Stochastic intensity is modelled by a Lévy based random field while the animal path is simplified to a discrete random walk. In a simulation study first a method suggested previously is used. Then it is shown that a solution of the filtering problem yields the desired inference to the random intensity. Two approaches are suggested and the new one based on finite point process density is applied. Using Markov chain Monte Carlo we obtain numerical results from the simulated model. The methodology is discussed.

  18. Travel cost inference from sparse, spatio-temporally correlated time series using markov models

    DEFF Research Database (Denmark)

    Yang, B.; Guo, C.; Jensen, C.S.

    2013-01-01

    of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each......The monitoring of a system can yield a set of measurements that can be modeled as a collection of time series. These time series are often sparse, due to missing measurements, and spatiotemporally correlated, meaning that spatially close time series exhibit temporal correlation. The analysis...... road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending...

  19. A software framework for construction of process-based stochastic spatio-temporal models and data assimilation

    NARCIS (Netherlands)

    Karssenberg, D.J.; Schmitz, O.; Salamon, P.; Jong, K. de; Bierkens, M.F.P.

    2010-01-01

    Process-based spatio-temporal models simulate changes over time using equations that represent real world processes. They are widely applied in geography and earth science. Software implementation of the model itself and integrating model results with observations through data assimilation are two i

  20. Spatio-temporal correlations in models of collective motion ruled by different dynamical laws

    CERN Document Server

    Cavagna, Andrea; Giardina, Irene; Grigera, Tomas S; Melillo, Stefania; Viale, Massimiliano

    2016-01-01

    Information transfer is an essential factor in determining the robustness of collective behaviour in biological systems with distributed control. The most direct way to study the information transfer mechanisms is to experimentally detect the propagation across the system of a signal triggered by some perturbation. However, for field experiments this method is inefficient, as the possibilities of the observer to perturb the group are limited and empirical observations must rely on rare natural perturbations. An alternative way is to use spatio-temporal correlations to assess the information transfer mechanism directly from the spontaneous fluctuations of the system, without the need to have an actual propagating signal on record. We test the approach on ground truth data provided by numerical simulations in three dimensions of two models of collective behaviour characterized by very different dynamical equations and information transfer mechanisms: the classic Vicsek model, describing an overdamped noninertia...

  1. Incorporating social contact data in spatio-temporal models for infectious disease spread

    CERN Document Server

    Meyer, Sebastian

    2015-01-01

    Routine public health surveillance of notifiable infectious diseases gives rise to weekly counts of reported cases - possibly stratified by region and/or age group. A well-established approach to the statistical analysis of such surveillance data are endemic-epidemic time-series models. The temporal dependence inherent to communicable diseases is thereby taken into account by an observation-driven formulation conditioning on past counts. Additional spatial dynamics in areal-level counts are largely driven by human travel and can be captured by power-law weights based on the order of adjacency. However, social contacts are highly assortative also with respect to age. For example, characteristic pathways of directly transmitted pathogens are linked to childcare facilities, schools and nursing homes. We therefore investigate how a spatio-temporal endemic-epidemic model can be extended to take social contact data into account. The approach is illustrated in a case study on norovirus gastroenteritis in Berlin, 201...

  2. A customized light sheet microscope to measure spatio-temporal protein dynamics in small model organisms.

    Directory of Open Access Journals (Sweden)

    Matthias Rieckher

    Full Text Available We describe a customizable and cost-effective light sheet microscopy (LSM platform for rapid three-dimensional imaging of protein dynamics in small model organisms. The system is designed for high acquisition speeds and enables extended time-lapse in vivo experiments when using fluorescently labeled specimens. We demonstrate the capability of the setup to monitor gene expression and protein localization during ageing and upon starvation stress in longitudinal studies in individual or small groups of adult Caenorhabditis elegans nematodes. The system is equipped to readily perform fluorescence recovery after photobleaching (FRAP, which allows monitoring protein recovery and distribution under low photobleaching conditions. Our imaging platform is designed to easily switch between light sheet microscopy and optical projection tomography (OPT modalities. The setup permits monitoring of spatio-temporal expression and localization of ageing biomarkers of subcellular size and can be conveniently adapted to image a wide range of small model organisms and tissue samples.

  3. Mechanisms for spatio-temporal pattern formation in highway traffic models.

    Science.gov (United States)

    Wilson, R Eddie

    2008-06-13

    A key qualitative requirement for highway traffic models is the ability to replicate a type of traffic jam popularly referred to as a phantom jam, shock wave or stop-and-go wave. Despite over 50 years of modelling, the precise mechanisms for the generation and propagation of stop-and-go waves and the associated spatio-temporal patterns are in dispute. However, the increasing availability of empirical datasets, such as those collected from motorway incident detection and automatic signalling system (MIDAS) inductance loops in the UK or the next-generation simulation trajectory data (NGSIM) project in the USA, means that we can expect to resolve these questions definitively in the next few years. This paper will survey the essence of the competing explanations of highway traffic pattern formation and introduce and analyse a new mechanism, based on dynamical systems theory and bistability, which can help resolve the conflict.

  4. Spatio-Temporal Modeling of Seismic Provinces of Iran Using DBSCAN Algorithm

    Science.gov (United States)

    Kazemi-Beydokhti, Mohammad; Ali Abbaspour, Rahim; Mojarab, Masoud

    2017-05-01

    One of the most important issues in the field of engineering seismology is identification and classification of seismic provinces. Due to the importance of this issue in Iran, various studies have been conducted using different methods such as expert judgment, computational methods, data-driven methods, and smart methods. The purpose of the present research is to develop a spatio-temporal seismic model for Iran using robust and objective clustering tools. In the present study, one of the most powerful clustering methods, DBSCAN, is selected based on its ability to analyze huge amounts of data. The DBSCAN algorithm, which acts based on the density of seismic events, is capable of detecting arbitrarily shaped clusters. The seismic datasets used in this study, which were obtained from the seismic catalog of Iran from 1900 to 2015, have been divided into three window periods including 2- , 5- , and 10-year intervals. Afterward, different seismicity patterns for each period are obtained by applying DBSCAN algorithm. Then, those exhibited high agreements in terms of shapes and locations of clusters with the other models are determined. Ultimately, by considering these models and using expert judgments, a unified spatio-temporal model is presented. The results reveal meaningful information in different parts of Iran especially in Zagros, Alborz, and Azerbaijan zones and are generally in good agreement with previous studies. Moreover, the results emphasize that a seismic model, which is obtained based on considering seismogenic zones in various time periods along with the application of density-based clustering tools, will produce reliable results.

  5. Spatio-Temporal Modeling of Seismic Provinces of Iran Using DBSCAN Algorithm

    Science.gov (United States)

    Kazemi-Beydokhti, Mohammad; Ali Abbaspour, Rahim; Mojarab, Masoud

    2017-03-01

    One of the most important issues in the field of engineering seismology is identification and classification of seismic provinces. Due to the importance of this issue in Iran, various studies have been conducted using different methods such as expert judgment, computational methods, data-driven methods, and smart methods. The purpose of the present research is to develop a spatio-temporal seismic model for Iran using robust and objective clustering tools. In the present study, one of the most powerful clustering methods, DBSCAN, is selected based on its ability to analyze huge amounts of data. The DBSCAN algorithm, which acts based on the density of seismic events, is capable of detecting arbitrarily shaped clusters. The seismic datasets used in this study, which were obtained from the seismic catalog of Iran from 1900 to 2015, have been divided into three window periods including 2- , 5- , and 10-year intervals. Afterward, different seismicity patterns for each period are obtained by applying DBSCAN algorithm. Then, those exhibited high agreements in terms of shapes and locations of clusters with the other models are determined. Ultimately, by considering these models and using expert judgments, a unified spatio-temporal model is presented. The results reveal meaningful information in different parts of Iran especially in Zagros, Alborz, and Azerbaijan zones and are generally in good agreement with previous studies. Moreover, the results emphasize that a seismic model, which is obtained based on considering seismogenic zones in various time periods along with the application of density-based clustering tools, will produce reliable results.

  6. Spatio-temporal modelling of foot-and-mouth disease outbreaks.

    Science.gov (United States)

    Malesios, C; Demiris, N; Kostoulas, P; Dadousis, K; Koutroumanidis, T; Abas, Z

    2016-09-01

    We present and analyse data collected during a severe epidemic of foot-and-mouth disease (FMD) that occurred between July and September 2000 in a region of northeastern Greece with strategic importance since it represents the southeastern border of Europe and Asia. We implement generic Bayesian methodology, which offers flexibility in the ability to fit several realistically complex models that simultaneously capture the presence of 'excess' zeros, the spatio-temporal dependence of the cases, assesses the impact of environmental noise and controls for multicollinearity issues. Our findings suggest that the epidemic was mostly driven by the size and the animal type of each farm as well as the distance between farms while environmental and other endemic factors were not important during this outbreak. Analyses of this kind may prove useful to informing decisions related to optimal control measures for potential future FMD outbreaks as well as other acute epidemics such as FMD.

  7. On the spatio-temporal analysis of hydrological droughts from global hydrological models

    Directory of Open Access Journals (Sweden)

    G. A. Corzo Perez

    2011-09-01

    Full Text Available The recent concerns for world-wide extreme events related to climate change have motivated the development of large scale models that simulate the global water cycle. In this context, analysis of hydrological extremes is important and requires the adaptation of identification methods used for river basin models. This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are classified as non-contiguous and contiguous drought area analyses (i.e. NCDA and CDA. The NCDA presents time series of percentages of areas in drought at the global scale and for pre-defined regions of known hydroclimatology. The CDA is introduced as a complementary method that generates information on the spatial coherence of drought events at the global scale. Spatial drought events are found through CDA by clustering patterns (contiguous areas. In this study the global hydrological model WaterGAP was used to illustrate the methodology development. Global gridded time series of subsurface runoff (resolution 0.5° simulated with the WaterGAP model from land points were used. The NCDA and CDA were developed to identify drought events in runoff. The percentages of area in drought calculated with both methods show complementary information on the spatial and temporal events for the last decades of the 20th century. The NCDA provides relevant information on the average number of droughts, duration and severity (deficit volume for pre-defined regions (globe, 2 selected hydroclimatic regions. Additionally, the CDA provides information on the number of spatially linked areas in drought, maximum spatial event and their geographic location on the globe. Some results capture the overall spatio-temporal drought extremes over the last decades of the 20th century. Events like the El Niño Southern Oscillation (ENSO in South America and

  8. A Spatio-Temporal Model for Forest Fire Detection Using MODIS Data

    Science.gov (United States)

    Li, Jing; Gong, Adu; Chen, Yanling; Wang, Jingmei

    2017-04-01

    Contextual algorithm and Muti-temporal analysis are currently the most widely used in fire detection based on remote sensing technology. However, muti-temporal analysis ignores the correlation between the inspected pixel and its neighboring pixels (spatial heterogeneity) (Equation (1)). Contextual algorithm only focuses on a single scene, and ignores the internal differences of the background pixels, which increases the commission error. Due to the muti-temporal analysis and contextual algorithm are used for different processes of fire detection, the combination between them will increase the accuracy for fire detection. BTti1- BT-it2- BT-it3 BTto1 = αBT ot2= βBT ot3=... (1) Where BTtin is the bright temperature (BT) of the valid neighboring pixel i of the inspected pixel o at time tn ,(i=1,2,…,N), N is the number of the valid neighboring pixels(Which depends on the condition of context), BT otn is the BT of o at time tn . In this paper, We coupled the muti-temporal analysis with contextual algorithm and proposed a region-adaptive spatio-temporal model for forest fire detection: (1) Pre-processing: Cloud, water, potential background fires and bright fire-free targets masking (refer to the context method); (2) Adjust the threshold for identifying potential fire-points for different study areas (Equation 2); (3) The spatial relationship of BT between the inspected pixels and its neighboring pixels in current time is build based on the spatial relationship of BT between them in the multiple previous images, and the BT of the inspected pixels is estimated based on the present spatial relationship and the BT of its neighboring pixels and using inverse distance weighted method (Equation 3). (4) The predicted BT value of the inspected pixel at a certain time is the weighted sum of the value obtained by (3) and the real BT value of the inspected pixel at the previous time (Equation 4); (5) Relative fire pixels judgment(refer to the context method). BT4 > BT4S,DBT > 10k

  9. 基于时空克里格的土壤重金属时空建模与预测%Spatio-temporal modeling and prediction of soil heavy metal based on spatio-temporal Kriging

    Institute of Scientific and Technical Information of China (English)

    杨勇; 梅杨; 张楚天; 张若兮; 廖祥森

    2014-01-01

    Soil plays a very important role in the food chain, and hence is a very important pathway through which humans come into contact with most pollutants. Therefore, there is considerable interest in the best way to monitor the quality of the soil to ensure that it is managed sustainably. However, when the need to monitor the status of soil heavy metals for one area continuously occurs, the sampling and analysis procedures are expensive and time-consuming. Therefore, space-time interpolation is necessary because we can use previous soil sampling points to predict present spatial distribution with fewer soil samples. In this paper, spatio-temporal kriging was utilized to model and predict the spatio-temporal distribution of soil heavy metals. The main objectives of this study were 1) to explore the methods of obtaining an experimental spatio-temporal semivariogram; 2) to fit models for experimental spatio-temporal semivariogram;3) to perform the algorithm of spatio-temporal kriging interpolation; 4) to evaluate the accuracy and uncertainty of spatio-temporal kriging under the conditions of different neighborhoods; and 5) to predict the spatio-temporal distribution of soil heavy metals of a study area using spatio-temporal kriging. The study area was east of Qingshan district, Wuhan city, Hubei province, China. To monitor the degree of soil contamination, we collected topsoil samples from the study area every year from 2011 to 2014. The number of soil samples from 2011, 2012, 2013, and 2014 were 45, 48, 55, and 48, respectively. The concentrations of cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) were analyzed and used as experimental data. For Cd, Cu, and Zn, soil concentrations showed a constant increase from 2010 to 2014. However, the concentrations of Pb showed an increase from 2010 to 2013, followed by a small decrease in 2014. The results of K-S tests showed that Cd, Pb, and Cu did not follow a normal distribution, however, Zn followed a normal distribution

  10. Stochastic Spatio-Temporal Models for Analysing NDVI Distribution of GIMMS NDVI3g Images

    Directory of Open Access Journals (Sweden)

    Ana F. Militino

    2017-01-01

    Full Text Available The normalized difference vegetation index (NDVI is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10 as auxiliary information. The model will be estimated with the Expectation-Maximization (EM algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions.

  11. Zebrafish: an exciting model for investigating the spatio-temporal pattern of enteric nervous system development.

    LENUS (Irish Health Repository)

    Doodnath, Reshma

    2012-02-01

    AIM: Recently, the zebrafish (Danio rerio) has been shown to be an excellent model for human paediatric research. Advantages over other models include its small size, externally visually accessible development and ease of experimental manipulation. The enteric nervous system (ENS) consists of neurons and enteric glia. Glial cells permit cell bodies and processes of neurons to be arranged and maintained in a proper spatial arrangement, and are essential in the maintenance of basic physiological functions of neurons. Glial fibrillary acidic protein (GFAP) is expressed in astrocytes, but also expressed outside of the central nervous system. The aim of this study was to investigate the spatio-temporal pattern of GFAP expression in developing zebrafish ENS from 24 h post-fertilization (hpf), using transgenic fish that express green fluorescent protein (GFP). METHODS: Zebrafish embryos were collected from transgenic GFP Tg(GFAP:GFP)(mi2001) adult zebrafish from 24 to 120 hpf, fixed and processed for whole mount immunohistochemistry. Antibodies to Phox2b were used to identify enteric neurons. Specimens were mounted on slides and imaging was performed using a fluorescent laser confocal microscope. RESULTS: GFAP:GFP labelling outside the spinal cord was identified in embryos from 48 hpf. The patterning was intracellular and consisted of elongated profiles that appeared to migrate away from the spinal cord into the periphery. At 72 and 96 hpf, GFAP:GFP was expressed dorsally and ventrally to the intestinal tract. At 120 hpf, GFAP:GFP was expressed throughout the intestinal wall, and clusters of enteric neurons were identified using Phox2b immunofluorescence along the pathway of GFAP:GFP positive processes, indicative of a migratory pathway of ENS precursors from the spinal cord into the intestine. CONCLUSION: The pattern of migration of GFAP:GFP expressing cells outside the spinal cord suggests an organized, early developing migratory pathway to the ENS. This shows for the

  12. Spatio-temporal dynamic climate model for Neoleucinodes elegantalis using CLIMEX

    Science.gov (United States)

    da Silva, Ricardo Siqueira; Kumar, Lalit; Shabani, Farzin; da Silva, Ezio Marques; da Silva Galdino, Tarcisio Visintin; Picanço, Marcelo Coutinho

    2016-10-01

    Seasonal variations are important components in understanding the ecology of insect population of crops. Ecological studies through modeling may be a useful tool for enhancing knowledge of seasonal patterns of insects on field crops as well as seasonal patterns of favorable climatic conditions for species. Recently CLIMEX, a semi-mechanistic niche model, was upgraded and enhanced to consider spatio-temporal dynamics of climate suitability through time. In this study, attempts were made to determine monthly variations of climate suitability for Neoleucinodes elegantalis (Guenée) (Lepidoptera: Crambidae) in five commercial tomato crop localities through the latest version of CLIMEX. We observed that N. elegantalis displays seasonality with increased abundance in tomato crops during summer and autumn, corresponding to the first 6 months of the year in monitored areas in this study. Our model demonstrated a strong accord between the CLIMEX weekly growth index (GIw) and the density of N. elegantalis for this period, thus indicating a greater confidence in our model results. Our model shows a seasonal variability of climatic suitability for N. elegantalis and provides useful information for initiating methods for timely management, such as sampling strategies and control, during periods of high degree of suitability for N. elegantalis. In this study, we ensure that the simulation results are valid through our verification using field data.

  13. A spatio-temporal model to describe the spread of Salmonella within a laying flock.

    Science.gov (United States)

    Zongo, Pascal; Viet, Anne-France; Magal, Pierre; Beaumont, Catherine

    2010-12-21

    Salmonella is one of the major sources of toxi-infection in humans, most often because of consumption of poultry products. The main reason for this association is the presence in hen flocks of silent carriers, i.e. animals harboring Salmonella without expressing any visible symptoms. Many prophylactic means have been developed to reduce the prevalence of Salmonella carrier-state. While none allows a total reduction of the risk, synergy could result in a drastic reduction of it. Evaluating the risk by modeling would be very useful to estimate such gain in food safety. Here, we propose an individual-based model which describes the spatio-temporal spread of Salmonella within a laying flock and takes into account the host response to bacterial infection. The model includes the individual bacterial load and the animals' ability to reduce it thanks to the immune response, i.e. maximum bacterial dose that the animals may resist without long term carriage and, when carriers, length of bacterial clearance. For model validation, we simulated the Salmonella spread under published experimental conditions. There was a good agreement between simulated and observed published data. This model will thus allow studying the effects, on the spatiotemporal distribution of the bacteria, of both mean and variability of different elements of host response.

  14. Spatio-temporal and stochastic modelling of severe acute respiratory syndrome

    Directory of Open Access Journals (Sweden)

    Poh-Chin Lai

    2013-11-01

    Full Text Available This study describes the development of a spatio-temporal disease model based on the episodes of severe acute respiratory syndrome (SARS that took place in Hong Kong in 2003. In contrast to conventional, deterministic modelling approaches, the model described here is predominantly spatial. It incorporates stochastic processing of environmental and social variables that interact in space and time to affect the patterns of disease transmission in a community. The model was validated through a comparative assessment between actual and modelled distribution of diseased locations. Our study shows that the inclusion of location-specific characteristics satisfactorily replicates the spatial dynamics of an infectious disease. The Pearson’s correlation coefficients for five trials based on 3-day aggregation of disease counts for 1-3, 4-6 and 7-9 day forecasts were 0.57- 0.95, 0.54-0.86 and 0.57-0.82, respectively, while the correlation based on 5-day aggregation for the 1-5 day forecast was 0.55- 0.94 and 0.58-0.81 for the 6-10 day forecast. The significant and strong relationship between actual results and forecast is encouraging for the potential development of an early warning system for detecting this type of disease outbreaks.

  15. An accessible method for implementing hierarchical models with spatio-temporal abundance data

    Science.gov (United States)

    Ross, Beth E.; Hooten, Melvin B.; Koons, David N.

    2012-01-01

    A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.

  16. Compressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

    KAUST Repository

    Castruccio, Stefano

    2015-04-02

    One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific data sets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a non-trivial model to a data set of one billion data points with a covariance matrix comprising of 10^18 entries.

  17. A Model of the Spatio-temporal Dynamics of Drosophila Eye Disc Development

    Science.gov (United States)

    Fried, Patrick; Sánchez-Aragón, Máximo; Lehtinen, Birgitta; Casares, Fernando; Iber, Dagmar

    2016-01-01

    Patterning and growth are linked during early development and have to be tightly controlled to result in a functional tissue or organ. During the development of the Drosophila eye, this linkage is particularly clear: the growth of the eye primordium mainly results from proliferating cells ahead of the morphogenetic furrow (MF), a moving signaling wave that sweeps across the tissue from the posterior to the anterior side, that induces proliferating cells anterior to it to differentiate and become cell cycle quiescent in its wake. Therefore, final eye disc size depends on the proliferation rate of undifferentiated cells and on the speed with which the MF sweeps across the eye disc. We developed a spatio-temporal model of the growing eye disc based on the regulatory interactions controlled by the signals Decapentaplegic (Dpp), Hedgehog (Hh) and the transcription factor Homothorax (Hth) and explored how the signaling patterns affect the movement of the MF and impact on eye disc growth. We used published and new quantitative data to parameterize the model. In particular, two crucial parameter values, the degradation rate of Hth and the diffusion coefficient of Hh, were measured. The model is able to reproduce the linear movement of the MF and the termination of growth of the primordium. We further show that the model can explain several mutant phenotypes, but fails to reproduce the previously observed scaling of the Dpp gradient in the anterior compartment. PMID:27626238

  18. Spatio-temporal Rich Model Based Video Steganalysis on Cross Sections of Motion Vector Planes.

    Science.gov (United States)

    Tasdemir, Kasim; Kurugollu, Fatih; Sezer, Sakir

    2016-05-11

    A rich model based motion vector steganalysis benefiting from both temporal and spatial correlations of motion vectors is proposed in this work. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this work. Firstly, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring motion vectors for longer distances. Therefore, temporal motion vector dependency along side the spatial dependency is utilized for rigorous motion vector steganalysis. Secondly, unlike the filters previously used, which were heuristically designed against a specific motion vector steganography, a diverse set of many filters which can capture aberrations introduced by various motion vector steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent motion vector steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in motion vector steganalysis field including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.

  19. A comparison of predictive soil-carbon models across multiple spatio-temporal catchment scales.

    Science.gov (United States)

    Hancock, G. R.; Kunkel, V.; Wells, T.

    2014-12-01

    Soil's potential as a carbon sink for atmospheric CO2 has been widely discussed. Studies of soil organic carbon (SOC) controls, and the subsequent models derived from their findings, have focussed mainly on North American and European regions, and more recently, in regions such as China. In Australia, agricultural practices have led to losses in SOC. This implies that Australian soils have a large potential for increases in SOC. Building on previous work, here we examine the spatial and temporal variation in soil organic carbon (SOC) and its controlling factors controls across a large catchment of approximately 600 km2 in the Upper Hunter Valley, New South Wales, Australia, using data collected from two sampling campaigns, (April 2006 and June-July 2014). Remote sensing using Landsat (30m) and MODIS (250m) NDVI was used to determine if catchment SOC could be predicted using both low and high resolution remote sensing . Relationships between SOC and elevation, aboveground biomass (as represented by NDVI), topographic wetness index (TWI), and incident solar radiation as a surrogate for soil temperature were compared. Initial results demonstrate that higher spatio-temporal resolution may not be necessary for predicting SOC at larger scales. The relationship between SOC and the environmental tracer 137-Cesium as a surrogate for the loss of SOC by erosion also suggests that sediment transport and deposition influences the distribution of SOC. A model developed for the site suggests that simple linear relationships between vegetation, climate and sediment transport could improve SOC predictions.

  20. Modelling of spatio-temporal precipitation relevant for urban hydrology with focus on scales, extremes and climate change

    DEFF Research Database (Denmark)

    Sørup, Hjalte Jomo Danielsen

    Time series of precipitation are necessary for assessment of urban hydrological systems. In a changed climate this is challenging as climate model output is not directly comparable to observations at the scales relevant for urban hydrology. The focus of this PhD thesis is downscaling...... of precipitation to spatio-temporal scales used in urban hydrology. It investigates several observational data products and identifies relevant scales where climate change and precipitation can be assessed for urban use. Precipitation is modelled at different scales using different stochastic techniques. A weather...... generator is used to produce an artificial spatio-temporal precipitation product that can be used both directly in large scale urban hydrological modelling and for derivation of extreme precipitation statistics relevant for urban hydrology. It is discussed why precipitation time series from a changed...

  1. Diffusive spatio-temporal noise in a first-passage time model for intracellular calcium release

    KAUST Repository

    Flegg, Mark B.

    2013-01-01

    The intracellular release of calcium from the endoplasmic reticulum is controlled by ion channels. The resulting calcium signals exhibit a rich spatio-temporal signature, which originates at least partly from microscopic fluctuations. While stochasticity in the gating transition of ion channels has been incorporated into many models, the distribution of calcium is usually described by deterministic reaction-diffusion equations. Here we test the validity of the latter modeling approach by using two different models to calculate the frequency of localized calcium signals (calcium puffs) from clustered IP3 receptor channels. The complexity of the full calcium system is here limited to the basic opening mechanism of the ion channels and, in the mathematical reduction simplifies to the calculation of a first passage time. Two models are then studied: (i) a hybrid model, where channel gating is treated stochastically, while calcium concentration is deterministic and (ii) a fully stochastic model with noisy channel gating and Brownian calcium ion motion. The second model utilises the recently developed two-regime method [M. B. Flegg, S. J. Chapman, and R. Erban, "The two-regime method for optimizing stochastic reaction-diffusion simulations," J. R. Soc., Interface 9, 859-868 (2012)] in order to simulate a large domain with precision required only near the Ca2+ absorbing channels. The expected time for a first channel opening that results in a calcium puff event is calculated. It is found that for a large diffusion constant, predictions of the interpuff time are significantly overestimated using the model (i) with a deterministic non-spatial calcium variable. It is thus demonstrated that the presence of diffusive noise in local concentrations of intracellular Ca2+ ions can substantially influence the occurrence of calcium signals. The presented approach and results may also be relevant for other cell-physiological first-passage time problems with small ligand concentration

  2. Spatio-Temporal Data Analysis at Scale Using Models Based on Gaussian Processes

    Energy Technology Data Exchange (ETDEWEB)

    Stein, Michael [Univ. of Chicago, IL (United States)

    2017-03-13

    Gaussian processes are the most commonly used statistical model for spatial and spatio-temporal processes that vary continuously. They are broadly applicable in the physical sciences and engineering and are also frequently used to approximate the output of complex computer models, deterministic or stochastic. We undertook research related to theory, computation, and applications of Gaussian processes as well as some work on estimating extremes of distributions for which a Gaussian process assumption might be inappropriate. Our theoretical contributions include the development of new classes of spatial-temporal covariance functions with desirable properties and new results showing that certain covariance models lead to predictions with undesirable properties. To understand how Gaussian process models behave when applied to deterministic computer models, we derived what we believe to be the first significant results on the large sample properties of estimators of parameters of Gaussian processes when the actual process is a simple deterministic function. Finally, we investigated some theoretical issues related to maxima of observations with varying upper bounds and found that, depending on the circumstances, standard large sample results for maxima may or may not hold. Our computational innovations include methods for analyzing large spatial datasets when observations fall on a partially observed grid and methods for estimating parameters of a Gaussian process model from observations taken by a polar-orbiting satellite. In our application of Gaussian process models to deterministic computer experiments, we carried out some matrix computations that would have been infeasible using even extended precision arithmetic by focusing on special cases in which all elements of the matrices under study are rational and using exact arithmetic. The applications we studied include total column ozone as measured from a polar-orbiting satellite, sea surface temperatures over the

  3. Spatio-temporal auxiliary particle filtering with l1-norm-based appearance model learning for robust visual tracking.

    Science.gov (United States)

    Kim, Du Yong; Jeon, Moongu

    2013-02-01

    In this paper, we propose an efficient and accurate visual tracker equipped with a new particle filtering algorithm and robust subspace learning-based appearance model. The proposed visual tracker avoids drifting problems caused by abrupt motion changes and severe appearance variations that are well-known difficulties in visual tracking. The proposed algorithm is based on a type of auxiliary particle filtering that uses a spatio-temporal sliding window. Compared to conventional particle filtering algorithms, spatio-temporal auxiliary particle filtering is computationally efficient and successfully implemented in visual tracking. In addition, a real-time robust principal component pursuit (RRPCP) equipped with l(1)-norm optimization has been utilized to obtain a new appearance model learning block for reliable visual tracking especially for occlusions in object appearance. The overall tracking framework based on the dual ideas is robust against occlusions and out-of-plane motions because of the proposed spatio-temporal filtering and recursive form of RRPCP. The designed tracker has been evaluated using challenging video sequences, and the results confirm the advantage of using this tracker.

  4. Spatio-temporal modelling of informal settlement development in Sancaktepe district, Istanbul, Turkey

    Science.gov (United States)

    Dubovyk, Olena; Sliuzas, Richard; Flacke, Johannes

    In less developed countries, the recent high rates of urban expansion are often associated with the emergence of informal settlements that may exaggerate social and environmental problems and impede sustainable development. An enhanced understanding of informal development may, therefore, be a key for future success in its effective management. This paper explores the possibilities offered by progress in Geo-Information Science and spatial modelling to improve understanding of informal settlement development through comprehensive spatio-temporal analyses. First, it investigates spatial and temporal patterns of the growth of the informal settlements in Sancaktepe district of Istanbul between 1990 and 2005. Second, using a logistic regression model, an analysis of the driving forces of informal development and prediction of probable locations of new informal settlements are performed. A list of spatial factors that are correlated to informal development is compiled and used to build six logistic regression models for different time steps between 1990 and 2005. Population density, slope, and proportion of informal settlements in the neighbourhood were found to be the main predictors influencing the spatial development of informal settlements during the study period. The performance of the models is evaluated and validated to identify those which best explain the informal development in the study area. As a result, three models built for 1990-1995 and 1995-2000 were selected to generate probability maps of informal settlement development, showing the likelihood for each location to be informally developed in the future. These results can be used as a basis for the evaluation of the process of informal development in other parts of Istanbul, as well as in other cities and countries. At the same time, the technique may serve as a decision-making tool for urban planners and policy makers.

  5. Modeling and Statistical Analysis of the Spatio-Temporal Patterns of Seasonal Influenza in Israel

    Science.gov (United States)

    Katriel, Guy; Yaari, Rami; Roll, Uri; Stone, Lewi

    2012-01-01

    Background Seasonal influenza outbreaks are a serious burden for public health worldwide and cause morbidity to millions of people each year. In the temperate zone influenza is predominantly seasonal, with epidemics occurring every winter, but the severity of the outbreaks vary substantially between years. In this study we used a highly detailed database, which gave us both temporal and spatial information of influenza dynamics in Israel in the years 1998–2009. We use a discrete-time stochastic epidemic SIR model to find estimates and credible confidence intervals of key epidemiological parameters. Findings Despite the biological complexity of the disease we found that a simple SIR-type model can be fitted successfully to the seasonal influenza data. This was true at both the national levels and at the scale of single cities.The effective reproductive number Re varies between the different years both nationally and among Israeli cities. However, we did not find differences in Re between different Israeli cities within a year. Re was positively correlated to the strength of the spatial synchronization in Israel. For those years in which the disease was more “infectious”, then outbreaks in different cities tended to occur with smaller time lags. Our spatial analysis demonstrates that both the timing and the strength of the outbreak within a year are highly synchronized between the Israeli cities. We extend the spatial analysis to demonstrate the existence of high synchrony between Israeli and French influenza outbreaks. Conclusions The data analysis combined with mathematical modeling provided a better understanding of the spatio-temporal and synchronization dynamics of influenza in Israel and between Israel and France. Altogether, we show that despite major differences in demography and weather conditions intra-annual influenza epidemics are tightly synchronized in both their timing and magnitude, while they may vary greatly between years. The predominance of

  6. Spatio-temporal change detection from multidimensional arrays

    NARCIS (Netherlands)

    Lu, Meng; Pebesma, Edzer; Sanchez, Alber; Verbesselt, Jan

    2016-01-01

    Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over

  7. Sensitivity Analysis of a Spatio-Temporal Avalanche Forecasting Model Based on Support Vector Machines

    Science.gov (United States)

    Matasci, G.; Pozdnoukhov, A.; Kanevski, M.

    2009-04-01

    The recent progress in environmental monitoring technologies allows capturing extensive amount of data that can be used to assist in avalanche forecasting. While it is not straightforward to directly obtain the stability factors with the available technologies, the snow-pack profiles and especially meteorological parameters are becoming more and more available at finer spatial and temporal scales. Being very useful for improving physical modelling, these data are also of particular interest regarding their use involving the contemporary data-driven techniques of machine learning. Such, the use of support vector machine classifier opens ways to discriminate the ``safe'' and ``dangerous'' conditions in the feature space of factors related to avalanche activity based on historical observations. The input space of factors is constructed from the number of direct and indirect snowpack and weather observations pre-processed with heuristic and physical models into a high-dimensional spatially varying vector of input parameters. The particular system presented in this work is implemented for the avalanche-prone site of Ben Nevis, Lochaber region in Scotland. A data-driven model for spatio-temporal avalanche danger forecasting provides an avalanche danger map for this local (5x5 km) region at the resolution of 10m based on weather and avalanche observations made by forecasters on a daily basis at the site. We present the further work aimed at overcoming the ``black-box'' type modelling, a disadvantage the machine learning methods are often criticized for. It explores what the data-driven method of support vector machine has to offer to improve the interpretability of the forecast, uncovers the properties of the developed system with respect to highlighting which are the important features that led to the particular prediction (both in time and space), and presents the analysis of sensitivity of the prediction with respect to the varying input parameters. The purpose of the

  8. Spatio-Temporal Modelling of Dust Transport over Surface Mining Areas and Neighbouring Residential Zones

    Directory of Open Access Journals (Sweden)

    Eva Gulikova

    2008-06-01

    Full Text Available Projects focusing on spatio-temporal modelling of the living environment need to manage a wide range of terrain measurements, existing spatial data, time series, results of spatial analysis and inputs/outputs from numerical simulations. Thus, GISs are often used to manage data from remote sensors, to provide advanced spatial analysis and to integrate numerical models. In order to demonstrate the integration of spatial data, time series and methods in the framework of the GIS, we present a case study focused on the modelling of dust transport over a surface coal mining area, exploring spatial data from 3D laser scanners, GPS measurements, aerial images, time series of meteorological observations, inputs/outputs form numerical models and existing geographic resources. To achieve this, digital terrain models, layers including GPS thematic mapping, and scenes with simulation of wind flows are created to visualize and interpret coal dust transport over the mine area and a neighbouring residential zone. A temporary coal storage and sorting site, located near the residential zone, is one of the dominant sources of emissions. Using numerical simulations, the possible effects of wind flows are observed over the surface, modified by natural objects and man-made obstacles. The coal dust drifts with the wind in the direction of the residential zone and is partially deposited in this area. The simultaneous display of the digital map layers together with the location of the dominant emission source, wind flows and protected areas enables a risk assessment of the dust deposition in the area of interest to be performed. In order to obtain a more accurate simulation of wind flows over the temporary storage and sorting site, 3D laser scanning and GPS thematic mapping are used to create a more detailed digital terrain model. Thus, visualization of wind flows over the area of interest combined with 3D map layers enables the exploration of the processes of coal dust

  9. Modelling natural grass production and its spatio-temporal variations in a semiarid Mediterranean watershed

    Science.gov (United States)

    Schnabel, Susanne; Lozano-Parra, Javier; Maneta-López, Marco

    2014-05-01

    Natural grasses are found in semiarid rangelands with disperse tree cover of part of the Iberian Peninsula and constitute a resource with high ecologic and economic value worth, being an important source of food for livestock, playing a significant role in the hydrologic cycle, controlling the soil thermal regime, and are a key factor in reducing soil erosion and degradation. However, increasing pressure on the resources, changes in land use as well as possible climate variations threaten the sustainability of natural grasses. Despite of their importance, the spatio-temporal variations of pasture production over whole watersheds are poorly known. In this sense, previous studies by other authors have indicated its dependence on a balance of positive and negative effects brought about by the main limiting factors: water, light, nutrients and space. Nevertheless, the specific weight of each factor is not clear because they are highly variable due to climate characteristics and the structure of these agroforestry systems. We have used a physical spatially-distributed ecohydrologic model to investigate the specific weight of factors that contribute to pasture production in a semiarid watershed of 99.5 ha in western Spain. This model couples a two layer (canopy and understory) vertical local closure energy balance scheme, a hydrologic model and a carbon uptake and vegetation growth component, and it was run using a synthetic daily climate dataset generated by a stochastic weather generator, which reproduced the range of climatic variations observed under mediterranean current climate. The modelling results reproduced satisfactorily the seasonality effects of climate as precipitation and temperatures, as well as annual and inter-annual variations of pasture production. Spatial variations of pasture production were largely controlled by topographic and tree effects, showing medium-low values depending of considered areas. These low values require introduction of feed to

  10. Spatio-Temporal Patterns of Demyelination and Remyelination in the Cuprizone Mouse Model.

    Directory of Open Access Journals (Sweden)

    Ian Tagge

    Full Text Available Cuprizone administration in mice provides a reproducible model of demyelination and spontaneous remyelination, and has been useful in understanding important aspects of human disease, including multiple sclerosis. In this study, we apply high spatial resolution quantitative MRI techniques to establish the spatio-temporal patterns of acute demyelination in C57BL/6 mice after 6 weeks of cuprizone administration, and subsequent remyelination after 6 weeks of post-cuprizone recovery. MRI measurements were complemented with Black Gold II stain for myelin and immunohistochemical stains for associated tissue changes. Gene expression was evaluated using the Allen Gene Expression Atlas. Twenty-five C57BL/6 male mice were split into control and cuprizone groups; MRI data were obtained at baseline, after 6 weeks of cuprizone, and 6 weeks post-cuprizone. High-resolution (100 μm isotropic whole-brain coverage magnetization transfer ratio (MTR parametric maps demonstrated concurrent caudal-to-rostral and medial-to-lateral gradients of MTR decrease within corpus callosum (CC that correlated well with demyelination assessed histologically. Our results show that demyelination was not limited to the midsagittal line of the corpus callosum, and also that opposing gradients of demyelination occur in the lateral and medial CC. T2-weighted MRI gray/white matter contrast was strong at baseline, weak after 6 weeks of cuprizone treatment, and returned to a limited extent after recovery. MTR decreases during demyelination were observed throughout the brain, most clearly in callosal white matter. Myelin damage and repair appear to be influenced by proximity to oligodendrocyte progenitor cell populations and exhibit an inverse correlation with myelin basic protein gene expression. These findings suggest that susceptibility to injury and ability to repair vary across the brain, and whole-brain analysis is necessary to accurately characterize this model. Whole

  11. Automatic calibration of a parsimonious ecohydrological model in a sparse basin using the spatio-temporal variation of the NDVI

    Science.gov (United States)

    Ruiz-Pérez, Guiomar; Manfreda, Salvatore; Caylor, Kelly; Francés, Félix

    2016-04-01

    Drylands are extensive, covering 30% of the Earth's land surface and 50% of Africa. In these water-controlled areas, vegetation plays a key role in the water cycle. Ecohydrological models provide a tool to investigate the relationships between vegetation and water resources. However, studies in Africa often face the problem that many ecohydrological models have quite extensive parametrical requirements, while available data are scarce. Therefore, there is a need for searching new sources of information such as satellite data. The advantages of the use of satellite data in dry regions has been deeply demonstrated and studied. But, the use of this kind of data forces to introduce the concept of spatio-temporal information. In this context, we have to deal with the fact that there is a lack in terms of statistics and methodologies to incorporate the spatio-temporal data during the calibration and validation processes. This research wants to be a contribution in that sense. The used ecohydrological model was calibrated in the Upper Ewaso river basin in Kenya only using NDVI (Normalized Difference Vegetation Index) data from MODIS. An automatic calibration methodology based on Singular Value Decomposition techniques was proposed in order to calibrate the model taking into account the temporal variation and, also, the spatial pattern of the observed NDVI and the simulated LAI. The obtained results have demonstrated: (1) the satellite data is an extraordinary useful tool of information and it can be used to implement ecohydrological models in dry regions; (2) the proposed model calibrated only using satellite data is able to reproduce the vegetation dynamics (in time and in space) and, also, the observed discharge at the outlet point; and (3) the proposed automatic calibration methodology works satisfactorily and it includes spatio-temporal data, in other words, it takes into account the temporal variation and the spatial pattern of the analyzed data.

  12. A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking.

    Science.gov (United States)

    Pârvu, Ovidiu; Gilbert, David

    2016-01-01

    Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour

  13. Assessing spatio-temporal variability of rainfall using a simple physically based statistical model

    Science.gov (United States)

    Hutchinson, M. F.; Xu, T.; Kesteven, J.

    2010-12-01

    Reliable assessment of spatio-temporal variability of observed rainfall is difficult in the current climate because of the complex spatial variability displayed by daily and shorter time scale rainfall data. As demonstrated in a recent analysis of Canadian daily precipitation data by Hutchinson et al. (2009), direct interpolation of short time scale precipitation data is a poor way to address spatial patterns of rainfall extremes. Addressing the behaviour of projected future precipitation extremes is made even more difficult by the limited temporal and spatial resolution of precipitation as simulated by global climate models. The “uniform drizzle” that tends to be produced by these models makes the assessment of even straightforward statistics, such as daily rainfall occurrence, problematic. Putting aside significant inter-model variability, the more reliable outputs of global models include mean fluxes, such as monthly rainfall amounts, and associated insight into the nature of the modelled precipitation in relation to forcing synoptic systems. The truncated power of normal distribution, as described by Hutchinson (1995), offers a relatively simple way to make progress. Two of the three model parameters are simply calibrated in terms of monthly mean fluxes and the model is able to accurately describe precipitation extremes. These model parameters can also be robustly determined from serially incomplete data. It can be argued that the model has a broad physical process basis by modelling rainfall as an event that occurs as an appropriate threshold is exceeded. This analysis extends the approach of Stidd (1954, 1973) who suggested the cube root as a universal normalising power. We show that the power parameter, once robustly calibrated, displays a broadly spatially varying distribution of around 0.5. This corresponds well with the two dimensional synoptic convergence that is required to produce precipitation. The power parameter appears to be related to the

  14. Spatio-temporal modelling of zero-inflated deep-sea shrimp data by Tweedie generalized additive

    Directory of Open Access Journals (Sweden)

    Simona Arcuti

    2013-10-01

    Full Text Available In theMediterrean Sea the population features of demersal resources fluctuate over spatial and temporal scales due to the variability of abiotic and biotic factors as well as to human activities. The two shrimps Parapenaeus longirostris and Aristaeomorpha foliacea are among the most important deep-sea demersal resources in the North-Western Ionian Sea. Their changes in terms of density, biomass andmedian length induced by anthropogenic and environmental variables (fishing effort, sea surface temperature, precipitations, Winter North Atlantic Oscillation (NAO and Annual MediterraneanOscillation (MO indices were investigated. Biological data were collected during trawl surveys carried out from 1995 to 2006 as part of the international program MEDITS (International Bottom Trawl Survey in the Mediterranean. Generalized AdditiveModels were used to evaluate the spatio-temporal variation of both species, together with the possible nonlinear effects of biotic and abiotic factors. Density and biomass were assumed to be distributed according to a member of the Tweedie family in order to account for zero-inflation in the relative data. Spacetime interaction was consideredwithin a non-separablemodel with smooth spatio-temporal component based on tensor product splines. The results show significant spatio-temporal and depth effects in the three population parameters of these resources. Winter NAO index significantly influenced the density, biomass and length of P. longirostris. Sea surface temperature significantly influenced the size of this species and the three population features of A. foliacea. The size of this shrimp resulted also influenced negatively by fishing effort and positively by the MO index.

  15. Modelling Spatio-Temporal Relevancy in Urban Context-Aware Pervasive Systems Using Voronoi Continuous Range Query and Multi-Interval Algebra

    Directory of Open Access Journals (Sweden)

    Najmeh Neysani Samany

    2013-01-01

    Full Text Available Space and time are two dominant factors in context-aware pervasive systems which determine whether an entity is related to the moving user or not. This paper specifically addresses the use of spatio-temporal relations for detecting spatio-temporally relevant contexts to the user. The main contribution of this work is that the proposed model is sensitive to the velocity and direction of the user and applies customized Multi Interval Algebra (MIA with Voronoi Continuous Range Query (VCRQ to introduce spatio-temporally relevant contexts according to their arrangement in space. In this implementation the Spatio-Temporal Relevancy Model for Context-Aware Systems (STRMCAS helps the tourist to find his/her preferred areas that are spatio-temporally relevant. The experimental results in a scenario of tourist navigation are evaluated with respect to the accuracy of the model, performance time and satisfaction of users in 30 iterations of the algorithm. The evaluation process demonstrated the efficiency of the model in real-world applications.

  16. Spatio-temporal Modeling of Lasing Action in Core–Shell Metallic Nanoparticles

    Science.gov (United States)

    2016-01-01

    Nanoscale laser sources based on single metallic nanoparticles (spasers) have attracted significant interest for their fundamental implications and technological potential. Here we theoretically investigate the spatio-temporal dynamics of lasing action in core–shell metallic nanoparticles that include optically pumped four-level gain media. By using detailed semiclassical simulations based on a time-domain generalization of the finite-element method, we study the evolution of the lasing dynamics when going from a spherical case to an elongated nanorod configuration. Our calculations show that there exists an optimal nanoparticle elongation that exhibits significantly improved lasing threshold and slope efficiency over those obtained for its spherical counterpart. These results are accounted for in terms of a coupled-mode theory analysis of the variation with elongation of the light confinement properties of localized surface plasmons. This work could be of importance for further development of nanoscale light sources based on localized surface plasmon resonances.

  17. Integration of various data sources for transient groundwater modeling with spatio-temporally variable fluxes—Sardon study case, Spain

    Science.gov (United States)

    Lubczynski, Maciek W.; Gurwin, Jacek

    2005-05-01

    Spatio-temporal variability of recharge ( R) and groundwater evapotranspiration ( ETg) fluxes in a granite Sardon catchment in Spain (˜80 km 2) have been assessed based on integration of various data sources and methods within the numerical groundwater MODFLOW model. The data sources and methods included: remote sensing solution of surface energy balance using satellite data, sap flow measurements, chloride mass balance, automated monitoring of climate, depth to groundwater table and river discharges, 1D reservoir modeling, GIS modeling, field cartography and aerial photo interpretation, slug and pumping tests, resistivity, electromagnetic and magnetic resonance soundings. The presented study case provides not only detailed evaluation of the complexity of spatio-temporal variable fluxes, but also a complete and generic methodology of modern data acquisition and data integration in transient groundwater modeling for spatio-temporal groundwater balancing. The calibrated numerical model showed spatially variable patterns of R and ETg fluxes despite a uniform rainfall pattern. The seasonal variability of fluxes indicated: (1) R in the range of 0.3-0.5 mm/d within ˜8 months of the wet season with exceptional peaks as high as 0.9 mm/d in January and February and no recharge in July and August; (2) a year round stable lateral groundwater outflow ( Qg) in the range of 0.08-0.24 mm/d; (3) ETg=0.64, 0.80, 0.55 mm/d in the dry seasons of 1997, 1998, 1999, respectively, and <0.05 mm/d in wet seasons; (4) temporally variable aquifer storage, which gains water in wet seasons shortly after rain showers and looses water in dry seasons mainly due to groundwater evapotranspiration. The dry season sap flow measurements of tree transpiration performed in the homogenous stands of Quercus ilex and Quercus pyrenaica indicated flux rates of 0.40 and 0.15 mm/d, respectively. The dry season tree transpiration for the entire catchment was ˜0.16 mm/d. The availability of dry season

  18. Testing the accuracy ratio of the Spatio-Temporal Epidemiological Modeler (STEM) through Ebola haemorrhagic fever outbreaks.

    Science.gov (United States)

    Baldassi, F; D'Amico, F; Carestia, M; Cenciarelli, O; Mancinelli, S; Gilardi, F; Malizia, A; DI Giovanni, D; Soave, P M; Bellecci, C; Gaudio, P; Palombi, L

    2016-05-01

    Mathematical modelling is an important tool for understanding the dynamics of the spread of infectious diseases, which could be the result of a natural outbreak or of the intentional release of pathogenic biological agents. Decision makers and policymakers responsible for strategies to contain disease, prevent epidemics and fight possible bioterrorism attacks, need accurate computational tools, based on mathematical modelling, for preventing or even managing these complex situations. In this article, we tested the validity, and demonstrate the reliability, of an open-source software, the Spatio-Temporal Epidemiological Modeler (STEM), designed to help scientists and public health officials to evaluate and create models of emerging infectious diseases, analysing three real cases of Ebola haemorrhagic fever (EHF) outbreaks: Uganda (2000), Gabon (2001) and Guinea (2014). We discuss the cases analysed through the simulation results obtained with STEM in order to demonstrate the capability of this software in helping decision makers plan interventions in case of biological emergencies.

  19. Estimation of ocean primary productivity and its spatio-temporal variation mechanism for East China Sea based on VGPM model

    Institute of Scientific and Technical Information of China (English)

    LIGuosheng; GAOPing; WANGFang; LIANGQiang

    2004-01-01

    According to calculation results of ocean chlorophyll concentration based on SeaWiFS data by SeaBAM model and synchronous ship-measured data, this research set up an improved model for Case I and Case Ⅱ water bodies respectively. The monthly chlorophyll distribution in the East China Sea in 1998 was obtained from this improved model on calculation results of SeaBAM. The euphotic depth distribution in 1998 in the East China Sea is calculated by using remote sensing data of K490 from SeaWiFS according to the relation between the euphotic depth and the oceanic diffuse attenuation coefficient. With data of ocean chlorophyll concentration, euphotic depth, ocean surface photosynthetic available radiation (PAR), daily photoperiod and optimal rate of daily carbon fixation within a water column, the monthly and annual primary productivity spatio-temporal distributions in the East China Sea in 1998 were obtained based on VGPM model. Based on analysis of those distributions, the conclusion can be drawn that there is a clear bimodality character of primary productivity in the monthly distribution in the East China Sea. In detail, the monthly distribution of primary productivity stays the lowest level in winter and rises rapidly to the peak in spring. It gets down a little in summer, and gets up a little in autumn. The daily average of primary productivity in the whole East China Sea is 560.03 mg/m2/d, which is far higher than the average of subtropical ocean areas. The annual average of primary productivity is 236.95 g/m2/a. The research on the seasonal variety mechanism of primary productivity shows that several factors that affect the spatio-temporal distribution may include the chlorophyll concentration distribution, temperature condition, the Yangtze River diluted water variety, the euphotic depth, ocean current variety, etc. But the main influencing factors may be different in each local sea area.

  20. ANALISIS KETERKAITAN PERUBAHAN LAHAN PERTANIAN TERHADAP KETAHANAN PANGAN KABUPATEN MAGELANG BERBASIS MODEL SPATIO TEMPORAL SIG

    Directory of Open Access Journals (Sweden)

    Rifki Destianto

    2014-04-01

    Full Text Available As mandated by the 1945 Constitution of the Republic of Indonesia, agricultural land is part of the earth as a gift from God that is controlled by the state and utilized for the prosperity and welfare of the people. The agricultural land can provide great benefits in terms of economic, social, and environmental benefits. Land use conversion from agricultural to non-agricultural use can cause productivity decrease in agriculture, so it triggers a hypothesis that the decrease of agricultural land will negatively affect food security. Based on the problem, the research is conducted to assess the relationship between conversion in agricultural land use with food security using a case study of Magelang Regency (Kabupaten in the 2009-11 period. The methods used are spatio-temporal GIS, quantitative analysis, and field calibration. The results have shown that the agricultural land area has decreased (6.31% but the food security has not declined. It is because the food sources for Magelang Regency come from several adjacent areas. It can then be concluded that the reduction of agricultural land does not significantly affect the food security status of the study area. However, in the long run the decrease of the agricultural land will affect national food security. So, to maintain food security, agricultural land use control is necessary to prevent the unnecessary conversion of agricultural land.

  1. Modeling the spatio-temporal dynamics of porcine reproductive & respiratory syndrome cases at farm level using geographical distance and pig trade network matrices

    National Research Council Canada - National Science Library

    Sara Amirpour Haredasht; Dale Polson; Rodger Main; Kyuyoung Lee; Derald Holtkamp; Beatriz Martinez-Lopez

    2017-01-01

    .... In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US...

  2. Spatio-Temporal Rule Mining

    DEFF Research Database (Denmark)

    Gidofalvi, Gyozo; Pedersen, Torben Bach

    2005-01-01

    Recent advances in communication and information technology, such as the increasing accuracy of GPS technology and the miniaturization of wireless communication devices pave the road for Location-Based Services (LBS). To achieve high quality for such services, spatio-temporal data mining techniques...... are needed. In this paper, we describe experiences with spatio-temporal rule mining in a Danish data mining company. First, a number of real world spatio-temporal data sets are described, leading to a taxonomy of spatio-temporal data. Second, the paper describes a general methodology that transforms...... the spatio-temporal rule mining task to the traditional market basket analysis task and applies it to the described data sets, enabling traditional association rule mining methods to discover spatio-temporal rules for LBS. Finally, unique issues in spatio-temporal rule mining are identified and discussed....

  3. The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review

    Science.gov (United States)

    Aburas, Maher Milad; Ho, Yuek Ming; Ramli, Mohammad Firuz; Ash'aari, Zulfa Hanan

    2016-10-01

    In recent years, several types of simulation and prediction models have been used within a GIS environment to determine a realistic future for urban growth patterns. These models include quantitative and spatio-temporal techniques that are implemented to monitor urban growth. The results derived through these techniques are used to create future policies that take into account sustainable development and the demands of future generations. The aim of this paper is to provide a basis for a literature review of urban Cellular Automata (CA) models to find the most suitable approach for a realistic simulation of land use changes. The general characteristics of simulation models of urban growth and urban CA models are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various models are identified based on the analysis and discussion of the characteristics of these models. The results of the review confirm that the CA model is one of the strongest models for simulating urban growth patterns owing to its structure, simplicity, and possibility of evolution. Limitations of the CA model, namely weaknesses in the quantitative aspect, and the inability to include the driving forces of urban growth in the simulation process, may be minimized by integrating it with other quantitative models, such as via the Analytic Hierarchy Process (AHP), Markov Chain and frequency ratio models. Realistic simulation can be achieved when socioeconomic factors and spatial and temporal dimensions are integrated in the simulation process.

  4. Deciphering the spatio-temporal complexity of climate change of the last deglaciation: a model analysis

    Directory of Open Access Journals (Sweden)

    D. M. Roche

    2010-12-01

    Full Text Available Understanding the sequence of events occuring during the last major glacial to interglacial transition (21 ka BP to 9 ka BP is a challenging task that has the potential to unveil the mechanisms behind large scale climate changes. Though many studies have focused at a complex understanding of the sequence of rapid climatic change that accompanied or interrupted the deglaciation, few have analysed it in a more theoretical framework with simple forcings. In the following, we address when and where the first significant temperature anomalies appear when using slow varying forcing of the last deglaciation. We use here coupled transient simulations of the last deglaciation, including ocean, atmosphere and vegetation components to analyse the spatial timing of the deglaciation. To keep the analysis in a simple framework, we do not include rapid freshwater forcings that have led to rapid climate shifts during that time period. We aim to disentangle the direct and subsequent response of the climate system to slow forcing and moreover the location where those changes are more clearly expressed. In a data-modelling comparison perspective this could help understanding the physically plausible phasing between known forcings and recorded climatic changes. Our analysis of climate variability could also help to distinguish deglacial warming signals from internal climate variability. We thus are able to better pinpoint the onset of local deglaciation, as defined by the first significant local warming, and further show that there is a large regional variability associated with it, even with the set of slow forcings used here.

  5. The effect of neighbourhood definitions on spatio-temporal models of disease outbreaks: Separation distance versus range overlap.

    Science.gov (United States)

    Laffan, Shawn W; Wang, Zhaoyuan; Ward, Michael P

    2011-12-01

    The definition of the spatial relatedness between infectious and susceptible animal groups is a fundamental component of spatio-temporal modelling of disease outbreaks. A common neighbourhood definition for disease spread in wild and feral animal populations is the distance between the centroids of neighbouring group home ranges. This distance can be used to define neighbourhood interactions, and also to describe the probability of successful disease transmission. Key limitations of this approach are (1) that a susceptible neighbour of an infectious group with an overlapping home range - but whose centroid lies outside the home range of an infectious group - will not be considered for disease transmission, and (2) the degree of overlap between the home ranges is not taken into account for those groups with centroids inside the infectious home range. We assessed the impact of both distance-based and range overlap methods of disease transmission on model-predicted disease spread. Range overlap was calculated using home ranges modelled as circles. We used the Sirca geographic automata model, with the population data from a nine-county study area in Texas that we have previously described. For each method we applied 100 model repetitions, each of 100 time steps, to 30 index locations. The results show that the rate of disease spread for the range-overlap method is clearly less than the distance-based method, with median outbreaks modelled using the latter being 1.4-1.45 times larger. However, the two methods show similar overall trends in the area infected, and the range-overlap median (48 and 120 for cattle and pigs, respectively) falls within the 5th-95th percentile range of the distance-based method (0-96 and 0-252 for cattle and pigs, respectively). These differences can be attributed to the calculation of the interaction probabilities in the two methods, with overlap weights generally resulting in lower interaction probabilities. The definition of spatial

  6. Supplementary Material for: Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

    KAUST Repository

    Castruccio, Stefano

    2016-01-01

    One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific datasets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a nontrivial model to a dataset of 1 billion data points with a covariance matrix comprising of 1018 entries. Supplementary materials for this article are available online.

  7. A Bayesian Hierarchical Model for Spatio-Temporal Prediction and Uncertainty Assessment Using Repeat LiDAR Acquisitions for the Kenai Peninsula, AK, USA

    Science.gov (United States)

    Babcock, C. R.; Andersen, H. E.; Finley, A. O.; Cook, B.; Morton, D. C.

    2015-12-01

    Models using repeat LiDAR and field campaigns may be one mechanism to monitor carbon storage and flux in forested regions. Considering the ability of multi-temporal LiDAR to estimate growth, it is not surprising that there is great interest in developing forest carbon monitoring strategies that rely on repeated LiDAR acquisitions. Allowing for sparser field campaigns, LiDAR stands to make monitoring forest carbon cheaper and more efficient than field-only sampling procedures. Here, we look to the spatio-temporally data-rich Kenai Peninsula in Alaska to examine the potential for Bayesian spatio-temporal mapping of forest carbon storage and uncertainty. The framework explored here can predict forest carbon through space and time, while formally propagating uncertainty through to prediction. Bayesian spatio-temporal models are flexible frameworks allowing for forest growth processes to be formally integrated into the model. By incorporating a mechanism for growth---using temporally repeated field and LiDAR data---we can more fully exploit the information-rich inventory network to improve prediction accuracy. LiDAR data for the Kenai Peninsula has been collected on four different occasions---spatially coincident LiDAR strip samples in 2004, 09 and 14, along with a wall-to-wall collection in 2008. There were 436 plots measured twice between 2002 and 2014. LiDAR was acquired at least once over most inventory plots with many having LiDAR collected during 2, 3 or 4 different campaigns. Results from this research will impact how forests are inventoried. It is too expensive to monitor terrestrial carbon using field-only sampling strategies and currently proposed LiDAR model-based techniques lack the ability to properly utilize temporally repeated and misaligned data. Bayesian hierarchical spatio-temporal models offer a solution to these shortcomings and allow for formal predictive error assessment, which is useful for policy development and decision making.

  8. Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System

    Science.gov (United States)

    Librero, Julián; Martínez-Lizaga, Natalia; Peiró, Salvador; Bernal-Delgado, Enrique

    2017-01-01

    Objective To illustrate the ability of hierarchical Bayesian spatio-temporal models in capturing different geo-temporal structures in order to explain hospital risk variations using three different conditions: Percutaneous Coronary Intervention (PCI), Colectomy in Colorectal Cancer (CCC) and Chronic Obstructive Pulmonary Disease (COPD). Research design This is an observational population-based spatio-temporal study, from 2002 to 2013, with a two-level geographical structure, Autonomous Communities (AC) and Health Care Areas (HA). Setting The Spanish National Health System, a quasi-federal structure with 17 regional governments (AC) with full responsibility in planning and financing, and 203 HA providing hospital and primary care to a defined population. Methods A poisson-log normal mixed model in the Bayesian framework was fitted using the INLA efficient estimation procedure. Measures The spatio-temporal hospitalization relative risks, the evolution of their variation, and the relative contribution (fraction of variation) of each of the model components (AC, HA, year and interaction AC-year). Results Following PCI-CCC-CODP order, the three conditions show differences in the initial hospitalization rates (from 4 to 21 per 10,000 person-years) and in their trends (upward, inverted V shape, downward). Most of the risk variation is captured by phenomena occurring at the HA level (fraction variance: 51.6, 54.7 and 56.9%). At AC level, the risk of PCI hospitalization follow a heterogeneous ascending dynamic (interaction AC-year: 17.7%), whereas in COPD the AC role is more homogenous and important (37%). Conclusions In a system where the decisions loci are differentiated, the spatio-temporal modeling allows to assess the dynamic relative role of different levels of decision and their influence on health outcomes. PMID:28166233

  9. Mechanistic spatio-temporal point process models for marked point processes, with a view to forest stand data

    DEFF Research Database (Denmark)

    Møller, Jesper; Ghorbani, Mohammad; Rubak, Ege Holger

    We show how a spatial point process, where to each point there is associated a random quantitative mark, can be identified with a spatio-temporal point process specified by a conditional intensity function. For instance, the points can be tree locations, the marks can express the size of trees...

  10. A million-plus neuron model of the hippocampal dentate gyrus: Dependency of spatio-temporal network dynamics on topography.

    Science.gov (United States)

    Hendrickson, Phillip J; Yu, Gene J; Song, Dong; Berger, Theodore W

    2015-01-01

    This paper describes a million-plus granule cell compartmental model of the rat hippocampal dentate gyrus, including excitatory, perforant path input from the entorhinal cortex, and feedforward and feedback inhibitory input from dentate interneurons. The model includes experimentally determined morphological and biophysical properties of granule cells, together with glutamatergic AMPA-like EPSP and GABAergic GABAA-like IPSP synaptic excitatory and inhibitory inputs, respectively. Each granule cell was composed of approximately 200 compartments having passive and active conductances distributed throughout the somatic and dendritic regions. Modeling excitatory input from the entorhinal cortex was guided by axonal transport studies documenting the topographical organization of projections from subregions of the medial and lateral entorhinal cortex, plus other important details of the distribution of glutamatergic inputs to the dentate gyrus. Results showed that when medial and lateral entorhinal cortical neurons maintained Poisson random firing, dentate granule cells expressed, throughout the million-cell network, a robust, non-random pattern of spiking best described as spatiotemporal "clustering". To identify the network property or properties responsible for generating such firing "clusters", we progressively eliminated from the model key mechanisms such as feedforward and feedback inhibition, intrinsic membrane properties underlying rhythmic burst firing, and/or topographical organization of entorhinal afferents. Findings conclusively identified topographical organization of inputs as the key element responsible for generating a spatio-temporal distribution of clustered firing. These results uncover a functional organization of perforant path afferents to the dentate gyrus not previously recognized: topography-dependent clusters of granule cell activity as "functional units" that organize the processing of entorhinal signals.

  11. Quantifying the spatio-temporal dynamics of woody plant encroachment using an integrative remote sensing, GIS, and spatial modeling approach

    Science.gov (United States)

    Buenemann, Michaela

    Despite a longstanding universal concern about and intensive research into woody plant encroachment (WPE)---the replacement of grasslands by shrub- and woodlands---our accumulated understanding of the process has either not been translated into sustainable rangeland management strategies or with only limited success. In order to increase our scientific insights into WPE, move us one step closer toward the sustainable management of rangelands affected by or vulnerable to the process, and identify needs for a future global research agenda, this dissertation presents an unprecedented critical, qualitative and quantitative assessment of the existing literature on the topic and evaluates the utility of an integrative remote sensing, GIS, and spatial modeling approach for quantifying the spatio-temporal dynamics of WPE. Findings from this research suggest that gaps in our current understanding of WPE and difficulties in devising sustainable rangeland management strategies are in part due to the complex spatio-temporal web of interactions between geoecological and anthropogenic variables involved in the process as well as limitations of presently available data and techniques. However, an in-depth analysis of the published literature also reveals that aforementioned problems are caused by two further crucial factors: the absence of information acquisition and reporting standards and the relative lack of long-term, large-scale, multi-disciplinary research efforts. The methodological framework proposed in this dissertation yields data that are easily standardized according to various criteria and facilitates the integration of spatially explicit data generated by a variety of studies. This framework may thus provide one common ground for scientists from a diversity of fields. Also, it has utility for both research and management. Specifically, this research demonstrates that the application of cutting-edge remote sensing techniques (Multiple Endmember Spectral Mixture

  12. Fourier Analysis of an Expanded Gravity Model for Spatio-Temporal Interactions

    CERN Document Server

    Chen, Yanguang

    2013-01-01

    Fourier analysis and cross-correlation function are successfully applied to improving the conventional gravity model of interaction between cities by introducing a time variable to the attraction measures (e.g., city sizes). The traditional model assumes spatial interaction as instantaneous, while the new model considers the interaction as a temporal process and measures it as an aggregation over a period of time. By doing so, the new model not only is more theoretically sound, but also enables us to integrate the analysis of temporal process into spatial interaction modeling. Based on cross-correlation function, the developed model is calibrated by Fourier analysis techniques, and the computation process is demonstrated in four steps. The paper uses a simple case study to illustrate the approach to modeling the interurban interaction, and highlight the relationship between the new model and the conventional gravity model.

  13. Synchronizing spatio-temporal chaos with imperfect models: A stochastic surface growth picture

    Energy Technology Data Exchange (ETDEWEB)

    Pazó, Diego, E-mail: pazo@ifca.unican.es; López, Juan M.; Rodríguez, Miguel A. [Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, 39005 Santander (Spain); Gallego, Rafael [Departamento de Matemáticas, Universidad de Oviedo, Campus de Viesques, 33203 Gijón (Spain)

    2014-12-01

    We study the synchronization of two spatially extended dynamical systems where the models have imperfections. We show that the synchronization error across space can be visualized as a rough surface governed by the Kardar-Parisi-Zhang equation with both upper and lower bounding walls corresponding to nonlinearities and model discrepancies, respectively. Two types of model imperfections are considered: parameter mismatch and unresolved fast scales, finding in both cases the same qualitative results. The consistency between different setups and systems indicates that the results are generic for a wide family of spatially extended systems.

  14. A spatio-temporal model for spontaneous thrombus formation in cerebral aneurysms.

    Science.gov (United States)

    Malaspinas, O; Turjman, A; Ribeiro de Sousa, D; Garcia-Cardena, G; Raes, M; Nguyen, P-T T; Zhang, Y; Courbebaisse, G; Lelubre, C; Zouaoui Boudjeltia, K; Chopard, B

    2016-04-07

    We propose a new numerical model to describe thrombus formation in cerebral aneurysms. This model combines CFD simulations with a set of bio-mechanical processes identified as being the most important to describe the phenomena at a large space and time scales. The hypotheses of the model are based on in vitro experiments and clinical observations. We document that we can reproduce very well the shape and volume of patient specific thrombus segmented in giant aneurysms. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. SPATIO-TEMPORAL MODELING OF AGRICULTURAL YIELD DATA WITH AN APPLICATION TO PRICING CROP INSURANCE CONTRACTS

    Science.gov (United States)

    Ozaki, Vitor A.; Ghosh, Sujit K.; Goodwin, Barry K.; Shirota, Ricardo

    2009-01-01

    This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Paraná (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited. PMID:19890450

  16. SPATIO-TEMPORAL MODELING OF AGRICULTURAL YIELD DATA WITH AN APPLICATION TO PRICING CROP INSURANCE CONTRACTS.

    Science.gov (United States)

    Ozaki, Vitor A; Ghosh, Sujit K; Goodwin, Barry K; Shirota, Ricardo

    2008-11-01

    This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Paraná (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited.

  17. An online spatio-temporal prediction model for dengue fever epidemic in Kaohsiung,Taiwan

    Science.gov (United States)

    Cheng, Ming-Hung; Yu, Hwa-Lung; Angulo, Jose; Christakos, George

    2013-04-01

    Dengue Fever (DF) is one of the most serious vector-borne infectious diseases in tropical and subtropical areas. DF epidemics occur in Taiwan annually especially during summer and fall seasons. Kaohsiung city has been one of the major DF hotspots in decades. The emergence and re-emergence of the DF epidemic is complex and can be influenced by various factors including space-time dynamics of human and vector populations and virus serotypes as well as the associated uncertainties. This study integrates a stochastic space-time "Susceptible-Infected-Recovered" model under Bayesian maximum entropy framework (BME-SIR) to perform real-time prediction of disease diffusion across space-time. The proposed model is applied for spatiotemporal prediction of the DF epidemic at Kaohsiung city during 2002 when the historical series of high DF cases was recorded. The online prediction by BME-SIR model updates the parameters of SIR model and infected cases across districts over time. Results show that the proposed model is rigorous to initial guess of unknown model parameters, i.e. transmission and recovery rates, which can depend upon the virus serotypes and various human interventions. This study shows that spatial diffusion can be well characterized by BME-SIR model, especially at the district surrounding the disease outbreak locations. The prediction performance at DF hotspots, i.e. Cianjhen and Sanmin, can be degraded due to the implementation of various disease control strategies during the epidemics. The proposed online disease prediction BME-SIR model can provide the governmental agency with a valuable reference to timely identify, control, and efficiently prevent DF spread across space-time.

  18. RESRO: A spatio-temporal model to optimise regional energy systems emphasising renewable energies

    OpenAIRE

    2012-01-01

    RESRO (Reference Energy System Regional Optimization) optimises the simultaneous fulfilment of the heat and power demand in regional energy systems. It is a mixed-integer program realised in the modelling language GAMS. The model handles information on geographically disaggregated data describing heat demand and renewable energy potentials (e.g. biomass, solar energy, ambient heat). Power demand is handled spatially aggregated in an hourly time resolution within 8 type days. The major idea is...

  19. The Role of Spatio-Temporal Resolution of Rainfall Inputs on a Landscape Evolution Model

    Science.gov (United States)

    Skinner, C. J.; Coulthard, T. J.

    2015-12-01

    Landscape Evolution Models are important experimental tools for understanding the long-term development of landscapes. Designed to simulate timescales ranging from decades to millennia, they are usually driven by precipitation inputs that are lumped, both spatially across the drainage basin, and temporally to daily, monthly, or even annual rates. This is based on an assumption that the spatial and temporal heterogeneity of the rainfall will equalise over the long timescales simulated. However, recent studies (Coulthard et al., 2012) have shown that such models are sensitive to event magnitudes, with exponential increases in sediment yields generated by linear increases in flood event size at a basin scale. This suggests that there may be a sensitivity to the spatial and temporal scales of rainfall used to drive such models. This study uses the CAESAR-Lisflood Landscape Evolution Model to investigate the impact of spatial and temporal resolution of rainfall input on model outputs. The sediment response to a range of temporal (15 min to daily) and spatial (5 km to 50km) resolutions over three different drainage basin sizes was observed. The results showed the model was sensitive to both, generating up to 100% differences in modelled sediment yields with smaller spatial and temporal resolution precipitation. Larger drainage basins also showed a greater sensitivity to both spatial and temporal resolution. Furthermore, analysis of the distribution of erosion and deposition patterns suggested that small temporal and spatial resolution inputs increased erosion in drainage basin headwaters and deposition in the valley floors. Both of these findings may have implications for existing models and approaches for simulating landscape development.

  20. Multiple spatio-temporal scale modeling of composites subjected to cyclic loading

    Science.gov (United States)

    Crouch, Robert; Oskay, Caglar; Clay, Stephen

    2013-01-01

    This manuscript presents a multiscale modeling methodology for failure analysis of composites subjected to cyclic loading conditions. Computational homogenization theory with multiple spatial and temporal scales is employed to devise the proposed methodology. Multiple spatial scales address the disparity between the length scale of material heterogeneities and the overall structure, whereas multiple temporal scales with almost periodic fields address the disparity between the load period and overall life under cyclic loading. The computational complexity of the multiscale modeling approach is reduced by employing a meso-mechanical model based on eigendeformation based homogenization with symmetric coefficients in the space domain, and an adaptive time stepping strategy based on a quadratic multistep method with error control in the time domain. The proposed methodology is employed to simulate the response of graphite fiber-reinforced epoxy composites. Model parameters are calibrated using a suite of experiments conducted on unidirectionally reinforced specimens subjected to monotonic and cyclic loading. The calibrated model is employed to predict damage progression in quasi-isotropic specimens. The capabilities of the model are validated using acoustic emission testing.

  1. On the spatio-temporal analysis of hydrological droughts from global hydrological models

    NARCIS (Netherlands)

    Corzo Perez, G.; Huijgevoort, van M.H.J.; Voss, F.; Lanen, van H.A.J.

    2011-01-01

    The recent concerns for world-wide extreme events related to climate change have motivated the development of large scale models that simulate the global water cycle. In this context, analysis of hydrological extremes is important and requires the adaptation of identification methods used for river

  2. Regional and local scale modeling of stream temperatures and spatio-temporal variation in thermal sensitivities.

    Science.gov (United States)

    Hilderbrand, Robert H; Kashiwagi, Michael T; Prochaska, Anthony P

    2014-07-01

    Understanding variation in stream thermal regimes becomes increasingly important as the climate changes and aquatic biota approach their thermal limits. We used data from paired air and water temperature loggers to develop region-scale and stream-specific models of average daily water temperature and to explore thermal sensitivities, the slopes of air-water temperature regressions, of mostly forested streams across Maryland, USA. The region-scale stream temperature model explained nearly 90 % of the variation (root mean square error = 0.957 °C), with the mostly flat coastal plain streams having significantly higher thermal sensitivities than the steeper highlands streams with piedmont streams intermediate. Model R (2) for stream-specific models was positively related to a stream's thermal sensitivity. Both the regional and the stream-specific air-water temperature regression models benefited from including mean daily discharge from regional gaging stations, but the degree of improvement declined as a stream's thermal sensitivity increased. Although catchment size had no relationship to thermal sensitivity, steeper streams or those with greater amounts of forest in their upstream watershed were less thermally sensitive. The subset of streams with three or more summers of temperature data exhibited a wide range of annual variation in thermal sensitivity at a site, with the variation not attributable to discharge, precipitation patterns, or physical attributes of streams or their watersheds. Our findings are a useful starting point to better understand patterns in stream thermal regimes. However, a more spatially and temporally comprehensive monitoring network should increase understanding of stream temperature variation and its controls as climatic patterns change.

  3. RESRO: A spatio-temporal model to optimise regional energy systems emphasising renewable energies

    Directory of Open Access Journals (Sweden)

    Gadocha S.

    2012-10-01

    Full Text Available RESRO (Reference Energy System Regional Optimization optimises the simultaneous fulfilment of the heat and power demand in regional energy systems. It is a mixed-integer program realised in the modelling language GAMS. The model handles information on geographically disaggregated data describing heat demand and renewable energy potentials (e.g. biomass, solar energy, ambient heat. Power demand is handled spatially aggregated in an hourly time resolution within 8 type days. The major idea is to use a high-spatial, low-temporal heat resolution and a low-spatial, hightemporal power resolution with both demand levels linked with each other. Due to high transport losses the possibilities for heat transport over long distances are unsatisfying. Thus, the spatial, raster-based approach is used to identify and utilise renewable energy resources for heat generation close to the customers as well as to optimize district heating grids and related energy flows fed by heating plants or combined heat and power (CHP plants fuelled by renewables. By combining the heat and electricity sector within the model, it is possible to evaluate relationships between these energy fields such as the use of CHP or heat pump technologies and also to examine relationships between technologies such as solar thermal and photovoltaic facilities, which are in competition for available, suitable roof or ground areas.

  4. Comparing hierarchical models for spatio-temporally misaligned data using the deviance information criterion.

    Science.gov (United States)

    Zhu, L; Carlin, B P

    Bayes and empirical Bayes methods have proven effective in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining overall geographic trends and patterns. Recent work extends these methods to the analysis of areal data which are spatially misaligned, that is, involving variables (typically counts or rates) which are aggregated over differing sets of regional boundaries. The addition of a temporal aspect complicates matters further, since now the misalignment can arise either within a given time point, or across time points (as when the regional boundaries themselves evolve over time). Hierarchical Bayesian methods (implemented via modern Markov chain Monte Carlo computing methods) enable the fitting of such models, but a formal comparison of their fit is hampered by their large size and often improper prior specifications. In this paper, we accomplish this comparison using the deviance information criterion (DIC), a recently proposed generalization of the Akaike information criterion (AIC) designed for complex hierarchical model settings like ours. We investigate the use of the delta method for obtaining an approximate variance estimate for DIC, in order to attach significance to apparent differences between models. We illustrate our approach using a spatially misaligned data set relating a measure of traffic density to paediatric asthma hospitalizations in San Diego County, California.

  5. Modeling change from large-scale high-dimensional spatio-temporal array data

    Science.gov (United States)

    Lu, Meng; Pebesma, Edzer

    2014-05-01

    The massive data that come from Earth observation satellite and other sensors provide significant information for modeling global change. At the same time, the high dimensionality of the data has brought challenges in data acquisition, management, effective querying and processing. In addition, the output of earth system modeling tends to be data intensive and needs methodologies for storing, validation, analyzing and visualization, e.g. as maps. An important proportion of earth system observations and simulated data can be represented as multi-dimensional array data, which has received increasingly attention in big data management and spatial-temporal analysis. Study cases will be developed in natural science such as climate change, hydrological modeling, sediment dynamics, from which the addressing of big data problems is necessary. Multi-dimensional array-based database management and analytics system such as Rasdaman, SciDB, and R will be applied to these cases. From these studies will hope to learn the strengths and weaknesses of these systems, how they might work together or how semantics of array operations differ, through addressing the problems associated with big data. Research questions include: • How can we reduce dimensions spatially and temporally, or thematically? • How can we extend existing GIS functions to work on multidimensional arrays? • How can we combine data sets of different dimensionality or different resolutions? • Can map algebra be extended to an intelligible array algebra? • What are effective semantics for array programming of dynamic data driven applications? • In which sense are space and time special, as dimensions, compared to other properties? • How can we make the analysis of multi-spectral, multi-temporal and multi-sensor earth observation data easy?

  6. Improving the spatio-temporal erodibility of dust emission models using MODIS data (Invited)

    Science.gov (United States)

    Chappell, A.

    2013-12-01

    Wind erosion and dust emission models are essential to estimate the lateral and vertical fluxes of carbon and nutrients between terrestrial and marine ecosystems and reduce uncertainty about dust radiative forcing of climate models. Wind erosion models approximate turbulent transfer of momentum by surface roughness elements using roughness density (lateral cover or the frontal area index; λ). Raupach et al. (1993) demonstrated using Marshall's (1971) wind tunnel data how the threshold friction velocity ratio Rt could be estimated as Rt(λ). Unfortunately, estimation of λ over large areas (regions etc.) is difficult and often approximated using classification of cover and vegetation type from satellite remote sensing and in the case of bare surfaces, using geometry. These approximations create surface discontinuities between classes and largely exclude heterogeneity due to mixtures of different surface types and canopies. Consequently, there is a need to reduce the uncertainty associated with the estimation of λ and develop a holistic approach common for all scales and surface mixtures of roughness types. When rough surfaces are exposed to the wind, wakes or areas of flow separation are created downwind of all obstacles. These sheltered areas reduce the area of exposed substrate and protect the erodible surface and some of the roughness elements from the wind (depending on their size and spacing). These sheltered areas have been conceptualised using shadow (Raupach, 1992) and shown to be proportional to shadow (Chappell et al., 2010). We made virtual reconstructions of Marshall's (1971) wind tunnel surfaces and applied a ray-tracer to estimate their at-nadir hemispherical ';black-sky' albedo (ωdir) and develop Rt(ωdir). In addition to that relationship being dependent on view zenith angle it is also dependent on waveband as evident by ω being influenced by soil moisture, soil colour etc. We use the ray-tracing simulations to normalise the data (Nω) and remove

  7. Logistic Models of Fractal Dimension Growth for Spatio-Temporal Dynamics of Urban Morphology

    CERN Document Server

    Chen, Yanguang

    2016-01-01

    Urban form and growth can be described with fractal dimension, which is a measurement of space filling of urban evolution. Based on empirical analyses, a discovery is made that the time series of fractal dimension of urban form can be treated as a sigmoid function of time. Among various sigmoid functions, the logistic function is the most probable selection. However, how to use the model of fractal dimension growth to explain and predict urban growth is a pending problem remaining to be solved. This paper is devoted to modeling fractal dimension evolution of different types of cities. A interesting discovery is as follows: for the cities in developed countries such as UK, USA and Israel, the comparable fractal dimension values of a city's morphology in different years can be fitted to the logistic function; while for the cities in developing countries such as China, the fractal dimension data of urban form can be fitted to a quadratic logistic function. A generalized logistic function is thus proposed to mode...

  8. Optimal Control of a Spatio-Temporal Model for Malaria: Synergy Treatment and Prevention

    Directory of Open Access Journals (Sweden)

    Malicki Zorom

    2012-01-01

    Full Text Available We propose a metapopulation model for malaria with two control variables, treatment and prevention, distributed between different patches (localities. Malaria spreads between these localities through human travel. We used the theory of optimal control and applied a mathematical model for three connected patches. From previous studies with the same data, two patches were identified as reservoirs of malaria infection, namely, the patches that sustain malaria epidemic in the other patches. We argue that to reduce the number of infections and semi-immunes (i.e., asymptomatic carriers of parasites in overall population, two considerations are needed, (a For the reservoir patches, we need to apply both treatment and prevention to reduce the number of infections and to reduce the number of semi-immunes; neither the treatment nor prevention were specified at the beginning of the control application, except prevention that seems to be effective at the end. (b For unreservoir patches, we should apply the treatment to reduce the number of infections, and the same strategy should be applied to semi-immune as in (a.

  9. Persistence, extinction and spatio-temporal synchronization of SIRS spatial models

    Science.gov (United States)

    Liu, Quan-Xing; Wang, Rong-Hua; Jin, Zhen

    2009-07-01

    Spatially explicit models are widely used in today's mathematical ecology and epidemiology to study persistence and extinction of populations as well as their spatial patterns. Here we extend the earlier work on static dispersal between neighboring individuals to the mobility of individuals as well as multi-patch environments. As is commonly found, the basic reproductive ratio is maximized for the evolutionarily stable strategy for disease persistence in mean field theory. This has important implications, as it implies that for a wide range of parameters the infection rate tends to a maximum. This is opposite to the present result obtained from spatially explicit models, which is that the infection rate is limited by an upper bound. We observe the emergence of trade-offs of extinction and persistence for the parameters of the infection period and infection rate, and show the extinction time as having a linear relationship with respect to system size. We further find that higher mobility can pronouncedly promote the persistence of the spread of epidemics, i.e., a phase transition occurs from the extinction domain to the persistence domain, and the wavelength of the spirals increases with the mobility ratio enhancement and will ultimately saturate at a certain value. Furthermore, for the multi-patch case, we find that lower coupling strength leads to anti-phase oscillation of the infected fraction, while higher coupling strength corresponds to in-phase oscillation.

  10. Spatio-temporal tumour model for analysis and mechanism of action of intracellular drug accumulation

    Indian Academy of Sciences (India)

    Somna Mishra; V K Katiyar

    2008-09-01

    We have developed a one-dimensional tumour simulator to describe the biodistribution of chemotherapeutic drugs to a tumoral lesion and the tumour cell’s response to therapy. A three-compartment model is used for drug dynamics within the tumour. The first compartment represents the extracellular space in which cells move, the second corresponds to the intracellular fluid space (including cell membrane) which is in direct equilibrium with the extracellular space, and the third is a non-exchangeable compartment that represents sequestered drug which is trapped in the nucleus to damage the cellular DNA, directly triggering cell death. Analytical and numerical techniques (Finite Element Method) are used to describe the tumour’s response to therapy and the effect of parameter variation on the drug concentration profiles in the three compartments.

  11. Probabilistic daily ILI syndromic surveillance with a spatio-temporal Bayesian hierarchical model.

    Directory of Open Access Journals (Sweden)

    Ta-Chien Chan

    Full Text Available BACKGROUND: For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty. METHODS AND FINDINGS: Based on the Bayesian posterior probability of influenza-like illness (ILI visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006-2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1-2 days prior to the rise of ILI visits. CONCLUSIONS: This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs.

  12. Modeling Spatio-Temporal Dynamics of Optimum Tilt Angles for Solar Collectors in Turkey

    Directory of Open Access Journals (Sweden)

    Recep Kulcu

    2008-05-01

    Full Text Available Quantifying spatial and temporal variations in optimal tilt angle of a solar collector relative to a horizontal position assists in maximizing its performance for energy collection depending on changes in time and space. In this study, optimal tilt angles were quantified for solar collectors based on the monthly global and diffuse solar radiation on a horizontal surface across Turkey. The dataset of monthly average daily global solar radiation was obtained from 158 places, and monthly diffuse radiation data were estimated using an empirical model in the related literature. Our results showed that high tilt angles during the autumn (September to November and winter (December to February and low tilt angles during the summer (March to August enabled the solar collector surface to absorb the maximum amount of solar radiation. Monthly optimum tilt angles were estimated devising a sinusoidal function of latitude and day of the year, and their validation resulted in a high R2 value of 98.8%, with root mean square error (RMSE of 2.06o.

  13. Continental Spatio-temporal Data Analysis with Linear Spectral Mixture Model using FOSS

    Science.gov (United States)

    Kumar, U.; Nemani, R. R.; Ganguly, S.; Milesi, C.; Raja, K. S.; Wang, W.; Votava, P.; Michaelis, A.

    2015-12-01

    This work demonstrates the development and implementation of a Fully Constrained Least Squares (FCLS) unmixing model developed in C++ programming language with OpenCV package and boost C++ libraries in the NASA Earth Exchange (NEX). Visualization of the results is supported by GRASS GIS and statistical analysis is carried in R in a Linux system environment. FCLS was first tested on computer simulated data with Gaussian noise of various signal-to-noise ratio, and Landsat data of an agricultural scenario and an urban environment using a set of global endmembers of substrate (soils, sediments, rocks, and non-photosynthetic vegetation), vegetation that includes green photosynthetic plants and dark objects which encompasses absorptive substrate materials, clear water, deep shadows, etc. For the agricultural scenario, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data of Fresno, California, USA were unmixed and the results were validated with the corresponding ground data. To study an urbanized landscape, a clear sky Landsat-5 TM data were unmixed and validated with coincident World View-2 abundance maps (of 2 m spatial resolution) for an area of San Francisco, California, USA. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success, boxplot and bivariate distribution function. Finally, FCLS was used for sub-pixel land cover analysis of the monthly WELD (Wen-enabled Landsat data) repository from 2008 to 2011 of North America. The abundance maps in conjunction with DMSP-OLS nighttime lights data were used to extract the urban land cover features and analyze their spatial-temporal growth.

  14. Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing

    Science.gov (United States)

    Edirisinghe, Asoka; Clark, Dave; Waugh, Deanne

    2012-06-01

    Pasture biomass is a vital input for management of dairy systems in New Zealand. An accurate estimate of pasture biomass information is required for the calculation of feed budget, on which decisions are made for farm practices such as conservation, nitrogen use, rotational lengths and supplementary feeding leading to profitability and sustainable use of pasture resources. The traditional field based methods of measuring pasture biomass such as using rising plate metres (RPM) are largely inefficient in providing the timely information at the spatial extent and temporal frequency demanded by commercial environments. In recent times remote sensing has emerged as an alternative tool. In this paper we have examined the Normalised Difference Vegetation Index (NDVI) derived from medium resolution imagery of SPOT-4 and SPOT-5 satellite sensors to predict pasture biomass of intensively grazed dairy pastures. In the space and time domain analysis we have found a significant dependency of time over the season and no dependency of space across the scene at a given time for the relationship between NDVI and field based pasture biomass. We have established a positive correlation (81%) between the two variables in a pixel scale analysis. The application of the model on 2 selected farms over 3 images and aggregation of the predicted biomass to paddock scale has produced paddock average pasture biomass values with a coefficient of determination of 0.71 and a standard error of 260 kg DM ha-1 in the field observed range between 1500 and 3500 kg DM ha-1. This result indicates a high potential for operational use of remotely sensed data to predict pasture biomass of intensively grazed dairy pastures.

  15. Stochastic spatio-temporal modelling of African swine fever spread in the European Union during the high risk period.

    Science.gov (United States)

    Nigsch, Annette; Costard, Solenne; Jones, Bryony A; Pfeiffer, Dirk U; Wieland, Barbara

    2013-03-01

    African swine fever (ASF) is a notifiable viral pig disease with high mortality and serious socio-economic consequences. Since ASF emerged in Georgia in 2007 the disease has spread to several neighbouring countries and cases have been detected in areas bordering the European Union (EU). It is uncertain how fast the virus would be able to spread within the unrestricted European trading area if it were introduced into the EU. This project therefore aimed to develop a model for the spread of ASF within and between the 27 Member States (MS) of the EU during the high risk period (HRP) and to identify MS during that period would most likely contribute to ASF spread ("super-spreaders") or MS that would most likely receive cases from other MS ("super-receivers"). A stochastic spatio-temporal state-transition model using simulated individual farm records was developed to assess silent ASF virus spread during different predefined HRPs of 10-60 days duration. Infection was seeded into farms of different pig production types in each of the 27 MS. Direct pig-to-pig transmission and indirect transmission routes (pig transport lorries and professional contacts) were considered the main pathways during the early stages of an epidemic. The model was parameterised using data collated from EUROSTAT, TRACES, a questionnaire sent to MS, and the scientific literature. Model outputs showed that virus circulation was generally limited to 1-2 infected premises per outbreak (95% IQR: 1-4; maximum: 10) with large breeder farms as index case resulting in most infected premises. Seven MS caused between-MS spread due to intra-Community trade during the first 10 days after seeding infection. For a HRP of 60 days from virus introduction, movements of infected pigs will originate at least once from 16 MS, with 6 MS spreading ASF in more than 10% of iterations. Two thirds of all intra-Community spread was linked to six trade links only. Denmark, the Netherlands, Lithuania and Latvia were identified

  16. The Huaihe Basin Water Resource and Water Quality Management Platform Implemented with a Spatio-Temporal Data Model

    Science.gov (United States)

    Liu, Y.; Zhang, W.; Yan, C.

    2012-07-01

    Presently, planning and assessment in maintenance, renewal and decision-making for watershed hydrology, water resource management and water quality assessment are evolving toward complex, spatially explicit regional environmental assessments. These problems have to be addressed with object-oriented spatio-temporal data models that can restore, manage, query and visualize various historic and updated basic information concerning with watershed hydrology, water resource management and water quality as well as compute and evaluate the watershed environmental conditions so as to provide online forecasting to police-makers and relevant authorities for supporting decision-making. The extensive data requirements and the difficult task of building input parameter files, however, has long been an obstacle to use of such complex models timely and effectively by resource managers. Success depends on an integrated approach that brings together scientific, education and training advances made across many individual disciplines and modified to fit the needs of the individuals and groups who must write, implement, evaluate, and adjust their watershed management plans. The centre for Hydro-science Research, Nanjing University, in cooperation with the relevant watershed management authorities, has developed a WebGIS management platform to facilitate this complex process. Improve the management of watersheds over the Huaihe basin through the development, promotion and use of a web-based, user-friendly, geospatial watershed management data and decision support system (WMDDSS) involved many difficulties for the development of this complicated System. In terms of the spatial and temporal characteristics of historic and currently available information on meteorological, hydrological, geographical, environmental and other relevant disciplines, we designed an object-oriented spatiotemporal data model that combines spatial, attribute and temporal information to implement the management

  17. Measuring and modeling spatio-temporal patterns of groundwater storage dynamics to better understand nonlinear streamflow response

    Science.gov (United States)

    Rinderer, Michael; van Meerveld, Ilja; McGlynn, Brian

    2017-04-01

    area concept. However, isolated active parts of the catchment did not connect to the stream during some events. In a sensitivity analysis, we tested the influence of the different groundwater response thresholds and the influence of the uncertainty due to the time series clustering on the extent of the active and connected area. While the size and shape of the active and connected area differed between the scenarios, the dynamics of the connected areas and the relation between storage and streamflow did not differ significantly. This suggests that in steep catchments with shallow perched groundwater tables, groundwater dynamics can be upscaled based on topographic site characteristic to obtain catchment-scale groundwater response patterns, which can then be used to predict the spatio-temporal evolution of the active and connected runoff source areas.

  18. Statistical methods for spatio-temporal systems

    CERN Document Server

    Finkenstadt, Barbel

    2006-01-01

    Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities.Contributed by leading researchers in the field, each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis, gastroenteric disease, and the U.K. foot-and-mouth outbreak, the first chapter uses stochastic models, such as point process models, to provide the probabilistic backbone that facilitates statistical inference from data. The next chapter discusses the critical issue of modeling random growth objects in diverse biological systems, such as bacteria colonies, tumors, and plant populations. The subsequent chapter examines data transformation tools using examples from ecology and air quality data, followed by a chapter on space-time co...

  19. Integrating Map Algebra and Statistical Modeling for Spatio-Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain

    Science.gov (United States)

    Evrendilek, Fatih

    2007-01-01

    This study aims at quantifying spatio-temporal dynamics of monthly mean daily incident photosynthetically active radiation (PAR) over a vast and complex terrain such as Turkey. The spatial interpolation method of universal kriging, and the combination of multiple linear regression (MLR) models and map algebra techniques were implemented to generate surface maps of PAR with a grid resolution of 500 × 500 m as a function of five geographical and 14 climatic variables. Performance of the geostatistical and MLR models was compared using mean prediction error (MPE), root-mean-square prediction error (RMSPE), average standard prediction error (ASE), mean standardized prediction error (MSPE), root-mean-square standardized prediction error (RMSSPE), and adjusted coefficient of determination (R2adj.). The best-fit MLR- and universal kriging-generated models of monthly mean daily PAR were validated against an independent 37-year observed dataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifing method. The spatial variability patterns of monthly mean daily incident PAR were more accurately reflected in the surface maps created by the MLR-based models than in those created by the universal kriging method, in particular, for spring (May) and autumn (November). The MLR-based spatial interpolation algorithms of PAR described in this study indicated the significance of the multifactor approach to understanding and mapping spatio-temporal dynamics of PAR for a complex terrain over meso-scales.

  20. Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model

    KAUST Repository

    Angel, Yoseline

    2016-10-25

    Hyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological cycles of crops. This paper explores a cloud removal approach based on reflectance prediction using multitemporal data and spatio-Temporal statistical models. In particular, a covariance model that captures the behavior of spatial and temporal components in data simultaneously (i.e. non-separable) is considered. Eight weekly images collected from the Hyperion hyper-spectrometer instrument over an agricultural region of Saudi Arabia were used to reconstruct a scene with the presence of cloudy affected pixels over a center-pivot crop. A subset of reflectance values of cloud-free pixels from 50 bands in the spectral range from 426.82 to 884.7 nm at each date, were used as input to fit a parametric family of non-separable and stationary spatio-Temporal covariance functions. Applying simple kriging as an interpolator, cloud affected pixels were replaced by cloud-free predicted values per band, obtaining their respective predicted spectral profiles at the same time. An exercise of reconstructing simulated cloudy pixels in a different swath was conducted to assess the model accuracy, achieving root mean square error (RMSE) values per band less than or equal to 3%. The spatial coherence of the results was also checked through absolute error distribution maps demonstrating their consistency.

  1. Spatio-temporal models to estimate daily concentrations of fine particulate matter in Montreal: Kriging with external drift and inverse distance-weighted approaches.

    Science.gov (United States)

    Ramos, Yuddy; St-Onge, Benoît; Blanchet, Jean-Pierre; Smargiassi, Audrey

    2016-06-01

    Air pollution is a major environmental and health problem, especially in urban agglomerations. Estimating personal exposure to fine particulate matter (PM2.5) remains a great challenge because it requires numerous point measurements to explain the daily spatial variation in pollutant levels. Furthermore, meteorological variables have considerable effects on the dispersion and distribution of pollutants, which also depends on spatio-temporal emission patterns. In this study we developed a hybrid interpolation technique that combined the inverse distance-weighted (IDW) method with Kriging with external drift (KED), and applied it to daily PM2.5 levels observed at 10 monitoring stations. This provided us with downscaled high-resolution maps of PM2.5 for the Island of Montreal. For the KED interpolation, we used spatio-temporal daily meteorological estimates and spatial covariates as land use and vegetation density. Different KED and IDW daily estimation models for the year 2010 were developed for each of the six synoptic weather classes. These clusters were developed using principal component analysis and unsupervised hierarchical classification. The results of the interpolation models were assessed with a leave-one-station-out cross-validation. The performance of the hybrid model was better than that of the KED or the IDW alone for all six synoptic weather classes (the daily estimate for R(2) was 0.66-0.93 and for root mean square error (RMSE) 2.54-1.89 μg/m(3)).

  2. Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model

    KAUST Repository

    Angel, Yoseline

    2016-09-26

    Hyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological cycles of crops. This paper explores a cloud removal approach based on reflectance prediction using multi-temporal data and spatio-temporal statistical models. In particular, a covariance model that captures the behavior of spatial and temporal components in data simultaneously (i.e. non-separable) is considered. Eight weekly images collected from the Hyperion hyper-spectrometer instrument over an agricultural region of Saudi Arabia were used to reconstruct a scene with the presence of cloudy affected pixels over a center-pivot crop. A subset of reflectance values of cloud-free pixels from 50 bands in the spectral range from 426.82 to 884.7 nm at each date, were used as input to fit a parametric family of non-separable and stationary spatio-temporal covariance functions. Applying simple kriging as an interpolator, cloud affected pixels were replaced by cloud-free predicted values per band, obtaining their respective predicted spectral profiles at the same time. An exercise of reconstructing simulated cloudy pixels in a different swath was conducted to assess the model accuracy, achieving root mean square error (RMSE) values per band less than or equal to 3%. The spatial coherence of the results was also checked through absolute error distribution maps demonstrating their consistency.

  3. Spatio-temporal rectification of tower-based eddy-covariance flux measurements for consistently informing process-based models

    Science.gov (United States)

    Metzger, S.; Xu, K.; Desai, A. R.; Taylor, J. R.; Kljun, N.; Schneider, D.; Kampe, T. U.; Fox, A. M.

    2013-12-01

    Process-based models, such as land surface models (LSMs), allow insight in the spatio-temporal distribution of stocks and the exchange of nutrients, trace gases etc. among environmental compartments. More recently, LSMs also become capable of assimilating time-series of in-situ reference observations. This enables calibrating the underlying functional relationships to site-specific characteristics, or to constrain the model results after each time-step in an attempt to minimize drift. The spatial resolution of LSMs is typically on the order of 10^2-10^4 km2, which is suitable for linking regional to continental scales and beyond. However, continuous in-situ observations of relevant stock and exchange variables, such as tower-based eddy-covariance (EC) fluxes, represent orders of magnitude smaller spatial scales (10^-6-10^1 km2). During data assimilation, this significant gap in spatial representativeness is typically either neglected, or side-stepped using simple tiling approaches. Moreover, at ';coarse' resolutions, a single LSM evaluation per time-step implies linearity among the underlying functional relationships as well as among the sub-grid land cover fractions. This, however, is not warranted for land-atmosphere exchange processes over more complex terrain. Hence, it is desirable to explicitly consider spatial variability at LSM sub-grid scales. Here we present a procedure that determines from a single EC tower the spatially integrated probability density function (PDF) of the surface-atmosphere exchange for individual land covers. These PDFs allow quantifying the expected value, as well as spatial variability over a target domain, can be assimilated in tiling-capable LSMs, and mitigate linearity assumptions at ';coarse' resolutions. The procedure is based on the extraction and extrapolation of environmental response functions (ERFs), for which a technical-oriented companion poster is submitted. In short, the subsequent steps are: (i) Time

  4. Spatio-Temporal Changes in the Rice Planting Area and Their Relationship to Climate Change in Northeast China:A Model-Based Analysis

    Institute of Scientific and Technical Information of China (English)

    XIA Tian; WU Wen-bin; ZHOU Qing-bo; YU Qiang-yi; Peter H Verburg; YANG Peng; LU Zhong-jun; TANG Hua-jun

    2014-01-01

    Rice is one of the most important grain crops in Northeast China (NEC) and its cultivation is sensitive to climate change. This study aimed to explore the spatio-temporal changes in the NEC rice planting area over the period of 1980-2010 and to analyze their relationship to climate change. To do so, the CLUE-S (conversion of land use and its effects at small region extent) model was ifrst updated and used to simulate dynamic changes in the rice planting area in NEC to understand spatio-temporal change trends during three periods: 1980-1990, 1990-2000 and 2000-2010. The changing results in individual periods were then linked to climatic variables to investigate the climatic drivers of these changes. Results showed that the NEC rice planting area expanded quickly and increased by nearly 4.5 times during 1980-2010. The concentration of newly planted rice areas in NEC constantly moved northward and the changes were strongly dependent on latitude. This conifrmed that climate change, increases in temperature in particular, greatly inlfuenced the shift in the rice planting area. The shift in the north limit of the NEC rice planting area generally followed a 1°C isoline migration pattern, but with an obvious time-lag effect. These ifndings can help policy makers and crop producers take proper adaptation measures even when exposed to the global warming situation in NEC.

  5. Climate-driven mathematical models to understand the spatio-temporal heterogeneity of a chikungunya outbreak in the presence of widespread asymptomatic infection

    Science.gov (United States)

    Dommar, Carlos J.; Robinson, Marguerite; Lowe, Rachel; Conan, Anne; Buchy, Philippe; Tarantola, Arnaud; Rodó, Xavier

    2014-05-01

    The emergence and persistence of human pathogens in the environment represents a constant threat to society, with global implications for human health, economies and ecosystems. Of particular concern are vector-borne diseases, such as dengue, malaria and chikungunya, which are increasing across their traditional ranges and continuing to infiltrate new regions. This unprecedented situation has been partly attributed to the increase in global temperatures in recent decades which has allowed non-native mosquito species to invade and successfully colonise previously inhospitable environments. The spatio-temporal evolution of these diseases is determined by the interaction of the host and vector, which is strongly dependent on social structures and mobility patterns. In turn, vector populations are thought to be driven by external environmental variables, such as precipitation and temperature. Furthermore, the ability of asymptomatic individuals to successfully transmit the infection and evade control measures can undermine public health interventions. We employed a stochastic model, which explicitly included asymptomatic and undocumented laboratory confirmed cases, and applied it to a documented outbreak in Cambodia in 2012 (Trapeang Roka village, Kampong Speu Province). The resulting estimate of the reproduction number was considerably higher than values obtained for previous outbreaks and highlights the importance of asymptomatic transmission. Subsequently, we develop an agent-based model (ABM), in which each individual is explicitly represented and vector populations are linked to precipitation estimates in a tropical setting. The model is implemented on both scale-free and regular networks. The spatio-temporal transmission of chikungunya is analysed and the presence of asymptomatic silent spreaders within the population is investigated in the context of implementing travel restrictions during an outbreak. Preventing the movement of symptomatic individuals alone is

  6. Earthquake forecasting in Italy, before and after Umbria-Marche seismic sequence 1997. A review of the earthquake occurrence modeling at different spatio-temporal-magnitude scales.

    Directory of Open Access Journals (Sweden)

    W. Marzocchi

    2008-06-01

    Full Text Available The main goal of this work is to review the scientific researches carried out before and after the Umbria-Marche sequence related to the earthquake forecasting/prediction in Italy. In particular, I focus the attention on models that aim addressing three main practical questions: was (is Umbria-Marche a region with high probability of occurrence of a destructive earthquake? Was a precursory activity recorded before the mainshock(s? What was our capability to model the spatio-temporal-magnitude evolution of that seismic sequence? The models are reviewed pointing out what we have learned after the Umbria-Marche earthquakes, in terms of physical understanding of earthquake occurrence process, and of improving our capability to forecast earthquakes and to track in real-time seismic sequences.

  7. DynaPop-X: A population dynamics model applied to spatio-temporal exposure assessment - Implementation aspects from the CRISMA project

    Science.gov (United States)

    Aubrecht, Christoph; Steinnocher, Klaus; Humer, Heinrich; Huber, Hermann

    2014-05-01

    In the context of proactive disaster risk as well as immediate situational crisis management knowledge of locational social aspects in terms of spatio-temporal population distribution dynamics is considered among the most important factors for disaster impact minimization (Aubrecht et al., 2013a). This applies to both the pre-event stage for designing appropriate preparedness measures and to acute crisis situations when an event chain actually unfolds for efficient situation-aware response. The presented DynaPop population dynamics model is developed at the interface of those interlinked crisis stages and aims at providing basic input for social impact evaluation and decision support in crisis management. The model provides the starting point for assessing population exposure dynamics - thus here labeled as DynaPop-X - which can either be applied in a sense of illustrating the changing locations and numbers of affected people at different stages during an event or as ex-ante estimations of probable and maximum expected clusters of affected population (Aubrecht et al., 2013b; Freire & Aubrecht, 2012). DynaPop is implemented via a gridded spatial disaggregation approach and integrates previous efforts on spatio-temporal modeling that account for various aspects of population dynamics such as human mobility and activity patterns that are particularly relevant in picturing the highly dynamic daytime situation (Ahola et al., 2007; Bhaduri, 2008; Cockings et al., 2010). We will present ongoing developments particularly focusing on the implementation logic of the model using the emikat software tool, a data management system initially designed for inventorying and analysis of spatially resolved regional air pollutant emission scenarios. This study was performed in the framework of the EU CRISMA project. CRISMA is funded from the European Community's Seventh Framework Programme FP7/2007-2013 under grant agreement no. 284552. REFERENCES Ahola, T., Virrantaus, K., Krisp, J

  8. How about a Bayesian M/EEG imaging method correcting for incomplete spatio-temporal priors

    DEFF Research Database (Denmark)

    Stahlhut, Carsten; Attias, Hagai T.; Sekihara, Kensuke;

    2013-01-01

    In this contribution we present a hierarchical Bayesian model, sAquavit, to tackle the highly ill-posed problem that follows with MEG and EEG source imaging. Our model facilitates spatio-temporal patterns through the use of both spatial and temporal basis functions. While in contrast to most...... previous spatio-temporal inverse M/EEG models, the proposed model benefits of consisting of two source terms, namely, a spatio-temporal pattern term limiting the source configuration to a spatio-temporal subspace and a source correcting term to pick up source activity not covered by the spatio...

  9. Hierarchical stochastic modeling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example

    Science.gov (United States)

    Wildhaber, Mark L.; Wikle, Christopher K.; Moran, Edward H.; Anderson, Christopher J.; Franz, Kristie J.; Dey, Rima

    2017-01-01

    We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere – ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, cli- mate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our down- scaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.

  10. Enhancing Spatio-Temporal Identity: States of Existence and Presence

    Directory of Open Access Journals (Sweden)

    Pierre Hallot

    2016-05-01

    Full Text Available This work presents a new approach that aims to characterize the spatio-temporal relationships that exist between geographical objects that are absent or non-existent at the moment of analysis. First, we would like to propose a formal analysis of the spatio-temporal states of presence and existence of a geographical object. We will then use a combination of these states in order to define a set of life and motion configurations. The model developed then serves as a formal basis for the realization of a series of spatio-temporal queries based on an analysis of patterns in the succession of spatio-temporal states. The entire approach is then demonstrated by using the example of the organization of a scientific conference by defining the spatio-temporal relationships between the conference participants. The research methodology is finally compared with a real dataset taken from a geolocalized social network to show the efficiency of this type of management.

  11. Evaluation of high resolution spatio-temporal precipitation extremes from a stochastic weather generator

    DEFF Research Database (Denmark)

    Sørup, Hjalte Jomo Danielsen; Christensen, O. B.; Arnbjerg-Nielsen, Karsten

    Spatio-temporal rainfall is modelled for the North-Eastern part of Zealand (Denmark) using the Spatio-Temporal Neyman-Scott Rectangular Pulses model as implemented in the RainSim software. Hourly precipitation series for fitting the model are obtained from a dense network of tipping bucket rain g...

  12. Evaluation of high resolution spatio-temporal precipitation extremes from a stochastic weather generator

    DEFF Research Database (Denmark)

    Sørup, Hjalte Jomo Danielsen; Christensen, O. B.; Arnbjerg-Nielsen, Karsten;

    Spatio-temporal rainfall is modelled for the North-Eastern part of Zealand (Denmark) using the Spatio-Temporal Neyman-Scott Rectangular Pulses model as implemented in the RainSim software. Hourly precipitation series for fitting the model are obtained from a dense network of tipping bucket rain g...

  13. Reaction diffusion equation with spatio-temporal delay

    Science.gov (United States)

    Zhao, Zhihong; Rong, Erhua

    2014-07-01

    We investigate reaction-diffusion equation with spatio-temporal delays, the global existence, uniqueness and asymptotic behavior of solutions for which in relation to constant steady-state solution, included in the region of attraction of a stable steady solution. It is shown that if the delay reaction function satisfies some conditions and the system possesses a pair of upper and lower solutions then there exists a unique global solution. In terms of the maximal and minimal constant solutions of the corresponding steady-state problem, we get the asymptotic stability of reaction-diffusion equation with spatio-temporal delay. Applying this theory to Lotka-Volterra model with spatio-temporal delay, we get the global solution asymptotically tend to the steady-state problem's steady-state solution.

  14. Spatio-temporal data analytics for wind energy integration

    CERN Document Server

    Yang, Lei; Zhang, Junshan

    2014-01-01

    This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic di

  15. Analysis and Modeling of spatio-temporal Patterns of Carbon and Water Fluxes in Production Fields of Winter Wheat and Sugar Beet

    Science.gov (United States)

    Kupisch, M.; Langensiepen, M.; van Wijk, M.; Stadler, A.; Ewert, F.

    2011-12-01

    Gas exchange of CO2 and water vapour are important processes that determine crop growth and yield. Understanding their spatio-temporal variability at field level is necessary for accurate simulation of crop growth in fields with heterogeneous growing conditions and for parameterizing soil-vegetation-atmosphere transfer (SVAT) models. Accordingly, relationships between the spatio-temporal patterns of assimilation and transpiration rates and environmental (e.g. soil) heterogeneity are of specific interest. A particular challenge refers then to the appropriate method of up-scaling of these relationships from the leaf to the canopy and field level. Therefore, gas-exchange (CO2 and water vapour) was measured at different points in winter wheat and sugar beet fieldsboth at leaf and at canopy level in a nearly biweekly cycle during the growing seasons 2010 and 2011. The measurements comprised also C/N-content of leaf, leaf area index, soil water content and soil nitrogen content. The results revealed a strong spatial heterogeneity of carbon and water canopy fluxes across the fields. While canopy measurements had a temporal variability with distinct diurnal and seasonal patterns, the temporal (and spatial) variability of leaf level photosynthesisand transpirationwas comparably small.Further analysis suggests that the observed spatial and seasonal variability of canopy measurements was mainly caused by field heterogeneity in LAI and less by gas exchange rates per unit leaf area. However, both crops differed in their response to drought stress: while wheat responded mainly through irreversible reduction in green leaf area, the canopy assimilation rate of sugar beets decreases only temporarily with no observed effects in LAI. The obtained datasets from both years are the basis for parameterizing a crop growth model with canopy assimilation and transpiration components and for developing appropriate up-scaling methods from leaf to field. Our results indicate that it is

  16. A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques

    Science.gov (United States)

    Chang, Fi-John; Tsai, Meng-Jung

    2016-04-01

    Accurate multi-step-ahead inflow forecasting during typhoon periods is extremely crucial for real-time reservoir flood control. We propose a spatio-temporal lumping of radar rainfall for modeling inflow forecasts to mitigate time-lag problems and improve forecasting accuracy. Spatial aggregation of radar cells is made based on the sub-catchment partitioning obtained from the Self-Organizing Map (SOM), and then flood forecasting is made by the Adaptive Neuro Fuzzy Inference System (ANFIS) models coupled with a 2-staged Gamma Test (2-GT) procedure that identifies the optimal non-trivial rainfall inputs. The Shihmen Reservoir in northern Taiwan is used as a case study. The results show that the proposed methods can, in general, precisely make 1- to 4-hour-ahead forecasts and the lag time between predicted and observed flood peaks could be mitigated. The constructed ANFIS models with only two fuzzy if-then rules can effectively categorize inputs into two levels (i.e. high and low) and provide an insightful view (perspective) of the rainfall-runoff process, which demonstrate their capability in modeling the complex rainfall-runoff process. In addition, the confidence level of forecasts with acceptable error can reach as high as 97% at horizon t+1 and 77% at horizon t+4, respectively, which evidently promotes model reliability and leads to better decisions on real-time reservoir operation during typhoon events.

  17. Advaced Spatio-Temporal Thermal Analysis of Electronic Systems

    Directory of Open Access Journals (Sweden)

    Miroslav Hrianka

    2003-01-01

    Full Text Available The article gives a brief review the of diagnostics and analysis possibilities by a spatio-temporal approach into electronic system in infrared bandwidth. The two dimensional image grabbed by the thermo vision camera provides information about the surface temperature distribution of an electronic system. The main idea is based on the analysis of the object which consists of a temporal sequence of a spatial thermal images. Advanced analysis is achieved by morphological image gradient spatio-temporal model: The mentioned method provides a total temperature system evaluation as well as it allows separate analysis in the chosen determined temperature area.

  18. Integrating real-time and manual monitored data to predict hillslope soil moisture dynamics with high spatio-temporal resolution using linear and non-linear models

    Science.gov (United States)

    Zhu, Qing; Zhou, Zhiwen; Duncan, Emily W.; Lv, Ligang; Liao, Kaihua; Feng, Huihui

    2017-02-01

    Spatio-temporal variability of soil moisture (θ) is a challenge that remains to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time θ monitoring methods. This restricted the comprehensive and intensive examination of θ dynamics. In this study, we integrated the manual and real-time monitored data to depict the hillslope θ dynamics with good spatial coverage and temporal resolution. Linear (stepwise multiple linear regression-SMLR) and non-linear (support vector machines-SVM) models were used to predict θ at 39 manual sites (collected 1-2 times per month) with θ collected at three real-time monitoring sites (collected every 5 mins). By comparing the accuracies of SMLR and SVM for each depth and manual site, an optimal prediction model was then determined at this depth of this site. Results showed that θ at the 39 manual sites can be reliably predicted (root mean square errors model. The subsurface flow dynamics was an important factor that determined whether the relationship was linear or non-linear. Depth to bedrock, elevation, topographic wetness index, profile curvature, and θ temporal stability influenced the selection of prediction model since they were related to the subsurface soil water distribution and movement. Using this approach, hillslope θ spatial distributions at un-sampled times and dates can be predicted. Missing information of hillslope θ dynamics can be acquired successfully.

  19. On spatio-temporal Lévy based Cox processes

    DEFF Research Database (Denmark)

    Prokesova, Michaela; Hellmund, Gunnar; Jensen, Eva Bjørn Vedel

    2006-01-01

    The paper discusses a new class of models for spatio-temporal Cox point processes. In these models, the driving field is defined by means of an integral of a weight function with respect to a Lévy basis. The relations to other Cox process models studied previously are discussed and formulas for t...

  20. Assessment of spatio-temporal gait parameters from trunk accelerations during human walking

    NARCIS (Netherlands)

    Zijlstra, W; Hof, AL

    2003-01-01

    This paper studies the feasibility of an analysis of spatio-temporal gait parameters based upon accelerometry. To this purpose, acceleration patterns of the trunk and their relationships with spatio-temporal gait parameters were analysed in healthy subjects. Based on model predictions of the body's

  1. A top-down hierarchical spatio-temporal process description method and its data organization

    Science.gov (United States)

    Xie, Jiong; Xue, Cunjin

    2009-10-01

    Modeling and representing spatio-temporal process is the key foundation for analyzing geographic phenomenon and acquiring spatio-temporal high-level knowledge. Spatio-temporal representation methods with bottom-up approach based on object modeling view lack of explicit definition of geographic phenomenon and finer-grained representation of spatio-temporal causal relationships. Based on significant advances in data modeling of spatio-temporal object and event, aimed to represent discrete regional dynamic phenomenon composed with group of spatio-temporal objects, a regional spatio-temporal process description method using Top-Down Hierarchical approach (STP-TDH) is proposed and a data organization structure based on relational database is designed and implemented which builds up the data structure foundation for carrying out advanced data utilization and decision-making. The land use application case indicated that process modeling with top-down approach was proved to be good with the spatio-temporal cognition characteristic of our human, and its hierarchical representation framework can depict dynamic evolution characteristic of regional phenomenon with finer-grained level and can reduce complexity of process description.

  2. Assessment of spatio-temporal gait parameters from trunk accelerations during human walking

    NARCIS (Netherlands)

    Zijlstra, W; Hof, AL

    2003-01-01

    This paper studies the feasibility of an analysis of spatio-temporal gait parameters based upon accelerometry. To this purpose, acceleration patterns of the trunk and their relationships with spatio-temporal gait parameters were analysed in healthy subjects. Based on model predictions of the body's

  3. Glutamate-bound NMDARs arising from in vivo-like network activity extend spatio-temporal integration in a L5 cortical pyramidal cell model.

    Directory of Open Access Journals (Sweden)

    Matteo Farinella

    2014-04-01

    Full Text Available In vivo, cortical pyramidal cells are bombarded by asynchronous synaptic input arising from ongoing network activity. However, little is known about how such 'background' synaptic input interacts with nonlinear dendritic mechanisms. We have modified an existing model of a layer 5 (L5 pyramidal cell to explore how dendritic integration in the apical dendritic tuft could be altered by the levels of network activity observed in vivo. Here we show that asynchronous background excitatory input increases neuronal gain and extends both temporal and spatial integration of stimulus-evoked synaptic input onto the dendritic tuft. Addition of fast and slow inhibitory synaptic conductances, with properties similar to those from dendritic targeting interneurons, that provided a 'balanced' background configuration, partially counteracted these effects, suggesting that inhibition can tune spatio-temporal integration in the tuft. Excitatory background input lowered the threshold for NMDA receptor-mediated dendritic spikes, extended their duration and increased the probability of additional regenerative events occurring in neighbouring branches. These effects were also observed in a passive model where all the non-synaptic voltage-gated conductances were removed. Our results show that glutamate-bound NMDA receptors arising from ongoing network activity can provide a powerful spatially distributed nonlinear dendritic conductance. This may enable L5 pyramidal cells to change their integrative properties as a function of local network activity, potentially allowing both clustered and spatially distributed synaptic inputs to be integrated over extended timescales.

  4. Spatio-temporal optical vortices

    CERN Document Server

    Jhajj, N; Rosenthal, E W; Zahedpour, S; Wahlstrand, J K; Milchberg, H M

    2016-01-01

    We present the first experimental, theoretical, and numerical evidence of spatio-temporal optical vortices (STOVs). Quantized STOVs are a fundamental element of the nonlinear collapse and subsequent propagation of short optical pulses in material media. A STOV consists of a ring-shaped null in the electromagnetic field about which the phase is spiral, forming a dynamic torus which is concentric with and tracks the propagating pulse. Depending on the sign of the material dispersion, the local electromagnetic energy flow is saddle or spiral about the STOV. STOVs are born and evolve conserving topological charge; they can be simultaneously created in pairs with opposite windings, or generated from a point null. Our results, here obtained for optical pulse collapse and filamentation in air, are generalizable to a broad class of nonlinearly propagating waves.

  5. Modeling spatio-temporal risk changes in the incidence of dengue fever in Saudi Arabia: a geographical information system case study

    Directory of Open Access Journals (Sweden)

    Hassan M. Khormi

    2011-11-01

    Full Text Available The aim of this study was to use geographical information systems to demonstrate the Dengue fever (DF risk on a monthly basis in Jeddah, Saudi Arabia with the purpose to provide documentation serving to improve surveillance and monitor the Aedes aegypti mosquito vector. Getis-Ord Gi* statistics and a frequency index covering a five-year period (2006- 2010 were used to map DF and model the risk spatio-temporally. The results show that monthly hotspots were mainly concentrated in central Jeddah districts and that the pattern changes considerably with time. For example, on a yearly basis, for the month of January, the Burman district was identified as a low risk area in 2006, a high-risk area in 2007, medium risk in 2008, very low risk in 2009 and low risk in 2010. The results demonstrate that it would be useful to follow the monthly DF pattern, based on the average weekly frequency, as this can facilitate the allocation of resources for the treatment of the disease, preventing its prevalence and monitoring its vector.

  6. Modeling spatio-temporal risk changes in the incidence of Dengue fever in Saudi Arabia: a geographical information system case study.

    Science.gov (United States)

    Khormi, Hassan M; Kumar, Lalit; Elzahrany, Ramze A

    2011-11-01

    The aim of this study was to use geographical information systems to demonstrate the Dengue fever (DF) risk on a monthly basis in Jeddah, Saudi Arabia with the purpose to provide documentation serving to improve surveillance and monitor the Aedes aegypti mosquito vector. Getis-Ord Gi* statistics and a frequency index covering a five-year period (2006-2010) were used to map DF and model the risk spatio-temporally. The results show that monthly hotspots were mainly concentrated in central Jeddah districts and that the pattern changes considerably with time. For example, on a yearly basis, for the month of January, the Burman district was identified as a low risk area in 2006, a high-risk area in 2007, medium risk in 2008, very low risk in 2009 and low risk in 2010. The results demonstrate that it would be useful to follow the monthly DF pattern, based on the average weekly frequency, as this can facilitate the allocation of resources for the treatment of the disease, preventing its prevalence and monitoring its vector.

  7. Scaling issues and spatio-temporal variability in ecohydrological modeling on mountain topography: Methods for improving the VELMA model

    Science.gov (United States)

    The interactions between vegetation and hydrology in mountainous terrain are difficult to represent in mathematical models. There are at least three primary reasons for this difficulty. First, expanding plot-scale measurements to the watershed scale requires finding the balance...

  8. Mathematical modelling of host-parasitoid systems: effects of chemically mediated parasitoid foraging strategies on within- and between-generation spatio-temporal dynamics.

    Science.gov (United States)

    Schofield, Peter; Chaplain, Mark; Hubbard, Stephen

    2002-01-07

    In this paper we develop a novel discrete, individual-based mathematical model to investigate the effect of parasitoid foraging strategies on the spatial and temporal dynamics of host-parasitoid systems. The model is used to compare naïve or random search strategies with search strategies that depend on experience and sensitivity to semiochemicals in the environment. It focuses on simple mechanistic interactions between individual hosts, parasitoids, and an underlying field of a volatile semiochemical (emitted by the hosts during feeding) which acts as a chemoattractant for the parasitoids. The model addresses movement at different spatial scales, where scale of movement also depends on the internal state of an individual. Individual interactions between hosts and parasitoids are modelled at a discrete (micro-scale) level using probabilistic rules. The resulting within-generation dynamics produced by these interactions are then used to generate the population levels for successive generations. The model simulations examine the effect of various key parameters of the model on (i) the spatio-temporal patterns of hosts and parasitoids within generations; (ii) the population levels of the hosts and parasitoids between generations. Key results of the model simulations show that the following model parameters have an important effect on either the development of patchiness within generations or the stability/instability of the population levels between generations: (i) the rate of diffusion of the kairomones; (ii) the specific search strategy adopted by the parasitoids; (iii) the rate of host increase between successive generations. Finally, evolutionary aspects concerning competition between several parasitoid subpopulations adopting different search strategies are also examined.

  9. A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change.

    Science.gov (United States)

    Jackson, Faye L; Fryer, Robert J; Hannah, David M; Millar, Colin P; Malcolm, Iain A

    2017-09-14

    The thermal suitability of riverine habitats for cold water adapted species may be reduced under climate change. Riparian tree planting is a practical climate change mitigation measure, but it is often unclear where to focus effort for maximum benefit. Recent developments in data collection, monitoring and statistical methods have facilitated the development of increasingly sophisticated river temperature models capable of predicting spatial variability at large scales appropriate to management. In parallel, improvements in temporal river temperature models have increased the accuracy of temperature predictions at individual sites. This study developed a novel large scale spatio-temporal model of maximum daily river temperature (Twmax) for Scotland that predicts variability in both river temperature and climate sensitivity. Twmax was modelled as a linear function of maximum daily air temperature (Tamax), with the slope and intercept allowed to vary as a smooth function of day of the year (DoY) and further modified by landscape covariates including elevation, channel orientation and riparian woodland. Spatial correlation in Twmax was modelled at two scales; (1) river network (2) regional. Temporal correlation was addressed through an autoregressive (AR1) error structure for observations within sites. Additional site level variability was modelled with random effects. The resulting model was used to map (1) spatial variability in predicted Twmax under current (but extreme) climate conditions (2) the sensitivity of rivers to climate variability and (3) the effects of riparian tree planting. These visualisations provide innovative tools for informing fisheries and land-use management under current and future climate. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  10. Spatio-temporal Laplacian pyramid coding for action recognition.

    Science.gov (United States)

    Shao, Ling; Zhen, Xiantong; Tao, Dacheng; Li, Xuelong

    2014-06-01

    We present a novel descriptor, called spatio-temporal Laplacian pyramid coding (STLPC), for holistic representation of human actions. In contrast to sparse representations based on detected local interest points, STLPC regards a video sequence as a whole with spatio-temporal features directly extracted from it, which prevents the loss of information in sparse representations. Through decomposing each sequence into a set of band-pass-filtered components, the proposed pyramid model localizes features residing at different scales, and therefore is able to effectively encode the motion information of actions. To make features further invariant and resistant to distortions as well as noise, a bank of 3-D Gabor filters is applied to each level of the Laplacian pyramid, followed by max pooling within filter bands and over spatio-temporal neighborhoods. Since the convolving and pooling are performed spatio-temporally, the coding model can capture structural and motion information simultaneously and provide an informative representation of actions. The proposed method achieves superb recognition rates on the KTH, the multiview IXMAS, the challenging UCF Sports, and the newly released HMDB51 datasets. It outperforms state of the art methods showing its great potential on action recognition.

  11. Spatio-Temporal Data Exchange Standards

    DEFF Research Database (Denmark)

    Jensen, Christian Søndergaard; Schmidt, Albrecht

    2003-01-01

    and briefly surveys standardization in relation to primarily the exchange and integration of spatio-temporal data. An overview of several data exchange languages is offered, along with reviews their potential for facilitating the collection of test data and the leveraging of prototypes. The standards, most......We believe that research that concerns aspects of spatio-temporal data management may benefit from taking into account the various standards for spatio-temporal data formats. For example, this may contribute to rendering prototype software “open” and more readily useful. This paper thus identifies...

  12. Spatio-Temporal Data Exchange Standards

    DEFF Research Database (Denmark)

    Jensen, Christian Søndergaard; Schmidt, Albrecht

    2003-01-01

    We believe that research that concerns aspects of spatio-temporal data management may benefit from taking into account the various standards for spatio-temporal data formats. For example, this may contribute to rendering prototype software “open” and more readily useful. This paper thus identifies...... and briefly surveys standardization in relation to primarily the exchange and integration of spatio-temporal data. An overview of several data exchange languages is offered, along with reviews their potential for facilitating the collection of test data and the leveraging of prototypes. The standards, most...

  13. Spatio-Temporal Variation and Futuristic Emission Scenario of Ambient Nitrogen Dioxide over an Urban Area of Eastern India Using GIS and Coupled AERMOD–WRF Model

    Science.gov (United States)

    Dey, Sharadia; Gupta, Srimanta; Sibanda, Precious; Chakraborty, Arun

    2017-01-01

    The present study focuses on the spatio-temporal variation of nitrogen dioxide (NO2) during June 2013 to May 2015 and its futuristic emission scenario over an urban area (Durgapur) of eastern India. The concentration of ambient NO2 shows seasonal as well as site specific characteristics. The site with high vehicular density (Muchipara) shows highest NO2 concentration followed by industrial site (DVC- DTPS Colony) and the residential site (B Zone), respectively. The seasonal variation of ambient NO2 over the study area is portrayed by means of Geographical Information System based Digital Elevation Model. Out of the total urban area under consideration (114.982 km2), the concentration of NO2 exceeded the National Ambient Air Quality Standard (NAAQS) permissible limit over an area of 5.000 km2, 0.786 km2 and 0.653 km2 in post monsoon, winter and pre monsoon, respectively. Wind rose diagrams, correlation and regression analyses show that meteorology plays a crucial role in dilution and dispersion of NO2 near the earth’s surface. Principal component analysis identifies vehicular source as the major source of NO2 in all the seasons over the urban region. Coupled AMS/EPA Regulatory Model (AERMOD)–Weather Research and Forecasting (WRF) model is used for predicting the concentration of NO2. Comparison of the observed and simulated data shows that the model overestimates the concentration of NO2 in all the seasons (except winter). The results show that coupled AERMOD–WRF model can overcome the unavailability of hourly surface as well as upper air meteorological data required for predicting the pollutant concentration, but improvement of emission inventory along with better understanding of the sinks and sources of ambient NO2 is essential for capturing the more realistic scenario. PMID:28141866

  14. Spatio-Temporal Variation and Futuristic Emission Scenario of Ambient Nitrogen Dioxide over an Urban Area of Eastern India Using GIS and Coupled AERMOD-WRF Model.

    Science.gov (United States)

    Dey, Sharadia; Gupta, Srimanta; Sibanda, Precious; Chakraborty, Arun

    2017-01-01

    The present study focuses on the spatio-temporal variation of nitrogen dioxide (NO2) during June 2013 to May 2015 and its futuristic emission scenario over an urban area (Durgapur) of eastern India. The concentration of ambient NO2 shows seasonal as well as site specific characteristics. The site with high vehicular density (Muchipara) shows highest NO2 concentration followed by industrial site (DVC- DTPS Colony) and the residential site (B Zone), respectively. The seasonal variation of ambient NO2 over the study area is portrayed by means of Geographical Information System based Digital Elevation Model. Out of the total urban area under consideration (114.982 km2), the concentration of NO2 exceeded the National Ambient Air Quality Standard (NAAQS) permissible limit over an area of 5.000 km2, 0.786 km2 and 0.653 km2 in post monsoon, winter and pre monsoon, respectively. Wind rose diagrams, correlation and regression analyses show that meteorology plays a crucial role in dilution and dispersion of NO2 near the earth's surface. Principal component analysis identifies vehicular source as the major source of NO2 in all the seasons over the urban region. Coupled AMS/EPA Regulatory Model (AERMOD)-Weather Research and Forecasting (WRF) model is used for predicting the concentration of NO2. Comparison of the observed and simulated data shows that the model overestimates the concentration of NO2 in all the seasons (except winter). The results show that coupled AERMOD-WRF model can overcome the unavailability of hourly surface as well as upper air meteorological data required for predicting the pollutant concentration, but improvement of emission inventory along with better understanding of the sinks and sources of ambient NO2 is essential for capturing the more realistic scenario.

  15. A spatio-temporal simulation model of the response of solid tumours to radiotherapy in vivo: parametric validation concerning oxygen enhancement ratio and cell cycle duration

    Science.gov (United States)

    Antipas, Vassilis P.; Stamatakos, Georgios S.; Uzunoglu, Nikolaos K.; Dionysiou, Dimitra D.; Dale, Roger G.

    2004-04-01

    Advanced bio-simulation methods are expected to substantially improve radiotherapy treatment planning. To this end a novel spatio-temporal patient-specific simulation model of the in vivo response of malignant tumours to radiotherapy schemes has been recently developed by our group. This paper discusses recent improvements to the model: an optimized algorithm leading to conformal shrinkage of the tumour as a response to radiotherapy, the introduction of the oxygen enhancement ratio (OER), a realistic initial cell phase distribution and finally an advanced imaging-based algorithm simulating the neovascularization field. A parametric study of the influence of the cell cycle duration Tc, OER, OERbgr for the beta LQ parameter on tumour growth, shrinkage and response to irradiation under two different fractionation schemes has been made. The model has been applied to two glioblastoma multiforme (GBM) cases, one with wild type (wt) and another one with mutated (mt) p53 gene. Furthermore, the model has been applied to a hypothetical GBM tumour with agr and bgr values corresponding to those of generic radiosensitive tumours. According to the model predictions, a whole tumour with shorter Tc tends to repopulate faster, as is to be expected. Furthermore, a higher OER value for the dormant cells leads to a more radioresistant whole tumour. A small variation of the OERbgr value does not seem to play a major role in the tumour response. Accelerated fractionation proved to be superior to the standard scheme for the whole range of the OER values considered. Finally, the tumour with mt p53 was shown to be more radioresistant compared to the tumour with wt p53. Although all simulation predictions agree at least qualitatively with the clinical experience and literature, a long-term clinical adaptation and quantitative validation procedure is in progress.

  16. Spatio-Temporal Analysis of Model and Satellite Based Soil Moisture Estimations for Assessing Coupling Hot Spots in the Southern La Plata Basin

    Science.gov (United States)

    Bruscantini, C. A.; Karszenbaum, H.; Ruscica, R. C.; Spennemann, P.; Salvia, M.; Sorensson, A. A.; Grings, F. M.; Saulo, C.

    2015-12-01

    The southern La Plata Basin, located in southeastern South America (SESA), a region of great importance because of its hydrological characteristics, the fact that it has the largest population density and is one of the most productive regions in terms of agriculture, cattle raising and industry of the continent, has been identified as a strong hotspot between soil moisture (SM) and the atmosphere by different regional studies. Among them, Ruscica et al. (2014, Atmos. Sci. Let, Int. J. Climatol.), and Spennemann et al. (2015, Int. J. Climatol.) show, through different modeling approaches, the presence of strong soil moisture-precipitation and evapotranspiration interactions during austral summer in SESA, revealing similar hotspots. Nevertheless these studies have diverse limitations related to model assumptions and to vegetation parameterizations, as well as the lack of observational data for the evaluation of models performance (Ferguson and Wood, 2011, J. of Hydrometeorology). On the other hand, in the last decade several instruments on board satellites are providing soil moisture products globally and in a continuous way. A recent work by Grings et al. (2015, IEEE JSTARS, in press), done over the Pampas Plains in SESA showed characteristic soil moisture patterns that follow the Standardized Precipitation Index (SPI) under extreme wet and dry conditions In order to deepen and overcome some of the mentioned model limitations, this work adds satellite soil moisture and vegetation products in the spatio-temporal analysis of the regions of strong soil moisture-atmosphere interactions. The main objectives and related outcomes are: the verification of already identified regions where soil moisture anomalies may have an influence on subsequent precipitation, evapotranspiration and temperature anomalies, and the study of their seasonal characteristics and land cover influences.

  17. A model of photon cell killing based on the spatio-temporal clustering of DNA damage in higher order chromatin structures.

    Directory of Open Access Journals (Sweden)

    Lisa Herr

    Full Text Available We present a new approach to model dose rate effects on cell killing after photon radiation based on the spatio-temporal clustering of DNA double strand breaks (DSBs within higher order chromatin structures of approximately 1-2 Mbp size, so called giant loops. The main concept of this approach consists of a distinction of two classes of lesions, isolated and clustered DSBs, characterized by the number of double strand breaks induced in a giant loop. We assume a low lethality and fast component of repair for isolated DSBs and a high lethality and slow component of repair for clustered DSBs. With appropriate rates, the temporal transition between the different lesion classes is expressed in terms of five differential equations. These allow formulating the dynamics involved in the competition of damage induction and repair for arbitrary dose rates and fractionation schemes. Final cell survival probabilities are computable with a cell line specific set of three parameters: The lethality for isolated DSBs, the lethality for clustered DSBs and the half-life time of isolated DSBs. By comparison with larger sets of published experimental data it is demonstrated that the model describes the cell line dependent response to treatments using either continuous irradiation at a constant dose rate or to split dose irradiation well. Furthermore, an analytic investigation of the formulation concerning single fraction treatments with constant dose rates in the limiting cases of extremely high or low dose rates is presented. The approach is consistent with the Linear-Quadratic model extended by the Lea-Catcheside factor up to the second moment in dose. Finally, it is shown that the model correctly predicts empirical findings about the dose rate dependence of incidence probabilities for deterministic radiation effects like pneumonitis and the bone marrow syndrome. These findings further support the general concepts on which the approach is based.

  18. Spatio-temporal Assessment Of The Land Use Implications Of Solar PV And Bioenergy Deployment In The UK TM Energy Model

    Science.gov (United States)

    Sobral Mourao, Z.; Konadu, D. D.; Skelton, S.; Lupton, R.

    2015-12-01

    The UK TIMES model (UKTM) succeeds the UK MARKAL as the underlying model of the UK Department of Energy and Climate Change (DECC) for long term energy system planning and policy development. It generates energy system pathways which achieve the 80% greenhouse gas (GHG) emissions reduction target by 2050, stipulated in the UK Climate Change Act (2008), at the least possible cost. Some of these pathways prescribe large-scale deployment of solar PV and indigenously sourced bioenergy, which are land intensive and could result in significant land use transitions; but would this create competition and stress for UK land use? To answer the above question, this study uses an integrated spatio-temporal modelling approach, ForeseerTM, which characterises the interdependencies between the energy and land systems by evaluating the land required under each pathways for solar PV and bioenergy, based on scenarios of a range of PV conversion efficiencies, and energy crop yield projections. The outcome is compared with availability of suitable locations for solar PV and sustainable limits of agricultural land appropriation for bioenergy production to assess potential stresses and competition with other land use services. Preliminary results show UKTM pathways could pose significant impact on the UK land use system. Bioenergy deployment could potentially compete with other land services by taking up a significant part of the available UK agricultural land thus competing directly with food production, most notably livestock production. For pathways with significant solar PV deployment, direct competition would not be focussed on the high quality land used for food crop production but rather for land used for livestock production and other ecosystem services.

  19. Space-borne Chlorophyll Fluorescence, Greenness, Vegetation Models and Interannual Variability of Photosynthetic Activity: Spatio-temporal Patterns, Mechanisms, and Environmental Sensitivities

    Science.gov (United States)

    Walther, S.; Guanter, L.; Jung, M.; Frankenberg, C.; Sun, Y.; Forkel, M.; Zhang, Y.; Duveiller, G.; Cescatti, A.; Camps-Valls, G.; Köhler, P.

    2016-12-01

    It is much debated whether respiration or photosynthesis drive net ecosystem productivity andwhich regions contribute strongest to the observed interannual variability (IAV) of the strengthof the land sink. Several studies point to photosynthetic productivity in semi-arid regions as avery important factor influencing atmospheric CO2 variability globally (e.g. Jung et al., 2011;Poulter et al., 2014; Ahlstr ̈ om et al., 2015). Here, we aim at a comprehensive comparison ofthe strength, timing and spatial extent of anomalies of photosynthesis as they are indicated bysatellite observations of greenness, vegetation optical depth, and sun-induced chlorophyll fluo-rescence (SIF). We will compare them to the results of diagnostic, empirical and process-basedvegetation models. Except for the evergreen tropics, the spatio-temporal patterns of monthlydominant vegetation variability are generally consistently shown in semi-arid areas, albeit withdiffering magnitudes between greenness and photosynthesis globally. Relative anomalies (to themean seasonal cycle) are particularly widespread in high northern latitudes. Further researchsteps will include i) the repeated analysis at higher temporal resolution to better refine the dif-ferent time scales of reaction between light-use-efficiency and APAR and between forestedand non-forested ecosystems, ii) investigate on characteristic time scales at which the proxies(dis-)agree and why, iii) study the relative contributions of anomalies in peak and length of thegrowing season to IAV (similar to Xia et al., 2015; Zhou et al., 2016), iv) analyse the proxiesfor possibly differing hydrological sensitivities, and v) vegetation models have long been knownto have very diverse abilities to capture GPP IAV. Our preliminary results confirm this and wewill further study possible limitations and possible ways for improvement of the simulations.

  20. Forecasting Italian seismicity through a spatio-temporal physical model: importance of considering time-dependency and reliability of the forecast

    Directory of Open Access Journals (Sweden)

    Amir Hakimhashemi

    2010-11-01

    Full Text Available We apply here a forecasting model to the Italian region for the spatio-temporal distribution of seismicity based on a smoothing Kernel function, Coulomb stress variations, and a rate-and-state friction law. We tested the feasibility of this approach, and analyzed the importance of introducing time-dependency in forecasting future events. The change in seismicity rate as a function of time was estimated by calculating the Coulomb stress change imparted by large earthquakes. We applied our approach to the region of Italy, and used all of the cataloged earthquakes that occurred up to 2006 to generate the reference seismicity rate. For calculation of the time-dependent seismicity rate changes, we estimated the rate-and-state stress transfer imparted by all of the ML≥4.0 earthquakes that occurred during 2007 and 2008. To validate the results, we first compared the reference seismicity rate with the distribution of ML≥1.8 earthquakes since 2007, using both a non-declustered and a declustered catalog. A positive correlation was found, and all of the forecast earthquakes had locations within 82% and 87% of the study area with the highest seismicity rate, respectively. Furthermore, 95% of the forecast earthquakes had locations within 27% and 47% of the study area with the highest seismicity rate, respectively. For the time-dependent seismicity rate changes, the number of events with locations in the regions with a seismicity rate increase was 11% more than in the regions with a seismicity rate decrease.

  1. Population Immunity against Serotype-2 Poliomyelitis Leading up to the Global Withdrawal of the Oral Poliovirus Vaccine: Spatio-temporal Modelling of Surveillance Data

    Science.gov (United States)

    O’Reilly, Kathleen M.; Etsano, Andrew; Vaz, Rui Gama; Jafari, Hamid; Grassly, Nicholas C.; Blake, Isobel M.

    2016-01-01

    Background Global withdrawal of serotype-2 oral poliovirus vaccine (OPV2) took place in April 2016. This marked a milestone in global polio eradication and was a public health intervention of unprecedented scale, affecting 155 countries. Achieving high levels of serotype-2 population immunity before OPV2 withdrawal was critical to avoid subsequent outbreaks of serotype-2 vaccine-derived polioviruses (VDPV2s). Methods and Findings In August 2015, we estimated vaccine-induced population immunity against serotype-2 poliomyelitis for 1 January 2004–30 June 2015 and produced forecasts for April 2016 by district in Nigeria and Pakistan. Population immunity was estimated from the vaccination histories of children poliomyelitis. District immunity estimates were spatio-temporally smoothed using a Bayesian hierarchical framework. Coverage estimates for immunisation activities were also obtained, allowing for heterogeneity within and among districts. Forward projections of immunity, based on these estimates and planned immunisation activities, were produced through to April 2016 using a cohort model. Estimated population immunity was negatively correlated with the probability of VDPV2 poliomyelitis being reported in a district. In Nigeria and Pakistan, declines in immunity during 2008–2009 and 2012–2013, respectively, were associated with outbreaks of VDPV2. Immunity has since improved in both countries as a result of increased use of trivalent OPV, and projections generally indicated sustained or improved immunity in April 2016, such that the majority of districts (99% [95% uncertainty interval 97%–100%] in Nigeria and 84% [95% uncertainty interval 77%–91%] in Pakistan) had >70% population immunity among children poliomyelitis was forecasted to improve in April 2016 compared to the first half of 2015 in Nigeria and Pakistan. These analyses informed the endorsement of OPV2 withdrawal in April 2016 by the WHO Strategic Advisory Group of Experts on Immunization. PMID

  2. Nonstationary, Nonparametric, Nonseparable Bayesian Spatio-Temporal Modeling using Kernel Convolution of Order Based Dependent Dirichlet Process

    OpenAIRE

    Das, Moumita; Bhattacharya, Sourabh

    2014-01-01

    In this paper, using kernel convolution of order based dependent Dirichlet process (Griffin & Steel (2006)) we construct a nonstationary, nonseparable, nonparametric space-time process, which, as we show, satisfies desirable properties, and includes the stationary, separable, parametric processes as special cases. We also investigate the smoothness properties of our proposed model. Since our model entails an infinite random series, for Bayesian model fitting purpose we must either truncate th...

  3. Spatio-temporal modelling of electrical supply systems to optimize the site planning process for the "power to mobility" technology

    Science.gov (United States)

    Karl, Florian; Zink, Roland

    2016-04-01

    The transformation of the energy sector towards decentralized renewable energies (RE) requires also storage systems to ensure security of supply. The new "Power to Mobility" (PtM) technology is one potential solution to use electrical overproduction to produce methane for i.e. gas vehicles. Motivated by these fact, the paper presents a methodology for a GIS-based temporal modelling of the power grid, to optimize the site planning process for the new PtM-technology. The modelling approach is based on a combination of the software QuantumGIS for the geographical and topological energy supply structure and OpenDSS for the net modelling. For a case study (work in progress) of the city of Straubing (Lower Bavaria) the parameters of the model are quantified. The presentation will discuss the methodology as well as the first results with a view to the application on a regional scale.

  4. Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors

    Science.gov (United States)

    2014-01-01

    Background Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations. Methods We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM2.5–10) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV). Results The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988–1998 and 1999–2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988–1998 and 1999–2007) and PM2.5–10 (CV R2=0.46 and 0.52 for 1988–1998 and 1999–2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999–2007). Conclusions Our models provide

  5. My Corporis Fabrica Embryo: An ontology-based 3D spatio-temporal modeling of human embryo development.

    Science.gov (United States)

    Rabattu, Pierre-Yves; Massé, Benoit; Ulliana, Federico; Rousset, Marie-Christine; Rohmer, Damien; Léon, Jean-Claude; Palombi, Olivier

    2015-01-01

    Embryology is a complex morphologic discipline involving a set of entangled mechanisms, sometime difficult to understand and to visualize. Recent computer based techniques ranging from geometrical to physically based modeling are used to assist the visualization and the simulation of virtual humans for numerous domains such as surgical simulation and learning. On the other side, the ontology-based approach applied to knowledge representation is more and more successfully adopted in the life-science domains to formalize biological entities and phenomena, thanks to a declarative approach for expressing and reasoning over symbolic information. 3D models and ontologies are two complementary ways to describe biological entities that remain largely separated. Indeed, while many ontologies providing a unified formalization of anatomy and embryology exist, they remain only descriptive and make the access to anatomical content of complex 3D embryology models and simulations difficult. In this work, we present a novel ontology describing the development of the human embryology deforming 3D models. Beyond describing how organs and structures are composed, our ontology integrates a procedural description of their 3D representations, temporal deformation and relations with respect to their developments. We also created inferences rules to express complex connections between entities. It results in a unified description of both the knowledge of the organs deformation and their 3D representations enabling to visualize dynamically the embryo deformation during the Carnegie stages. Through a simplified ontology, containing representative entities which are linked to spatial position and temporal process information, we illustrate the added-value of such a declarative approach for interactive simulation and visualization of 3D embryos. Combining ontologies and 3D models enables a declarative description of different embryological models that capture the complexity of human

  6. Modelling the spatio-temporal evolution of {sup 3}H in the waters of the River Tagus

    Energy Technology Data Exchange (ETDEWEB)

    Baeza, A. [Universidad de Extremadura, Facultad de Veterinaria, Dpto. de Fisica, Avda. Universidad sn, 10071 Caceres (Spain)]. E-mail: ymiralle@unex.es; Garcia, E. [Universidad de Extremadura, Ingenieria Tecnica Forestal, Dpto. de Fisica, 10600 Plasencia (Caceres) (Spain); Miro, C. [Universidad de Extremadura, Facultad de Veterinaria, Dpto. de Fisica, Avda. Universidad sn, 10071 Caceres (Spain); Perianez, R. [Universidad de Sevilla, E.U. de Ingenieria Tecnica Agricola, Dpto. de Fisica Aplicada, 1, 41013 Sevilla (Spain)

    2006-07-01

    Measurements of tritium specific activity levels and of temperatures in waters of the Torrejon-Tagus reservoir (Spain) showed that their radioactive characteristics were basically influenced by the radioactive liquid effluent from the Almaraz Nuclear Power Plant. This enters the Torrejon-Tagus reservoir via the Arrocampo cooling reservoir, which exchanges water with the first. We studied the temporal and spatial (in two dimensions) evolution of the mentioned parameters for years 1997 and 1998. The tritium levels were found to be significantly correlated with temperature. Two numerical models were constructed for a quantitative study of the tritium levels along Torrejon reservoir: a 1D model was used for the dispersion of tritium along the whole length of the reservoir, and a 2D depth-averaged model was used for a detailed study of the area where tritium is released into the reservoir. Both models solve the hydrodynamic equations, to obtain the currents induced by the exchanges of water between the reservoirs in the River Tagus and Arrocampo, and the advection/diffusion equation to calculate the dispersion of tritium. In general, the model results were in agreement with the experimental observations.

  7. Height-dependence of spatio-temporal spectra of wall-bounded turbulence - LES results and model predictions

    CERN Document Server

    Wilczek, Michael; Meneveau, Charles

    2016-01-01

    Wavenumber-frequency spectra of the streamwise velocity component obtained from large-eddy simulations are presented. Following a recent paper [Wilczek et al., J. Fluid. Mech., 769:R1, 2015] we show that the main features, a Doppler shift and a Doppler broadening of frequencies, are captured by an advection model based on the Tennekes-Kraichnan random-sweeping hypothesis with additional mean flow. In this paper, we focus on the height-dependence of the spectra within the logarithmic layer of the flow. We furthermore benchmark an analytical model spectrum that takes the predictions of the random-sweeping model as a starting point and find good agreement with the LES data. We also quantify the influence of LES grid resolution on the wavenumber-frequency spectra.

  8. SESAM – a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

    DEFF Research Database (Denmark)

    Guisan, Antoine; Rahbek, Carsten

    2011-01-01

    , and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both......Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realized properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts...... with constituent species to approximate the properties of assemblages. Here, we propose to unify the two approaches in a single ‘spatially explicit species assemblage modelling’ (SESAM) framework. This framework uses relevant designations of initial species source pools for modelling, macroecological variables...

  9. Brain connections of words, perceptions and actions: A neurobiological model of spatio-temporal semantic activation in the human cortex.

    Science.gov (United States)

    Tomasello, Rosario; Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

    2017-04-01

    Neuroimaging and patient studies show that different areas of cortex respectively specialize for general and selective, or category-specific, semantic processing. Why are there both semantic hubs and category-specificity, and how come that they emerge in different cortical regions? Can the activation time-course of these areas be predicted and explained by brain-like network models? In this present work, we extend a neurocomputational model of human cortical function to simulate the time-course of cortical processes of understanding meaningful concrete words. The model implements frontal and temporal cortical areas for language, perception, and action along with their connectivity. It uses Hebbian learning to semantically ground words in aspects of their referential object- and action-related meaning. Compared with earlier proposals, the present model incorporates additional neuroanatomical links supported by connectivity studies and downscaled synaptic weights in order to control for functional between-area differences purely due to the number of in- or output links of an area. We show that learning of semantic relationships between words and the objects and actions these symbols are used to speak about, leads to the formation of distributed circuits, which all include neuronal material in connector hub areas bridging between sensory and motor cortical systems. Therefore, these connector hub areas acquire a role as semantic hubs. By differentially reaching into motor or visual areas, the cortical distributions of the emergent 'semantic circuits' reflect aspects of the represented symbols' meaning, thus explaining category-specificity. The improved connectivity structure of our model entails a degree of category-specificity even in the 'semantic hubs' of the model. The relative time-course of activation of these areas is typically fast and near-simultaneous, with semantic hubs central to the network structure activating before modality-preferential areas carrying

  10. The Spatio-Temporal Modeling of Urban Growth Using Remote Sensing and Intelligent Algorithms, Case of Mahabad, Iran

    Directory of Open Access Journals (Sweden)

    Alì Soltani

    2013-06-01

    Full Text Available The simulation of urban growth can be considered as a useful way for analyzing the complex process of urban physical evolution. The aim of this study is to model and simulate the complex patterns of land use change by utilizing remote sensing and artificial intelligence techniques in the fast growing city of Mahabad, north-west of Iran which encountered with several environmental subsequences. The key subject is how to allocate optimized weight into effective parameters upon urban growth and subsequently achieving an improved simulation. Artificial Neural Networks (ANN algorithm was used to allocate the weight via an iteration approach. In this way, weight allocation was carried out by the ANN training accomplishing through time-series satellite images representing urban growth process. Cellular Automata (CA was used as the principal motor of the model and then ANN applied to find suitable scale of parameters and relations between potential factors affecting urban growth. The general accuracy of the suggested model and obtained Fuzzy Kappa Coefficient confirms achieving better results than classic CA models in simulating nonlinear urban evolution process.

  11. A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States.

    Science.gov (United States)

    Liu, Yan; Watson, Stella C; Gettings, Jenna R; Lund, Robert B; Nordone, Shila K; Yabsley, Michael J; McMahan, Christopher S

    2017-01-01

    This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast's construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011-2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year's regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011-2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases.

  12. SPATIO-TEMPORAL SEGMENTATION AND REGIONS TRACKING OF HIGH DEFINITION VIDEO SEQUENCES USING A MARKOV RANDOM FIELD MODEL

    OpenAIRE

    Brouard, Olivier; Delannay, Fabrice; Ricordel, Vincent; Barba, Dominique

    2008-01-01

    International audience; In this paper, we proposed a Markov Random field sequence segmentation and regions tracking model, which aims at combining color, texture, and motion features. First a motion-based segmentation is realized. The global motion of the video sequence is estimated and compensated. From the remaining motion information, the motion segmentation is achieved. Then, we use a Markovian approach to update and track over time the video objects. By video object, we mean typically, a...

  13. My Corporis Fabrica Embryo: An ontology-based 3D spatio-temporal modeling of human embryo development

    OpenAIRE

    Rabattu, Pierre-Yves; Massé, Benoit; Ulliana, Federico; Rousset, Marie-Christine; Rohmer, Damien; Léon, Jean-Claude; Palombi, Olivier

    2015-01-01

    Background Embryology is a complex morphologic discipline involving a set of entangled mechanisms, sometime difficult to understand and to visualize. Recent computer based techniques ranging from geometrical to physically based modeling are used to assist the visualization and the simulation of virtual humans for numerous domains such as surgical simulation and learning. On the other side, the ontology-based approach applied to knowledge representation is more and more successfully adopted in...

  14. Spatio-temporal variability of concentrations and speciation of particulate matter across Spain in the CALIOPE modeling system

    Science.gov (United States)

    Pay, M. T.; Jiménez-Guerrero, P.; Jorba, O.; Basart, S.; Querol, X.; Pandolfi, M.; Baldasano, J. M.

    2012-01-01

    The CALIOPE high-resolution air quality modeling system (4 km × 4 km, 1 h) estimates particulate matter from two aerosol models, CMAQv4.5 (AERO4) and BSC-DREAM8b. While CMAQv4.5 calculates biogenic, anthropogenic and sea-salt aerosols; BSC-DREAM8b provides hourly estimates of the natural mineral dust contribution from North Africa deserts. This paper presents an evaluation of the CALIOPE system to reproduce the spatial and temporal variability levels of PM2.5, PM10 and chemical composition (nitrate, non-marine sulfate, ammonium, organic and elemental carbon, sea-salt, and desert dust) across Spain. The evaluation is performed against ground-based observations for the year 2004, when a number of time series of chemically speciated compounds were available. A new data set of Saharan dust PM10 concentration is used to evaluate the PM10 contribution modeled by BSC-DREAM8b. The results indicate that both natural aerosol sea-salt and desert dust accomplish the model performance criteria (MFE ≤ 75% and MFB ± 60%). Modeled PM10 sea-salt is highly dependent on wind speed and presents high correlation with experimental data in coastal areas ( r = 0.67). The BSC-DREAM8b is able to reproduce the daily variability of the observed levels of desert dust and most of the outbreaks affecting southern Spain. Species in the equilibrium (e.g. sulfate/nitrate/ammonium) are highly correlated each other and show high dependency on ammonia emissions. Non-marine sulfate and ammonium are underestimated by a factor of 3. An underestimation of nitrate was also seen (factor of 2). Fine carbonaceous aerosols present the highest underestimations (factor of 4) in part related to the state-of-the-science concerning secondary organic aerosol formation pathways. Spatial and seasonal variability of PM2.5, PM10 and their chemical compounds increase the correlation with observations when multiplicative bias-correction factors for the aforementioned underestimated species are taking into account

  15. A Unified Spatio-Temporal Framework of the Cuerno-Barabasi Stochastic Continuum Model of Surface Sputtering

    Institute of Scientific and Technical Information of China (English)

    Oluwole Emmanuel Oyewande

    2012-01-01

    The nonlinear continuum model proposed by Cuerno and Barabasi is the most successful and widely acceptable theoretical description of oblique incidence ion sputtered surfaces to date and is quite robust in its predictions of the time evolution and scaling of interfaces driven by ion bombardment. However, this theory has thus far predicted only ripple topographies and rough surfaces for short and large scales, respectively. As a result, its application to the interpretation and study of nanodots, predicted by Monte Carlo simulations for, and observed in experiments of, oblique incidence sputtering is still unclear and, hence, an open problem. In this paper, we provide a new insight to the theory, within the same length scale, that explains nanodot formation on off-normal incidence sputtered surfaces, among others, and propose ways of observing the predicted topographies of the MC simulations, as well as possible control of the size of the nanodots, in the framework of the Cuerno-Barabasi continuum theory.

  16. Characterization of regional influenza seasonality patterns in China and implications for vaccination strategies: spatio-temporal modeling of surveillance data.

    Directory of Open Access Journals (Sweden)

    Hongjie Yu

    2013-11-01

    Full Text Available BACKGROUND: The complexity of influenza seasonal patterns in the inter-tropical zone impedes the establishment of effective routine immunization programs. China is a climatologically and economically diverse country, which has yet to establish a national influenza vaccination program. Here we characterize the diversity of influenza seasonality in China and make recommendations to guide future vaccination programs. METHODS AND FINDINGS: We compiled weekly reports of laboratory-confirmed influenza A and B infections from sentinel hospitals in cities representing 30 Chinese provinces, 2005-2011, and data on population demographics, mobility patterns, socio-economic, and climate factors. We applied linear regression models with harmonic terms to estimate influenza seasonal characteristics, including the amplitude of annual and semi-annual periodicities, their ratio, and peak timing. Hierarchical Bayesian modeling and hierarchical clustering were used to identify predictors of influenza seasonal characteristics and define epidemiologically-relevant regions. The annual periodicity of influenza A epidemics increased with latitude (mean amplitude of annual cycle standardized by mean incidence, 140% [95% CI 128%-151%] in the north versus 37% [95% CI 27%-47%] in the south, p0.6 in provinces located within 27.4°N-31.3°N, slope of latitudinal gradient with latitude -0.016 [95% CI -0.025 to -0.008], p<0.001. In contrast, influenza B activity predominated in colder months throughout most of China. Climate factors were the strongest predictors of influenza seasonality, including minimum temperature, hours of sunshine, and maximum rainfall. Our main study limitations include a short surveillance period and sparse influenza sampling in some of the southern provinces. CONCLUSIONS: Regional-specific influenza vaccination strategies would be optimal in China; in particular, annual campaigns should be initiated 4-6 months apart in Northern and Southern China

  17. Calibrating spatio-temporal models of leukocyte dynamics against in vivo live-imaging data using approximate Bayesian computation

    Science.gov (United States)

    Barnes, Chris P.; Huvet, Maxime; Bugeon, Laurence; Thorne, Thomas; Lamb, Jonathan R.; Dallman, Margaret J.; Stumpf, Michael P. H.

    2016-01-01

    In vivo studies allow us to investigate biological processes at the level of the organism. But not all aspects of in vivo systems are amenable to direct experimental measurements. In order to make the most of such data we therefore require statistical tools that allow us to obtain reliable estimates for e.g. kinetic in vivo parameters. Here we show how we can use approximate Bayesian computation approaches in order to analyse leukocyte migration in zebrafish embryos in response to injuries. We track individual leukocytes using live imaging following surgical injury to the embryos’ tail-fins. The signalling gradient that leukocytes follow towards the site of the injury cannot be directly measured but we can estimate its shape and how it changes with time from the directly observed patterns of leukocyte migration. By coupling simple models of immune signalling and leukocyte migration with the unknown gradient shape into a single statistical framework we can gain detailed insights into the tissue-wide processes that are involved in the innate immune response to wound injury. In particular we find conclusive evidence for a temporally and spatially changing signalling gradient that modulates the changing activity of the leukocyte population in the embryos. We conclude with a robustness analysis which highlights the most important factors determining the leukocyte dynamics. Our approach relies only on the ability to simulate numerically the process under investigation and is therefore also applicable in other in vivo contexts and studies. PMID:22327539

  18. Spatio-temporal modeling of the African swine fever epidemic in the Russian Federation, 2007-2012.

    Science.gov (United States)

    Korennoy, F I; Gulenkin, V M; Malone, J B; Mores, C N; Dudnikov, S A; Stevenson, M A

    2014-10-01

    In 2007 African swine fever (ASF) entered Georgia and in the same year the disease entered the Russian Federation. From 2007 to 2012 ASF spread throughout the southern region of the Russian Federation. At the same time several cases of ASF were detected in the central and northern regions of the Russian Federation, forming a northern cluster of outbreaks in 2011. This northern cluster is of concern because of its proximity to mainland Europe. The aim of this study was to use details of recorded ASF outbreaks and human and swine population details to estimate the spatial distribution of ASF risk in the southern region of the European part of the Russian Federation. Our model of ASF risk was comprised of two components. The first was an estimate of ASF suitability scores calculated using maximum entropy methods. The second was an estimate of ASF risk as a function of Euclidean distance from index cases. An exponential distribution fitted to a frequency histogram of the Euclidean distance between consecutive ASF cases had a mean value of 156 km, a distance greater than the surveillance zone radius of 100-150 km stated in the ASF control regulations for the Russian Federation. We show that the spatial and temporal risk of ASF expansion is related to the suitability of the area of potential expansion, which is in turn a function of socio-economic and geographic variables. We propose that the methodology presented in this paper provides a useful tool to optimize surveillance for ASF in affected areas.

  19. Spatio-temporal modelling of heat stress and climate change implications for the Murray dairy region, Australia

    Science.gov (United States)

    Nidumolu, Uday; Crimp, Steven; Gobbett, David; Laing, Alison; Howden, Mark; Little, Stephen

    2014-08-01

    The Murray dairy region produces approximately 1.85 billion litres of milk each year, representing about 20 % of Australia's total annual milk production. An ongoing production challenge in this region is the management of the impacts of heat stress during spring and summer. An increase in the frequency and severity of extreme temperature events due to climate change may result in additional heat stress and production losses. This paper assesses the changing nature of heat stress now, and into the future, using historical data and climate change projections for the region using the temperature humidity index (THI). Projected temperature and relative humidity changes from two global climate models (GCMs), CSIRO MK3.5 and CCR-MIROC-H, have been used to calculate THI values for 2025 and 2050, and summarized as mean occurrence of, and mean length of consecutive high heat stress periods. The future climate scenarios explored show that by 2025 an additional 12-15 days (compared to 1971 to 2000 baseline data) of moderate to severe heat stress are likely across much of the study region. By 2050, larger increases in severity and occurrence of heat stress are likely (i.e. an additional 31-42 moderate to severe heat stress days compared with baseline data). This increasing trend will have a negative impact on milk production among dairy cattle in the region. The results from this study provide useful insights on the trends in THI in the region. Dairy farmers and the dairy industry could use these results to devise and prioritise adaptation options to deal with projected increases in heat stress frequency and severity.

  20. Geo-information Based Spatio-temporal Modeling of Urban Land Use and Land Cover Change in Butwal Municipality, Nepal

    Science.gov (United States)

    Mandal, U. K.

    2014-11-01

    Unscientific utilization of land use and land cover due to rapid growth of urban population deteriorates urban condition. Urban growth, land use change and future urban land demand are key concerns of urban planners. This paper is aimed to model urban land use change essential for sustainable urban development. GI science technology was employed to study the urban change dynamics using Markov Chain and CA-Markov and predicted the magnitude and spatial pattern. It was performed using the probability transition matrix from the Markov chain process, the suitability map of each land use/cover types and the contiguity filter. Suitability maps were generated from the MCE process where weight was derived from the pair wise comparison in the AHP process considering slope, land capability, distance to road, and settlement and water bodies as criterion of factor maps. Thematic land use land cover types of 1999, 2006, and 2013 of Landsat sensors were classified using MLC algorithm. The spatial extent increase from 1999 to 2013 in built up , bush and forest was observed to be 48.30 percent,79.48 percent and 7.79 percent, respectively, while decrease in agriculture and water bodies were 30.26 percent and 28.22 percent. The predicted urban LULC for 2020 and 2027 would provide useful inputs to the decision makers. Built up and bush expansion are explored as the main driving force for loss of agriculture and river areas and has the potential to continue in future also. The abandoned area of river bed has been converted to built- up areas.

  1. Spatio-temporal modelling of heat stress and climate change implications for the Murray dairy region, Australia.

    Science.gov (United States)

    Nidumolu, Uday; Crimp, Steven; Gobbett, David; Laing, Alison; Howden, Mark; Little, Stephen

    2014-08-01

    The Murray dairy region produces approximately 1.85 billion litres of milk each year, representing about 20 % of Australia's total annual milk production. An ongoing production challenge in this region is the management of the impacts of heat stress during spring and summer. An increase in the frequency and severity of extreme temperature events due to climate change may result in additional heat stress and production losses. This paper assesses the changing nature of heat stress now, and into the future, using historical data and climate change projections for the region using the temperature humidity index (THI). Projected temperature and relative humidity changes from two global climate models (GCMs), CSIRO MK3.5 and CCR-MIROC-H, have been used to calculate THI values for 2025 and 2050, and summarized as mean occurrence of, and mean length of consecutive high heat stress periods. The future climate scenarios explored show that by 2025 an additional 12-15 days (compared to 1971 to 2000 baseline data) of moderate to severe heat stress are likely across much of the study region. By 2050, larger increases in severity and occurrence of heat stress are likely (i.e. an additional 31-42 moderate to severe heat stress days compared with baseline data). This increasing trend will have a negative impact on milk production among dairy cattle in the region. The results from this study provide useful insights on the trends in THI in the region. Dairy farmers and the dairy industry could use these results to devise and prioritise adaptation options to deal with projected increases in heat stress frequency and severity.

  2. Can we determine what controls the spatio-temporal distribution of d-excess and 17O-excess in precipitation using the LMDZ general circulation model?

    Directory of Open Access Journals (Sweden)

    C. Risi

    2013-09-01

    Full Text Available Combined measurements of the H218O and HDO isotopic ratios in precipitation, leading to second-order parameter D-excess, have provided additional constraints on past climates compared to the H218O isotopic ratio alone. More recently, measurements of H217O have led to another second-order parameter: 17O-excess. Recent studies suggest that 17O-excess in polar ice may provide information on evaporative conditions at the moisture source. However, the processes controlling the spatio-temporal distribution of 17O-excess are still far from being fully understood. We use the isotopic general circulation model (GCM LMDZ to better understand what controls d-excess and 17O-excess in precipitation at present-day (PD and during the last glacial maximum (LGM. The simulation of D-excess and 17O-excess is evaluated against measurements in meteoric water, water vapor and polar ice cores. A set of sensitivity tests and diagnostics are used to quantify the relative effects of evaporative conditions (sea surface temperature and relative humidity, Rayleigh distillation, mixing between vapors from different origins, precipitation re-evaporation and supersaturation during condensation at low temperature. In LMDZ, simulations suggest that in the tropics convective processes and rain re-evaporation are important controls on precipitation D-excess and 17O-excess. In higher latitudes, the effect of distillation, mixing between vapors from different origins and supersaturation are the most important controls. For example, the lower d-excess and 17O-excess at LGM simulated at LGM are mainly due to the supersaturation effect. The effect of supersaturation is however very sensitive to a parameter whose tuning would require more measurements and laboratory experiments. Evaporative conditions had previously been suggested to be key controlling factors of d-excess and 17O-excess, but LMDZ underestimates their role. More generally, some shortcomings in the simulation of 17O

  3. Formally grounding spatio-temporal thinking.

    Science.gov (United States)

    Klippel, Alexander; Wallgrün, Jan Oliver; Yang, Jinlong; Li, Rui; Dylla, Frank

    2012-08-01

    To navigate through daily life, humans use their ability to conceptualize spatio-temporal information, which ultimately leads to a system of categories. Likewise, the spatial sciences rely heavily on conceptualization and categorization as means to create knowledge when they process spatio-temporal data. In the spatial sciences and in related branches of artificial intelligence, an approach has been developed for processing spatio-temporal data on the level of coarse categories: qualitative spatio-temporal representation and reasoning (QSTR). Calculi developed in QSTR allow for the meaningful processing of and reasoning with spatio-temporal information. While qualitative calculi are widely acknowledged in the cognitive sciences, there is little behavioral assessment whether these calculi are indeed cognitively adequate. This is an astonishing conundrum given that these calculi are ubiquitous, are often intended to improve processes at the human-machine interface, and are on several occasions claimed to be cognitively adequate. We have systematically evaluated several approaches to formally characterize spatial relations from a cognitive-behavioral perspective for both static and dynamically changing spatial relations. This contribution will detail our framework, which is addressing the question how formal characterization of space can help us understand how people think with, in, and about space.

  4. Estimating TCR using an integrated model-observation framework that accounts for spatio-temporal variability and pre-industrial radiative imbalances.

    Science.gov (United States)

    Haustein, K.; Schurer, A. P.; Venema, V.

    2016-12-01

    Apart from a few exceptions (e.g. Aldrin et al. 2012, Skeie et al. 2013) TCR estimates with EBMs are based on global data. Since these estimates don't represent the true spatial-temporal behaviour for observed temperature as well as external forcing (Marvel et al. 2015), we have developed a two-box EBM framework that accounts for these effects. In addition, external forcing from anthropogenic aerosol and GHGs tends to have different response times in comparison to volcanic stratospheric aerosols. Using PMIP3 and an extended ensemble of HadCM3 simulations (Euro500; Schurer et al. 2014) GCM simulations for the pre-industrial period, we obtain the fast and slow response time constants required in the EBM. With the most recent anthropogenic and natural forcing estimates, we test a range of TCR values against observations. The TCR/ECS ratio necessary to achieve that goal is taken from CMIP5 as observationally OHC-based estimates are notoriously unreliable. Given that observed and modelled OHC estimates are in agreement (Cheng et al. 2016), we argue that this should be the standard procedure the make inferences about ECS. Alternatively, it should be distinguished between equilibrium and effective climate sensitivity. The preliminary best estimate for TCR is 1.6K (1.1-2.2K) with an associated ECS value of 2.9K (2.0-4.0K). This is in good agreement with other D&A techniques that do use spatio-temporal patterns as well (e.g. Jones et al. 2016, Gillet et al. 2013). Correcting for natural ENSO variability and tas/tos-related inaccuracies (Richardson et al. 2016) further increases the robustness of the estimated sensitivity range. Our results also indicate that the small radiative imbalance caused by the period of very strong volcanic eruptions just before the CMIP5 historical period starts (1809-1840) has noteworthy implications for the response to later volcanic eruptions and the temperature evolution after 1850. Simply put, CMIP5-type simulations are slightly more sensitive

  5. Effective and efficient analysis of spatio-temporal data

    Science.gov (United States)

    Zhang, Zhongnan

    Spatio-temporal data mining, i.e., mining knowledge from large amount of spatio-temporal data, is a highly demanding field because huge amounts of spatio-temporal data have been collected in various applications, ranging from remote sensing, to geographical information systems (GIS), computer cartography, environmental assessment and planning, etc. The collection data far exceeded human's ability to analyze which make it crucial to develop analysis tools. Recent studies on data mining have extended to the scope of data mining from relational and transactional datasets to spatial and temporal datasets. Among the various forms of spatio-temporal data, remote sensing images play an important role, due to the growing wide-spreading of outer space satellites. In this dissertation, we proposed two approaches to analyze the remote sensing data. The first one is about applying association rules mining onto images processing. Each image was divided into a number of image blocks. We built a spatial relationship for these blocks during the dividing process. This made a large number of images into a spatio-temporal dataset since each image was shot in time-series. The second one implemented co-occurrence patterns discovery from these images. The generated patterns represent subsets of spatial features that are located together in space and time. A weather analysis is composed of individual analysis of several meteorological variables. These variables include temperature, pressure, dew point, wind, clouds, visibility and so on. Local-scale models provide detailed analysis and forecasts of meteorological phenomena ranging from a few kilometers to about 100 kilometers in size. When some of above meteorological variables have some special change tendency, some kind of severe weather will happen in most cases. Using the discovery of association rules, we found that some special meteorological variables' changing has tight relation with some severe weather situation that will happen

  6. Sex & vision I: Spatio-temporal resolution

    Directory of Open Access Journals (Sweden)

    Abramov Israel

    2012-09-01

    Full Text Available Abstract Background Cerebral cortex has a very large number of testosterone receptors, which could be a basis for sex differences in sensory functions. For example, audition has clear sex differences, which are related to serum testosterone levels. Of all major sensory systems only vision has not been examined for sex differences, which is surprising because occipital lobe (primary visual projection area may have the highest density of testosterone receptors in the cortex. We have examined a basic visual function: spatial and temporal pattern resolution and acuity. Methods We tested large groups of young adults with normal vision. They were screened with a battery of standard tests that examined acuity, color vision, and stereopsis. We sampled the visual system’s contrast-sensitivity function (CSF across the entire spatio-temporal space: 6 spatial frequencies at each of 5 temporal rates. Stimuli were gratings with sinusoidal luminance profiles generated on a special-purpose computer screen; their contrast was also sinusoidally modulated in time. We measured threshold contrasts using a criterion-free (forced-choice, adaptive psychophysical method (QUEST algorithm. Also, each individual’s acuity limit was estimated by fitting his or her data with a model and extrapolating to find the spatial frequency corresponding to 100% contrast. Results At a very low temporal rate, the spatial CSF was the canonical inverted-U; but for higher temporal rates, the maxima of the spatial CSFs shifted: Observers lost sensitivity at high spatial frequencies and gained sensitivity at low frequencies; also, all the maxima of the CSFs shifted by about the same amount in spatial frequency. Main effect: there was a significant (ANOVA sex difference. Across the entire spatio-temporal domain, males were more sensitive, especially at higher spatial frequencies; similarly males had significantly better acuity at all temporal rates. Conclusion As with other sensory systems

  7. Estimating spatio-temporal dynamics of size-structured populations

    DEFF Research Database (Denmark)

    Kristensen, Kasper; Thygesen, Uffe Høgsbro; Andersen, Ken Haste

    2014-01-01

    Spatial distributions of structured populations are usually estimated by fitting abundance surfaces for each stage and at each point of time separately, ignoring correlations that emerge from growth of individuals. Here, we present a statistical model that combines spatio-temporal correlations...... with simple stock dynamics, to estimate simultaneously how size distributions and spatial distributions develop in time. We demonstrate the method for a cod population sampled by trawl surveys. Particular attention is paid to correlation between size classes within each trawl haul due to clustering...... of individuals with similar size. The model estimates growth, mortality and reproduction, after which any aspect of size-structure, spatio-temporal population dynamics, as well as the sampling process can be probed. This is illustrated by two applications: 1) tracking the spatial movements of a single cohort...

  8. Smart video crime detection based on 3D model spatio-temporal subspace%三维模型时空子空间引导的智能视频侦查系统

    Institute of Scientific and Technical Information of China (English)

    卢涤非; 斯进; 王秋

    2016-01-01

    In order to overcome the problem of the "semantic gap" faced by the traditional video processing technology, this paper proposes a three-dimensional model of spatio-temporal subspace guided smart video detecting technology. Its core idea is that the video data is processed and analyzed with the information contained in the 3D model of spatio-temporal subspace. This paper include: 1, matching 3D target model with the video under the guidance of the shape subspace; 2, 3D model of spatio-temporal subspace guided extraction of video events: monitor object video + spatio-temporal subspace of 3D model → 3D monitored object movement; 3, Comparison of movements in 3D event Library: Sports data + 3D event library → video type and nature. This paper is related to graphics, video processing and criminal technology. It establishes new channels for the use of 3D graphics technology to solve the problem of video detection and has an important academic significance.%为了克服传统视频处理技术面临的“语义鸿沟”等难题,借助三维模型时空子空间所蕴含的信息进行视频处理分析,提出了三维模型时空子空间引导的智能视频侦查技术。①在体形子空间的引导下从视频中匹配三维目标模型。②三维模型时空子空间引导下提取视频事件:监控对象视频+三维模型时空子空间→监控对象三维动作。③三维事件库中的动作比对分类:运动数据+三维事件库→视频类型和性质。文章涉及图形学、视频处理和刑事技术,探索了使用三维图形学技术解决视频侦查难题的新渠道。

  9. Spatio-Temporal Complex Networks: Reachability, Centrality, and Robustness

    CERN Document Server

    Williams, Matthew J

    2015-01-01

    While recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems, existing models do not capture the combined constraint that space and time impose on the relationships and interactions present in a spatio-temporal complex network. This has important consequences, often resulting in an over-simplification of the resilience of a system and obscuring the network's true structure. In this paper, we study the response of spatio-temporal complex networks to random error and systematic attack. Firstly, we propose a model of spatio-temporal paths in time-varying spatially embedded networks. This model captures the property that, in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Secondly, using numerical experiments on four empirical examples of such systems, we study the effect of node failure on a network's topological, temporal, and spatial structure. We find that networ...

  10. Spatio-temporal Reasoning by Combined Topological and Directional Relations Information

    OpenAIRE

    Salamat, Nadeem; Zahzah, El-Hadi

    2011-01-01

    Spatio-temporal reasoning is extensively used in many areas of computer vision and Artificial Intelligence. Different models for spatio-temporal reasoning are proposed based on topological and directional relations sepa- rately in respective domains. Reasoning about moving objects in a spatial scene or description about the two-dimensional scene needs both the reasoning systems simultaneously. We introduced a reasoning system of a two-dimensional spatial scene based on Combined Topological an...

  11. Towards understanding of the spatio-temporal composition of Terrestrial Water Storage variations in Northern Latitudes using a model-data fusion approach

    Science.gov (United States)

    Trautmann, Tina; Koirala, Sujan; Carvalhais, Nuno; Niemann, Christoph; Fink, Manfred; Jung, Martin

    2017-04-01

    Understanding variations in the terrestrial water storage (TWS) and its components is essential to gain insights into the dynamics of the hydrological cycle, and to assess temporal and spatial variations of water availability under global changes. We investigated spatio-temporal patterns of TWS variations and their composition in the humid regions of northern mid-to-high latitudes during 2001-2014 by using a simple hydrological model with few effective parameters. Compared to traditional modelling studies, our simple model was informed and constrained by multiple state-of-the-art earth observation products including TWS from Gravity Recovery and Climate Experiment (GRACE) satellites (Wiese 2015), Snow Water Equivalent (SWE) from GlobSnow project (Loujous et al. 2014), evapotranspiration fluxes from eddy covariance measurements (Tramontana et al. 2016), and gridded runoff estimates for Europe (Gudmundsson & Seneviratne 2016). Thorough evaluation of model demonstrates that the model reproduces the observed patterns of hydrological fluxes and states well. The validated model results are then used to assess the contributions of snow pack, soil moisture and groundwater on the integrated TWS across spatial (local grid scale, spatially integrated) and temporal (seasonal, inter-annual) scales. Interestingly, our results show that TWS variations on different scales are dominated by different components. On both, seasonal and inter-annual time scales, the spatially integrated TWS signal mainly originates from dynamics of snow pack. On the local grid scale, mean seasonal TWS variations are driven by snow dynamics as well, whereas inter-annual variations are found to originate from soil moisture availability. Thus, we show that the determinants of TWS variations are scale-dependent, while coincidently underline the potential of model-data fusion techniques to gain insights into the complex hydrological system. References: Gudmundsson, L. and S. I. Seneviratne (2016

  12. Analysis of the sensitivity to rainfall spatio-temporal variability of an operational urban rainfall-runoff model in a multifractal framework

    Science.gov (United States)

    Gires, A.; Tchiguirinskaia, I.; Schertzer, D. J.; Lovejoy, S.

    2011-12-01

    In large urban areas, storm water management is a challenge with enlarging impervious areas. Many cities have implemented real time control (RTC) of their urban drainage system to either reduce overflow or limit urban contamination. A basic component of RTC is hydraulic/hydrologic model. In this paper we use the multifractal framework to suggest an innovative way to test the sensitivity of such a model to the spatio-temporal variability of its rainfall input. Indeed the rainfall variability is often neglected in urban context, being considered as a non-relevant issue at the scales involve. Our results show that on the contrary the rainfall variability should be taken into account. Universal multifractals (UM) rely on the concept of multiplicative cascade and are a standard tool to analyze and simulate with a reduced number of parameters geophysical processes that are extremely variable over a wide range of scales. This study is conducted on a 3 400 ha urban area located in Seine-Saint-Denis, in the North of Paris (France). We use the operational semi-distributed model that was calibrated by the local authority (Direction Eau et Assainnissement du 93) that is in charge of urban drainage. The rainfall data comes from the C-Band radar of Trappes operated by Météo-France. The rainfall event of February 9th, 2009 was used. A stochastic ensemble approach was implemented to quantify the uncertainty on discharge associated to the rainfall variability occurring at scales smaller than 1 km x 1 km x 5 min that is usually available with C-band radar networks. An analysis of the quantiles of the simulated peak flow showed that the uncertainty exceeds 20 % for upstream links. To evaluate a potential gain from a direct use of the rainfall data available at the resolution of X-band radar, we performed similar analysis of the rainfall fields of the degraded resolution of 9 km x 9 km x 20 min. The results show a clear decrease in uncertainty when the original resolution of C

  13. SPATIO-TEMPORAL CLUSTER ANALYSIS OF DISEASE

    Directory of Open Access Journals (Sweden)

    M. S. Abramovich

    2014-01-01

    Full Text Available The robust version of the spatial scanning statistics for clustering is proposed. Spatio-temporal cluster analysis algorithms were used for the cluster detection of incidence of thyroid carcinoma. Me-thods and algorithms of detection and building clusters for disease on studying territories are consi-dered.

  14. Spatio-temporal dynamics of growth and survival of Lesser Sandeel early life-stages in the North Sea: Predictions from a coupled individual-based and hydrodynamic-biogeochemical model

    DEFF Research Database (Denmark)

    Gurkan, Zeren; Christensen, Asbjørn; Maar, Marie

    2013-01-01

    of larval and early juvenile Lesser Sandeel (Ammodytes marinus) in the North Sea to local feeding conditions by an adapted version of a generic bioenergetic individual-based model for larval fish describing growth and survival. Prey encounter and physiological processes are described explicitly in the model......, which allows analyzing the influence of prey on the growth and survival of sandeel. The model is coupled to a hydrodynamic-biogeochemical model with physical and prey fields and implemented in temporal and three-dimensional spatial settings. Zooplankton biomass simulated by the biogeochemical model...... is validated by Continuous Plankton Recorder survey time series data. Spatio-temporal dynamics of the sandeel cohorts are simulated by the integrated model framework for the period 2004-2006 and five major area divisions of suitable sandeel habitats in the North Sea. This allows obtaining insight...

  15. 一种基于历史事件的多维星座快照模型%A Multi-dimension Constellation Snapshot Spatio-temporal Data Model Based on Historical Events

    Institute of Scientific and Technical Information of China (English)

    戴红; 于宁; 常子冠

    2014-01-01

    To support the studies of urban evolution and development about urban form and spatial structure , the data expression of the characteristics of urban form and spatial structure is a basis .The characteristics are based on some typical events during ur-ban development as the tangible footprints carved on regional space .As an important tool to express information of history , geog-raphy and culture , the spatio-temporal data model can completely express time , space and semantic feature of the objective world . The typical events of Beijing during urban development are extracted .A multi-dimension constellation snapshot data model is presented to study the facts and characteristics of spatio-temporal data based on the historical events .This model based on rela-tional frameworks synthesizes the traits of multi-dimension constellation model and snapshot data model to express features of his -tory and geography at different stages in Beijing .It offers a conceptual representation for dataware model for spatio-temporal data integration and analysis of spatial features .%城市发展进程中典型事件下的城市形态和空间结构诸方面特征,是城市历史文化演变在地域空间上留刻的有形足迹,它的数据表达能够对城市演变和发展研究形成基础和有效支持。时空数据模型因其能够完整表达客观世界时间、空间和特征语义,是重要的历史、地理和文化信息描述工具。以北京为背景,提取城市发展过程中典型历史事件,研究其下的时空数据事实和特征,提出基于历史事件的多维星座快照模型。该模型综合多维星座模型和时间快照模型的特点,以关系模型为主题框架,表达北京各时期历史和地理特征,为建立时空数据集成和城市空间特征分析的数据仓库模型提供概念表示。

  16. Bisimulation for Single-Agent Plausibility Models

    DEFF Research Database (Denmark)

    Andersen, Mikkel Birkegaard; Bolander, Thomas; van Ditmarsch, H.;

    2013-01-01

    Epistemic plausibility models are Kripke models agents use to reason about the knowledge and beliefs of themselves and each other. Restricting ourselves to the single-agent case, we determine when such models are indistinguishable in the logical language containing conditional belief, i.e., we...... define a proper notion of bisimulation, and prove that bisimulation corresponds to logical equivalence on image-finite models. We relate our results to other epistemic notions, such as safe belief and degrees of belief. Our results imply that there are only finitely many non-bisimilar single......-agent epistemic plausibility models on a finite set of propositions. This gives decidability for single-agent epistemic plausibility planning....

  17. RFID Spatio-Temporal Data Management

    Directory of Open Access Journals (Sweden)

    WANG Yonghui

    2013-01-01

    Full Text Available Radio-frequency Identification (RFID technology promises to revolutionize the way we track items in supply chain, retail store, and asset management applications. The size and different characteristics of RFID data pose many interesting challenges in the current data management systems. In this paper, we provide a brief overview of RFID technology and highlight a few of the spatio-temporal data management challenges that we believe are suitable topics for exploratory research.

  18. Spatio-temporal dynamics of growth and survival of Lesser Sandeel early life-stages in the North Sea: Predictions from a coupled individual-based and hydrodynamic-biogeochemical model

    DEFF Research Database (Denmark)

    Gurkan, Zeren; Christensen, Asbjørn; Maar, Marie

    2013-01-01

    of larval and early juvenile Lesser Sandeel (Ammodytes marinus) in the North Sea to local feeding conditions by an adapted version of a generic bioenergetic individual-based model for larval fish describing growth and survival. Prey encounter and physiological processes are described explicitly in the model...... is validated by Continuous Plankton Recorder survey time series data. Spatio-temporal dynamics of the sandeel cohorts are simulated by the integrated model framework for the period 2004-2006 and five major area divisions of suitable sandeel habitats in the North Sea. This allows obtaining insight...... into the influence of temperature variation and zooplankton availability on the growth and survival. To determine areas promising for recruitment, area divisions are compared and optimal time of hatching for higher survival to recruitment due to match-mismatch with prey is determined by comparing different hatching...

  19. 一种基于对象和快照的混合地表覆盖时空数据存储模型%A Mixed Land Cover Spatio-temporal Data Model Based on Object-oriented and Snapshot

    Institute of Scientific and Technical Information of China (English)

    李寅超; 李建松

    2016-01-01

    Spatio-temporal data model (STDM)is one of the hot topics in the domains of spatio-temporal database and data analysis.There is a common view that a universal STDM is always of high complexity due to the various situation of spatio-temporal data.In this article,a mixed STDM is proposed based on object-oriented and snapshot models for modelling and analyzing landcover change (LCC).This model uses the object-oriented STDM to describe the spatio-temporal processes of land cover patches and organize their spatial and attributive properties.In the meantime,it uses the snapshot STDM to present the spatio-temporal distribution of LCC on the whole via snapshot images.The two types of models are spatially and temporally combined into a mixed version.In addition to presenting the spatio-temporal events themselves, this model could express the transformation events between different classes of spatio-temporal objects.It can be used to create database for historical data of LCC,do spatio-temporal statistics,simulation and data mining with the data.In this article,the LCC data in Heilongjiang province is used for case study to validate spatio-temporal data management and analysis abilities of mixed STDM,including creating database, spatio-temporal query, global evolution analysis and patches spatio-temporal process expressi on.%时空数据模型是数据库和时空数据分析研究热点之一。由于时空数据变化类型多、分析计算复杂及应用范围广,一种观点认为建立通用的时空数据模型具有高度复杂性。本文针对地表覆盖变化时空数据建库和时空分析应用需求,提出了基于对象和快照的混合时空数据模型。该模型用面向对象描述地表覆盖的斑块对象时空过程,组织管理斑块对象时空事件和空间、属性信息,同时用快照描述地表覆盖整体时空分布,组织管理栅格快照,两者通过基于时间和空间位置的逻辑关联关系形成混合模型。模

  20. 东海初级生产力遥感反演及其时空演化机制%Estimation of ocean primary productivity and its spatio-temporal variation mechanism for East China Sea based on VGPM model

    Institute of Scientific and Technical Information of China (English)

    李国胜; 高平; 王芳

    2004-01-01

    According to calculation results of ocean chlorophyll concentration based on SeaWiFS data by SeaBAM model and synchronous ship-measured data, this research set up an improved model for Case Ⅰ and Case Ⅱ water bodies respectively. The monthly chlorophyll distribution in the East China Sea in 1998 was obtained kom this improved model on calculation results of SeaBAM. The euphotic depth distribution in 1998 in the East China Sea is calculated by using remote sensing data of K490 from SeaWiFS according to the relation between the euphotic depth and the oceanic diffuse attenuation coefficient. With data of ocean chlorophyll concentration, euphotic depth, ocean surface photosynthetic available radiation (PAR), daily photoperiod and optimal rate of daily carbon fixation within a water column, the monthly and annual primary productivity spatio-temporal distributions in the East China Sea in 1998 were obtained based on VGPM model. Based on analysis of those distributions, the conclusion can be drawn that there is a clear bimodality character of primary productivity in the monthly distribution in the East China Sea. In detail, the monthly distribution of primary productivity stays the lowest level in winter and rises rapidly to the peak in spring. It gets down a little in summer, and gets up a little in autumn. The daily average of primary productivity in the whole East China Sea is 560.03 mg/m2/d, which is far higher than the average of subtropical ocean areas. The annual average of primary productivity is 236.95 g/m2/a. The research on the seasonal variety mechanism of primary productivity shows that several factors that affect the spatio-temporal distribution may include the chlorophyll concentration distribution, temperature condition, the Yangtze River diluted water variety, the euphotic depth, ocean current variety, etc. But the main influencing factors may be different in each local sea area.

  1. Impact of Lipid Composition and Receptor Conformation on the Spatio-temporal Organization of μ-Opioid Receptors in a Multi-component Plasma Membrane Model.

    Science.gov (United States)

    Marino, Kristen A; Prada-Gracia, Diego; Provasi, Davide; Filizola, Marta

    2016-12-01

    The lipid composition of cell membranes has increasingly been recognized as playing an important role in the function of various membrane proteins, including G Protein-Coupled Receptors (GPCRs). For instance, experimental and computational evidence has pointed to lipids influencing receptor oligomerization directly, by physically interacting with the receptor, and/or indirectly, by altering the bulk properties of the membrane. While the exact role of oligomerization in the function of class A GPCRs such as the μ-opioid receptor (MOR) is still unclear, insight as to how these receptors oligomerize and the relevance of the lipid environment to this phenomenon is crucial to our understanding of receptor function. To examine the effect of lipids and different MOR conformations on receptor oligomerization we carried out extensive coarse-grained molecular dynamics simulations of crystal structures of inactive and/or activated MOR embedded in an idealized mammalian plasma membrane composed of 63 lipid types asymmetrically distributed across the two leaflets. The results of these simulations point, for the first time, to specific direct and indirect effects of the lipids, as well as the receptor conformation, on the spatio-temporal organization of MOR in the plasma membrane. While sphingomyelin-rich, high-order lipid regions near certain transmembrane (TM) helices of MOR induce an effective long-range attractive force on individual protomers, both long-range lipid order and interface formation are found to be conformation dependent, with a larger number of different interfaces formed by inactive MOR compared to active MOR.

  2. Pattern formations in chaotic spatio-temporal systems

    Indian Academy of Sciences (India)

    Ying Zhang; Shihong Wang; Jinhua Xiao; Hilda A Cerdeira; S Chen; Gang Hu

    2005-06-01

    Pattern formations in chaotic spatio-temporal systems modelled by coupled chaotic oscillators are investigated. We focus on various symmetry breakings and different kinds of chaos synchronization–desynchronization transitions, which lead to certain types of spontaneous spatial orderings and the emergence of some typical ordered patterns, such as rotating wave patterns with splay phase ordering (orientational symmetry breaking) and partially synchronous standing wave patterns with in-phase ordering (translational symmetry breaking). General pictures of the global behaviors of pattern formations and transitions in coupled chaotic oscillators are provided.

  3. Spatio-temporal evolution of Beijing 2003 SARS epidemic

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township. Two smoothing methods, Bayesian adjustment and spatial smoothing, were applied to identify the spatial risks and spatial transmission trends. Furthermore, we explored various spatial patterns and spatio-temporal evolution of Beijing 2003 SARS epidemic using spatial statistics such as Moran’s I and LISA. Part of this study is targeted at evaluating the effectiveness of public health control measures implemented during the SARS epidemic. The main findings are as follows. (1) The diffusion speed of SARS in the northwest-southeast direction is weaker than that in northeast-southwest direction. (2) SARS’s spread risk is positively spatially associated and the strength of this spatial association has experienced changes from weak to strong and then back to weak during the lifetime of the Beijing SARS epidemic. (3) Two spatial clusters of disease cases are identified: one in the city center and the other in the eastern suburban area. These two clusters followed different evolutionary paths but interacted with each other as well. (4) Although the government missed the opportunity to contain the early outbreak of SARS in March 2003, the response strategies implemented after the mid of April were effective. These response measures not only controlled the growth of the disease cases, but also mitigated the spatial diffusion.

  4. Visualization of Spatio-Temporal Relations in Movement Event Using Multi-View

    Science.gov (United States)

    Zheng, K.; Gu, D.; Fang, F.; Wang, Y.; Liu, H.; Zhao, W.; Zhang, M.; Li, Q.

    2017-09-01

    Spatio-temporal relations among movement events extracted from temporally varying trajectory data can provide useful information about the evolution of individual or collective movers, as well as their interactions with their spatial and temporal contexts. However, the pure statistical tools commonly used by analysts pose many difficulties, due to the large number of attributes embedded in multi-scale and multi-semantic trajectory data. The need for models that operate at multiple scales to search for relations at different locations within time and space, as well as intuitively interpret what these relations mean, also presents challenges. Since analysts do not know where or when these relevant spatio-temporal relations might emerge, these models must compute statistical summaries of multiple attributes at different granularities. In this paper, we propose a multi-view approach to visualize the spatio-temporal relations among movement events. We describe a method for visualizing movement events and spatio-temporal relations that uses multiple displays. A visual interface is presented, and the user can interactively select or filter spatial and temporal extents to guide the knowledge discovery process. We also demonstrate how this approach can help analysts to derive and explain the spatio-temporal relations of movement events from taxi trajectory data.

  5. VISUALIZATION OF SPATIO-TEMPORAL RELATIONS IN MOVEMENT EVENT USING MULTI-VIEW

    Directory of Open Access Journals (Sweden)

    K. Zheng

    2017-09-01

    Full Text Available Spatio-temporal relations among movement events extracted from temporally varying trajectory data can provide useful information about the evolution of individual or collective movers, as well as their interactions with their spatial and temporal contexts. However, the pure statistical tools commonly used by analysts pose many difficulties, due to the large number of attributes embedded in multi-scale and multi-semantic trajectory data. The need for models that operate at multiple scales to search for relations at different locations within time and space, as well as intuitively interpret what these relations mean, also presents challenges. Since analysts do not know where or when these relevant spatio-temporal relations might emerge, these models must compute statistical summaries of multiple attributes at different granularities. In this paper, we propose a multi-view approach to visualize the spatio-temporal relations among movement events. We describe a method for visualizing movement events and spatio-temporal relations that uses multiple displays. A visual interface is presented, and the user can interactively select or filter spatial and temporal extents to guide the knowledge discovery process. We also demonstrate how this approach can help analysts to derive and explain the spatio-temporal relations of movement events from taxi trajectory data.

  6. Spatio-Temporal Features of China’s Urban Fires: An Investigation with Reference to Gross Domestic Product and Humidity

    OpenAIRE

    Zhenbo Wang; Xiaorui Zhang; Bo Xu

    2015-01-01

    Frequent fire accidents pose a serious threat to human life and property. The spatio-temporal features of China’s urban fires, and their drivers should be investigated. Based on the Spatio-temporal Dynamic panel data Model (SDM), and using fire data gathered from 337 Chinese cities in 2000 to 2009, the influence of spatio-temporal factors on the frequency of urban fires was analyzed. The results show that (1) the overall fire incidence of China increased annually before 2002 and reduced signi...

  7. Spatio-temporal avalanche forecasting with Support Vector Machines

    Directory of Open Access Journals (Sweden)

    A. Pozdnoukhov

    2011-02-01

    Full Text Available This paper explores the use of the Support Vector Machine (SVM as a data exploration tool and a predictive engine for spatio-temporal forecasting of snow avalanches. Based on the historical observations of avalanche activity, meteorological conditions and snowpack observations in the field, an SVM is used to build a data-driven spatio-temporal forecast for the local mountain region. It incorporates the outputs of simple physics-based and statistical approaches used to interpolate meteorological and snowpack-related data over a digital elevation model of the region. The interpretation of the produced forecast is discussed, and the quality of the model is validated using observations and avalanche bulletins of the recent years. The insight into the model behaviour is presented to highlight the interpretability of the model, its abilities to produce reliable forecasts for individual avalanche paths and sensitivity to input data. Estimates of prediction uncertainty are obtained with ensemble forecasting. The case study was carried out using data from the avalanche forecasting service in the Locaber region of Scotland, where avalanches are forecast on a daily basis during the winter months.

  8. An autoregressive approach to spatio-temporal disease mapping.

    Science.gov (United States)

    Martínez-Beneito, M A; López-Quilez, A; Botella-Rocamora, P

    2008-07-10

    Disease mapping has been a very active research field during recent years. Nevertheless, time trends in risks have been ignored in most of these studies, yet they can provide information with a very high epidemiological value. Lately, several spatio-temporal models have been proposed, either based on a parametric description of time trends, on independent risk estimates for every period, or on the definition of the joint covariance matrix for all the periods as a Kronecker product of matrices. The following paper offers an autoregressive approach to spatio-temporal disease mapping by fusing ideas from autoregressive time series in order to link information in time and by spatial modelling to link information in space. Our proposal can be easily implemented in Bayesian simulation software packages, for example WinBUGS. As a result, risk estimates are obtained for every region related to those in their neighbours and to those in the same region in adjacent periods. (c) 2007 John Wiley & Sons, Ltd.

  9. Standards-Based Services for Big Spatio-Temporal Data

    Science.gov (United States)

    Baumann, P.; Merticariu, V.; Dumitru, A.; Misev, D.

    2016-06-01

    With the unprecedented availability of continuously updated measured and generated data there is an immense potential for getting new and timely insights - yet, the value is not fully leveraged as of today. The quest is up for high-level service interfaces for dissecting datasets and rejoining them with other datasets - ultimately, to allow users to ask "any question, anytime, on any size" enabling them to "build their own product on the go". With OGC Coverages, a concrete, interoperable data model has been established which unifies n-D spatio-temporal regular and irregular grids, point clouds, and meshes. The Web Coverage Service (WCS) suite provides versatile streamlined coverage functionality ranging from simple access to flexible spatio-temporal analytics. Flexibility and scalability of the WCS suite has been demonstrated in practice through massive services run by large-scale data centers. We present the current status in OGC Coverage data and service models, contrast them to related work, and describe a scalable implementation based on the rasdaman array engine.

  10. plotKML: Scientific Visualization of Spatio-Temporal Data

    Directory of Open Access Journals (Sweden)

    Tomislav Hengl

    2015-02-01

    Full Text Available plotKML is an R package that provides methods for writing the most common R spatial classes into KML files. It builds up on the existing XML parsing functionality (XML package, and provides similar plotting functionality as the lattice package. Its main objective is to provide a simple interface to generate KML files with a small number of arguments, and allows users to visually explore spatio-temporal data available in R: points, polygons, gridded maps, trajectory-type data, vertical profiles, ground photographs, time series vector objects or raster images, along with the results of spatial analysis such as geostatistical mapping, spatial simulations of vector and gridded objects, optimized sampling designs, species distribution models and similar. A generic plotKML( function automatically determines the parsing order and visualizes data directly from R; lower level functions can be combined to allow for new user-created visualization templates. In comparison to other packages writing KML, plotKML seems to be more object oriented, it links more closely to the existing R classes for spatio-temporal data (sp, spacetime and raster packages than the alternatives, and provides users with the possibility to create their own templates.

  11. Adjusted functional boxplots for spatio-temporal data visualization and outlier detection

    KAUST Repository

    Sun, Ying

    2011-10-24

    This article proposes a simulation-based method to adjust functional boxplots for correlations when visualizing functional and spatio-temporal data, as well as detecting outliers. We start by investigating the relationship between the spatio-temporal dependence and the 1.5 times the 50% central region empirical outlier detection rule. Then, we propose to simulate observations without outliers on the basis of a robust estimator of the covariance function of the data. We select the constant factor in the functional boxplot to control the probability of correctly detecting no outliers. Finally, we apply the selected factor to the functional boxplot of the original data. As applications, the factor selection procedure and the adjusted functional boxplots are demonstrated on sea surface temperatures, spatio-temporal precipitation and general circulation model (GCM) data. The outlier detection performance is also compared before and after the factor adjustment. © 2011 John Wiley & Sons, Ltd.

  12. Spatio-Temporal Stochastic Pattern Generator for ensemble prediction and ensemble data assimilation in geophysical applications

    CERN Document Server

    Tsyrulnikov, Michael

    2016-01-01

    A generator of spatio-temporal pseudo-random Gaussian fields that satisfy the "proportionality of scales" property (Tsyroulnikov 2001) is presented. The generator is a third-order in time stochastic differential equation with a pseudo-differential spatial operator defined on a limited area domain in Cartesian coordinate system. The spatial covariance functions of the generated fields belong to the Mat\\'ern class. The spatio-temporal covariances are non-separable. A spectral-space numerical solver is implemented and accelerated exploiting properties of real-world geophysical fields, in particular, smoothness of their spatial spectra. The generator is designed to simulate additive or multiplicative, or other spatio-temporal perturbations that represent uncertainties in numerical prediction models in geophysics. The program code of the generator is publicly available.

  13. Spatio-temporal map generalizations with the hierarchical Voronoi data structure

    DEFF Research Database (Denmark)

    Mioc, Darka; Anton, François; Gold, Christopher M.

    of map objects, together with their temporal and spatial adjacency relationships. In this paper, we present new solutions to the problems of spatio-temporal generalizations using the hierarchical Voronoi spatio-temporal data structure. The application of the hierarchical Voronoi data structure presented...... in this research is in spatio-temporal map generalization, which is needed for reasoning about dynamic aspects of the world, primarily about actions, events and processes. This provides an advance in the domain of map generalization as we are able to deal not only with the cartographic objects, but also...... implemented in commercial GIS systems. In this research, we used the Voronoi spatial data model for map generalizations. We were able to demonstrate that the map generalization does not affect only spatial objects (points, lines or polygons), but also the events corresponding to the creation and modification...

  14. Nonlinear system identification NARMAX methods in the time, frequency, and spatio-temporal domains

    CERN Document Server

    Billings, Stephen A

    2013-01-01

    Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) modelThe orthogonal least squares algorithm that allows models to be built term by

  15. Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

    KAUST Repository

    Xie, Le

    2014-01-01

    We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.

  16. Application of Bayesian spatio-temporal modeling in describing the brucellosis infections%贝叶斯时空模型在布鲁氏菌病疫情数据分析中的应用

    Institute of Scientific and Technical Information of China (English)

    郑杨; 冯子健; 李晓松

    2011-01-01

    以2000-2007年内蒙古地区布鲁氏菌病(布病)疫情数据为例,运用空间统计学和传染病流行病学的相关理论,应用贝叶斯理论框架建立时空模型,分析布病在时间和空间上呈现的格局及其演变,以及与之相关联的协变量及其变化,并与传统分析方法进行比较.结果 显示,拟合协变量的贝叶斯时空模型相对较佳(离差信息准则值最小,为2388.000).2000-2007年内蒙古自治区101个旗县的布病疫情呈现较强的空间相关性,时空格局存在较明显的共变现象,每年空间相关性不尽相同,空间相关系数后验中位数位于0.968~0.973之间,总体上随时间变化略呈下降趋势.地区类型和牛羊存栏数量与内蒙古布病流行可能有关,且牛羊存栏数量对布病的影响随年份而变化.与传统描述流行病学分析方法比较,贝叶斯方法对布病发病相对危险度的估计更加稳定.%Based on the number of brucellosis cases reported from the national infectious diseases reporting system in Inner Mongolia from 2000 to 2007, a model was developed. Theories of spatial statistics were used, together with knowledge on infectious disease epidemiology and the frame of Bayesian statistics, before the Bayesian spatio-temporal models were respectively set. The effects of space, time, space-time and the relative covariates were also considered. These models were applied to analyze the brucellosis distribution and time trend in Inner Mongolia during 2000-2007. The results of Bayesian spatio-temporal models was expressed by mapping of the disease and compared to the conventional statistical methods. Results showed that the Bayesian models, under consideration of space-time effect and the relative covariates (deviance information criterion, DIC=2388.000) ,seemed to be the best way to serve the purpose. The county-level spatial correlation of brucellosis epidemics was positive and quite strong in Inner Mongolia. However, the spatial

  17. Spatio-Temporal Detection of Fine-Grained Dyadic Human Interactions

    NARCIS (Netherlands)

    van Gemeren, C.J.; Poppe, R.W.; Veltkamp, R.C.

    2016-01-01

    We introduce a novel spatio-temporal deformable part model for offline detection of fine-grained interactions in video. One novelty of the model is that part detectors model the interacting individuals in a single graph that can contain different combinations of feature descriptors. This allows us t

  18. Spatio-Temporal Detection of Fine-Grained Dyadic Human Interactions

    NARCIS (Netherlands)

    van Gemeren, C.J.; Poppe, R.W.; Veltkamp, R.C.

    2016-01-01

    We introduce a novel spatio-temporal deformable part model for offline detection of fine-grained interactions in video. One novelty of the model is that part detectors model the interacting individuals in a single graph that can contain different combinations of feature descriptors. This allows us

  19. Spatio-temporal simulation in subthreshold CMOS

    Science.gov (United States)

    Neeley, John; Harris, John G.

    1997-05-01

    This paper reports on the design and chip measurements from a CMOS chaotic oscillator operating by itself and connected in a ring of four similar oscillators. The oscillator is autonomous and generates signals with three state variables analogous to Chua's circuit. For commensurate bandwidth, this design utilizes currents and capacitors over 200 times smaller than above threshold CMOS realizations. Also, all circuit elements are on chip. The resulting voltage-controlled bifurcation parameters simplify exploration of the circuit's dynamics, alleviating the need to interchange physical components. This combination of reduced size and variable parameters make the design suitable for single-chip VLSI synthesis of higher dimensional chaotic circuits, including coupled maps generating spatio-temporal chaos and systems exploiting chaos synchronization.

  20. Spatio-temporal properties of letter crowding

    Science.gov (United States)

    Chung, Susana T. L.

    2016-01-01

    Crowding between adjacent letters has been investigated primarily as a spatial effect. The purpose of this study was to investigate the spatio-temporal properties of letter crowding. Specifically, we examined the systematic changes in the degradation effects in letter identification performance when adjacent letters were presented with a temporal asynchrony, as a function of letter separation and between the fovea and the periphery. We measured proportion-correct performance for identifying the middle target letter in strings of three lowercase letters at the fovea and 10° in the inferior visual field, for a range of center-to-center letter separations and a range of stimulus onset asynchronies (SOA) between the target and flanking letters (positive SOAs: target preceded flankers). As expected, the accuracy for identifying the target letters reduces with decreases in letter separation. This crowding effect shows a strong dependency on SOAs, such that crowding is maximal between 0 and ∼100 ms (depending on conditions) and diminishes for larger SOAs (positive or negative). Maximal crowding does not require the target and flanking letters to physically coexist for the entire presentation duration. Most importantly, crowding can be minimized even for closely spaced letters if there is a large temporal asynchrony between the target and flankers. The reliance of letter identification performance on SOAs and how it changes with letter separations imply that the crowding effect can be traded between space and time. Our findings are consistent with the notion that crowding should be considered as a spatio-temporal, and not simply a spatial, effect. PMID:27088895

  1. Spatio-temporal properties of letter crowding.

    Science.gov (United States)

    Chung, Susana T L

    2016-01-01

    Crowding between adjacent letters has been investigated primarily as a spatial effect. The purpose of this study was to investigate the spatio-temporal properties of letter crowding. Specifically, we examined the systematic changes in the degradation effects in letter identification performance when adjacent letters were presented with a temporal asynchrony, as a function of letter separation and between the fovea and the periphery. We measured proportion-correct performance for identifying the middle target letter in strings of three lowercase letters at the fovea and 10° in the inferior visual field, for a range of center-to-center letter separations and a range of stimulus onset asynchronies (SOA) between the target and flanking letters (positive SOAs: target preceded flankers). As expected, the accuracy for identifying the target letters reduces with decreases in letter separation. This crowding effect shows a strong dependency on SOAs, such that crowding is maximal between 0 and ∼100 ms (depending on conditions) and diminishes for larger SOAs (positive or negative). Maximal crowding does not require the target and flanking letters to physically coexist for the entire presentation duration. Most importantly, crowding can be minimized even for closely spaced letters if there is a large temporal asynchrony between the target and flankers. The reliance of letter identification performance on SOAs and how it changes with letter separations imply that the crowding effect can be traded between space and time. Our findings are consistent with the notion that crowding should be considered as a spatio-temporal, and not simply a spatial, effect.

  2. Spatio-Temporal Clustering of Monitoring Network

    Science.gov (United States)

    Hussain, I.; Pilz, J.

    2009-04-01

    Pakistan has much diversity in seasonal variation of different locations. Some areas are in desserts and remain very hot and waterless, for example coastal areas are situated along the Arabian Sea and have very warm season and a little rainfall. Some areas are covered with mountains, have very low temperature and heavy rainfall; for instance Karakoram ranges. The most important variables that have an impact on the climate are temperature, precipitation, humidity, wind speed and elevation. Furthermore, it is hard to find homogeneous regions in Pakistan with respect to climate variation. Identification of homogeneous regions in Pakistan can be useful in many aspects. It can be helpful for prediction of the climate in the sub-regions and for optimizing the number of monitoring sites. In the earlier literature no one tried to identify homogeneous regions of Pakistan with respect to climate variation. There are only a few papers about spatio-temporal clustering of monitoring network. Steinhaus (1956) presented the well-known K-means clustering method. It can identify a predefined number of clusters by iteratively assigning centriods to clusters based. Castro et al. (1997) developed a genetic heuristic algorithm to solve medoids based clustering. Their method is based on genetic recombination upon random assorting recombination. The suggested method is appropriate for clustering the attributes which have genetic characteristics. Sap and Awan (2005) presented a robust weighted kernel K-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data. Soltani and Modarres (2006) used hierarchical and divisive cluster analysis to categorize patterns of rainfall in Iran. They only considered rainfall at twenty-eight monitoring sites and concluded that eight clusters

  3. A Hierarchical Bayesian M/EEG Imaging Method Correcting for Incomplete Spatio-Temporal Priors

    DEFF Research Database (Denmark)

    Stahlhut, Carsten; Attias, Hagai T.; Sekihara, Kensuke;

    2013-01-01

    In this paper we present a hierarchical Bayesian model, to tackle the highly ill-posed problem that follows with MEG and EEG source imaging. Our model promotes spatiotemporal patterns through the use of both spatial and temporal basis functions. While in contrast to most previous spatio-temporal ...

  4. Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series

    Science.gov (United States)

    Lu, Meng; Pebesma, Edzer; Sanchez, Alber; Verbesselt, Jan

    2016-07-01

    Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis.

  5. Design and implementation of spatio-temporal database of water and soil loss

    Science.gov (United States)

    Lu, XinHai; Bian, Fulin; Tan, Xiaojun

    2008-12-01

    This paper analyzed the features and limitations of several typical spatio-temporal data models. "spatio-temporal cube": the main disadvantage is that the target change will produce great data redundancy when under non-consecutive circumstances. "Snapshots": it repeatedly saves graphics and attribute of no changes which resulted in waste of storage spaces, and it is impossible to reflect space objects under the same domain and the relationship between the attributes. "Base State with Amendments": merely modify changing object, but it's not suitable for continuous variation space object. "space-frame composite": currently, the model is lacking of sound framework structure and application model. "Object-oriented spatio-temporal model": The modeling concept, theoretical foundation and technical realization has not yet reached a consensus, it's not mature enough. In allusion to the features of the spatial database of water and soil loss, this essay expounded the characteristics of spatiotemporal databases. Spatial features in many practical circumstances ( such as thematic maps in soil and water conservation projects and space elements of soil erosion distribution map) have spatial data features, and also change with time, consequently, required us to establish spatio-temporal database, STDB, which can capture time data and space data at the same time. This analysis based on "ArcSDE versioning mechanisms" temporal and spatial database implement technologies, discussed the construction methods, process and data features of the database, and introduced the implementation of historical data rebuilding and version merging.

  6. A spatio-temporal analysis of suicide in El Salvador.

    Science.gov (United States)

    Carcach, Carlos

    2017-04-20

    In 2012, international statistics showed El Salvador's suicide rate as 40th in the world and the highest in Latin America. Over the last 15 years, national statistics show the suicide death rate declining as opposed to an increasing rate of homicide. Though completed suicide is an important social and health issue, little is known about its prevalence, incidence, etiology and spatio-temporal behavior. The primary objective of this study was to examine completed suicide and homicide using the stream analogy to lethal violence within a spatio-temporal framework. A Bayesian model was applied to examine the spatio-temporal evolution of the tendency of completed suicide over homicide in El Salvador. Data on numbers of suicides and homicides at the municipal level were obtained from the Instituto de Medicina Legal (IML) and population counts, from the Dirección General de Estadística y Censos (DIGESTYC), for the period of 2002 to 2012. Data on migration were derived from the 2007 Population Census, and inequality data were obtained from a study by Damianović, Valenzuela and Vera. The data reveal a stable standardized rate of total lethal violence (completed suicide plus homicide) across municipalities over time; a decline in suicide; and a standardized suicide rate decreasing with income inequality but increasing with social isolation. Municipalities clustered in terms of both total lethal violence and suicide standardized rates. Spatial effects for suicide were stronger among municipalities located in the north-east and center-south sides of the country. New clusters of municipalities with large suicide standardized rates were detected in the north-west, south-west and center-south regions, all of which are part of time-stable clusters of homicide. Prevention efforts to reduce income inequality and mitigate the negative effects of weak relational systems should focus upon municipalities forming time-persistent clusters with a large rate of death by suicide. In

  7. Probabilistic M/EEG source imaging from sparse spatio-temporal event structure

    DEFF Research Database (Denmark)

    Stahlhut, Carsten; Attias, Hagai T.; Wipf, David

    While MEG and EEG source imaging methods have to tackle a severely ill-posed problem their success can be stated as their ability to constrain the solutions using appropriate priors. In this paper we propose a hierarchical Bayesian model facilitating spatio-temporal patterns through the use of bo...

  8. Spatio-temporal observations of tertiary ozone maximum

    Directory of Open Access Journals (Sweden)

    V. F. Sofieva

    2009-03-01

    Full Text Available We present spatio-temporal distributions of tertiary ozone maximum (TOM, based on GOMOS (Global Ozone Monitoring by Occultation of Stars ozone measurements in 2002–2006. The tertiary ozone maximum is typically observed in the high-latitude winter mesosphere at altitude ~72 km. Although the explanation for this phenomenon has been found recently – low concentrations of odd-hydrogen cause the subsequent decrease in odd-oxygen losses – models have had significant deviations from existing observations until recently. Good coverage of polar night regions by GOMOS data has allowed for the first time obtaining spatial and temporal observational distributions of night-time ozone mixing ratio in the mesosphere.

    The distributions obtained from GOMOS data have specific features, which are variable from year to year. In particular, due to a long lifetime of ozone in polar night conditions, the downward transport of polar air by the meridional circulation is clearly observed in the tertiary ozone maximum time series. Although the maximum tertiary ozone mixing ratio is achieved close to the polar night terminator (as predicted by the theory, TOM can be observed also at very high latitudes, not only in the beginning and at the end, but also in the middle of winter. We have compared the observational spatio-temporal distributions of tertiary ozone maximum with that obtained using WACCM (Whole Atmosphere Community Climate Model and found that the specific features are reproduced satisfactorily by the model.

    Since ozone in the mesosphere is very sensitive to HOx concentrations, energetic particle precipitation can significantly modify the shape of the ozone profiles. In particular, GOMOS observations have shown that the tertiary ozone maximum was temporarily destroyed during the January 2005 and December 2006 solar proton events as a result of the HOx enhancement from the increased ionization.

  9. Sparse cortical source localization using spatio-temporal atoms.

    Science.gov (United States)

    Korats, Gundars; Ranta, Radu; Le Cam, Steven; Louis-Dorr, Valérie

    2015-01-01

    This paper addresses the problem of sparse localization of cortical sources from scalp EEG recordings. Localization algorithms use propagation model under spatial and/or temporal constraints, but their performance highly depends on the data signal-to-noise ratio (SNR). In this work we propose a dictionary based sparse localization method which uses a data driven spatio-temporal dictionary to reconstruct the measurements using Single Best Replacement (SBR) and Continuation Single Best Replacement (CSBR) algorithms. We tested and compared our methods with the well-known MUSIC and RAP-MUSIC algorithms on simulated realistic data. Tests were carried out for different noise levels. The results show that our method has a strong advantage over MUSIC-type methods in case of synchronized sources.

  10. Spatio-temporal phenomena in complex systems with time delays

    Science.gov (United States)

    Yanchuk, Serhiy; Giacomelli, Giovanni

    2017-03-01

    Real-world systems can be strongly influenced by time delays occurring in self-coupling interactions, due to unavoidable finite signal propagation velocities. When the delays become significantly long, complicated high-dimensional phenomena appear and a simple extension of the methods employed in low-dimensional dynamical systems is not feasible. We review the general theory developed in this case, describing the main destabilization mechanisms, the use of visualization tools, and commenting on the most important and effective dynamical indicators as well as their properties in different regimes. We show how a suitable approach, based on a comparison with spatio-temporal systems, represents a powerful instrument for disclosing the very basic mechanism of long-delay systems. Various examples from different models and a series of recent experiments are reported.

  11. SPATIO-TEMPORAL COMPLEXITY OF THE AORTIC SINUS VORTEX.

    Science.gov (United States)

    Moore, Brandon; Dasi, Lakshmi Prasad

    2014-06-01

    The aortic sinus vortex is a classical flow structure of significant importance to aortic valve dynamics and the initiation and progression of calific aortic valve disease. We characterize the spatio-temporal characteristics of aortic sinus voxtex dynamics in relation to the viscosity of blood analog solution as well as heart rate. High resolution time-resolved (2KHz) particle image velocimetry was conducted to capture 2D particle streak videos and 2D instantaneous velocity and streamlines along the sinus midplane using a physiological but rigid aorta model fitted with a porcine bioprosthetic heart valve. Blood analog fluids used include a water-glycerin mixture and saline to elucidate the sensitivity of vortex dynamics to viscosity. Experiments were conducted to record 10 heart beats for each combination of blood analog and heart rate condition. Results show that the topological characteristics of the velocity field vary in time-scales as revealed using time bin averaged vectors and corresponding instantaneous streamlines. There exist small time-scale vortices and a large time-scale main vortex. A key flow structure observed is the counter vortex at the upstream end of the sinus adjacent to the base (lower half) of the leaflet. The spatio-temporal complexity of vortex dynamics is shown to be profoundly influenced by strong leaflet flutter during systole with a peak frequency of 200Hz and peak amplitude of 4 mm observed in the saline case. While fluid viscosity influences the length and time-scales as well as the introduction of leaflet flutter, heart rate influences the formation of counter vortex at the upstream end of the sinus. Higher heart rates are shown to reduce the strength of the counter vortex that can greatly influence the directionality and strength of shear stresses along the base of the leaflet. This study demonstrates the impact of heart rate and blood analog viscosity on aortic sinus hemodynamics.

  12. Predicting saltwater intrusion into aquifers in vicinity of deserts using spatio-temporal kriging.

    Science.gov (United States)

    Bahrami Jovein, E; Hosseini, S M

    2017-02-01

    The primary objective of this study was to provide a detailed framework to use the spatio-temporal kriging to model the spatio-temporal variations of salinity data and predict saltwater intrusion into freshwater aquifers in the vicinity of deserts. EC data, measured in extraction wells in the Mahvelat plain located in the Northeastern part of Iran, available from 2007 to 2013, were used to demonstrate the developed framework. The source of data was not a well-designed measurement network. Therefore, to homogenize the data, spatial analysis was used to find EC distribution in the area in each year of study. To conduct the spatial analysis, a guideline and a systematic process were developed to select an appropriate kriging method and optimize its parameters. This process can be applied to different variables. After spatial analysis of EC data for all the years of the analysis period using empirical Bayesian kriging (EBK) method with manually optimized parameters, spatio-temporal and corresponding variogram analysis was conducted using R software. This process was based on a separable product-sum model applied to the data from 2007 to 2012. The data of 2013 and the data available for the years 1999 and 2006 were used for evaluating the performance of the spatio-temporal model. The EC distribution maps, developed for different years until 2021, show a high level of EC in the north, south, and west of the study area and growing saltwater intrusion into the central freshwater aquifer. This result can be attributed to the over-exploitation of the aquifer and hydraulic head and gradient distribution in the area. The framework provided in this study for spatio-temporal analysis of unstructured EC data is useful for groundwater managers in making proper decisions.

  13. Annotating spatio-temporal datasets for meaningful analysis in the Web

    Science.gov (United States)

    Stasch, Christoph; Pebesma, Edzer; Scheider, Simon

    2014-05-01

    More and more environmental datasets that vary in space and time are available in the Web. This comes along with an advantage of using the data for other purposes than originally foreseen, but also with the danger that users may apply inappropriate analysis procedures due to lack of important assumptions made during the data collection process. In order to guide towards a meaningful (statistical) analysis of spatio-temporal datasets available in the Web, we have developed a Higher-Order-Logic formalism that captures some relevant assumptions in our previous work [1]. It allows to proof on meaningful spatial prediction and aggregation in a semi-automated fashion. In this poster presentation, we will present a concept for annotating spatio-temporal datasets available in the Web with concepts defined in our formalism. Therefore, we have defined a subset of the formalism as a Web Ontology Language (OWL) pattern. It allows capturing the distinction between the different spatio-temporal variable types, i.e. point patterns, fields, lattices and trajectories, that in turn determine whether a particular dataset can be interpolated or aggregated in a meaningful way using a certain procedure. The actual annotations that link spatio-temporal datasets with the concepts in the ontology pattern are provided as Linked Data. In order to allow data producers to add the annotations to their datasets, we have implemented a Web portal that uses a triple store at the backend to store the annotations and to make them available in the Linked Data cloud. Furthermore, we have implemented functions in the statistical environment R to retrieve the RDF annotations and, based on these annotations, to support a stronger typing of spatio-temporal datatypes guiding towards a meaningful analysis in R. [1] Stasch, C., Scheider, S., Pebesma, E., Kuhn, W. (2014): "Meaningful spatial prediction and aggregation", Environmental Modelling & Software, 51, 149-165.

  14. Spatio-temporal analysis of female breast cancer incidence in Shenzhen, 2007-2012

    Institute of Scientific and Technical Information of China (English)

    Hai-Bin Zhou; Sheng-Yuan Liu; Lin Lei; Zhong-Wei Chen; Ji Peng; Ying-Zhou Yang; Xiao-Li Liu

    2015-01-01

    Introduction:Breast cancer is a leading tumor with a high mortality in women. This study examined the spatio-temporal distribution of the incidence of female breast cancer in Shenzhen between 2007 and 2012. Methods:The data on breast cancer incidence were obtained from the Shenzhen Cancer Registry System. To describe the temporal trend, the average annual percentage change (AAPC) was analyzed using a joinpoint regression model. Spatial autocorrelation and a retrospective spatio-temporal scan approach were used to detect the spatio-temporal cluster distribution of breast cancer cases. Results:Breast cancer ranked first among different types of cancer in women in Shenzhen between 2007 and 2012 with a crude incidence of 20.0/100,000 population. The age-standardized rate according to the world standard population was 21.1/100,000 in 2012, with an AAPC of 11.3%. The spatial autocorrelation analysis showed a spatial correlation characterized by the presence of a hotspot in south-central Shenzhen, which included the eastern part of Luohu District (Donghu and Liantang Streets) and Yantian District (Shatoujiao, Haishan, and Yantian Streets). Five spatio-temporal cluster areas were detected between 2010 and 2012, one of which was a Class 1 cluster located in southwestern Shenzhen in 2010, which included Yuehai, Nantou, Shahe, Shekou, and Nanshan Streets in Nanshan District with an incidence of 54.1/100,000 and a relative risk of 2.41;the other four were Class 2 clusters located in Yantian, Luohu, Futian, and Longhua Districts with a relative risk ranging from 1.70 to 3.25. Conclusions:This study revealed the spatio-temporal cluster pattern for the incidence of female breast cancer in Shenzhen, which will be useful for a better allocation of health resources in Shenzhen.

  15. Analytic Models of Plausible Gravitational Lens Potentials

    Energy Technology Data Exchange (ETDEWEB)

    Baltz, Edward A.; Marshall, Phil; Oguri, Masamune

    2007-05-04

    Gravitational lenses on galaxy scales are plausibly modeled as having ellipsoidal symmetry and a universal dark matter density profile, with a Sersic profile to describe the distribution of baryonic matter. Predicting all lensing effects requires knowledge of the total lens potential: in this work we give analytic forms for that of the above hybrid model. Emphasizing that complex lens potentials can be constructed from simpler components in linear combination, we provide a recipe for attaining elliptical symmetry in either projected mass or lens potential.We also provide analytic formulae for the lens potentials of Sersic profiles for integer and half-integer index. We then present formulae describing the gravitational lensing effects due to smoothly-truncated universal density profiles in cold dark matter model. For our isolated haloes the density profile falls off as radius to the minus fifth or seventh power beyond the tidal radius, functional forms that allow all orders of lens potential derivatives to be calculated analytically, while ensuring a non-divergent total mass. We show how the observables predicted by this profile differ from that of the original infinite-mass NFW profile. Expressions for the gravitational flexion are highlighted. We show how decreasing the tidal radius allows stripped haloes to be modeled, providing a framework for a fuller investigation of dark matter substructure in galaxies and clusters. Finally we remark on the need for finite mass halo profiles when doing cosmological ray-tracing simulations, and the need for readily-calculable higher order derivatives of the lens potential when studying catastrophes in strong lenses.

  16. Prediction of spatio-temporal patterns of neural activity from pairwise correlations

    OpenAIRE

    Marre, Olivier; Boustani, Sami El; Fregnac, Yves; Destexhe, Alain

    2009-01-01

    We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatio-temporal patterns significantly better than Ising models taking into acco...

  17. Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data

    Science.gov (United States)

    Heimhuber, Valentin; Tulbure, Mirela G.; Broich, Mark

    2016-06-01

    The usage of time series of Earth observation (EO) data for analyzing and modeling surface water extent (SWE) dynamics across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWE dynamics from a unique, statistically validated Landsat-based time series (1986-2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray-Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWE as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET), and rainfall (P). To enable a consistent modeling approach across space, we modeled SWE dynamics separately for hydrologically distinct floodplain, floodplain-lake, and non-floodplain areas within eco-hydrological zones and 10km × 10km grid cells. We applied this spatial modeling framework to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWE dynamics. Based on these automatically quantified flow lag times and variable combinations, SWE dynamics on 233 (64 %) out of 363 floodplain grid cells were modeled with a coefficient of determination (r2) greater than 0.6. The contribution of P, ET, and SM to the predictive performance of models differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The spatial modeling framework presented here is suitable for modeling SWE dynamics on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.

  18. Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of earth observation data

    Directory of Open Access Journals (Sweden)

    V. Heimhuber

    2015-11-01

    Full Text Available The usage of time series of earth observation (EO data for analyzing and modeling surface water dynamics (SWD across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWD from a unique validated Landsat-based time series (1986–2011 continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB in Australia. We used dynamic linear regression to model remotely sensed SWD as a function of river flow and spatially explicit time series of soil moisture (SM, evapotranspiration (ET and rainfall (P. To enable a consistent modeling approach across space, we modeled SWD separately for hydrologically distinct floodplain, floodplain-lake and non-floodplain areas within eco-hydrological zones and 10 km × 10 km grid cells. We applied this spatial modeling framework (SMF to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWD. Based on these automatically quantified flow lag times and variable combinations, SWD on 233 (64 % out of 363 floodplain grid cells were modeled with r2 ≥ 0.6. The contribution of P, ET and SM to the models' predictive performance differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The SMF presented here is suitable for modeling SWD on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.

  19. Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio

    Science.gov (United States)

    Aburas, Maher Milad; Ho, Yuek Ming; Ramli, Mohammad Firuz; Ash'aari, Zulfa Hanan

    2017-07-01

    The creation of an accurate simulation of future urban growth is considered one of the most important challenges in urban studies that involve spatial modeling. The purpose of this study is to improve the simulation capability of an integrated CA-Markov Chain (CA-MC) model using CA-MC based on the Analytical Hierarchy Process (AHP) and CA-MC based on Frequency Ratio (FR), both applied in Seremban, Malaysia, as well as to compare the performance and accuracy between the traditional and hybrid models. Various physical, socio-economic, utilities, and environmental criteria were used as predictors, including elevation, slope, soil texture, population density, distance to commercial area, distance to educational area, distance to residential area, distance to industrial area, distance to roads, distance to highway, distance to railway, distance to power line, distance to stream, and land cover. For calibration, three models were applied to simulate urban growth trends in 2010; the actual data of 2010 were used for model validation utilizing the Relative Operating Characteristic (ROC) and Kappa coefficient methods Consequently, future urban growth maps of 2020 and 2030 were created. The validation findings confirm that the integration of the CA-MC model with the FR model and employing the significant driving force of urban growth in the simulation process have resulted in the improved simulation capability of the CA-MC model. This study has provided a novel approach for improving the CA-MC model based on FR, which will provide powerful support to planners and decision-makers in the development of future sustainable urban planning.

  20. Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM model

    Directory of Open Access Journals (Sweden)

    A. Kuentz

    2015-06-01

    The ANATEM model has been also evaluated for the regional scale against independent long-term time series and was able to capture regional low-frequency variability over more than a century (1883–2010.

  1. Spatio-Temporal Matching for Human Pose Estimation in Video.

    Science.gov (United States)

    Zhou, Feng; Torre, Fernando De la

    2016-08-01

    Detection and tracking humans in videos have been long-standing problems in computer vision. Most successful approaches (e.g., deformable parts models) heavily rely on discriminative models to build appearance detectors for body joints and generative models to constrain possible body configurations (e.g., trees). While these 2D models have been successfully applied to images (and with less success to videos), a major challenge is to generalize these models to cope with camera views. In order to achieve view-invariance, these 2D models typically require a large amount of training data across views that is difficult to gather and time-consuming to label. Unlike existing 2D models, this paper formulates the problem of human detection in videos as spatio-temporal matching (STM) between a 3D motion capture model and trajectories in videos. Our algorithm estimates the camera view and selects a subset of tracked trajectories that matches the motion of the 3D model. The STM is efficiently solved with linear programming, and it is robust to tracking mismatches, occlusions and outliers. To the best of our knowledge this is the first paper that solves the correspondence between video and 3D motion capture data for human pose detection. Experiments on the CMU motion capture, Human3.6M, Berkeley MHAD and CMU MAD databases illustrate the benefits of our method over state-of-the-art approaches.

  2. Some Remarks on the Model Theory of Epistemic Plausibility Models

    CERN Document Server

    Demey, Lorenz

    2010-01-01

    Classical logics of knowledge and belief are usually interpreted on Kripke models, for which a mathematically well-developed model theory is available. However, such models are inadequate to capture dynamic phenomena. Therefore, epistemic plausibility models have been introduced. Because these are much richer structures than Kripke models, they do not straightforwardly inherit the model-theoretical results of modal logic. Therefore, while epistemic plausibility structures are well-suited for modeling purposes, an extensive investigation of their model theory has been lacking so far. The aim of the present paper is to fill exactly this gap, by initiating a systematic exploration of the model theory of epistemic plausibility models. Like in 'ordinary' modal logic, the focus will be on the notion of bisimulation. We define various notions of bisimulations (parametrized by a language L) and show that L-bisimilarity implies L-equivalence. We prove a Hennesy-Milner type result, and also two undefinability results. ...

  3. The potential for remote sensing and hydrologic modelling to assess the spatio-temporal dynamics of ponds in the Ferlo Region (Senegal)

    Science.gov (United States)

    Soti, V.; Puech, C.; Lo Seen, D.; Bertran, A.; Vignolles, C.; Mondet, B.; Dessay, N.; Tran, A.

    2010-08-01

    In the Ferlo Region in Senegal, livestock depend on temporary ponds for water but are exposed to the Rift Valley Fever (RVF), a disease transmitted to herds by mosquitoes which develop in these ponds. Mosquito abundance is related to the emptying and filling phases of the ponds, and in order to study the epidemiology of RVF, pond modelling is required. In the context of a data scarce region, a simple hydrologic model which makes use of remote sensing data was developed to simulate pond water dynamics from daily rainfall. Two sets of ponds were considered: those located in the main stream of the Ferlo Valley whose hydrological dynamics are essentially due to runoff, and the ponds located outside, which are smaller and whose filling mechanisms are mainly due to direct rainfall. Separate calibrations and validations were made for each set of ponds. Calibration was performed from daily field data (rainfall, water level) collected during the 2001 and 2002 rainy seasons and from three different sources of remote sensing data: 1) very high spatial resolution optical satellite images to access pond location and surface area at given dates, 2) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) data to estimate pond catchment area and 3) Tropical Rainfall Measuring Mission (TRMM) data for rainfall estimates. The model was applied to all ponds of the study area, the results were validated and a sensitivity analysis was performed. Water height simulations using gauge rainfall as input were compared to water level measurements from four ponds and Nash coefficients >0.7 were obtained. Comparison with simulations using TRMM rainfall data gave mixed results, with poor water height simulations for the year 2001 and good estimations for the year 2002. A pond map derived from a Quickbird satellite image was used to assess model accuracy for simulating pond water areas for all the ponds of the study area. The validation showed that

  4. A computational model of spatio-temporal cardiac intracellular calcium handling with realistic structure and spatial flux distribution from sarcoplasmic reticulum and t-tubule reconstructions.

    Directory of Open Access Journals (Sweden)

    Michael A Colman

    2017-08-01

    Full Text Available Intracellular calcium cycling is a vital component of cardiac excitation-contraction coupling. The key structures responsible for controlling calcium dynamics are the cell membrane (comprising the surface sarcolemma and transverse-tubules, the intracellular calcium store (the sarcoplasmic reticulum, and the co-localisation of these two structures to form dyads within which calcium-induced-calcium-release occurs. The organisation of these structures tightly controls intracellular calcium dynamics. In this study, we present a computational model of intracellular calcium cycling in three-dimensions (3-D, which incorporates high resolution reconstructions of these key regulatory structures, attained through imaging of tissue taken from the sheep left ventricle using serial block face scanning electron microscopy. An approach was developed to model the sarcoplasmic reticulum structure at the whole-cell scale, by reducing its full 3-D structure to a 3-D network of one-dimensional strands. The model reproduces intracellular calcium dynamics during control pacing and reveals the high-resolution 3-D spatial structure of calcium gradients and intracellular fluxes in both the cytoplasm and sarcoplasmic reticulum. We also demonstrated the capability of the model to reproduce potentially pro-arrhythmic dynamics under perturbed conditions, pertaining to calcium-transient alternans and spontaneous release events. Comparison with idealised cell models emphasised the importance of structure in determining calcium gradients and controlling the spatial dynamics associated with calcium-transient alternans, wherein the probabilistic nature of dyad activation and recruitment was constrained. The model was further used to highlight the criticality in calcium spark propagation in relation to inter-dyad distances. The model presented provides a powerful tool for future investigation of structure-function relationships underlying physiological and pathophysiological

  5. The potential for remote sensing and hydrologic modelling to assess the spatio-temporal dynamics of ponds in the Ferlo Region (Senegal

    Directory of Open Access Journals (Sweden)

    V. Soti

    2010-08-01

    Full Text Available In the Ferlo Region in Senegal, livestock depend on temporary ponds for water but are exposed to the Rift Valley Fever (RVF, a disease transmitted to herds by mosquitoes which develop in these ponds. Mosquito abundance is related to the emptying and filling phases of the ponds, and in order to study the epidemiology of RVF, pond modelling is required. In the context of a data scarce region, a simple hydrologic model which makes use of remote sensing data was developed to simulate pond water dynamics from daily rainfall. Two sets of ponds were considered: those located in the main stream of the Ferlo Valley whose hydrological dynamics are essentially due to runoff, and the ponds located outside, which are smaller and whose filling mechanisms are mainly due to direct rainfall. Separate calibrations and validations were made for each set of ponds. Calibration was performed from daily field data (rainfall, water level collected during the 2001 and 2002 rainy seasons and from three different sources of remote sensing data: 1 very high spatial resolution optical satellite images to access pond location and surface area at given dates, 2 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER Digital Elevation Model (DEM data to estimate pond catchment area and 3 Tropical Rainfall Measuring Mission (TRMM data for rainfall estimates. The model was applied to all ponds of the study area, the results were validated and a sensitivity analysis was performed. Water height simulations using gauge rainfall as input were compared to water level measurements from four ponds and Nash coefficients >0.7 were obtained. Comparison with simulations using TRMM rainfall data gave mixed results, with poor water height simulations for the year 2001 and good estimations for the year 2002. A pond map derived from a Quickbird satellite image was used to assess model accuracy for simulating pond water areas for all the ponds of the study area. The validation

  6. STSE: Spatio-Temporal Simulation Environment Dedicated to Biology.

    Science.gov (United States)

    Stoma, Szymon; Fröhlich, Martina; Gerber, Susanne; Klipp, Edda

    2011-04-28

    Recently, the availability of high-resolution microscopy together with the advancements in the development of biomarkers as reporters of biomolecular interactions increased the importance of imaging methods in molecular cell biology. These techniques enable the investigation of cellular characteristics like volume, size and geometry as well as volume and geometry of intracellular compartments, and the amount of existing proteins in a spatially resolved manner. Such detailed investigations opened up many new areas of research in the study of spatial, complex and dynamic cellular systems. One of the crucial challenges for the study of such systems is the design of a well stuctured and optimized workflow to provide a systematic and efficient hypothesis verification. Computer Science can efficiently address this task by providing software that facilitates handling, analysis, and evaluation of biological data to the benefit of experimenters and modelers. The Spatio-Temporal Simulation Environment (STSE) is a set of open-source tools provided to conduct spatio-temporal simulations in discrete structures based on microscopy images. The framework contains modules to digitize, represent, analyze, and mathematically model spatial distributions of biochemical species. Graphical user interface (GUI) tools provided with the software enable meshing of the simulation space based on the Voronoi concept. In addition, it supports to automatically acquire spatial information to the mesh from the images based on pixel luminosity (e.g. corresponding to molecular levels from microscopy images). STSE is freely available either as a stand-alone version or included in the linux live distribution Systems Biology Operational Software (SB.OS) and can be downloaded from http://www.stse-software.org/. The Python source code as well as a comprehensive user manual and video tutorials are also offered to the research community. We discuss main concepts of the STSE design and workflow. We

  7. STSE: Spatio-Temporal Simulation Environment Dedicated to Biology

    Directory of Open Access Journals (Sweden)

    Gerber Susanne

    2011-04-01

    Full Text Available Abstract Background Recently, the availability of high-resolution microscopy together with the advancements in the development of biomarkers as reporters of biomolecular interactions increased the importance of imaging methods in molecular cell biology. These techniques enable the investigation of cellular characteristics like volume, size and geometry as well as volume and geometry of intracellular compartments, and the amount of existing proteins in a spatially resolved manner. Such detailed investigations opened up many new areas of research in the study of spatial, complex and dynamic cellular systems. One of the crucial challenges for the study of such systems is the design of a well stuctured and optimized workflow to provide a systematic and efficient hypothesis verification. Computer Science can efficiently address this task by providing software that facilitates handling, analysis, and evaluation of biological data to the benefit of experimenters and modelers. Results The Spatio-Temporal Simulation Environment (STSE is a set of open-source tools provided to conduct spatio-temporal simulations in discrete structures based on microscopy images. The framework contains modules to digitize, represent, analyze, and mathematically model spatial distributions of biochemical species. Graphical user interface (GUI tools provided with the software enable meshing of the simulation space based on the Voronoi concept. In addition, it supports to automatically acquire spatial information to the mesh from the images based on pixel luminosity (e.g. corresponding to molecular levels from microscopy images. STSE is freely available either as a stand-alone version or included in the linux live distribution Systems Biology Operational Software (SB.OS and can be downloaded from http://www.stse-software.org/. The Python source code as well as a comprehensive user manual and video tutorials are also offered to the research community. We discuss main concepts

  8. Analysis Method of Traffic Congestion Degree Based on Spatio-Temporal Simulation

    OpenAIRE

    Shulin He

    2012-01-01

    The purpose of this research is to design and implement a road traffic congestion and traffic patterns simulation (TPS) model and integrate it with extension-information model (EIM). The problems of road traffic simulation and control are studied according to the method of extension information model, and from the spatio-temporal analysis point of view. The rules of the traffic simulation from existence to evolution are analyzed using theories. Based on this study, the concept of traffic syst...

  9. A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

    Science.gov (United States)

    Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

    2014-01-01

    The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions

  10. Hybrid fitting of a hydrosystem model using dense spatio-temporally distributed data: the Beauce aquifer functioning over 40 yr (France)

    Science.gov (United States)

    Monteil, C.; Flipo, N.; Poulin, M.; Krimissa, M.

    2011-12-01

    This study focuses on the Beauce aquifer (8 000 km2, unconfined) over a 40-year period. The mono-layer aquifer system is part of the hydrosystem Loire (surface basin of 117 000 km2) which is composed of a multi-layer aquifer system. This area is documented with various types of structural (land use, geology) and hydrological data (precipitation, potential evapotranspiration, water volume withdrawn at pumping wells and their location) from which a distributed process-based model has been implemented to model the surface, the unsaturated zone and the aquifer system. The surface model contains 37 620 cells ranging from 1 to 16 km2, 16 141 among them are river cells. Beauce aquifer unit is simulated with 4 489 groundwater cells. To understand the Beauce hydrological functioning and quantify exchanged fluxes, a pragmatic hybrid fitting method has been developed. First the parameters of the water mass balance module are calibrated based on in-river gauging stations selected from a morphological analysis. Then the surface and river routing modules are calibrated based on the analysis of flood discharge peaks at 157 gauging stations. After a pre-calibration of the surface modules for the whole Loire basin, the hybrid fitting methodology focuses on the Beauce aquifer system. It couples manual and automatic iterative calibration. Roughly, the automatic calibration aims at inversing a low water piezometric head map for a steady state using the successive flux estimation. Then the transient manual calibration aims at calibrating others parameters in transient state. The model performances are assessed with a multicriteria approach using global RMSE and bias, and criteria computed for 78 piezometers and for 157 gauging stations. Inspired from soft computing techniques, the hybrid fitting methodology involves three data subsets: a calibration one (10 yr), a validation one (10 yr) and a test one (35 yr). The global RMSE on piezometric head is around 2.5 m for the three subsets

  11. Characteristics of juvenile survivors reveal spatio-temporal differences in early life stage survival of Baltic cod

    DEFF Research Database (Denmark)

    Huwer, Bastian; Hinrichsen, H.H.; Böttcher, U.

    2014-01-01

    The spatio-temporal origin of surviving juvenile Baltic cod Gadus morhua was investigated by coupling age information from otolith microstructure analysis and hydrodynamic modeling, which allowed backtracking of drift routes in time and space. The suitability of hydrodynamic modeling for drift...... simulations of early life stages of Baltic cod up to the pelagic juvenile stage was validated by comparing model simulations with the catch distribution from a survey targeting pelagic juveniles, and mortality rates and hatch date distributions of pelagic and demersal juveniles were estimated. Hatch dates...... and hatch locations of juvenile survivors showed distinct patterns which did not agree well with the abundance and spatial distribution of eggs, suggesting marked spatio-temporal differences in larval survival. The good agreement of the spatio-temporal origin of survivors from this field investigation...

  12. A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health.

    Science.gov (United States)

    Lee, Duncan; Mukhopadhyay, Sabyasachi; Rushworth, Alastair; Sahu, Sujit K

    2017-04-01

    In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  13. Spatio-temporal Model of Multiscale Fracture in Brittle Solids%固体材料脆性断裂的多尺度时空模型

    Institute of Scientific and Technical Information of China (English)

    PETROV Y V; BRATOV V

    2012-01-01

    通过实例分析了动态断裂过程所固有的典型动态作用,基于断裂孵化时间理论的时空结构方法提出了固体材料断裂的统一解释.此外,还提出了计及尺度效应的广义断裂模型,该模型可以用于预测准脆性非均质材料的多尺度断裂,并证明此模型基于实验室尺度的实验数据可以预测更高尺度(真实尺度)的宏观断裂.%We analyze examples illustrating typical dynamic effects inherent in dynamic fracture process, and we propose a unified interpretation for fracture of solids utilizing structural-temporal approach based on the concept of the fracture incubation time. Corresponding generalized model accounting for fracture scale level will also be presented. The model will be used to predict fracture of quasi-brittle heterogeneous materials on different scale levels. It will be demonstrated that can give a possibility to predict fracture on a higher (real) scale level and having experimental data obtained on a lower (laboratory) scale level.

  14. Quantifying spatio-temporal stream-aquifer water exchanges along a multi-layer aquifer system using LOMOS and hydro-thermo modelling

    Science.gov (United States)

    Mouhri, Amer; flipo, Nicolas; Rejiba, Fayçal; Bodet, Ludovic; Jost, Anne; Goblet, Patrick

    2014-05-01

    The aim of this work is to understand the spatial and temporal variability of stream-aquifer water exchanges along a 6 km-stream network in a multi-layer aquifer system using both LOcal MOnitoring Stations (LOMOSs) coupled with the optimization of a hydro-thermo model per LOMOS. With an area of 45 km2, the Orgeval experimental basin is located 70 km east from Paris. It drains a multi-layer aquifer system, which is composed of two main geological formations: the Oligocene (upper aquifer unit) and the Eocene (lower aquifer unit). These two aquifer units are separated by a clayey aquitard. The connectivity status between streams and aquifer units has been evaluated using near surface geophysical investigations as well as drill cores. Five LOMOSs of the stream-aquifer exchanges have been deployed along the stream-network to monitor stream-aquifer exchanges over years, based on continuous pressure and temperature measurements (15 min-time step). Each LOMOS is composed of one or two shallow piezometers located 2 to 3 m away from the river edge; one surface water monitoring system; two hyporheic zone temperature profiles located close to each river bank. The five LOMOSs are distributed in two upstream, two intermediate, and one downstream site. The two upstream sites are connected to the upper aquifer unit, and the downstream one is connected to the lower aquifer unit. The 2012-April - 2013-december period of hydrological data are hereafter analyzed. We first focus on the spatial distribution of the stream-aquifer exchanges along the multi-layer aquifer system during the low flow period. Results display an upstream-downstream functional gradient, with upstream gaining stream and downstream losing stream. This spatial distribution is due to the multi-layer nature of the aquifer system, whose lower aquifer unit is depleted. Then it appears that the downstream losing streams temporally switch into gaining ones during extreme hydrological events, while the upstream streams

  15. Hydrological response to urbanization at different spatio-temporal scales simulated by coupling of CLUE-S and the SWAT model in the Yangtze River Delta region

    Science.gov (United States)

    Zhou, Feng; Xu, Youpeng; Chen, Ying; Xu, C.-Y.; Gao, Yuqin; Du, Jinkang

    2013-04-01

    SummaryThe Main objective of the study is to understand and quantify the hydrological responses of land use and land cover changes. The Yangtze River Delta is one of the most developed regions in China with the rapid development of urbanization which serves as an excellent case study site for understanding the hydrological response to urbanization and land use change. The Xitiaoxi River basin, one of the main upstream rivers to the Taihu Lake in the Yangtze River Delta, was selected to perform the study. The urban area in the basin increased from 37.8 km2 in 1985 to 105 km2 in 2008. SWAT model, which makes direct use of land cover and land use data in simulating streamflow, provides as a useful tool for performing such studies and is therefore used in this study. The results showed that (1) the expansion of urban areas had a slight influence on the simulated annual streamflow and evapotranspiration (ET) as far as the whole catchment is concerned; (2) surface runoff and baseflow were found more sensitive to urbanization, which had increased by 11.3% and declined by 11.2%, respectively; (3) changes in streamflow, evapotranspiration and surface runoff were more pronounced during the wet season (from May to August), while baseflow and lateral flow had a slight seasonal variation; (4) the model simulated peak discharge increased 1.6-4.3% and flood volume increased 0.7-2.3% for the selected storm rainfall events at the entire basin level, and the change rate was larger for smaller flood events than for larger events; (5) spatially, changes of hydrological fluxes were more remarkable in the suburban basin which had a relative larger increase in urbanization than in rural sub-basins; and (6) analysis of future scenarios showed the impacts of urbanization on hydrological fluxes would be more obvious with growth in impervious areas from 15% to 30%. In conclusion, the urbanization would have a slight impact on annual water yield, but a remarkable impact was found on surface

  16. Spatio-temporal evolution of forest fires in Portugal

    Science.gov (United States)

    Tonini, Marj; Pereira, Mário G.; Parente, Joana

    2017-04-01

    southern areas spread hot-spot are spatially randomly distributed and temporally more concentrated in the frame 2000 - 2004. To conclude, this study let us to identify a multitude of clustering space-time features of forest fires in Portugal, which can be useful for a better planning of educational activities and prevention campaigns as well as for a better allocation of monitoring systems and firefighting. References: Tonini M., Pereira M. G., Parente J. (2016) - Evolution of forest fires in Portugal: from spatio-temporal point events to smoothed density maps. Natural Hazard, doi:10.1007/s11069-016-2637-x Lu B., Harris P., Charlton M., Brunsdon C. (2014) - The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models. Geo-spatial Information Science, Vol. 17: 85-101 Rowlingson B., Diggle P., Bivand M.R. (2012) - Splancs: spatial point pattern analysis code in S-Plus. Computers and Geosciences, Vol. 19: 627-655 Acknowledgements: This work was supported by: (i) the FIREXTR project, PTDC/ATP¬GEO/0462/2014; (ii) the project Interact - Integrative Research in Environment,Agro-Chain and Technology, NORTE-01-0145-FEDER-000017, research line BEST, cofinanced by FEDER/NORTE 2020; and, (iii) European Investment Funds by FEDER/COMPETE/POCI-Operacional Competitiveness and Internacionalization Programme, under Project POCI-01-0145-FEDER-006958 and National Funds by FCT - Portuguese Foundation for Science and Technology, under the project UID/AGR/04033. We are especially grateful to ICNF for providing the fire.

  17. Hierarchical Spatio-Temporal Probabilistic Graphical Model with Multiple Feature Fusion for Binary Facial Attribute Classification in Real-World Face Videos.

    Science.gov (United States)

    Demirkus, Meltem; Precup, Doina; Clark, James J; Arbel, Tal

    2016-06-01

    Recent literature shows that facial attributes, i.e., contextual facial information, can be beneficial for improving the performance of real-world applications, such as face verification, face recognition, and image search. Examples of face attributes include gender, skin color, facial hair, etc. How to robustly obtain these facial attributes (traits) is still an open problem, especially in the presence of the challenges of real-world environments: non-uniform illumination conditions, arbitrary occlusions, motion blur and background clutter. What makes this problem even more difficult is the enormous variability presented by the same subject, due to arbitrary face scales, head poses, and facial expressions. In this paper, we focus on the problem of facial trait classification in real-world face videos. We have developed a fully automatic hierarchical and probabilistic framework that models the collective set of frame class distributions and feature spatial information over a video sequence. The experiments are conducted on a large real-world face video database that we have collected, labelled and made publicly available. The proposed method is flexible enough to be applied to any facial classification problem. Experiments on a large, real-world video database McGillFaces [1] of 18,000 video frames reveal that the proposed framework outperforms alternative approaches, by up to 16.96 and 10.13%, for the facial attributes of gender and facial hair, respectively.

  18. Identification of environmental determinants for spatio-temporal patterns of norovirus outbreaks in Korea using a geographic information system and binary response models.

    Science.gov (United States)

    Kim, Jin Hwi; Lee, Dong Hoon; Joo, Yongsung; Zoh, Kyung Duk; Ko, Gwangpyo; Kang, Joo-Hyon

    2016-11-01

    Although norovirus outbreaks are well-recognized to have strong winter seasonality relevant to low temperature and humidity, the role of artificial human-made features within geographical areas in norovirus outbreaks has rarely been studied. The aim of this study is to assess the natural and human-made environmental factors favoring the occurrence of norovirus outbreaks using nationwide surveillance data. We used a geographic information system and binary response models to examine whether the norovirus outbreaks are spatially patterned and whether these patterns are associated with specific environmental variables including service levels of water supply and sanitation systems and land-use types. The results showed that small-scale low-tech local sewage treatment plants and winter sports areas were statistically significant factors favoring norovirus outbreaks. Compactness of the land development also affected the occurrence of norovirus outbreaks; transportation, water, and forest land-uses were less favored for effective transmission of norovirus, while commercial areas were associated with an increased rate of norovirus outbreaks. We observed associations of norovirus outbreaks with various outcomes of human activities, including discharge of poorly treated sewage, overcrowding of people during winter season, and compactness of land development, which might help prioritize target regions and strategies for the management of norovirus outbreaks. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Spatio-temporal characteristics of global warming in the Tibetan Plateau during the last 50 years based on a generalised temperature zone-elevation model.

    Directory of Open Access Journals (Sweden)

    Yanqiang Wei

    Full Text Available Temperature is one of the primary factors influencing the climate and ecosystem, and examining its change and fluctuation could elucidate the formation of novel climate patterns and trends. In this study, we constructed a generalised temperature zone elevation model (GTEM to assess the trends of climate change and temporal-spatial differences in the Tibetan Plateau (TP using the annual and monthly mean temperatures from 1961-2010 at 144 meteorological stations in and near the TP. The results showed the following: (1 The TP has undergone robust warming over the study period, and the warming rate was 0.318°C/decade. The warming has accelerated during recent decades, especially in the last 20 years, and the warming has been most significant in the winter months, followed by the spring, autumn and summer seasons. (2 Spatially, the zones that became significantly smaller were the temperature zones of -6°C and -4°C, and these have decreased 499.44 and 454.26 thousand sq km from 1961 to 2010 at average rates of 25.1% and 11.7%, respectively, over every 5-year interval. These quickly shrinking zones were located in the northwestern and central TP. (3 The elevation dependency of climate warming existed in the TP during 1961-2010, but this tendency has gradually been weakening due to more rapid warming at lower elevations than in the middle and upper elevations of the TP during 1991-2010. The higher regions and some low altitude valleys of the TP were the most significantly warming regions under the same categorizing criteria. Experimental evidence shows that the GTEM is an effective method to analyse climate changes in high altitude mountainous regions.

  20. Spatio-temporal characteristics of global warming in the Tibetan Plateau during the last 50 years based on a generalised temperature zone-elevation model.

    Science.gov (United States)

    Wei, Yanqiang; Fang, Yiping

    2013-01-01

    Temperature is one of the primary factors influencing the climate and ecosystem, and examining its change and fluctuation could elucidate the formation of novel climate patterns and trends. In this study, we constructed a generalised temperature zone elevation model (GTEM) to assess the trends of climate change and temporal-spatial differences in the Tibetan Plateau (TP) using the annual and monthly mean temperatures from 1961-2010 at 144 meteorological stations in and near the TP. The results showed the following: (1) The TP has undergone robust warming over the study period, and the warming rate was 0.318°C/decade. The warming has accelerated during recent decades, especially in the last 20 years, and the warming has been most significant in the winter months, followed by the spring, autumn and summer seasons. (2) Spatially, the zones that became significantly smaller were the temperature zones of -6°C and -4°C, and these have decreased 499.44 and 454.26 thousand sq km from 1961 to 2010 at average rates of 25.1% and 11.7%, respectively, over every 5-year interval. These quickly shrinking zones were located in the northwestern and central TP. (3) The elevation dependency of climate warming existed in the TP during 1961-2010, but this tendency has gradually been weakening due to more rapid warming at lower elevations than in the middle and upper elevations of the TP during 1991-2010. The higher regions and some low altitude valleys of the TP were the most significantly warming regions under the same categorizing criteria. Experimental evidence shows that the GTEM is an effective method to analyse climate changes in high altitude mountainous regions.

  1. Vehicle Trajectory Estimation Using Spatio-Temporal MCMC

    Directory of Open Access Journals (Sweden)

    Francois Bardet

    2010-01-01

    Full Text Available This paper presents an algorithm for modeling and tracking vehicles in video sequences within one integrated framework. Most of the solutions are based on sequential methods that make inference according to current information. In contrast, we propose a deferred logical inference method that makes a decision according to a sequence of observations, thus processing a spatio-temporal search on the whole trajectory. One of the drawbacks of deferred logical inference methods is that the solution space of hypotheses grows exponentially related to the depth of observation. Our approach takes into account both the kinematic model of the vehicle and a driver behavior model in order to reduce the space of the solutions. The resulting proposed state model explains the trajectory with only 11 parameters. The solution space is then sampled with a Markov Chain Monte Carlo (MCMC that uses a model-driven proposal distribution in order to control random walk behavior. We demonstrate our method on real video sequences from which we have ground truth provided by a RTK GPS (Real-Time Kinematic GPS. Experimental results show that the proposed algorithm outperforms a sequential inference solution (particle filter.

  2. Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media

    CERN Document Server

    Xu, Jun-Ming; Nowak, Robert; Zhu, Xiaojin

    2012-01-01

    Many real-world phenomena can be represented by a spatio-temporal signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity -- issues inadequately addressed by prior work. We formulate signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.

  3. Management of spatio-temporal data for dynamic segmentation in transportation application

    Science.gov (United States)

    Gui, Lan; Gong, Jianya

    2005-10-01

    There have been many research studies focusing on linear data modeling for transportation application. However, research on spatio-temporal modeling is still in its infancy, which limits transportation agencies to implement improved solutions. Transportation applications offer challenges to GIS technology. Not only are the attributes of transportation features dynamic, but also many features are dynamic. The authors firstly review the definition and characteristics of dynamic segmentation, an important technology in transportation application. The paper then presents a data model for dynamic segmentation with timing dimension and its implementation. This paper shows the design of spatio-temporal data structure. Both linear elements and events are tagged with start and end temporal expressions, thus it makes temporal querying easier. Segment geocoding, segment overlay and data maintenance is discussed in detailed.

  4. Analysis Method of Traffic Congestion Degree Based on Spatio-Temporal Simulation

    Directory of Open Access Journals (Sweden)

    Shulin He

    2012-04-01

    Full Text Available The purpose of this research is to design and implement a road traffic congestion and traffic patterns simulation (TPS model and integrate it with extension-information model (EIM. The problems of road traffic simulation and control are studied according to the method of extension information model, and from the spatio-temporal analysis point of view. The rules of the traffic simulation from existence to evolution are analyzed using theories. Based on this study, the concept of traffic system entropy is introduced, and resulted in the establishment of a fundamental frame work for the road traffic simulation system based on extension spatio-temporal information system. Moreover, a practicable methodology is presented.

  5. Climate change in Bangladesh: a spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model

    Science.gov (United States)

    Rahman, Md. Rejaur; Lateh, Habibah

    2017-04-01

    In this paper, temperature and rainfall data series were analysed from 34 meteorological stations distributed throughout Bangladesh over a 40-year period (1971 to 2010) in order to evaluate the magnitude of these changes statistically and spatially. Linear regression, coefficient of variation, inverse distance weighted interpolation techniques and geographical information systems were performed to analyse the trends, variability and spatial patterns of temperature and rainfall. Autoregressive integrated moving average time series model was used to simulate the temperature and rainfall data. The results confirm a particularly strong and recent climate change in Bangladesh with a 0.20 °C per decade upward trend of mean temperature. The highest upward trend in minimum temperature (range of 0.80-2.4 °C) was observed in the northern, northwestern, northeastern, central and central southern parts while greatest warming in the maximum temperature (range of 1.20-2.48 °C) was found in the southern, southeastern and northeastern parts during 1971-2010. An upward trend of annual rainfall (+7.13 mm per year) and downward pre-monsoon (-0.75 mm per year) and post-monsoon rainfall (-0.55 mm per year) trends were observed during this period. Rainfall was erratic in pre-monsoon season and even more so during the post-monsoon season (variability of 44.84 and 85.25 % per year, respectively). The mean forecasted temperature exhibited an increase of 0.018 °C per year in 2011-2020, and if this trend continues, this would lead to approximately 1.0 °C warmer temperatures in Bangladesh by 2020, compared to that of 1971. A greater rise is projected for the mean minimum (0.20 °C) than the mean maximum (0.16 °C) temperature. Annual rainfall is projected to decline 153 mm from 2011 to 2020, and a drying condition will persist in the northwestern, western and southwestern parts of the country during the pre- and post-monsoonal seasons.

  6. Climate change in Bangladesh: a spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model

    Science.gov (United States)

    Rahman, Md. Rejaur; Lateh, Habibah

    2015-12-01

    In this paper, temperature and rainfall data series were analysed from 34 meteorological stations distributed throughout Bangladesh over a 40-year period (1971 to 2010) in order to evaluate the magnitude of these changes statistically and spatially. Linear regression, coefficient of variation, inverse distance weighted interpolation techniques and geographical information systems were performed to analyse the trends, variability and spatial patterns of temperature and rainfall. Autoregressive integrated moving average time series model was used to simulate the temperature and rainfall data. The results confirm a particularly strong and recent climate change in Bangladesh with a 0.20 °C per decade upward trend of mean temperature. The highest upward trend in minimum temperature (range of 0.80-2.4 °C) was observed in the northern, northwestern, northeastern, central and central southern parts while greatest warming in the maximum temperature (range of 1.20-2.48 °C) was found in the southern, southeastern and northeastern parts during 1971-2010. An upward trend of annual rainfall (+7.13 mm per year) and downward pre-monsoon (-0.75 mm per year) and post-monsoon rainfall (-0.55 mm per year) trends were observed during this period. Rainfall was erratic in pre-monsoon season and even more so during the post-monsoon season (variability of 44.84 and 85.25 % per year, respectively). The mean forecasted temperature exhibited an increase of 0.018 °C per year in 2011-2020, and if this trend continues, this would lead to approximately 1.0 °C warmer temperatures in Bangladesh by 2020, compared to that of 1971. A greater rise is projected for the mean minimum (0.20 °C) than the mean maximum (0.16 °C) temperature. Annual rainfall is projected to decline 153 mm from 2011 to 2020, and a drying condition will persist in the northwestern, western and southwestern parts of the country during the pre- and post-monsoonal seasons.

  7. Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance

    Directory of Open Access Journals (Sweden)

    Sebastian Meyer

    2017-05-01

    Full Text Available The availability of geocoded health data and the inherent temporal structure of communicable diseases have led to an increased interest in statistical models and software for spatio-temporal data with epidemic features. The open source R package surveillance can handle various levels of aggregation at which infective events have been recorded: individual-level time-stamped geo-referenced data (case reports in either continuous space or discrete space, as well as counts aggregated by period and region. For each of these data types, the surveillance package implements tools for visualization, likelihoood inference and simulation from recently developed statistical regression frameworks capturing endemic and epidemic dynamics. Altogether, this paper is a guide to the spatio-temporal modeling of epidemic phenomena, exemplified by analyses of public health surveillance data on measles and invasive meningococcal disease.

  8. Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance

    CERN Document Server

    Meyer, Sebastian; Höhle, Michael

    2014-01-01

    The availability of geocoded health data and the inherent temporal structure of communicable diseases have led to an increased interest in statistical models and software for spatio-temporal data with epidemic features. The open source R package surveillance can handle various levels of aggregation at which infective events have been recorded: individual-level time-stamped geo-referenced data (case reports) in either continuous space or discrete space, as well as counts aggregated by period and region. For each of these data types, the surveillance package implements tools for visualization, likelihoood inference and simulation from recently developed statistical regression frameworks capturing endemic and epidemic dynamics. Altogether, this paper is a guide to the spatio-temporal modeling of epidemic phenomena, exemplified by analyses of public health surveillance data on measles and invasive meningococcal disease.

  9. Self-consistent spatio-temporal simulation of pulsed microwave discharge

    Energy Technology Data Exchange (ETDEWEB)

    Bonaventura, Z; Trunec, D; Mesko, M; Vasina, P; Kudrle, V [Department of Physical Electronics, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno (Czech Republic)

    2008-01-07

    A spatio-temporal theoretical model of pulsed microwave discharge was developed. This model is based on the macroscopic continuity equation for electrons and on the wave equation for an electromagnetic wave passing through the discharge plasma. These equations were solved together and in a self-consistent manner. For simplicity, the continuity equation was solved in one dimension only and the electromagnetic wave was assumed to be plane and transversal. Both equations were solved numerically and the spatio-temporal dependences of electron concentration and the amplitude of the microwave electric field were obtained. It was found that the discharge development depends, significantly, on the initial spatial distribution of electron concentration. Two different cases were studied: the discharge development during the first microwave pulse only and after several successive pulses. The calculations were performed particularly for the discharge in nitrogen. The results were compared with experimental data from our previous work.

  10. A test for stationarity of spatio-temporal random fields on planar and spherical domains

    KAUST Repository

    Jun, Mikyoung

    2012-01-01

    A formal test for weak stationarity of spatial and spatio-temporal random fields is proposed. We consider the cases where the spatial domain is planar or spherical, and we do not require distributional assumptions for the random fields. The method can be applied to univariate or to multivariate random fields. Our test is based on the asymptotic normality of certain statistics that are functions of estimators of covariances at certain spatial and temporal lags under weak stationarity. Simulation results for spatial as well as spatio-temporal cases on the two types of spatial domains are reported. We describe the results of testing the stationarity of Pacific wind data, and of testing the axial symmetry of climate model errors for surface temperature using the NOAA GFDL model outputs and the observations from the Climate Research Unit in East Anglia and the Hadley Centre.

  11. Spatio-temporal credit assignment in neuronal population learning.

    Science.gov (United States)

    Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

    2011-06-01

    In learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

  12. SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA

    Directory of Open Access Journals (Sweden)

    Karsten Rodenacker

    2011-05-01

    Full Text Available Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D is gathered during relatively long time ranges (3-5 min. From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters. Filters applied are compared by classifications of activations.

  13. AN H∞ FUZZY TRACKING CONTROL SCHEME FOR AFFINE COUPLED SPATIO-TEMPORAL CHAOS

    Institute of Scientific and Technical Information of China (English)

    Dou Chunxia; Zhang Shuqing

    2005-01-01

    Due to the interactions among coupled spatio-temporal subsystems and the constant bias term of affine chaos, it is difficult to achieve tracking control for the affine coupled spatiotemporal chaos. However, every subsystem of the affine coupled spatio-temporal chaos can be approximated by a set of fuzzy models; every fuzzy model represents a linearized model of the subsystem corresponding to the operating point of the controlled system. Because the consequent parts of the fuzzy models have a constant bias term, it is very difficult to achieve tracking control for the affine system. Based on these fuzzy models, considering the affine constant bias term, an H∞ fuzzy tracking control scheme is proposed. A linear matrix inequality is employed to represent the feedback controller, and parameters of the controller are achieved by convex optimization techniques. The tracking control for the affine coupled spatio-temporal chaos is achieved, and the stability of the system is also guaranteed. The tracking performances are testified by simulation examples.

  14. Spatio-temporal image inpainting for video applications

    Directory of Open Access Journals (Sweden)

    Voronin Viacheslav

    2017-01-01

    Full Text Available Video inpainting or completion is a vital video improvement technique used to repair or edit digital videos. This paper describes a framework for temporally consistent video completion. The proposed method allows to remove dynamic objects or restore missing or tainted regions presented in a video sequence by utilizing spatial and temporal information from neighboring scenes. Masking algorithm is used for detection of scratches or damaged portions in video frames. The algorithm iteratively performs the following operations: achieve frame; update the scene model; update positions of moving objects; replace parts of the frame occupied by the objects marked for remove by using a background model. In this paper, we extend an image inpainting algorithm based texture and structure reconstruction by incorporating an improved strategy for video. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background. Experimental comparisons to state-of-the-art video completion methods demonstrate the effectiveness of the proposed approach. It is shown that the proposed spatio-temporal image inpainting method allows restoring a missing blocks and removing a text from the scenes on videos.

  15. Spatio-temporal diffusion of dynamic PET images

    Energy Technology Data Exchange (ETDEWEB)

    Tauber, C; Chalon, S; Guilloteau, D [Inserm U930, CNRS ERL3106, Universite Francois Rabelais, Tours (France); Stute, S; Buvat, I [IMNC, IN2P3, UMR 8165 CNRS-Paris 7 and Paris 11 Universities, Orsay (France); Chau, M [ASA-Advanced Solutions Accelerator, Montpellier (France); Spiteri, P, E-mail: clovis.tauber@univ-tours.fr [IRIT-ENSEEIHT, UMR CNRS 5505, Toulouse (France)

    2011-10-21

    Positron emission tomography (PET) images are corrupted by noise. This is especially true in dynamic PET imaging where short frames are required to capture the peak of activity concentration after the radiotracer injection. High noise results in a possible bias in quantification, as the compartmental models used to estimate the kinetic parameters are sensitive to noise. This paper describes a new post-reconstruction filter to increase the signal-to-noise ratio in dynamic PET imaging. It consists in a spatio-temporal robust diffusion of the 4D image based on the time activity curve (TAC) in each voxel. It reduces the noise in homogeneous areas while preserving the distinct kinetics in regions of interest corresponding to different underlying physiological processes. Neither anatomical priors nor the kinetic model are required. We propose an automatic selection of the scale parameter involved in the diffusion process based on a robust statistical analysis of the distances between TACs. The method is evaluated using Monte Carlo simulations of brain activity distributions. We demonstrate the usefulness of the method and its superior performance over two other post-reconstruction spatial and temporal filters. Our simulations suggest that the proposed method can be used to significantly increase the signal-to-noise ratio in dynamic PET imaging.

  16. Multi-Agent Geosimulation in Support to Qualitative Spatio-Temporal Reasoning: COAs’ “What if” Analysis as an Example

    OpenAIRE

    Haddad, Hedi; Moulin, Bernard

    2010-01-01

    In this chapter we proposed an approach that associates MAGS models with qualitative spatio-temporal reasoning techniques to support qualitative analysis in the context of dynamic geographic spaces. We studied the COAs' "What-if" analysis problem as a practical example. This multidisciplinary approach led to other interesting contributions such as the model of spatio-temporal situations and its application to the problem of causal reasoning in a dynamic spatial context. Besides, the MAGS-COA ...

  17. Diverse spatio-temporal dynamical patterns of p53 and cell fate decisions

    Science.gov (United States)

    Clairambault, Jean; Eliaš, Ján

    2016-06-01

    The protein p53 as a tumour suppressor protein accumulates in cells in response to DNA damage and transactivates a large variety of genes involved in apoptosis, cell cycle regulation and numerous other processes. Recent biological observations suggest that specific spatio-temporal dynamical patterns of p53 may be associated with specific cellular response, and thus the spatio-temporal heterogeneity of the p53 dynamics contributes to the overall complexity of p53 signalling. Reaction-diffusion equations taking into account spatial representation of the cell and motion of the species inside the cell can be used to model p53 protein network and could be thus of some help to biologists and pharmacologists in anticancer treatment.

  18. Sensing Solutions for Collecting Spatio-Temporal Data for Wildlife Monitoring Applications: A Review

    Directory of Open Access Journals (Sweden)

    Bert A. G. Toxopeus

    2013-05-01

    Full Text Available Movement ecology is a field which places movement as a basis for understanding animal behavior. To realize this concept, ecologists rely on data collection technologies providing spatio-temporal data in order to analyze movement. Recently, wireless sensor networks have offered new opportunities for data collection from remote places through multi-hop communication and collaborative capability of the nodes. Several technologies can be used in such networks for sensing purposes and for collecting spatio-temporal data from animals. In this paper, we investigate and review technological solutions which can be used for collecting data for wildlife monitoring. Our aim is to provide an overview of different sensing technologies used for wildlife monitoring and to review their capabilities in terms of data they provide for modeling movement behavior of animals.

  19. Real time eye tracking using Kalman extended spatio-temporal context learning

    Science.gov (United States)

    Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

    2017-06-01

    Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

  20. Quantification of annual wildfire risk; A spatio-temporal point process approach.

    Directory of Open Access Journals (Sweden)

    Paula Pereira

    2013-10-01

    Full Text Available Policy responses for local and global firemanagement depend heavily on the proper understanding of the fire extent as well as its spatio-temporal variation across any given study area. Annual fire risk maps are important tools for such policy responses, supporting strategic decisions such as location-allocation of equipment and human resources. Here, we define risk of fire in the narrow sense as the probability of its occurrence without addressing the loss component. In this paper, we study the spatio-temporal point patterns of wildfires and model them by a log Gaussian Cox processes. Themean of predictive distribution of randomintensity function is used in the narrow sense, as the annual fire risk map for next year.

  1. Spatio-temporal databases complex motion pattern queries

    CERN Document Server

    Vieira, Marcos R

    2013-01-01

    This brief presents several new query processing techniques, called complex motion pattern queries, specifically designed for very large spatio-temporal databases of moving objects. The brief begins with the definition of flexible pattern queries, which are powerful because of the integration of variables and motion patterns. This is followed by a summary of the expressive power of patterns and flexibility of pattern queries. The brief then present the Spatio-Temporal Pattern System (STPS) and density-based pattern queries. STPS databases contain millions of records with information about mobi

  2. Spatio-Temporal Data Mining for Location-Based Services

    DEFF Research Database (Denmark)

    Gidofalvi, Gyozo

    . The objectives of the presented thesis are three-fold. First, to extend popular data mining methods to the spatio-temporal domain. Second, to demonstrate the usefulness of the extended methods and the derived knowledge in promising LBS examples. Finally, to eliminate privacy concerns in connection with spatio......-temporal data mining by devising systems for privacy-preserving location data collection and mining.......Location-Based Services (LBS) are continuously gaining popularity. Innovative LBSes integrate knowledge about the users into the service. Such knowledge can be derived by analyzing the location data of users. Such data contain two unique dimensions, space and time, which need to be analyzed...

  3. Time reversal and the spatio-temporal matched filter

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S K; Poggio, A J; Kallman, J S; Meyer, A W; Candy, J V

    2004-03-08

    It is known that focusing of an acoustic field by a time-reversal mirror (TRM) is equivalent to a spatio-temporal matched filter under conditions where the Green's function of the field satisfies reciprocity and is time invariant, i.e. the Green's function is independent of the choice of time origin. In this letter, it is shown that both reciprocity and time invariance can be replaced by a more general constraint on the Green's function that allows a TRM to implement the spatio-temporal matched filter even when conditions are time varying.

  4. Spatio-Temporal Data Mining for Location-Based Services

    DEFF Research Database (Denmark)

    Gidofalvi, Gyozo

    . The objectives of the presented thesis are three-fold. First, to extend popular data mining methods to the spatio-temporal domain. Second, to demonstrate the usefulness of the extended methods and the derived knowledge in promising LBS examples. Finally, to eliminate privacy concerns in connection with spatio......-temporal data mining by devising systems for privacy-preserving location data collection and mining.......Location-Based Services (LBS) are continuously gaining popularity. Innovative LBSes integrate knowledge about the users into the service. Such knowledge can be derived by analyzing the location data of users. Such data contain two unique dimensions, space and time, which need to be analyzed...

  5. DETERMINING SPATIO-TEMPORAL CADASTRAL DATA REQUIREMENT FOR INFRASTRUCTURE OF LADM FOR TURKEY

    Directory of Open Access Journals (Sweden)

    M. Alkan

    2016-06-01

    Full Text Available Nowadays, the nature of land title and cadastral (LTC data in the Turkey is dynamic from a temporal perspective which depends on the LTC operations. Functional requirements with respect to the characteristics are investigated based upon interviews of professionals in public and private sectors. These are; Legal authorities, Land Registry and Cadastre offices, Highway departments, Foundations, Ministries of Budget, Transportation, Justice, Public Works and Settlement, Environment and Forestry, Agriculture and Rural Affairs, Culture and Internal Affairs, State Institute of Statistics (SIS, execution offices, tax offices, real estate offices, private sector, local governments and banks. On the other hand, spatio-temporal LTC data very important component for creating infrastructure of Land Administration Model (LADM. For this reason, spatio-temporal LTC data needs for LADM not only updated but also temporal. The investigations ended up with determine temporal analyses of LTC data, traditional LTC system and tracing temporal analyses in traditional LTC system. In the traditional system, the temporal analyses needed by all these users could not be performed in a rapid and reliable way. The reason for this is that the traditional LTC system is a manual archiving system. The aims and general contents of this paper: (1 define traditional LTC system of Turkey; (2 determining the need for spatio-temporal LTC data and analyses for core domain model for LADM. As a results of temporal and spatio-temporal analysis LTC data needs, new system design is important for the Turkish LADM model. Designing and realizing an efficient and functional Temporal Geographic Information Systems (TGIS is inevitable for the Turkish LADM core infrastructure. Finally this paper outcome is creating infrastructure for design and develop LADM for Turkey.

  6. Spatio-temporal transmission and environmental determinants of Schistosomiasis Japonica in Anhui Province, China.

    Directory of Open Access Journals (Sweden)

    Yi Hu

    2015-02-01

    Full Text Available Schistosomiasis japonica still remains of public health and economic significance in China, especially in the lake and marshland areas along the Yangtze River Basin, where the control of transmission has proven difficult. In the study, we investigated spatio-temporal variations of S. japonicum infection risk in Anhui Province and assessed the associations of the disease with key environmental factors with the aim of understanding the mechanism of the disease and seeking clues to effective and sustainable schistosomiasis control.Infection data of schistosomiasis from annual conventional surveys were obtained at the village level in Anhui Province, China, from 2000 to 2010 and used in combination with environmental data. The spatio-temporal kriging model was used to assess how these environmental factors affected the spatio-temporal pattern of schistosomiasis risk. Our results suggested that seasonal variation of the normalized difference vegetation index (NDVI, seasonal variation of land surface temperature at daytime (LSTD, and distance to the Yangtze River were negatively significantly associated with risk of schistosomiasis. Predictive maps showed that schistosomiasis prevalence remained at a low level and schistosomiasis risk mainly evolved along the Yangtze River. Schistosomiasis risk also followed a focal spatial pattern, fluctuating temporally with a peak (the largest spatial extent in 2005 and then contracting gradually but with a scattered distribution until 2010.The fitted spatio-temporal kriging model can capture variations of schistosomiasis risk over space and time. Combined with techniques of geographic information system (GIS and remote sensing (RS, this approach facilitates and enriches risk modeling of schistosomiasis, which in turn helps to identify prior areas for effective and sustainable control of schistosomiasis in Anhui Province and perhaps elsewhere in China.

  7. Determining Spatio-Temporal Cadastral Data Requirement for Infrastructure of Ladm for Turkey

    Science.gov (United States)

    Alkan, M.; Polat, Z. A.

    2016-06-01

    Nowadays, the nature of land title and cadastral (LTC) data in the Turkey is dynamic from a temporal perspective which depends on the LTC operations. Functional requirements with respect to the characteristics are investigated based upon interviews of professionals in public and private sectors. These are; Legal authorities, Land Registry and Cadastre offices, Highway departments, Foundations, Ministries of Budget, Transportation, Justice, Public Works and Settlement, Environment and Forestry, Agriculture and Rural Affairs, Culture and Internal Affairs, State Institute of Statistics (SIS), execution offices, tax offices, real estate offices, private sector, local governments and banks. On the other hand, spatio-temporal LTC data very important component for creating infrastructure of Land Administration Model (LADM). For this reason, spatio-temporal LTC data needs for LADM not only updated but also temporal. The investigations ended up with determine temporal analyses of LTC data, traditional LTC system and tracing temporal analyses in traditional LTC system. In the traditional system, the temporal analyses needed by all these users could not be performed in a rapid and reliable way. The reason for this is that the traditional LTC system is a manual archiving system. The aims and general contents of this paper: (1) define traditional LTC system of Turkey; (2) determining the need for spatio-temporal LTC data and analyses for core domain model for LADM. As a results of temporal and spatio-temporal analysis LTC data needs, new system design is important for the Turkish LADM model. Designing and realizing an efficient and functional Temporal Geographic Information Systems (TGIS) is inevitable for the Turkish LADM core infrastructure. Finally this paper outcome is creating infrastructure for design and develop LADM for Turkey.

  8. Response-mode decomposition of spatio-temporal haemodynamics.

    Science.gov (United States)

    Pang, J C; Robinson, P A; Aquino, K M

    2016-05-01

    The blood oxygen-level dependent (BOLD) response to a neural stimulus is analysed using the transfer function derived from a physiologically based poroelastic model of cortical tissue. The transfer function is decomposed into components that correspond to distinct poles, each related to a response mode with a natural frequency and dispersion relation; together these yield the total BOLD response. The properties of the decomposed components provide a deeper understanding of the nature of the BOLD response, via the components' frequency dependences, spatial and temporal power spectra, and resonances. The transfer function components are then used to separate the BOLD response to a localized impulse stimulus, termed the Green function or spatio-temporal haemodynamic response function, into component responses that are explicitly related to underlying physiological quantities. The analytical results also provide a quantitative tool to calculate the linear BOLD response to an arbitrary neural drive, which is faster to implement than direct Fourier transform methods. The results of this study can be used to interpret functional magnetic resonance imaging data in new ways based on physiology, to enhance deconvolution methods and to design experimental protocols that can selectively enhance or suppress particular responses, to probe specific physiological phenomena.

  9. Spatio-temporal effects of low impact development practices

    Science.gov (United States)

    Gilroy, Kristin L.; McCuen, Richard H.

    2009-04-01

    SummaryThe increase in land development and urbanization experienced in the US and worldwide is causing environmental degradation. Traditional off-site stormwater management does not protect small streams. To mitigate the negative effects of land development, best management practices (BMPs) are being implemented into stormwater management policies for the purposes of controlling minor flooding and improving water quality. Unfortunately, the effectiveness of BMPs has not been extensively studied. The purpose of this research was to analyze the effects of both location and quantity of two types of BMPs: cisterns and bioretention pits. A spatio-temporal model of a microwatershed was developed to determine the effects of BMPs on single-family, townhome, and commercial lots. The effects of development and the BMPs on peak runoff rates and volumes were compared to pre-development conditions. The results show that cisterns alone are capable of controlling rooftop runoff for small storms. Both the spatial location and the volume of BMP storage on a microwatershed influences the effectiveness of BMPs. The volume of BMP storage is positively correlated to the percent reduction in the peak discharge rate and total runoff volume; however, location is a factor in the peak reduction and a maximum volume of effective storage for both hydrologic metrics does exist. These results provide guidelines for developing stormwater management policies that can potentially reduce pollution of first-order streams, lower the cost and maintenance requirements, enhance aesthetics, and increase safety.

  10. Effects of climate change adaptation scenarios on perceived spatio-temporal characteristics of drought events

    Science.gov (United States)

    Vidal, J.-P.; Martin, E.; Kitova, N.; Najac, J.; Soubeyroux, J.-M.

    2012-04-01

    Drought events develop in both space and time and they are therefore best described through summary joint spatio-temporal characteristics, like mean duration, mean affected area and total magnitude. This study addresses the issue of future projections of such characteristics of drought events over France through three main research questions: (1) Are downscaled climate projections able to reproduce spatio-temporal characteristics of meteorological and agricultural droughts in France over a present-day period? (2) How such characteristics will evolve over the 21st century under different emissions scenarios? (3) How would perceived drought characteristics evolve under theoretical adaptation scenarios? These questions are addressed using the Isba land surface model, downscaled climate projections from the ARPEGE General Circulation Model under three emissions scenarios, as well as results from a previously performed 50-year multilevel and multiscale drought reanalysis over France (Vidal et al., 2010). Spatio-temporal characteristics of meteorological and agricultural drought events are computed using the Standardized Precipitation Index (SPI) and the Standardized Soil Wetness Index (SSWI), respectively, and for time scales of 3 and 12 months. Results first show that the distributions of joint spatio-temporal characteristics of observed events are well reproduced by the downscaled hydroclimate projections over a present-day period. All spatio-temporal characteristics of drought events are then found to dramatically increase over the 21st century under all considered emissions scenarios, with stronger changes for agricultural droughts. Two theoretical adaptation scenarios are eventually built based on hypotheses of adaptation to evolving climate and hydrological normals. The two scenarios differ by the way the transient adaptation is performed for a given date in the future, with reference to the normals over either the previous 30-year window ("retrospective

  11. A three-dimensional spatio-temporal EEG pattern analyzing system

    Institute of Scientific and Technical Information of China (English)

    LIU Hesheng; GAO Xiaorong; YANG Fusheng

    2003-01-01

    Spatio-temporal pattern analysis of EEG is an important tool in brain research. An EEG pattern analysis system based on a hierarchical multi-method approach is proposed here. The system consists of multiple steps including extraction of target signal, acquisition of intracranial electric activity distribution, adaptive segmentation of EEG and spatio-temporal pattern recognition. Some modern signal processing methods such as common spatial subspace decomposition, hidden Markov model are adopted. This paper also proposes an algorithm named LORETA-FOCUSS to estimate the current density inside the brain with a high spatial resolution. Microstate analysis of EEG is extended to the 3-D situation. The system was applied to the brain computer interface problem and achieved the highest accuracy of 88.89% with an average accuracy of 81.48% when classifying two imaginary movement tasks, while the data were not manually pre-selected. The result has proved spatio-temporal EEG pattern analysis is an efficient way in brain research.

  12. Simultaneous spatio-temporal focusing for tissue manipulation

    Directory of Open Access Journals (Sweden)

    Squier J.

    2013-11-01

    Full Text Available Simultaneous spatiotemporal focusing (SSTF is applied to lens tissue and compared directly with standard femtosecond micromachining of the tissue at the same numerical aperture. Third harmonic generation imaging is used for spatio-temporal characterization of the processing conditions obtained with both a standard and SSTF focus.

  13. Spatio-temporal Variations of Abundance, Biomass, Repproductive ...

    African Journals Online (AJOL)

    N'DOUA RAPHAEL

    2015-08-19

    Aug 19, 2015 ... dissolved organic materials and role in estuary tropho- dynamics ... together with their development times and growth rate. (Koichi, 2001). ... Zooplankton (P. hessei) and environmental variables were collected during the dry season .... EPR and SPF) show the same spatio-temporal variation. (Figure 4B-E).

  14. Cubic map algebra functions for spatio-temporal analysis

    Science.gov (United States)

    Mennis, J.; Viger, R.; Tomlin, C.D.

    2005-01-01

    We propose an extension of map algebra to three dimensions for spatio-temporal data handling. This approach yields a new class of map algebra functions that we call "cube functions." Whereas conventional map algebra functions operate on data layers representing two-dimensional space, cube functions operate on data cubes representing two-dimensional space over a third-dimensional period of time. We describe the prototype implementation of a spatio-temporal data structure and selected cube function versions of conventional local, focal, and zonal map algebra functions. The utility of cube functions is demonstrated through a case study analyzing the spatio-temporal variability of remotely sensed, southeastern U.S. vegetation character over various land covers and during different El Nin??o/Southern Oscillation (ENSO) phases. Like conventional map algebra, the application of cube functions may demand significant data preprocessing when integrating diverse data sets, and are subject to limitations related to data storage and algorithm performance. Solutions to these issues include extending data compression and computing strategies for calculations on very large data volumes to spatio-temporal data handling.

  15. Scalable Top-k Spatio-Temporal Term Querying

    DEFF Research Database (Denmark)

    Skovsgaard, Anders; Sidlauskas, Darius; Jensen, Christian S.

    2014-01-01

    that are capable of providing the top-k terms seen in posts in a user-specified spatio-temporal range. The techniques enable interactive response times in the millisecond range in a realistic setting where the arrival rate of posts exceeds today's average tweet arrival rate by a factor of 4-10. The techniques...

  16. Visual experience modulates spatio-temporal dynamics of circuit activation

    Directory of Open Access Journals (Sweden)

    Lang eWang

    2011-06-01

    Full Text Available Persistent reduction in sensory drive in early development results in multiple plastic changes of different cortical synapses. How these experience-dependent modifications affect the spatio-temporal dynamics of signal propagation in neocortical circuits is poorly understood. Here we demonstrate that brief visual deprivation significantly affects the propagation of electrical signals in the primary visual cortex. The spatio-temporal spread of circuit activation upon direct stimulation of its input layer (Layer 4 is reduced, as is the activation of Layer 2/3 – the main recipient of the output from Layer 4. Our data suggest that the decrease in spatio-temporal activation of L2/3 depends on reduced L4 output, and is not intrinsically generated within L2/3. The data shown here suggest that changes in the synaptic components of the visual cortical circuit result not only in alteration of local integration of excitatory and inhibitory inputs, but also in a significant decrease in overall circuit activation. Furthermore, our data indicate a differential effect of visual deprivation on L4 and L2/3, suggesting that while feedforward activation of L2/3 is reduced, its activation by long range, within layer inputs is unaltered. Thus, brief visual deprivation induces experience-dependent circuit re-organization by modulating not only circuit excitability, but also the spatio-temporal patterns of cortical activation within and between layers.

  17. Spatio-temporal analysis of Salmonella surveillance data in Thailand

    DEFF Research Database (Denmark)

    Coutinho Calado Domingues, Ana Rita; Vieira, Antonio; Hendriksen, Rene S.

    2014-01-01

    This study evaluates the usefulness of spatio-temporal statistical tools to detect outbreaks using routine surveillance data where limited epidemiological information is available. A dataset from 2002 to 2007 containing information regarding date, origin, source and serotype of 29 586 Salmonella ...

  18. Spatio-temporal Dynamics of Land-use and Land-cover Change: A Multi-agent Simulation Model and Its Application to an Upland Watershed in Central Vietnam

    Science.gov (United States)

    Le, Q.; Vlek, P. L.; Park, S.

    2005-12-01

    Land-use and land-cover change (LUCC) is an essential environmental process that should be monitored and prognosticated to provide a basis for better land management policy. However, LUCC modeling is a challenge due to the complex nature and unexpected behavior of both human drivers and natural constraints. This paper presents a multi-agent-based model to simulate spatio-temporal land-use changes and the interdependent socio-economic dynamics emerging from the complex socio-ecological interactions at micro levels resulting from land-use policy interventions. The model provides land-use scenarios under alternative policy to support decisions on land management for improved rural livelihoods while protecting the environment. In the multi-agent simulation model, the human community is represented by household agents (heterogeneous farming households) with their profiles and decision-making mechanisms about land use. The household profile defines the five asset dimensions of household livelihood (e.g., social, human, financial, natural and physical assets). The land-use decision-making program works by taking inputs from the household profile, perceived spatial environmental attributes, and introduced policies. The decision-making program is a logical procedure that combines a land-use choice model (multi-nominal logistic choices) and anthropological rules. The landscape environment is represented by landscape agents (congruent land patches of 30mx30m) with their state variables and ecological response mechanisms to environmental changes and human interventions. State variables of landscape agents correspond to spatial GIS-raster layers of biophysical, economic, and institutional variables. Ecological mechanisms of landscape agents are represented by internal sub-models of agricultural and forest productivity dynamics, which work in response to the current state, history, and spatial neighbourhood of the landscape agents. A multi-agent based protocol coordinates the

  19. Soil water content interpolation using spatio-temporal kriging with external drift

    NARCIS (Netherlands)

    Snepvangers, J.J.J.C.; Heuvelink, G.B.M.; Huisman, J.A.

    2003-01-01

    In this study, two techniques for spatio-temporal (ST) kriging of soil water content are compared. The first technique, spatio-temporal ordinary kriging, is the simplest of the two, and uses only information about soil water content. The second technique, spatio-temporal kriging with external drift,

  20. Spatio-temporal 3D terrain modeling and visualization based on measuring data%基于测量数据的时空三维地形构建与可视化

    Institute of Scientific and Technical Information of China (English)

    王宏健; 傅桂霞; 王晶; 李娟

    2011-01-01

    基于无人无缆自主水下航行器及其测量传感器开展海洋环境勘察与监测是未来海洋开发的重要应用领域,测量数据的矢量建模及其可视化显示是其中的关键技术之一.针对AUV的多波束声呐地形测量数据,开展了地形矢量模型构建与可视化研究,给出了传感器数据处理与时空配准、海底地形坐标变换等方法,设计了一种优化的Delaunay三角网建模算法,构建了三维海底地形矢量模型,提出了三维矢量模型真实感表达方法.基于海试测量的离散数据完成了具有真实感特点的三维海底地形矢量模型的构建与显示,实验结果表明:所提出的研究方法是合理、可行的,克服了多波束声呐只能提供离散地形高程数据的缺点,其地形数据的连续矢量化处理与可视化显示的优点对于海洋环境立体化与可视化监测具有重要的意义和应用前景.%Oceanography survey and surveillance is an important application field in future ocean exploitation, which is based on autonomous underwater vehicle ( AUV) and its measuring sensors; and one of the key technologies is vector modeling and visualization based on measuring data. Aiming at the measuring terrain data from multi-beam sonar , terrain vector modeling and visualization are studied, and the methods of sonar data processing, spatio-temporal registration and coordinates transformation are proposed. An improved Delaunay triangulation modeling algorithm is designed to set up the vector model of terrain, and a realistic expression method of 3D vector model is proposed on the OpenGL platform. Based on the multi-beam sonar terrain data from the AUV sea trial, which has the characteristics of discrete and redundant, all the above methods are tested and verified. Test results indicate that the methods designed in this paper are reasonable and feasible, which overcomes the weak points that multi-beam sonar could only provide discrete terrain

  1. Spatio-temporal càdlàg functional marked point processes: Unifying spatio-temporal frameworks

    NARCIS (Netherlands)

    O.J.A. Cronie (Ottmar); J. Mateu

    2014-01-01

    htmlabstractThis paper defines the class of càdlàg functional marked point processes (CFMPPs). These are (spatio-temporal) point processes marked by random elements which take values in a càdlàg function space, i.e. the marks are given by càdlàg stochastic processes. We generalise notions of marked

  2. Spatio-temporal representativeness of aerosol remote sensing observations

    Science.gov (United States)

    Schutgens, Nick; Gryspeerdt, Edward; Tsyro, Svetlana; Goto, Daisuke; Watson-Parris, Duncan; Weigum, Natalie; Schulz, Michael; Stier, Philip

    2016-04-01

    One characteristic of remote sensing observations is the strong intermittency with which they observe the same scene. Due to unfavourable conditions (due to e.g. low visible light, cloudiness or high surface albedo), sampling constraints (due to e.g. polar orbits) or instrument malfunction or maintenance, gaps in the observing record of hours to months exist. At the same time, satellite L3 products often are spatial aggregates over considerable distances (e.g. 1 by 1 degree). We study the impact of spatio-temporal sampling of observations on their representativeness: i.e. how well can satellite products represent the large scale (~ 100 by 100 km) aerosol field over periods of days, months, or years. This study was conducted by using diverse global and regional aerosol models as a truth and sub-sample them according to actual observations. In this way, we have been able to study the representativeness of different observing systems like MODIS, CALIOP and AERONET. Monthly and yearly averages allow serious sampling errors, that may still be present in multi-year climatologies due to recurring observing patterns. Even daily averages are affected as diurnal cycles can often not be observed. We discuss the implications these representativeness errors have for e.g. model evaluation or the construction of climatologies. We also assess similar representativeness issues in ground site in-situ observations from e.g. EMEP or IMPROVE and show that satellite datasets have distinct advantages due to their better spatial coverage provided temporal sampling is dealt with properly (i.e. through collocation of datasets). Finally, we briefly introduce a software tool (the Community Intercomparison Suite or CIS) that is designed to improve representativeness of datasets in intercomparion studies through aggregation and collocation of data.

  3. Spatio-temporal coupling of EEG signals in epilepsy

    Science.gov (United States)

    Senger, Vanessa; Müller, Jens; Tetzlaff, Ronald

    2011-05-01

    Approximately 1% of the world's population suffer from epileptic seizures throughout their lives that mostly come without sign or warning. Thus, epilepsy is the most common chronical disorder of the neurological system. In the past decades, the problem of detecting a pre-seizure state in epilepsy using EEG signals has been addressed in many contributions by various authors over the past two decades. Up to now, the goal of identifying an impending epileptic seizure with sufficient specificity and reliability has not yet been achieved. Cellular Nonlinear Networks (CNN) are characterized by local couplings of dynamical systems of comparably low complexity. Thus, they are well suited for an implementation as highly parallel analogue processors. Programmable sensor-processor realizations of CNN combine high computational power comparable to tera ops of digital processors with low power consumption. An algorithm allowing an automated and reliable detection of epileptic seizure precursors would be a"huge step" towards the vision of an implantable seizure warning device that could provide information to patients and for a time/event specific treatment directly in the brain. Recent contributions have shown that modeling of brain electrical activity by solutions of Reaction-Diffusion-CNN as well as the application of a CNN predictor taking into account values of neighboring electrodes may contribute to the realization of a seizure warning device. In this paper, a CNN based predictor corresponding to a spatio-temporal filter is applied to multi channel EEG data in order to identify mutual couplings for different channels which lead to a enhanced prediction quality. Long term EEG recordings of different patients are considered. Results calculated for these recordings with inter-ictal phases as well as phases with seizures will be discussed in detail.

  4. Plausible cloth animation using dynamic bending model

    Institute of Scientific and Technical Information of China (English)

    Chuan Zhou; Xiaogang Jin; Charlie C.L. Wang; Jieqing Feng

    2008-01-01

    Simulating the mechanical behavior of a cloth is a very challenging and important problem in computer animation. The models of bending in most existing cloth simulation approaches are taking the assumption that the cloth is little deformed from a plate shape.Therefore, based on the thin-plate theory, these bending models do not consider the condition that the current shape of the cloth under large deformations cannot be regarded as the approximation to that before deformation, which leads to an unreal static bending. [This paper introduces a dynamic bending model which is appropriate to describe large out-plane deformations such as cloth buckling and bending, and develops a compact implementation of the new model on spring-mass systems. Experimental results show that wrinkles and folds generated using this technique in cloth simulation, can appear and vanish in a more natural way than other approaches.

  5. Spatio-Temporal Features of China’s Urban Fires: An Investigation with Reference to Gross Domestic Product and Humidity

    Directory of Open Access Journals (Sweden)

    Zhenbo Wang

    2015-07-01

    Full Text Available Frequent fire accidents pose a serious threat to human life and property. The spatio-temporal features of China’s urban fires, and their drivers should be investigated. Based on the Spatio-temporal Dynamic panel data Model (SDM, and using fire data gathered from 337 Chinese cities in 2000 to 2009, the influence of spatio-temporal factors on the frequency of urban fires was analyzed. The results show that (1 the overall fire incidence of China increased annually before 2002 and reduced significantly after 2003, and then high fire incidence increased in western China; (2 Spatio-temporal factors play a significant role in the frequency of Chinese urban fires; specifically, the fire assimilation effect, fire inertia effect and fire caution effect. The ratio of fire incidence of China has reduced significantly, and the focus of fire incidence moved towards the western region of China. GDP and humidity have a significant effect on urban fire situation change in China, and these effects may be referred to as “fire assimilation effects”, “fire inertia effects” and “fire caution effects”.

  6. A Spatio-Temporal Analysis of Mitochondrial DNA Haplogroup I

    Directory of Open Access Journals (Sweden)

    Revesz Peter Z.

    2016-01-01

    Full Text Available The recent recovery of ancient DNA from a growing number of human samples shows that mitochondrial DNA haplogroup I was introduced to Europe after the end of the Last Glacial Maximum. This paper provides a spatio-temporal analysis of the various subhaplogroups of mitochondrial DNA I. The study suggests that haplogroup I diversified into haplogroups I1, I2’3, I4 and I5 at specific regions in Eurasia and then spread southward to Crete and Egypt.

  7. Multidimensional spatio-temporal data clustering, with applications to imaging.

    OpenAIRE

    Mure, Simon

    2016-01-01

    Due to the dramatic increase of longitudinal acquisitions in the past decades such as video sequences, global positioning system (GPS) tracking or medical follow-up, many applications for time-series data mining have been developed. Thus, unsupervised time-series data mining has become highly relevant with the aim to automatically detect and identify similar temporal patterns between time-series. In this work, we propose a new spatio-temporal filtering scheme based on the mean-shift procedure...

  8. Spatio-temporal availability of soft mast in clearcuts in the Southern Appalachians

    Science.gov (United States)

    Reynolds-Hogland, M. J.; Mitchell, M.S.; Powell, R.A.

    2006-01-01

    Soft mast is an important resource for many wild populations in the Southern Appalachians, yet the way clear-cutting affects availability of soft mast though time is not fully understood. We tested a theoretical model of temporal availability of soft mast in clearcuts using empirical data on percent cover and berry production of Gaylussacia, Vaccinium, and Rubus spp. plants in 100 stands that were clearcut (0-122 years old) in the Southern Appalachian Mountains. We modeled the relationship between soft mast availability and stand age, evaluated the effects of topography and forest type on soft mast, developed statistical models for predicting the spatio-temporal distribution of soft mast, and tested the hypothesis that percent cover of berry plants and berry production provided similar information about soft mast availability. We found temporal dynamics explained berry production better than it predicted percent plant cover, whereas topographic variables influenced percent plant cover more than they influenced berry production. Berry production and percent plant cover were highest in ???2-9-year-old stands. Percent plant cover was lowest in 10-69-year-old stands and intermediate in 70+-year-old stands. Three of our spatio-temporal models performed well during model testing and they were not biased by the training data, indicating the inferences about spatio-temporal availability of soft mast extended beyond our sample data. The methods we used to estimate the distribution of soft mast may be useful for modeling distributions of other resources. ?? 2006 Elsevier B.V. All rights reserved.

  9. Use of Google SketchUp to implement 3D spatio-temporal visualization

    Science.gov (United States)

    Li, Linhai; Qu, Lina; Ying, Shen; Liang, Dongdong; Hu, Zhenlong

    2009-10-01

    Geovisualization is an important means to understand the geographic features and phenomena. Urban space, especially buildings, keeps changing with social development. However, traditional 2D visualization can only represent the plane geometric description, which is unable to support 3D dynamic visualization. Only with 3D dynamic visualization can the buildings' spatial morphology be exhibited temporally, including buildings' creation, expansion, removing, etc. But these buildings' changes are impossible to be studied in traditional 2D and 3D static visualization systems. As a result, it becomes urgent to find an effective solution to implement 3D spatial-temporal visualization of buildings. Inspired by 2D spatial-temporal visualization methods, like snapshot and event-based spatio-temporal data model(ESTDM), we propose a new data model called Spatio-Temporal Page Model(STPM) and implement 3D spatial-temporal visualization in Google SketchUp based on STPM. This paper studies 3D visualization of real estate focusing on its spatio-temporal characteristics. First of all, 3D models are built for every temporal scenario by the Google SketchUp. And every Geo-object is identified by a unique and permanent ObjectID, the linkage of Geo-objects between different time spots. Then, each temporal scenario is represented as page. After having the page series, finally, it is possible to display its spatial-temporal changes and create an animation. Underlying this solution, we have built a prototype system on part of real estate data. It is proven that users are able to understand clearly the real estate's changes from our prototype system. Consequently, we believe our method for 3D spatial-temporal visualization definitely has many merits.

  10. Field scale spatio-temporal soil moisture variability for trafficability and crop water availability

    Science.gov (United States)

    Carranza, Coleen; van der Ploeg, Martine; Ritsema, Coen

    2016-04-01

    Spatio-temporal patterns of soil moisture have been studied mostly for inputs in land surface models for weather and climate predictions. Remote sensing techniques for estimation of soil moisture have been explored because of the good spatial coverage at different scales. Current available satellite data provide surface soil moisture as microwave systems only measure soil moisture content up to 5cm soil depth. The OWAS1S project will focus on estimation of soil moisture from freely available Sentinel-1 datasets for operational water management in agricultural areas. As part of the project, it is essential to develop spatio-temporal methods to estimate root zone soil moisture from surface soil moisture. This will be used for crop water availability and trafficability in selected agricultural fields in the Netherlands. A network of single capacitance sensors installed per field will provide continuous measurements of soil moisture in the study area. Ground penetrating radar will be used to measure soil moisture variability within a single field for different time periods. During wetter months, optimal conditions for traffic will be assessed using simultaneous soil strength and soil moisture measurements. Towards water deficit periods, focus is on the relation (or the lack thereof) between surface soil moisture and root zone soil moisture to determine the amount of water for crops. Spatio-temporal distribution will determine important physical controls for surface and root zone soil moisture and provide insights for root-zone soil moisture. Existing models for field scale soil-water balance and data assimilation methods (e.g. Kalman filter) will be combined to estimate root zone soil moisture. Furthermore, effects of root development on soil structure and soil hydraulic properties and subsequent effects on trafficability and crop water availability will be investigated. This research project has recently started, therefore we want to present methods and framework of

  11. Sensitivity of cochlear nucleus neurons to spatio-temporal changes in auditory nerve activity.

    Science.gov (United States)

    Wang, Grace I; Delgutte, Bertrand

    2012-12-01

    The spatio-temporal pattern of auditory nerve (AN) activity, representing the relative timing of spikes across the tonotopic axis, contains cues to perceptual features of sounds such as pitch, loudness, timbre, and spatial location. These spatio-temporal cues may be extracted by neurons in the cochlear nucleus (CN) that are sensitive to relative timing of inputs from AN fibers innervating different cochlear regions. One possible mechanism for this extraction is "cross-frequency" coincidence detection (CD), in which a central neuron converts the degree of coincidence across the tonotopic axis into a rate code by preferentially firing when its AN inputs discharge in synchrony. We used Huffman stimuli (Carney LH. J Neurophysiol 64: 437-456, 1990), which have a flat power spectrum but differ in their phase spectra, to systematically manipulate relative timing of spikes across tonotopically neighboring AN fibers without changing overall firing rates. We compared responses of CN units to Huffman stimuli with responses of model CD cells operating on spatio-temporal patterns of AN activity derived from measured responses of AN fibers with the principle of cochlear scaling invariance. We used the maximum likelihood method to determine the CD model cell parameters most likely to produce the measured CN unit responses, and thereby could distinguish units behaving like cross-frequency CD cells from those consistent with same-frequency CD (in which all inputs would originate from the same tonotopic location). We find that certain CN unit types, especially those associated with globular bushy cells, have responses consistent with cross-frequency CD cells. A possible functional role of a cross-frequency CD mechanism in these CN units is to increase the dynamic range of binaural neurons that process cues for sound localization.

  12. Feature-based Analysis of Large-scale Spatio-Temporal Sensor Data on Hybrid Architectures.

    Science.gov (United States)

    Saltz, Joel; Teodoro, George; Pan, Tony; Cooper, Lee; Kong, Jun; Klasky, Scott; Kurc, Tahsin

    2013-08-01

    Analysis of large sensor datasets for structural and functional features has applications in many domains, including weather and climate modeling, characterization of subsurface reservoirs, and biomedicine. The vast amount of data obtained from state-of-the-art sensors and the computational cost of analysis operations create a barrier to such analyses. In this paper, we describe middleware system support to take advantage of large clusters of hybrid CPU-GPU nodes to address the data and compute-intensive requirements of feature-based analyses in large spatio-temporal datasets.

  13. All Optical Three Dimensional Spatio-Temporal Correlator for Automatic Event Recognition Using Multiphoton Atomic System

    CERN Document Server

    Monjur, Mehjabin S; Shahriar, Selim M

    2015-01-01

    In this paper, we model and show the simulation results of a three-dimensional spatio-temporal correlator (STC) that combines the technique of holographic correlation and photon echo based temporal pattern recognition. The STC is shift invariant in space and time. It can be used to recognize rapidly an event (e.g., a short video clip) that may be present in a large video file, and determine the temporal location of the event. It can also determine multiple matches automatically if the event occurs more than once. We show how to realize the STC using Raman transitions in Rb atomic vapor.

  14. Spatio-Temporal Variation in Landscape Composition May Speed Resistance Evolution of Pests to Bt Crops

    Science.gov (United States)

    Ives, Anthony R.; Paull, Cate; Hulthen, Andrew; Downes, Sharon; Andow, David A.; Haygood, Ralph; Zalucki, Myron P.; Schellhorn, Nancy A.

    2017-01-01

    Transgenic crops that express insecticide genes from Bacillus thuringiensis (Bt) are used worldwide against moth and beetle pests. Because these engineered plants can kill over 95% of susceptible larvae, they can rapidly select for resistance. Here, we use a model for a pyramid two-toxin Bt crop to explore the consequences of spatio-temporal variation in the area of Bt crop and non-Bt refuge habitat. We show that variability over time in the proportion of suitable non-Bt breeding habitat, Q, or in the total area of Bt and suitable non-Bt habitat, K, can increase the overall rate of resistance evolution by causing short-term surges of intense selection. These surges can be exacerbated when temporal variation in Q and/or K cause high larval densities in refuges that increase density-dependent mortality; this will give resistant larvae in Bt fields a relative advantage over susceptible larvae that largely depend on refuges. We address the effects of spatio-temporal variation in a management setting for two bollworm pests of cotton, Helicoverpa armigera and H. punctigera, and field data on landscape crop distributions from Australia. Even a small proportion of Bt fields available to egg-laying females when refuges are sparse may result in high exposure to Bt for just a single generation per year and cause a surge in selection. Therefore, rapid resistance evolution can occur when Bt crops are rare rather than common in the landscape. These results highlight the need to understand spatio-temporal fluctuations in the landscape composition of Bt crops and non-Bt habitats in order to design effective resistance management strategies. PMID:28046073

  15. Spatio-Temporal Variation in Landscape Composition May Speed Resistance Evolution of Pests to Bt Crops.

    Science.gov (United States)

    Ives, Anthony R; Paull, Cate; Hulthen, Andrew; Downes, Sharon; Andow, David A; Haygood, Ralph; Zalucki, Myron P; Schellhorn, Nancy A

    2017-01-01

    Transgenic crops that express insecticide genes from Bacillus thuringiensis (Bt) are used worldwide against moth and beetle pests. Because these engineered plants can kill over 95% of susceptible larvae, they can rapidly select for resistance. Here, we use a model for a pyramid two-toxin Bt crop to explore the consequences of spatio-temporal variation in the area of Bt crop and non-Bt refuge habitat. We show that variability over time in the proportion of suitable non-Bt breeding habitat, Q, or in the total area of Bt and suitable non-Bt habitat, K, can increase the overall rate of resistance evolution by causing short-term surges of intense selection. These surges can be exacerbated when temporal variation in Q and/or K cause high larval densities in refuges that increase density-dependent mortality; this will give resistant larvae in Bt fields a relative advantage over susceptible larvae that largely depend on refuges. We address the effects of spatio-temporal variation in a management setting for two bollworm pests of cotton, Helicoverpa armigera and H. punctigera, and field data on landscape crop distributions from Australia. Even a small proportion of Bt fields available to egg-laying females when refuges are sparse may result in high exposure to Bt for just a single generation per year and cause a surge in selection. Therefore, rapid resistance evolution can occur when Bt crops are rare rather than common in the landscape. These results highlight the need to understand spatio-temporal fluctuations in the landscape composition of Bt crops and non-Bt habitats in order to design effective resistance management strategies.

  16. A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia

    Directory of Open Access Journals (Sweden)

    Craig Anderson

    2017-02-01

    Full Text Available The field of spatio-temporal modelling has witnessed a recent surge as a result of developments in computational power and increased data collection. These developments allow analysts to model the evolution of health outcomes in both space and time simultaneously. This paper models the trends in ischaemic heart disease (IHD in New South Wales, Australia over an eight-year period between 2006 and 2013. A number of spatio-temporal models are considered, and we propose a novel method for determining the goodness-of-fit for these models by outlining a spatio-temporal extension of the Moran’s I statistic. We identify an overall decrease in the rates of IHD, but note that the extent of this health improvement varies across the state. In particular, we identified a number of remote areas in the north and west of the state where the risk stayed constant or even increased slightly.

  17. An adaptive wavelet neural network for spatio-temporal system identification.

    Science.gov (United States)

    Wei, H L; Billings, S A; Zhao, Y F; Guo, L Z

    2010-12-01

    Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks (AWNN) is introduced for spatio-temporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework.

  18. Spatio-Temporal Encoding in Medical Ultrasound Imaging

    DEFF Research Database (Denmark)

    Gran, Fredrik

    2005-01-01

    In this dissertation two methods for spatio-temporal encoding in medical ultrasound imaging are investigated. The first technique is based on a frequency division approach. Here, the available spectrum of the transducer is divided into a set of narrow bands. A waveform is designed for each band...... the signal to noise ratio and simultaneously the penetration depth so that the medical doctor can image deeper lying structures. The method is tested both experimentally and in simulation and has also evaluated for the purpose of blood flow estimation. The work presented is based on four papers which...

  19. Spatio-temporal patterns of Campylobacter colonization in Danish broilers

    DEFF Research Database (Denmark)

    Chowdhury, S; Themudo, G E; Sandberg, M

    2013-01-01

    SUMMARY Despite a number of risk-factor studies in different countries, the epidemiology of Campylobacter colonization in broilers, particularly spatial dependencies, is still not well understood. A series of analyses (visualization and exploratory) were therefore conducted in order to obtain...... a better understanding of the spatial and temporal distribution of Campylobacter in the Danish broiler population. In this study, we observed a non-random temporal occurrence of Campylobacter, with high prevalence during summer and low during winter. Significant spatio-temporal clusters were identified...

  20. Pinning control of spatio temporal chaos in nonlinear optics

    Energy Technology Data Exchange (ETDEWEB)

    Mendoza, C; Martinez-Mardones, J [Institute of Physics, Pontifical Catholic University of Valparaiso, 234-0025 Valparaiso (Chile); Ramazza, P L; Boccaletti, S [CNR- Istituto dei Sistemi Complessi, Via Madonna del Piano 10, 50019 Sesto Fiorentino (Italy)], E-mail: caromendoza@gmail.com

    2008-11-01

    We have studied numerically the influence of the number of controllers in the control of a spatial pattern in an optical device. In this article, we focus on the liquid crystal light valve (LCLV) which is known to exhibit spatio-temporal chaotic states in some range of parameters. By applying a correcting term in the intensity proportional to the difference between the light intensity of the target pattern and the chaos state, the system is driven to the target pattern in finite time. In addition, we study the number of pinning points and their positions to reach the control of the pattern.

  1. Spatio-Temporal Stream Reasoning with Incomplete Spatial Information

    OpenAIRE

    Heintz, Fredrik; de Leng, Daniel

    2014-01-01

    Reasoning about time and space is essential for many applications, especially for robots and other autonomous systems that act in the real world and need to reason about it. In this paper we present a pragmatic approach to spatio-temporal stream reasoning integrated in the Robot Operating System through the DyKnow framework. The temporal reasoning is done in the Metric Temporal Logic and the spatial reasoning in the Region Connection Calculus RCC-8. Progression is used to evaluate spatio-temp...

  2. A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution

    Directory of Open Access Journals (Sweden)

    Marc Wieland

    2017-04-01

    Full Text Available With an ever-increasing volume and complexity of data collected from a variety of sources, the efficient management of geospatial information becomes a key topic in disaster risk management. For example, the representation of assets exposed to natural disasters is subjected to changes throughout the different phases of risk management reaching from pre-disaster mitigation to the response after an event and the long-term recovery of affected assets. Spatio-temporal changes need to be integrated into a sound conceptual and technological framework able to deal with data coming from different sources, at varying scales, and changing in space and time. Especially managing the information life-cycle, the integration of heterogeneous information and the distributed versioning and release of geospatial information are important topics that need to become essential parts of modern exposure modelling solutions. The main purpose of this study is to provide a conceptual and technological framework to tackle the requirements implied by disaster risk management for describing exposed assets in space and time. An information life-cycle management solution is proposed, based on a relational spatio-temporal database model coupled with Git and GeoGig repositories for distributed versioning. Two application scenarios focusing on the modelling of residential building stocks are presented to show the capabilities of the implemented solution. A prototype database model is shared on GitHub along with the necessary scenario data.

  3. Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification.

    Science.gov (United States)

    Wei, Hua-Liang; Billings, Stephen A; Zhao, Yifan; Guo, Lingzhong

    2009-01-01

    In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.

  4. A Hybrid Method for Interpolating Missing Data in Heterogeneous Spatio-Temporal Datasets

    Directory of Open Access Journals (Sweden)

    Min Deng

    2016-02-01

    Full Text Available Space-time interpolation is widely used to estimate missing or unobserved values in a dataset integrating both spatial and temporal records. Although space-time interpolation plays a key role in space-time modeling, existing methods were mainly developed for space-time processes that exhibit stationarity in space and time. It is still challenging to model heterogeneity of space-time data in the interpolation model. To overcome this limitation, in this study, a novel space-time interpolation method considering both spatial and temporal heterogeneity is developed for estimating missing data in space-time datasets. The interpolation operation is first implemented in spatial and temporal dimensions. Heterogeneous covariance functions are constructed to obtain the best linear unbiased estimates in spatial and temporal dimensions. Spatial and temporal correlations are then considered to combine the interpolation results in spatial and temporal dimensions to estimate the missing data. The proposed method is tested on annual average temperature and precipitation data in China (1984–2009. Experimental results show that, for these datasets, the proposed method outperforms three state-of-the-art methods—e.g., spatio-temporal kriging, spatio-temporal inverse distance weighting, and point estimation model of biased hospitals-based area disease estimation methods.

  5. A geomatic methodology for spatio-temporal analysis of climatologic variables and water related diseases

    Science.gov (United States)

    Quentin, E.; Gómez Albores, M. A.; Díaz Delgado, C.

    2009-04-01

    The main objective of this research is to propose, by the way of geomatic developments, an integrated tool to analyze and model the spatio-temporal pattern of human diseases related to environmental conditions, in particular the ones that are linked to water resources. The geomatic developments follows four generic steps : requirement analysis, conceptual modeling, geomatic modeling and implementation (in Idrisi GIS software). A first development consists of the preprocessing of water, population and health data in order to facilitate the conversion and validation of tabular data into the required structure for spatio-temporal analysis. Three parallel developments follow : water balance, demographic state and evolution, epidemiological measures (morbidity and mortality rates, diseases burden). The new geomatic modules in their actual state have been tested on various regions of Mexico Republic (Lerma watershed, Chiapas state) focusing on diarrhea and vector borne diseases (dengue and malaria) and considering records over the last decade : a yearly as well as seasonal spreading trend can be observed in correlation with precipitation and temperature data. In an ecohealth perspective, the geomatic approach results particularly appropriate since one of its purposes is the integration of the various spatial themes implied in the study problem, environmental as anthropogenic. By the use of powerful spatial analysis functions, it permits the detection of spatial trends which, combined to the temporal evolution, can be of particularly use for example in climate change context, if sufficiently valid historical data can be obtain.

  6. Spatio-temporal dynamics induced by competing instabilities in two asymmetrically coupled nonlinear evolution equations

    Energy Technology Data Exchange (ETDEWEB)

    Schüler, D.; Alonso, S.; Bär, M. [Physikalisch-Technische Bundesanstalt, Abbestrasse 2-12, 10587 Berlin (Germany); Torcini, A. [CNR-Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi - Via Madonna del Piano 10, I-50019 Sesto Fiorentino (Italy); INFN Sez. Firenze, via Sansone 1, I-50019 Sesto Fiorentino (Italy)

    2014-12-15

    Pattern formation often occurs in spatially extended physical, biological, and chemical systems due to an instability of the homogeneous steady state. The type of the instability usually prescribes the resulting spatio-temporal patterns and their characteristic length scales. However, patterns resulting from the simultaneous occurrence of instabilities cannot be expected to be simple superposition of the patterns associated with the considered instabilities. To address this issue, we design two simple models composed by two asymmetrically coupled equations of non-conserved (Swift-Hohenberg equations) or conserved (Cahn-Hilliard equations) order parameters with different characteristic wave lengths. The patterns arising in these systems range from coexisting static patterns of different wavelengths to traveling waves. A linear stability analysis allows to derive a two parameter phase diagram for the studied models, in particular, revealing for the Swift-Hohenberg equations, a co-dimension two bifurcation point of Turing and wave instability and a region of coexistence of stationary and traveling patterns. The nonlinear dynamics of the coupled evolution equations is investigated by performing accurate numerical simulations. These reveal more complex patterns, ranging from traveling waves with embedded Turing patterns domains to spatio-temporal chaos, and a wide hysteretic region, where waves or Turing patterns coexist. For the coupled Cahn-Hilliard equations the presence of a weak coupling is sufficient to arrest the coarsening process and to lead to the emergence of purely periodic patterns. The final states are characterized by domains with a characteristic length, which diverges logarithmically with the coupling amplitude.

  7. Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes.

    Science.gov (United States)

    Kratz, Louis; Nishino, Ko

    2012-05-01

    Tracking pedestrians is a vital component of many computer vision applications, including surveillance, scene understanding, and behavior analysis. Videos of crowded scenes present significant challenges to tracking due to the large number of pedestrians and the frequent partial occlusions that they produce. The movement of each pedestrian, however, contributes to the overall crowd motion (i.e., the collective motions of the scene's constituents over the entire video) that exhibits an underlying spatially and temporally varying structured pattern. In this paper, we present a novel Bayesian framework for tracking pedestrians in videos of crowded scenes using a space-time model of the crowd motion. We represent the crowd motion with a collection of hidden Markov models trained on local spatio-temporal motion patterns, i.e., the motion patterns exhibited by pedestrians as they move through local space-time regions of the video. Using this unique representation, we predict the next local spatio-temporal motion pattern a tracked pedestrian will exhibit based on the observed frames of the video. We then use this prediction as a prior for tracking the movement of an individual in videos of extremely crowded scenes. We show that our approach of leveraging the crowd motion enables tracking in videos of complex scenes that present unique difficulty to other approaches.

  8. Spatio-temporal dynamics of security investments in an interdependent risk environment

    Science.gov (United States)

    Shafi, Kamran; Bender, Axel; Zhong, Weicai; Abbass, Hussein A.

    2012-10-01

    In a globalised world where risks spread through contagion, the decision of an entity to invest in securing its premises from stochastic risks no longer depends solely on its own actions but also on the actions of other interacting entities in the system. This phenomenon is commonly seen in many domains including airline, logistics and computer security and is referred to as Interdependent Security (IDS). An IDS game models this decision problem from a game-theoretic perspective and deals with the behavioural dynamics of risk-reduction investments in such settings. This paper enhances this model and investigates the spatio-temporal aspects of the IDS games. The spatio-temporal dynamics are studied using simple replicator dynamics on a variety of network structures and for various security cost tradeoffs that lead to different Nash equilibria in an IDS game. The simulation results show that the neighbourhood configuration has a greater effect on the IDS game dynamics than network structure. An in-depth empirical analysis of game dynamics is carried out on regular graphs, which leads to the articulation of necessary and sufficient conditions for dominance in IDS games under spatial constraints.

  9. Event detection using Twitter: a spatio-temporal approach.

    Directory of Open Access Journals (Sweden)

    Tao Cheng

    Full Text Available BACKGROUND: Every day, around 400 million tweets are sent worldwide, which has become a rich source for detecting, monitoring and analysing news stories and special (disaster events. Existing research within this field follows key words attributed to an event, monitoring temporal changes in word usage. However, this method requires prior knowledge of the event in order to know which words to follow, and does not guarantee that the words chosen will be the most appropriate to monitor. METHODS: This paper suggests an alternative methodology for event detection using space-time scan statistics (STSS. This technique looks for clusters within the dataset across both space and time, regardless of tweet content. It is expected that clusters of tweets will emerge during spatio-temporally relevant events, as people will tweet more than expected in order to describe the event and spread information. The special event used as a case study is the 2013 London helicopter crash. RESULTS AND CONCLUSION: A spatio-temporally significant cluster is found relating to the London helicopter crash. Although the cluster only remains significant for a relatively short time, it is rich in information, such as important key words and photographs. The method also detects other special events such as football matches, as well as train and flight delays from Twitter data. These findings demonstrate that STSS is an effective approach to analysing Twitter data for event detection.

  10. Spatio-temporal point pattern analysis on Wenchuan strong earthquake

    Institute of Scientific and Technical Information of China (English)

    PeijianShi; Jie Liu; ZhenYang

    2009-01-01

    For exploring the aftershock occurrence process of the 2008 Wenchuan strong earthquake, the spatio-temporal point pattern analysis method is employed to study the sequences of aftershocks with magnitude M≥4.0, M≥4.5, and M≥5.0. It is found that these data exhibit the spatio-temporal clustering on a certain distance scale and on a certain time scale. In particular, the space-time interaction obviously strengthens when the distance is less than 60 km and the time is less than 260 h for the first two aftershock sequences; however, it becomes strong when the distance scale is less than 80 km and the time scale is less than 150 h for the last aftershock sequence. The completely spatial randomness analysis on the data regardless of time component shows that the spatial clustering of the aftershocks gradually strengthens on the condition that the distance is less than 60 km. The results are valuable for exploring the occurrence rules of the Wenchuan strong earthquake and for predicting the aftershocks.

  11. Multiscale recurrence analysis of spatio-temporal data

    Science.gov (United States)

    Riedl, M.; Marwan, N.; Kurths, J.

    2015-12-01

    The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for this purpose, the mapogram approach allows the specific focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics. We illustrate this approach by application on mixed dynamics, such as traveling parallel wave fronts with additive noise, as well as more complicate examples, pseudo-random numbers and coupled map lattices with a semi-logistic mapping rule. Especially the complicate examples show the usefulness of the multi-scale consideration in order to take spatial pattern of different scales and with different rhythms into account. So, this mapogram approach promises new insights in problems of climatology, ecology, or medicine.

  12. Rational spatio-temporal strategies for controlling a Chagas disease vector in urban environments.

    Science.gov (United States)

    Levy, Michael Z; Malaga Chavez, Fernando S; Cornejo Del Carpio, Juan G; Vilhena, Daril A; McKenzie, F Ellis; Plotkin, Joshua B

    2010-07-06

    The rational design of interventions is critical to controlling communicable diseases, especially in urban environments. In the case of the Chagas disease vector Triatoma infestans, successful control is stymied by the return of the insect after the effectiveness of the insecticide wanes. Here, we adapt a genetic algorithm, originally developed for the travelling salesman problem, to improve the spatio-temporal design of insecticide campaigns against T. infestans, in a complex urban environment. We find a strategy that reduces the expected instances of vector return 34-fold compared with the current strategy of sequential insecticide application to spatially contiguous communities. The relative success of alternative control strategies depends upon the duration of the effectiveness of the insecticide, and it shows chaotic fluctuations in response to unforeseen delays in a control campaign. We use simplified models to analyse the outcomes of qualitatively different spatio-temporal strategies. Our results provide a detailed procedure to improve control efforts for an urban Chagas disease vector, as well as general guidelines for improving the design of interventions against other disease agents in complex environments.

  13. Spatio-temporal evolution of biogeochemical processes at a landfill site

    Science.gov (United States)

    Arora, B.; Mohanty, B. P.; McGuire, J. T.

    2011-12-01

    Predictions of fate and transport of contaminants are strongly dependent on spatio-temporal variability of soil hydraulic and geochemical properties. This study focuses on time-series signatures of hydrological and geochemical properties at different locations within the Norman landfill site. Norman Landfill is a closed municipal landfill site with prevalent organic contamination. Monthly data at the site include specific conductance, δ18O, δ2H, dissolved organic carbon (DOC) and anions (chloride, sulfate, nitrate) from 1998-2006. Column scale data on chemical concentrations, redox gradients, and flow parameters are also available on daily and hydrological event (infiltration, drainage, etc.) scales. Since high-resolution datasets of contaminant concentrations are usually unavailable, Wavelet and Fourier analyses were used to infer the dominance of different biogeochemical processes at different spatio-temporal scales and to extract linkages between transport and reaction processes. Results indicate that time variability controls the progression of reactions affecting biodegradation of contaminants. Wavelet analysis suggests that iron-sulfide reduction reactions had high seasonal variability at the site, while fermentation processes dominated at the annual time scale. Findings also suggest the dominance of small spatial features such as layered interfaces and clay lenses in driving biogeochemical reactions at both column and landfill scales. A conceptual model that caters to increased understanding and remediating structurally heterogeneous variably-saturated media is developed from the study.

  14. EEG source imaging with spatio-temporal tomographic nonnegative independent component analysis.

    Science.gov (United States)

    Valdés-Sosa, Pedro A; Vega-Hernández, Mayrim; Sánchez-Bornot, José Miguel; Martínez-Montes, Eduardo; Bobes, María Antonieta

    2009-06-01

    This article describes a spatio-temporal EEG/MEG source imaging (ESI) that extracts a parsimonious set of "atoms" or components, each the outer product of both a spatial and a temporal signature. The sources estimated are localized as smooth, minimally overlapping patches of cortical activation that are obtained by constraining spatial signatures to be nonnegative (NN), orthogonal, sparse, and smooth-in effect integrating ESI with NN-ICA. This constitutes a generalization of work by this group on the use of multiple penalties for ESI. A multiplicative update algorithm is derived being stable, fast and converging within seconds near the optimal solution. This procedure, spatio-temporal tomographic NN ICA (STTONNICA), is equally able to recover superficial or deep sources without additional weighting constraints as tested with simulations. STTONNICA analysis of ERPs to familiar and unfamiliar faces yields an occipital-fusiform atom activated by all faces and a more frontal atom that only is active with familiar faces. The temporal signatures are at present unconstrained but can be required to be smooth, complex, or following a multivariate autoregressive model.

  15. Interesting Spatio-Temporal Region Discovery Computations Over Gpu and Mapreduce Platforms

    Science.gov (United States)

    McDermott, M.; Prasad, S. K.; Shekhar, S.; Zhou, X.

    2015-07-01

    Discovery of interesting paths and regions in spatio-temporal data sets is important to many fields such as the earth and atmospheric sciences, GIS, public safety and public health both as a goal and as a preliminary step in a larger series of computations. This discovery is usually an exhaustive procedure that quickly becomes extremely time consuming to perform using traditional paradigms and hardware and given the rapidly growing sizes of today's data sets is quickly outpacing the speed at which computational capacity is growing. In our previous work (Prasad et al., 2013a) we achieved a 50 times speedup over sequential using a single GPU. We were able to achieve near linear speedup over this result on interesting path discovery by using Apache Hadoop to distribute the workload across multiple GPU nodes. Leveraging the parallel architecture of GPUs we were able to drastically reduce the computation time of a 3-dimensional spatio-temporal interest region search on a single tile of normalized difference vegetative index for Saudi Arabia. We were further able to see an almost linear speedup in compute performance by distributing this workload across several GPUs with a simple MapReduce model. This increases the speed of processing 10 fold over the comparable sequential while simultaneously increasing the amount of data being processed by 384 fold. This allowed us to process the entirety of the selected data set instead of a constrained window.

  16. Automatic identification of seismic swarms and other spatio-temporal clustering from catalogs

    Science.gov (United States)

    Nava, F. Alejandro; Glowacka, Ewa

    1994-06-01

    Statistical analysis of seismic catalogs usually requires identification of swarms and foreshocks-main event-aftershocks sequences-a tedious and time-consuming chore. SWaRMSHoW, a simple but versatile QBASIC program for PC, graphically displays on screen catalog epicentral activity, with optional temporal distribution scaling; identifies spatio-temporal hypocentral clusters (SwrSeq) which may be swarms or foreshocks-main event-aftershocks sequences and discriminates between these; and displays SwrSeq locations and limits, and assigns them equivalent magnitudes corresponding to those of single events having seismic energy equal to that of the whole SwrSeq. SWaRMSHoW features optional detailed disk output of swarms and clusters, including origin time, location, constituent events, equivalent magnitudes, and current parameters, that allows easy application of results. Graphic screen display includes optional maps and drawings. Operation can be completely automatic or interactive. Working parameters can be reset at any time during operation. Besides swarm and sequence identification, this program's modeling of the seismicity, scaled in both space and time, is useful for studying many aspects of spatio-temporal seismicity, such as fault activation, migration of activity, quiescence, etc.

  17. Characterizing the behaviour of partially coherent detectors through spatio-temporal modes

    Science.gov (United States)

    Withington, S.; Saklatvala, G.

    2007-07-01

    By extending linear systems theory to include bilinear functionals, it is shown that the output of any power detector is given by the contraction of two tensor fields: one of which describes the spatio-temporal state of coherence of the incoming radiation, and the other characterizes the partially coherent response of the detector. A detector's coherence tensor is Hilbert-Schmidt, and can be decomposed into a superposition of natural spatio-temporal modes. It follows that any single power detector can be regarded as a number of independent power detectors acting in parallel; each of which is sensitive to radiation in some particular state of coherence; and each of which has a reception pattern that comprises an incoherent superposition of fully coherent beams. The work can be extended to allow the fluctuations in the output, and the correlations between the fluctuations in the outputs of two detectors, say in an array, to be calculated. The model has numerous applications in areas as diverse as pulsed communications systems, THz imaging, astronomical detectors, and insect and animal vision.

  18. Three-dimensional shape measurement using improved binary spatio-temporal encoded illumination and voting algorithm.

    Science.gov (United States)

    Xue, Kang; Li, Yong; Lu, Shijiang; Chen, Liangfeng

    2011-10-01

    Some regions of objects will be measured incorrectly or cannot be measured in optical three-dimensional (3D) measurement system based on coded structured light, due to occlusion, shadow, transfer function of measurement system, and noise. To obtain 3D data as much as possible and as correctly as possible, we proposed a method using improved binary spatio-temporal encoded illumination and voting algorithm. Firstly, the binary spatio-temporal encoded (BSE) pattern is improved with a redundancy encoding method. One code is assigned to two adjacent sections and distinguished with their temporal coordinates. The redundancy encoding method provides more cues for code correcting and retrieving. Secondly, symbols are estimated according to four coding cues--code redundancy, continuity of stripes, intensity variation in temporal direction, and neighbor symbols in sequence. Finally, a voting algorithm is adopted to obtain final symbols. A plaster model of a human head was measured to validate the method. The experimental results reveal that more valid points can be obtained and the reliability of the decoding results is improved.

  19. Context aware spatio-temporal cell tracking in densely packed multilayer tissues.

    Science.gov (United States)

    Chakraborty, Anirban; Roy-Chowdhury, Amit K

    2015-01-01

    Modern live imaging technique enables us to observe the internal part of a tissue over time by generating serial optical images containing spatio-temporal slices of hundreds of tightly packed cells. Automated tracking of plant and animal cells from such time lapse live-imaging datasets of a developing multicellular tissue is required for quantitative, high throughput analysis of cell division, migration and cell growth. In this paper, we present a novel cell tracking method that exploits the tight spatial topology of neighboring cells in a multicellular field as contextual information and combines it with physical features of individual cells for generating reliable cell lineages. The 2D image slices of multicellular tissues are modeled as a conditional random field and pairwise cell to cell similarities are obtained by estimating marginal probability distributions through loopy belief propagation on this CRF. These similarity scores are further used in a spatio-temporal graph labeling problem to obtain the optimal and feasible set of correspondences between individual cell slices across the 4D image dataset. We present results on (3D+t) confocal image stacks of Arabidopsis shoot meristem and show that the method is capable of handling many visual analysis challenges associated with such cell tracking problems, viz. poor feature quality of individual cells, low SNR in parts of images, variable number of cells across slices and cell division detection. Copyright © 2014 Elsevier B.V. All rights reserved.

  20. OFDM Radar Space-Time Adaptive Processing by Exploiting Spatio-Temporal Sparsity

    Energy Technology Data Exchange (ETDEWEB)

    Sen, Satyabrata [ORNL

    2013-01-01

    We propose a sparsity-based space-time adaptive processing (STAP) algorithm to detect a slowly-moving target using an orthogonal frequency division multiplexing (OFDM) radar. We observe that the target and interference spectra are inherently sparse in the spatio-temporal domain. Hence, we exploit that sparsity to develop an efficient STAP technique that utilizes considerably lesser number of secondary data and produces an equivalent performance as the other existing STAP techniques. In addition, the use of an OFDM signal increases the frequency diversity of our system, as different scattering centers of a target resonate at different frequencies, and thus improves the target detectability. First, we formulate a realistic sparse-measurement model for an OFDM radar considering both the clutter and jammer as the interfering sources. Then, we apply a residual sparse-recovery technique based on the LASSO estimator to estimate the target and interference covariance matrices, and subsequently compute the optimal STAP-filter weights. Our numerical results demonstrate a comparative performance analysis of the proposed sparse-STAP algorithm with four other existing STAP methods. Furthermore, we discover that the OFDM-STAP filter-weights are adaptable to the frequency-variabilities of the target and interference responses, in addition to the spatio-temporal variabilities. Hence, by better utilizing the frequency variabilities, we propose an adaptive OFDM-waveform design technique, and consequently gain a significant amount of STAP-performance improvement.

  1. Attribute Invariant Spatial and Spatio-Temporal Correlators

    Science.gov (United States)

    Monjur, Mehjabin Sultana

    PMT based correlators. We also develop the concept and design of an Automatic Event Recognition (AER) System based on a three-dimensional Spatio-Temporal Correlator (STC), that combines the techniques of holographic correlation and photon echo based temporal pattern recognition to match a video-clip contained in a video file, using atoms stored in a porous-glass material. By employing the nonlinear properties of inhomogenous broadened atomic media we show that it is possible to realize an AER system that can recognize rapidly the occurrence of events, the number of events, and the occurrence times. To model the response of such a system, one requires solving the Schrodinger Equation (SE), which is a computationally extensive task. We develop an analytical model to find the response of the STC and show that the analytical model agrees closely with the results obtained via explicit numerical simulation, but at a speed that is many orders of magnitude faster than the numerical model. We also show how such a practical AER system can be realized using a combination of a porous-glass based Rb vapor cell, a holographic video disc, and a lithium niobate crystal.

  2. Monitoring and validating spatio-temporal dynamics of biogeochemical properties in Mersin Bay (Turkey) using Landsat ETM+.

    Science.gov (United States)

    Karakaya, Nusret; Evrendilek, Fatih

    2011-10-01

    The objective of this study was to devise and validate simple models for estimating spatio-temporal dynamics of seven optically (in)active biogeochemical properties in Mersin Bay using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data and GIS. Spatio-temporal dynamics of Secchi depth (S (depth)), dissolved oxygen (DO), nitrite nitrogen (NO(2)-N), nitrate nitrogen (NO₃-N), silicate (SiO₄), 5-day biological oxygen demand (BOD5), and chlorophyll-a (Chl-a) were estimated using best-fit multiple linear regression (MLR) models as a function of Landsat 7 ETM+ and ground data in 2007 and 2008, latitude, longitude, and day of year. Validation of the MLR models against Landsat and ground data in 2005 led to r values ranging from 0.39 for NO₂-N (P = 0.008) to 0.79 for S (depth) (P < 0.001). Parsimonious MLR models built in this study appear to be promising for monitoring and predicting spatio-temporal dynamics of optically (in)active water quality characteristics in Mersin Bay.

  3. Spatio-temporal clustering of hand, foot, and mouth disease at the county level in Guangxi, China.

    Directory of Open Access Journals (Sweden)

    Yi-hong Xie

    Full Text Available BACKGROUND: Amid numerous outbreaks of hand, foot and mouth disease (HFMD in Asia over the past decade, studies on spatio-temporal clustering are limited. Without this information the distribution of severe cases assumed to be sporadic. We analyzed surveillance data with onset dates between 1 May 2008 to 31 October 2013 with the aim to document the spatio-temporal clustering of HFMD cases and severe cases at the county level. METHODS: Purely temporal and purely spatial descriptive analyses were done. These were followed by a space-time scan statistic for the whole study period and by year to detect the high risk clusters based on a discrete Poisson model. RESULTS: The annual incidence rate of HFMD in Guangxi increased whereas the severe cases peaked in 2010 and 2012. EV71 and CoxA16 were alternating viruses. Both HFMD cases and severe cases had a seasonal peak in April to July. The spatio-temporal cluster of HFMD cases were mainly detected in the northeastern, central and southwestern regions, among which three clusters were observed in Nanning, Liuzhou, Guilin city and their neighbouring areas lasting from 1.2 to 2.5 years. The clusters of severe cases were less consistent in location and included around 40-70% of all severe cases in each year. CONCLUSIONS: Both HFMD cases and severe cases occur in spatio-temporal clusters. The continuous epidemic in Nanning, Liuzhou, Guilin cities and their neighbouring areas and the clusters of severe cases indicate the need for further intensive surveillance.

  4. Spatio-Temporal Multiway Data Decomposition Using Principal Tensor Analysis on k-Modes: The R Package PTAk

    Directory of Open Access Journals (Sweden)

    Didier G. Leibovici

    2010-10-01

    Full Text Available The purpose of this paper is to describe the R package {PTAk and how the spatio-temporal context can be taken into account in the analyses. Essentially PTAk( is a multiway multidimensional method to decompose a multi-entries data-array, seen mathematically as a tensor of any order. This PTAk-modes method proposes a way of generalizing SVD (singular value decomposition, as well as some other well known methods included in the R package, such as PARAFAC or CANDECOMP and the PCAn-modes or Tucker-n model. The example datasets cover different domains with various spatio-temporal characteristics and issues: (i~medical imaging in neuropsychology with a functional MRI (magnetic resonance imaging study, (ii~pharmaceutical research with a pharmacodynamic study with EEG (electro-encephaloegraphic data for a central nervous system (CNS drug, and (iii~geographical information system (GIS with a climatic dataset that characterizes arid and semi-arid variations. All the methods implemented in the R package PTAk also support non-identity metrics, as well as penalizations during the optimization process. As a result of these flexibilities, together with pre-processing facilities, PTAk constitutes a framework for devising extensions of multidimensional methods such ascorrespondence analysis, discriminant analysis, and multidimensional scaling, also enabling spatio-temporal constraints.

  5. Inferring Synaptic Connectivity from Spatio-Temporal Spike Patterns

    Directory of Open Access Journals (Sweden)

    Frank eVan Bussel

    2011-02-01

    Full Text Available Networks of well-known dynamical units but unknown interaction topology arise across various fields of biology, including genetics, ecology and neuroscience. The collective dynamics of such networks is often sensitive to the presence (or absence of individual interactions, but there is usually no direct way to probe for their existence. Here we present an explicit method for reconstructing interaction networks of leaky integrate-and-fire neurons from the spike patterns they exhibit in response to external driving. Given the dynamical parameters are known, the approach works well for networks in simple collective states but is also applicable to networks exhibiting complex spatio-temporal spike patterns. In particular, stationarity of spiking time series is not required.

  6. Clinical gait data analysis based on Spatio-Temporal features

    CERN Document Server

    Katiyar, Rohit

    2010-01-01

    Analysing human gait has found considerable interest in recent computer vision research. So far, however, contributions to this topic exclusively dealt with the tasks of person identification or activity recognition. In this paper, we consider a different application for gait analysis and examine its use as a means of deducing the physical well-being of people. The proposed method is based on transforming the joint motion trajectories using wavelets to extract spatio-temporal features which are then fed as input to a vector quantiser; a self-organising map for classification of walking patterns of individuals with and without pathology. We show that our proposed algorithm is successful in extracting features that successfully discriminate between individuals with and without locomotion impairment.

  7. A spatio-temporal extension to the map cube operator

    Science.gov (United States)

    Alzate, Juan C.; Moreno, Francisco J.; Echeverri, Jaime

    2012-09-01

    OLAP (On Line Analytical Processing) is a set of techniques and operators to facilitate the data analysis usually stored in a data warehouse. In this paper, we extend the functionality of an OLAP operator known as Map Cube with the definition and incorporation of a function that allows the formulation of spatio-temporal queries. For example, consider a data warehouse about crimes that includes data about the places where the crimes were committed. Suppose we want to find and visualize the trajectory (a trajectory is just the path that an object follows through space as a function of time) of the crimes of a suspect beginning with his oldest crime and ending with his most recent one. In order to meet this requirement, we extend the Map Cube operator.

  8. Event-based incremental updating of spatio-temporal database

    Institute of Scientific and Technical Information of China (English)

    周晓光; 陈军; 蒋捷; 朱建军; 李志林

    2004-01-01

    Based on the relationship among the geographic events, spatial changes and the database operations, a new automatic (semi-automatic) incremental updating approach of spatio-temporal database (STDB) named as event-based incremental updating (E-BIU) is proposed in this paper. At first, the relationship among the events, spatial changes and the database operations is analyzed, then a total architecture of E-BIU implementation is designed, which includes an event queue, three managers and two sets of rules, each component is presented in detail. The process of the E-BIU of master STDB is described successively. An example of building's incremental updating is given to illustrate this approach at the end. The result shows that E-BIU is an efficient automatic updating approach for master STDB.

  9. Spatio-temporal Patterns in Inclined Layer Convection

    CERN Document Server

    Subramanian, Priya; Brausch, Oliver; Daniels, Karen E; Bodenschatz, Eberhard; Schneider, Tobias M

    2015-01-01

    This paper reports on a theoretical analysis of the rich variety of spatio-temporal patterns observed recently in inclined layer convection at medium Prandtl number when varying the inclination angle {\\gamma} and the Rayleigh number R. The patterns are shown to originate from a complicated competition of buoyancy-driven and shear-flow driven pattern forming mechanisms. The former is expressed as longitudinal convection rolls with their axes oriented parallel to the incline, the latter as perpendicular transverse rolls. Our investigation is based on the standard Oberbeck-Boussinesq equations. Besides conventional methods to study roll patterns and their stability, we employ in particular, direct numerical simulations in large spatial domains comparable with experimental ones. As a result we arrive at a phase diagram of the characteristic complex 3D convection patterns in the {\\gamma}-R- plane, which compares very well to the experiments. In particular it is demonstrated that interactions of specific Fourier mo...

  10. Spatio-temporal wavefront shaping in a microwave cavity

    CERN Document Server

    del Hougne, Philipp; Fink, Mathias; Lerosey, Geoffroy

    2016-01-01

    Controlling waves in complex media has become a major topic of interest, notably through the concepts of time reversal and wavefront shaping. Recently, it was shown that spatial light modulators can counter-intuitively focus waves both in space and time through multiple scattering media when illuminated with optical pulses. In this letter we transpose the concept to a microwave cavity using flat arrays of electronically tunable resonators. We prove that maximizing the Green's function between two antennas at a chosen time yields diffraction limited spatio-temporal focusing. Then, changing the photons' dwell time inside the cavity, we modify the relative distribution of the spatial and temporal degrees of freedom (DoF), and we demonstrate that it has no impact on the field enhancement: wavefront shaping makes use of all available DoF, irrespective of their spatial or temporal nature. Our results prove that wavefront shaping using simple electronically reconfigurable arrays of reflectors is a viable approach to...

  11. Spatio-temporal clustering of wildfires in Portugal

    Science.gov (United States)

    Costa, R.; Pereira, M. G.; Caramelo, L.; Vega Orozco, C.; Kanevski, M.

    2012-04-01

    Several studies have shown that wildfires in Portugal presenthigh temporal as well as high spatial variability (Pereira et al., 2005, 2011). The identification and characterization of spatio-temporal clusters contributes to a comprehensivecharacterization of the fire regime and to improve the efficiency of fire prevention and combat activities. The main goalsin this studyare: (i) to detect the spatio-temporal clusters of burned area; and, (ii) to characterize these clusters along with the role of human and environmental factors. The data were supplied by the National Forest Authority(AFN, 2011) and comprises: (a)the Portuguese Rural Fire Database, PRFD, (Pereira et al., 2011) for the 1980-2007period; and, (b) the national mapping burned areas between 1990 and 2009. In this work, in order to complement the more common cluster analysis algorithms, an alternative approach based onscan statistics and on the permutation modelwas used. This statistical methodallows the detection of local excess events and to test if such an excess can reasonably have occurred by chance.Results obtained for different simulations performed for different spatial and temporal windows are presented, compared and interpreted.The influence of several fire factors such as (climate, vegetation type, etc.) is also assessed. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005:"Synoptic patterns associated with large summer forest fires in Portugal".Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 AFN, 2011: AutoridadeFlorestalNacional (National Forest Authority). Available at http://www.afn.min-agricultura.pt/portal.

  12. Long-term spatio-temporal drought variability in Turkey

    Science.gov (United States)

    Dabanlı, İsmail; Mishra, Ashok K.; Şen, Zekai

    2017-09-01

    The spatio-temporal variability of drought is presented by evaluating homogeneously distributed 250 station records from 1931 to 2010 for 80 years' duration in Turkey. The drought analysis is implemented using Standardized Precipitation Index (SPI) in terms of SPI-1, SPI-3, SPI-6, SPI-6AS (SPI-6 April to September) and SPI-12. The principle component analysis (PCA) is applied to SPI time series to identify spatial and temporal drought patterns. SPI time series are classified into two groups (1st group: SPI-1, SPI-3, SPI-6AS; and 2nd group: SPI-6 and SPI-12) according to the similarity in spatial drought patterns. SPI-3 and SPI-12 are selected as representative members of each group for spatio-temporal analysis. A relationship among correlation area (An), correlation coefficient (CC), principle component numbers (Fn) and total variances explained (Vexp) are investigated for identifying four well-defined drought vulnerable homogeneous regions over Turkey mainland. Mean percentages of extreme, severe, and moderate drought areas are calculated as 3.13% (2.81%), 3.75% (4.06%) and 7.19% (7.50%) for SPI-3 (SPI-12) based on 80 years in all drought vulnerable regions. Spectral characteristics of drought are also investigated based on fast Fourier transform (FFT) method. It is observed that while southeastern and western parts of Turkey are more stable due to the highly-correlated variances of spatial patterns; central parts and few pockets in northern areas of Turkey are less stable regions because of the low-correlated variance scores (below 10%). Furthermore, the impact of extreme phases of the ENSO (El Nino/La Nina) on droughts in four drought regions over Turkey is discussed.

  13. Spatio-temporal simulation of first pass drug perfusion in the liver.

    Directory of Open Access Journals (Sweden)

    Lars Ole Schwen

    2014-03-01

    Full Text Available The liver is the central organ for detoxification of xenobiotics in the body. In pharmacokinetic modeling, hepatic metabolization capacity is typically quantified as hepatic clearance computed as degradation in well-stirred compartments. This is an accurate mechanistic description once a quasi-equilibrium between blood and surrounding tissue is established. However, this model structure cannot be used to simulate spatio-temporal distribution during the first instants after drug injection. In this paper, we introduce a new spatially resolved model to simulate first pass perfusion of compounds within the naive liver. The model is based on vascular structures obtained from computed tomography as well as physiologically based mass transfer descriptions obtained from pharmacokinetic modeling. The physiological architecture of hepatic tissue in our model is governed by both vascular geometry and the composition of the connecting hepatic tissue. In particular, we here consider locally distributed mass flow in liver tissue instead of considering well-stirred compartments. Experimentally, the model structure corresponds to an isolated perfused liver and provides an ideal platform to address first pass effects and questions of hepatic heterogeneity. The model was evaluated for three exemplary compounds covering key aspects of perfusion, distribution and metabolization within the liver. As pathophysiological states we considered the influence of steatosis and carbon tetrachloride-induced liver necrosis on total hepatic distribution and metabolic capacity. Notably, we found that our computational predictions are in qualitative agreement with previously published experimental data. The simulation results provide an unprecedented level of detail in compound concentration profiles during first pass perfusion, both spatio-temporally in liver tissue itself and temporally in the outflowing blood. We expect our model to be the foundation of further spatially

  14. Spatio-temporal distribution of global solar radiation for Mexico using GOES data

    Science.gov (United States)

    Bonifaz, R.; Cuahutle, M.; Valdes, M.; Riveros, D.

    2013-05-01

    Increased need of sustainable and renewable energies around the world requires studies about the amount and distribution of such types of energies. Global solar radiation distribution in space and time is a key component on order to know the availability of the energy for different applications. Using GOES hourly data, the heliosat model was implemented for Mexico. Details about the model and its components are discussed step by stem an once obtained the global solar radiation images, different time datasets (hourly, daily, monthly and seasonal) were built in order to know the spatio-temporal behavior of this type of energy. Preliminary maps of the available solar global radiation energy for Mexico are presented, the amount and variation of the solar radiation by regions are analyzed and discussed. Future work includes a better parametrization of the model using calibrated ground stations data and more use of more complex models for better results.

  15. Climate change forecasts, long-term spatio-temporal prediction and the resilience of dry ecosystems

    Science.gov (United States)

    Shafran-Natan, Rakefet; Svoray, Tal; Avi, Perevolotsky

    2010-05-01

    Primary production is an important indicator to climatic changes in drylands, while reduction in productivity has many consequences on ecosystem functioning. We suggest that the response of dry ecosystems to climate change should lead to a change in spatial patterns of grasses without a substantial change in ecosystem resilience. We used field data and a recently published spatio-temporally explicit model to study factors affecting long-term variation in primary production in two dry ecosystems: semi-arid (SAE) and Mediterranean (DME) dominated by annual vegetation. The model was operated in both patch and landscape scales and was executed along 30 years (1979-2008) at SAE and along 21 years (1986-1990; 1993-2008) at DME. Model predictions were validated against samples that were harvested in each site at the end of the growing season, over 15 seasons (1994-2008) at SAE (0.63

  16. Spatio-temporal dynamcis of a cell signal cascade with negative feedback

    Science.gov (United States)

    Maya Bernal, Jose Luis; Ramirez-Santiago, Guillermo

    2014-03-01

    We studied the spatio-temporal dynamics of a system of reactio-diffusion equations that models a cell signal transduction pathway with six cycles and negative feedback. The basic cycle consists of the phosphorylation-dephosphorylation of two antagonic proteins. We found two regimes of saturation of the enzimatic reaction in the kinetic parameters space and determined the conditions for the signal propagation in the steady state. The trajectories for which transduction occurs are defined in terms of the ratio of the enzimatic activities. We found that in spite of the negative feedback the cell signal cascade behaves as an amplifier and produces phosphoprotein concentration gradients within the cell. This model behaves also as a noise filter and as a switch. Supported by DGAPA-UNAM Contract IN118410-3.

  17. A collaborative large spatio-temporal data visual analytics architecture for emergence response

    Science.gov (United States)

    Guo, D.; Li, J.; Cao, H.; Zhou, Y.

    2014-02-01

    The unconventional emergency, usually outbreaks more suddenly, and is diffused more quickly, but causes more secondary damage and derives more disaster than what it is usually expected. The data volume and urgency of emergency exceeds the capacity of current emergency management systems. In this paper, we propose a three-tier collaborative spatio-temporal visual analysis architecture to support emergency management. The prototype system, based on cloud computation environment, supports aggregation of massive unstructured and semi-structured data, integration of various computing model sand algorithms; collaborative visualization and visual analytics among users with a diversity of backgrounds. The distributed data in 100TB scale is integrated in a unified platform and shared with thousands of experts and government agencies by nearly 100 models. The users explore, visualize and analyse the big data and make a collaborative countermeasures to emergencies.

  18. Spatio-temporal dynamics of cod nursery areas in the Baltic Sea

    DEFF Research Database (Denmark)

    Hinrichsen, Hans-Harald; Dewitz, B. von; Lehmann, M.

    2017-01-01

    In this study the drift of eastern Baltic cod larvae and juveniles spawned within the historical eastern Baltic cod spawning grounds was investigated by detailed drift model simulations for the years 1971–2010, to examine the spatio-temporal dynamics of environmental suitability in the nursery...... areas of juvenile cod settlement. The results of the long-term model scenario runs, where juvenile cod were treated as simulated passively drifting particles, enabled us to find strong indications for long-term variations of settlement and potentially the reproduction success of the historically...... in the Baltic Sea. Furthermore, transport of juveniles surviving to the age of settlement with origin in the Bornholm Basin contributed on average 13 and 11% to the total settlement in the Arkona Basin and in the Gdansk Deep, respectively. The time-series of the simulated occupied juvenile cod habitat...

  19. Propagation of seismic waves through a spatio-temporally fluctuating medium: Homogenization

    CERN Document Server

    Hanasoge, Shravan; Bal, Guillaume

    2013-01-01

    Measurements of seismic wave travel times at the photosphere of the Sun have enabled inferences of its interior structure and dynamics. In interpreting these measurements, the simplifying assumption that waves propagate through a temporally stationary medium is almost universally invoked. However, the Sun is in a constant state of evolution, on a broad range of spatio-temporal scales. At the zero wavelength limit, i.e., when the wavelength is much shorter than the scale over which the medium varies, the WKBJ (ray) approximation may be applied. Here, we address the other asymptotic end of the spectrum, the infinite wavelength limit, using the technique of homogenization. We apply homogenization to scenarios where waves are propagating through rapidly varying media (spatially and temporally), and derive effective models for the media. One consequence is that a scalar sound speed becomes a tensorial wavespeed in the effective model and anisotropies can be induced depending on the nature of the perturbation. The ...

  20. RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint.

    Science.gov (United States)

    Piao, Xinglin; Zhang, Yong; Li, Tingshu; Hu, Yongli; Liu, Hao; Zhang, Ke; Ge, Yun

    2016-11-03

    The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or temporal correlation existing in fingerprint data and measurement data, which is helpful for improving localization accuracy. In this paper, we propose an RSS fingerprint-based indoor localization method by integrating the spatio-temporal constraints into the sparse representation model. The proposed model utilizes the inherent spatial correlation of fingerprint data in the fingerprint matching and uses the temporal continuity of the RSS measurement data in the localization phase. Experiments on the simulated data and the localization tests in the real scenes show that the proposed method improves the localization accuracy and stability effectively compared with state-of-the-art indoor localization methods.

  1. Spatio-temporal patterns of forest carbon dioxide exchange based on global eddy covariance measurements

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Spatio-temporal patterns and driving mechanisms of forest carbon dioxide (CO2) exchange are the key issues on terrestrial ecosystem carbon cycles, which are the basis for developing and validating ecosystem carbon cycle models, assessing and predicting the role of forests in global carbon balance. Eddy covariance (EC) technique, an important method for measuring energy and material exchanges between terrestrial ecosystems and the atmosphere, has made a great contribution to understanding CO2 exchanges in the biosphere during the past decade. Here, we synthesized published EC flux measurements at various forest sites in the global network of eddy flux tower sites (FLUXNET) and regional flux networks. Our objective was to explore spatio-temporal patterns and driving factors on forest carbon fluxes, i.e. net ecosystem productivity (NEP), gross primary productivity (GPP) and total ecosystem respiration (TER). Globally, forest NEP exhibited a significant latitudinal pattern jointly controlled by GPP and TER. The NEP decreased in an order of warm temperate forest > cold temperate and tropical rain forests > boreal and subalpine forests. Mean annual temperature (MAT) made a greater contribution to forest carbon fluxes than sum of annual precipitation (SAP). As MAT increased, the GPP increased linearly, whereas the TER increased exponentially, resulting in the NEP decreasing beyond an MAT threshold of 20°C. The GPP, TER and NEP varied substantially when the SAP was less than 1500 mm, but tended to increase with increasing SAP. Temporal dynamics in forest carbon fluxes and determinants depended upon time scales. NEP showed a significant interannual variability mainly driven by climate fluctuations and different responses of the GPP and TER to environmental forcing. In a longer term, forest carbon fluxes had a significant age effect. The ecosystem was a net carbon source right after clearcutting, gradually switched to a net carbon sink when the relative stand age (i

  2. Spatio-temporal patterns of forest carbon dioxide ex change based on global eddy covariance measurements

    Institute of Scientific and Technical Information of China (English)

    WANG XingChang; WANG ChuanKuan; YU GuiRui

    2008-01-01

    Spatio-temporal patterns and driving mechanisms of forest carbon dioxide (CO2) exchange are the key issues on terrestrial ecosystem carbon cycles, which are the basis for developing and validating ecosystem carbon cycle models, assessing and predicting the role of forests in global carbon balance.Eddy covariance (EC) technique, an important method for measuring energy and material exchanges between terrestrial ecosystems and the atmosphere, has made a great contribution to understanding CO2 exchanges in the biosphere during the past decade. Here, we synthesized published EC flux measurements at various forest sites in the global network of eddy flux tower sites (FLUXNET) and regional flux networks. Our objective was to explore spatio-temporal patterns and driving factors on forest carbon fluxes, i.e. net ecosystem productivity (NEP), gross primary productivity (GPP) and total ecosystem respiration (TER). Globally, forest NEP exhibited a significant latitudinal pattern jointly controlled by GPP and TER. The NEP decreased in an order of warm temperate forest > cold temperate and tropical rain forests > boreal and subalpine forests. Mean annual temperature (MAT) made a greater contribution to forest carbon fluxes than sum of annual precipitation (SAP). As MAT increased, the GPP increased linearly, whereas the TER increased exponentially, resulting in the NEP decreasing beyond an MAT threshold of 20℃. The GPP, TER and NEP varied substantially when the SAP was less than 1500 mm, but tended to increase with increasing SAP. Temporal dynamics in forest carbon fluxes and determinants depended upon time scales. NEP showed a significant interannual variability mainly driven by climate fluctuations and different responses of the GPP and TER to environmental forcing. In a longer term, forest carbon fluxes had a significant age effect. The ecosystem was a net carbon source right after clearcutting, gradually switched to a net carbon sink when the relative stand age (i

  3. Spatio-temporal variability of shallow groundwater quality in a typical agricultural catchment in subtropical central China

    Science.gov (United States)

    Liu, X.

    2015-12-01

    Excessive agriculture-sourced N leaching into shallow groundwater has deteriorated the domestic water quality in rural China. To effectively prevent the above environmental contamination issue, it is an essential prerequisite of exploring the spatio-temporal variability (stV) of the groundwater quality. In this study, a large observation program was deployed to observe ammonium-N (NH4N), nitrate-N (NO3N) and total N (TN) concentrations in 194 groundwater observation wells (1.5 m deep from soil surface) from April 2010 to November 2012 in the Jinjing river catchment in Hunan Province of China. A logit function was applied to transform NH4N, NO3N and TN data for normality; the resultant variables were thus named as NH4Nt, NO3Nt and TNt, respectively. A spatio-temporal semivariogram model in a sum-metric form was used to quantify the stV of NH4Nt, NO3Nt and TNt. The results indicated that the 33-month means ± standard deviations of the NH4N, NO3N and TN concentrations were 0.75±0.10, 1.60±0.19 and 2.99±0.29 mg N L-1, respectively. NH4Nt and NO3Nt exhibited a strong spatio-temporal dependence, while TNt only presented a strong temporal structure. Spatio-temporal ordinary kriging (stOK) was applied to predict the spatio-temporal distributions of NH4N, NO3N and TN over the catchment. The cross-validation results indicated that the stOK predictions for NH4N (r=0.48, RMSE=1.11 mg N L-1), NO3N (r=0.46, RMSE=1.21 mg N L-1) outperformed that for TN (r=0.29, RMSE=2.11 mg N L-1). Referenced to the Chinese Environmental Quality Standards for Groundwater (GB/T 14848-93), the proportions of areas contaminated by NH4N, NO3N and TN in the catchment over a 33-month period were 20.5%, 1.46%, and 5.07%, respectively. Our findings suggested that the Jinjing groundwater was mainly polluted by NH4N, which is probably attributed to the intensive rice agriculture featured with high urea fertilizer applications in the catchment.

  4. A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

    Directory of Open Access Journals (Sweden)

    Shengnan Ke

    2014-07-01

    Full Text Available In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

  5. A hybrid spatio-temporal data indexing method for trajectory databases.

    Science.gov (United States)

    Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting

    2014-07-21

    In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

  6. Taming of Modulation Instability by Spatio-Temporal Modulation of the Potential

    CERN Document Server

    Kumar, S; Botey, M; Staliunas, K

    2015-01-01

    Spontaneous pattern formation in a variety of spatially extended nonlinear system always occurs through a modulation instability: homogeneous state of the system becomes unstable with respect to growing modulation modes. Therefore, the manipulation of the modulation instability is of primary importance in controlling and manipulating the character of spatial patterns initiated by that instability. We show that the spatio-temporal periodic modulation of the potential of the spatially extended system results in a modification of its pattern forming instability. Depending on the modulation character the instability can be partially suppressed, can change its spectrum (for instance the long wave instability can transform into short wave instability), can split into two, or can be completely eliminated. The latter result is of especial practical interest, as can be used to stabilize the intrinsically unstable system. The result bears general character, as it is shown here on a universal model of Complex Ginzburg-L...

  7. Spatio-temporal study of non-degenerate two-wave mixing in bacteriorhodopsin films.

    Science.gov (United States)

    Blaya, Salvador; González, Alejandro; Acebal, Pablo; Carretero, Luis

    2016-10-31

    A spatio-temporal analysis of non-degenerate two-wave mixing in a saturable absorber, such as bacteriorhodopsin (bR) film, is performed. To do this, a theoretical model describing the temporal variation of the intensities is developed by taking into account the dielectric constant as a function of bR population. A good agreement between theory and experimental measurements is obtained. Thus, the dependence of the optical gain and the main dielectric constant parameters are studied at different intensities and frequencies. As a result, the best intensity-frequency zones where higher coupling is reached are proposed, and it is also demonstrated that non-uniform patterns, which evolve over time as a function of frequency difference, can be observed.

  8. Weather dependent estimation of continent-wide wind power generation based on spatio-temporal clustering

    Science.gov (United States)

    Schyska, Bruno U.; Couto, António; von Bremen, Lueder; Estanqueiro, Ana; Heinemann, Detlev

    2017-05-01

    Europe is facing the challenge of increasing shares of energy from variable renewable sources. Furthermore, it is heading towards a fully integrated electricity market, i.e. a Europe-wide electricity system. The stable operation of this large-scale renewable power system requires detailed information on the amount of electricity being transmitted now and in the future. To estimate the actual amount of electricity, upscaling algorithms are applied. Those algorithms - until now - however, only exist for smaller regions (e.g. transmission zones and single wind farms). The aim of this study is to introduce a new approach to estimate Europe-wide wind power generation based on spatio-temporal clustering. We furthermore show that training the upscaling model for different prevailing weather situations allows to further reduce the number of reference sites without losing accuracy.

  9. Mean-field equations for stochastic neural fields with spatio-temporal delays

    CERN Document Server

    Touboul, Jonathan

    2011-01-01

    Neurons form large-scale cell assemblies sharing the same individual properties and receiving the same input, in charge of certain functions. Such assemblies have specific space locations and hence interact after some (space dependent) delay due the transport and transfer of the information. Both delays and spatial connectivity structures are understood to shape the collective response of neural assemblies and brain states that are observed through usual recording techniques. Abstracting this setting, we consider here the problem of the asymptotics, as the number of neurons increases, of bio-inspired neuronal networks composed of several populations (up to a continuum), interacting with spatio-temporal delays. The propagation of chaos property is proved under mild assumptions on the neuronal dynamics, valid for most models used in neuroscience, in both the case of finite number and infinite continuum populations (called neural fields). The mean-field equations in these cases are derived and analyzed from the ...

  10. Exploiting Spatio-Temporal Correlation for Reliable Information Transport in WSNs

    Directory of Open Access Journals (Sweden)

    Faisal Karim Shaikh

    2011-01-01

    Full Text Available Delivering reliable services in service oriented architectures entails the underlying basis of having communication network models and well structured systems. With the rapid proliferation of ad-hoc mode of communication the reliable delivery of services increasingly encounter new communication and network perturbations. Empirically the core of service delivery in WSNs (Wireless Sensor Networks is information transport from the sensor nodes to the sink node where the service resides. In this work we provide a reliable information transport for enhanced service delivery by using spatio-temporal correlation in WSN. The classification for different types of information required by the services is also presented. To overcome dynamic network conditions and evolving service requirements an adaptive retransmission mechanism based on spatial correlation is utilized. Simulation results show that the proposed solutions provide service specific reliability and save expensive retransmissions and thus provide energy efficient solution.

  11. Robust spatio-temporal error concealment for packet- lossy H.264 video transmission

    Institute of Scientific and Technical Information of China (English)

    LIAO Ning; YAN Dan; QUAN Zi-yi; MEN Ai-dong

    2006-01-01

    In this article, a spatio-temporal post-processing error concealment algorithm designed initially for a H. 264video-streaming scheme over packet-lossy networks has been presented. It aims at optimizing subjective quality of restored video and the conventional objective metric, peak signal-to-noise ratio (PSNR), as well, under the constraints of low delay and computational complexity, which are critical to real-time applications and portable devices having limited resources.Specifically, it takes into consideration physical property of motion to achieve more meaningful perceptual video quality.Further, a content-adaptive bilinear spatial interpolation approach and a temporal error concealment approach are combined under a unified boundary match criterion based on texture and motion activity analysis. Extensive experiments have demonstrated that the proposal not only result in better reconstruction, objectively and subjectively, than the reference software model benchmark, but also results in better robustness to different video sequences.

  12. Spatial distribution of tree species governs the spatio-temporal interaction of leaf area index and soil moisture across a forested landscape.

    Directory of Open Access Journals (Sweden)

    Kusum J Naithani

    Full Text Available Quantifying coupled spatio-temporal dynamics of phenology and hydrology and understanding underlying processes is a fundamental challenge in ecohydrology. While variation in phenology and factors influencing it have attracted the attention of ecologists for a long time, the influence of biodiversity on coupled dynamics of phenology and hydrology across a landscape is largely untested. We measured leaf area index (L and volumetric soil water content (θ on a co-located spatial grid to characterize forest phenology and hydrology across a forested catchment in central Pennsylvania during 2010. We used hierarchical Bayesian modeling to quantify spatio-temporal patterns of L and θ. Our results suggest that the spatial distribution of tree species across the landscape created unique spatio-temporal patterns of L, which created patterns of water demand reflected in variable soil moisture across space and time. We found a lag of about 11 days between increase in L and decline in θ. Vegetation and soil moisture become increasingly homogenized and coupled from leaf-onset to maturity but heterogeneous and uncoupled from leaf maturity to senescence. Our results provide insight into spatio-temporal coupling between biodiversity and soil hydrology that is useful to enhance ecohydrological modeling in humid temperate forests.

  13. Spatial distribution of tree species governs the spatio-temporal interaction of leaf area index and soil moisture across a forested landscape.

    Science.gov (United States)

    Naithani, Kusum J; Baldwin, Doug C; Gaines, Katie P; Lin, Henry; Eissenstat, David M

    2013-01-01

    Quantifying coupled spatio-temporal dynamics of phenology and hydrology and understanding underlying processes is a fundamental challenge in ecohydrology. While variation in phenology and factors influencing it have attracted the attention of ecologists for a long time, the influence of biodiversity on coupled dynamics of phenology and hydrology across a landscape is largely untested. We measured leaf area index (L) and volumetric soil water content (θ) on a co-located spatial grid to characterize forest phenology and hydrology across a forested catchment in central Pennsylvania during 2010. We used hierarchical Bayesian modeling to quantify spatio-temporal patterns of L and θ. Our results suggest that the spatial distribution of tree species across the landscape created unique spatio-temporal patterns of L, which created patterns of water demand reflected in variable soil moisture across space and time. We found a lag of about 11 days between increase in L and decline in θ. Vegetation and soil moisture become increasingly homogenized and coupled from leaf-onset to maturity but heterogeneous and uncoupled from leaf maturity to senescence. Our results provide insight into spatio-temporal coupling between biodiversity and soil hydrology that is useful to enhance ecohydrological modeling in humid temperate forests.

  14. Spatio-temporal regulations and functions of neuronal alternative RNA splicing in developing and adult brains.

    Science.gov (United States)

    Iijima, Takatoshi; Hidaka, Chiharu; Iijima, Yoko

    2016-08-01

    Alternative pre-mRNA splicing is a fundamental mechanism that generates molecular diversity from a single gene. In the central nervous system (CNS), key neural developmental steps are thought to be controlled by alternative splicing decisions, including the molecular diversity underlying synaptic wiring, plasticity, and remodeling. Significant progress has been made in understanding the molecular mechanisms and functions of alternative pre-mRNA splicing in neurons through studies in invertebrate systems; however, recent studies have begun to uncover the potential role of neuronal alternative splicing in the mammalian CNS. This article provides an overview of recent findings regarding the regulation and function of neuronal alternative splicing. In particular, we focus on the spatio-temporal regulation of neurexin, a synaptic adhesion molecule, by neuronal cell type-specific factors and neuronal activity, which are thought to be especially important for characterizing neural development and function within the mammalian CNS. Notably, there is increasing evidence that implicates the dysregulation of neuronal splicing events in several neurological disorders. Therefore, understanding the detailed mechanisms of neuronal alternative splicing in the mammalian CNS may provide plausible treatment strategies for these diseases.

  15. Spatio-temporal analysis of sub-hourly rainfall over Mumbai, India: Is statistical forecasting futile?

    Science.gov (United States)

    Singh, Jitendra; Sekharan, Sheeba; Karmakar, Subhankar; Ghosh, Subimal; Zope, P. E.; Eldho, T. I.

    2017-04-01

    Mumbai, the commercial and financial capital of India, experiences incessant annual rain episodes, mainly attributable to erratic rainfall pattern during monsoons and urban heat-island effect due to escalating urbanization, leading to increasing vulnerability to frequent flooding. After the infamous episode of 2005 Mumbai torrential rains when only two rain gauging stations existed, the governing civic body, the Municipal Corporation of Greater Mumbai (MCGM) came forward with an initiative to install 26 automatic weather stations (AWS) in June 2006 (MCGM 2007), which later increased to 60 AWS. A comprehensive statistical analysis to understand the spatio-temporal pattern of rainfall over Mumbai or any other coastal city in India has never been attempted earlier. In the current study, a thorough analysis of available rainfall data for 2006-2014 from these stations was performed; the 2013-2014 sub-hourly data from 26 AWS was found useful for further analyses due to their consistency and continuity. Correlogram cloud indicated no pattern of significant correlation when we considered the closest to the farthest gauging station from the base station; this impression was also supported by the semivariogram plots. Gini index values, a statistical measure of temporal non-uniformity, were found above 0.8 in visible majority showing an increasing trend in most gauging stations; this sufficiently led us to conclude that inconsistency in daily rainfall was gradually increasing with progress in monsoon. Interestingly, night rainfall was lesser compared to daytime rainfall. The pattern-less high spatio-temporal variation observed in Mumbai rainfall data signifies the futility of independently applying advanced statistical techniques, and thus calls for simultaneous inclusion of physics-centred models such as different meso-scale numerical weather prediction systems, particularly the Weather Research and Forecasting (WRF) model.

  16. Spatio-temporal analysis of sub-hourly rainfall over Mumbai, India: Is statistical forecasting futile?

    Indian Academy of Sciences (India)

    Jitendra Singh; Sheeba Sekharan; Subhankar Karmakar; Subimal Ghosh; P E Zope; T I Eldho

    2017-04-01

    Mumbai, the commercial and financial capital of India, experiences incessant annual rain episodes, mainly attributable to erratic rainfall pattern during monsoons and urban heat-island effect due to escalating urbanization, leading to increasing vulnerability to frequent flooding. After the infamous episode of 2005 Mumbai torrential rains when only two rain gauging stations existed, the governing civic body, the Municipal Corporation of Greater Mumbai (MCGM) came forward with an initiative to install 26 automatic weather stations (AWS) in June 2006 (MCGM 2007), which later increased to 60 AWS. A comprehensive statistical analysis to understand the spatio-temporal pattern of rainfall over Mumbai or any other coastal city in India has never been attempted earlier. In the current study, a thorough analysis of available rainfall data for 2006–2014 from these stations was performed; the 2013–2014 subhourly data from 26 AWS was found useful for further analyses due to their consistency and continuity. Correlogram cloud indicated no pattern of significant correlation when we considered the closest to the farthest gauging station from the base station; this impression was also supported by the semivariogram plots. Gini index values, a statistical measure of temporal non-uniformity, were found above 0.8 in visible majority showing an increasing trend in most gauging stations; this sufficiently led us to conclude that inconsistency in daily rainfall was gradually increasing with progress in monsoon. Interestingly, night rainfall was lesser compared to daytime rainfall. The pattern-less high spatio-temporal variation observed in Mumbai rainfall data signifies the futility of independently applying advanced statistical techniques, and thus calls for simultaneous inclusion of physics-centred models such as different meso-scale numerical weather prediction systems, particularly the Weather Research and Forecasting (WRF) model.

  17. Localized spatio-temporal constraints for accelerated CMR perfusion.

    Science.gov (United States)

    Akçakaya, Mehmet; Basha, Tamer A; Pflugi, Silvio; Foppa, Murilo; Kissinger, Kraig V; Hauser, Thomas H; Nezafat, Reza

    2014-09-01

    To develop and evaluate an image reconstruction technique for cardiac MRI (CMR) perfusion that uses localized spatio-temporal constraints. CMR perfusion plays an important role in detecting myocardial ischemia in patients with coronary artery disease. Breath-hold k-t-based image acceleration techniques are typically used in CMR perfusion for superior spatial/temporal resolution and improved coverage. In this study, we propose a novel compressed sensing-based image reconstruction technique for CMR perfusion, with applicability to free-breathing examinations. This technique uses local spatio-temporal constraints by regularizing image patches across a small number of dynamics. The technique was compared with conventional dynamic-by-dynamic reconstruction, and sparsity regularization using a temporal principal-component (pc) basis, as well as zero-filled data in multislice two-dimensional (2D) and three-dimensional (3D) CMR perfusion. Qualitative image scores were used (1 = poor, 4 = excellent) to evaluate the technique in 3D perfusion in 10 patients and five healthy subjects. On four healthy subjects, the proposed technique was also compared with a breath-hold multislice 2D acquisition with parallel imaging in terms of signal intensity curves. The proposed technique produced images that were superior in terms of spatial and temporal blurring compared with the other techniques, even in free-breathing datasets. The image scores indicated a significant improvement compared with other techniques in 3D perfusion (x-pc regularization, 2.8 ± 0.5 versus 2.3 ± 0.5; dynamic-by-dynamic, 1.7 ± 0.5; zero-filled, 1.1 ± 0.2). Signal intensity curves indicate similar dynamics of uptake between the proposed method with 3D acquisition and the breath-hold multislice 2D acquisition with parallel imaging. The proposed reconstruction uses sparsity regularization based on localized information in both spatial and temporal domains for highly accelerated CMR perfusion with potential use

  18. Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

    Science.gov (United States)

    Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Devers, Alexandre; Graff, Benjamin

    2017-06-01

    The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

  19. Spatio-temporal effects of low severity grassland fire on soil colour

    Science.gov (United States)

    Pereira, Paulo; Cerdà, Artemi; Bolutiene, Violeta; Pranskevicius, Mantas; Úbeda, Xavier; Jordán, Antonio; Zavala, Lorena; Mataix-Solera, Jorge

    2013-04-01

    Fire changes soil properties directly, through temperature, or indirectly with ash deposition and the temporal elimination of vegetal cover. Both influences change soil colour and soil properties. The degree of changes depends on fire severity that has important implications on soil organic matter, texture, mineralogy and hydrological properties and type of ash produced. The ash colour is different according to the temperature of combustion and burned specie and this property will have implications on soil colour. In addition, ash properties have a strong spatial variability. The aim of this work is to study the spatio-temporal effects of a low severity grassland fire on soil colour occurred in Lithuania, near Vilnius city (54° 42' N, 25° 08' E, 158 m.a.s.l.). After the fire it was designed a plot of 20x20m in a burned and unburned flat area. Soil colour was analysed immediately after the fire, and 2, 5, 7 and 9 months after the fire. In each sampling 25 soil samples were collected, carried out to the laboratory, dried at room temperature (20-24° C) and sieved with the Mataix-Solera, J. Arcenegui, V., Zavala, L. (2013a) Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development (In press) DOI: 10.1002/ldr.2195 Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J., Martin, D.A., Jordan, A. Burguet, M. (2013b) Effects of fire on ash thickness in a Lithuanian grassland and short-term spatio-temporal changes. Solid Earth Discussions, 4 (1), 1545-1584. doi:10.5194/sed-4-1-2012 Pereira, P., Pranskevicius, M., Cepanko, V., Vaitkute, D., Pundyte, N., Ubeda, X., Mataix-Soler, J., Cerda, A., Martin, D.A. (2013c) Short time vegetation recovers after a spring grassland fire in Lithuania. Temporal and slope position effect, Flamma, 4(1), 13-17.

  20. Spatio-temporal variability of the North Sea cod recruitment in relation to temperature and zooplankton.

    Directory of Open Access Journals (Sweden)

    Delphine Nicolas

    Full Text Available The North Sea cod (Gadus morhua, L. stock has continuously declined over the past four decades linked with overfishing and climate change. Changes in stock structure due to overfishing have made the stock largely dependent on its recruitment success, which greatly relies on environmental conditions. Here we focus on the spatio-temporal variability of cod recruitment in an effort to detect changes during the critical early life stages. Using International Bottom Trawl Survey (IBTS data from 1974 to 2011, a major spatio-temporal change in the distribution of cod recruits was identified in the late 1990s, characterized by a pronounced decrease in the central and southeastern North Sea stock. Other minor spatial changes were also recorded in the mid-1980s and early 1990s. We tested whether the observed changes in recruits distribution could be related with direct (i.e. temperature and/or indirect (i.e. changes in the quantity and quality of zooplankton prey effects of climate variability. The analyses were based on spatially-resolved time series, i.e. sea surface temperature (SST from the Hadley Center and zooplankton records from the Continuous Plankton Recorder Survey. We showed that spring SST increase was the main driver for the most recent decrease in cod recruitment. The late 1990s were also characterized by relatively low total zooplankton biomass, particularly of energy-rich zooplankton such as the copepod Calanus finmarchicus, which have further contributed to the decline of North Sea cod recruitment. Long-term spatially-resolved observations were used to produce regional distribution models that could further be used to predict the abundance of North Sea cod recruits based on temperature and zooplankton food availability.

  1. Workload induced spatio-temporal distortions and safety of flight

    Energy Technology Data Exchange (ETDEWEB)

    Barrett, C.L.; Weisgerber, S.A. (Los Alamos National Lab., NM (USA); Naval Weapons Center, China Lake, CA (USA))

    1989-01-01

    A theoretical analysis of the relationship between cognitive complexity and the perception of time and distance is presented and experimentally verified. Complex tasks produce high rates of mental representation which affect the subjective sense of duration and, through the subjective time scale, the percept of distance derived from dynamic visual cues (i.e., visual cues requiring rate integration). The analysis of the interrelationship of subjective time and subjective distance yields the prediction that, as a function of cognitive complexity, distance estimates derived from dynamic visual cues will be longer than the actual distance whereas estimates based on perceived temporal duration will be shorter than the actual distance. This prediction was confirmed in an experiment in which subjects (both pilots and non-pilots) estimated distances using either temporal cues or dynamic visual cues. The distance estimation task was also combined with secondary loading tasks in order to vary the overall task complexity. The results indicated that distance estimates based on temporal cues were underestimated while estimates based on visual cues were overestimated. This spatio-temporal distortion effect increased with increases in overall task complexity. 30 refs., 6 figs., 1 tab.

  2. Exploring the spatio-temporal neural basis of face learning.

    Science.gov (United States)

    Yang, Ying; Xu, Yang; Jew, Carol A; Pyles, John A; Kass, Robert E; Tarr, Michael J

    2017-06-01

    Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150-250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150-250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces.

  3. Spatio-Temporal Mapping and the Enteric Nervous System.

    Science.gov (United States)

    Hennig, Grant W

    Study of the enteric nervous system (ENS) is somewhat less glamorous than other body systems but offers a unique opportunity to study the sensory, interneuronal and motor outputs of a highly developed neural network in the same tissue. This has not been a trivial task, and even after a century, we still struggle to understand both the simple (e.g. reflexes) and complex (e.g. MMCs) behaviors the gut produces. On top of that, other control networks (such as ICC) that are integrated with ENS at varying levels, can modify ENS activity directly or indirectly. While many of the methods used to study the ENS were originally developed in other systems (e.g. brain/heart), a few were spawned "in the offal" so to speak, due to the unique characteristics of the gut. The brief perspective below outlines how spatio-temporal maps (ST Maps) originated and continue to flourish in GI research as a tool to describe and analyze the complexity of GI movements.I apologize that I am not able to specifically mention all the people involved in the development and use of ST Maps in enteric/motility research due to space constraints (GWH, July 2014).

  4. Involuntary Eye Movement during Fixation is Influenced by Spatio-Temporal Frequency of Visual Stimuli

    Directory of Open Access Journals (Sweden)

    Masae Yokota

    2011-05-01

    Full Text Available Involuntary eye movement during fixation is essential for visual information acquisition. Previous studies have suggested that such eye movement depends on the attributes of visual stimuli (e.g. Yokota, APCV2010. In this study, we focus on spatio-temporal frequency, as an attribute of visual stimuli in order to understand spatio-temporal frequency property in the pathway of human vision. We measured eye movement during fixation for three subjects when 16 random-dot dynamic textures that have various frequency bands in spatially and temporally, are presented to the subjects as visual stimuli. The result shows that eye movement depends on the spatio-temporal frequency of visual stimuli. The eye movement includes higher frequency components, in other words, higher velocity components, when visual stimulus has higher spatial frequency and/or higher temporal frequency. Future detailed experiments will show that involuntary eye movement during fixation might be influenced by spatio-temporal frequency sensitivity in vision.

  5. Comparison of feature extraction methods within a spatio-temporal land cover change detection framework

    CSIR Research Space (South Africa)

    Kleynhans, W

    2011-07-01

    Full Text Available value yields a change or no-change decision [2]. The objective of this paper is to compare the EKF derived pa- rameter sequence with a sliding window Fast Fourier Trans- form (FFT) alternative [3] within the afore mentioned spatio- temporal change... detection framework. When considering the sliding window FFT approach in the context of the afore- mentioned spatio-temporal change detection framework. The underlying idea is that a sliding window FFT is computed for the entire time series...

  6. A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions

    OpenAIRE

    Hana Koorehdavoudi; Paul Bogdan

    2016-01-01

    Biological systems are frequently categorized as complex systems due to their capabilities of generating spatio-temporal structures from apparent random decisions. In spite of research on analyzing biological systems, we lack a quantifiable framework for measuring their complexity. To fill this gap, in this paper, we develop a new paradigm to study a collective group of N agents moving and interacting in a three-dimensional space. Our paradigm helps to identify the spatio-temporal states of t...

  7. 时空数据库索引机制研究%Research of Index Mechanisms in Spatio-temporal Databases

    Institute of Scientific and Technical Information of China (English)

    黄曙荣; 秦小麟

    2003-01-01

    Spatio-temporal database manages the large amount of spatial objects that change over time. It is necessary to query the spatio-temporal objects of the past and the current and to anticipate the future of spatio-temporal objects. It Is important to design an efficient index mechanism for accessing the spatio-temporal data efficiently. The paper analyzes the features of the spatio-temporal objects, studies the methods of spatio-temporal index mechanisms, classifies the index mechanisms, and discusses the key technologies of spatio-temporal indexes. And it also presents the index methods of STADBS that we are studying.

  8. Statistical forecasting for precipitation over West Africa based on spatio-temporal precipitation properties and tropical wave activity

    Science.gov (United States)

    Vogel, Peter; Klar, Manuel; Schlüter, Andreas; Knippertz, Peter; Fink, Andreas H.; Gneiting, Tilmann

    2017-04-01

    Precipitation forecasts for one up to several days are of high socioeconomic importance for agriculturally dominated societies in West Africa, regarding both the occurrence as well as the amount of precipitation. However, disappointingly forecasts based on numerical weather prediction models and even statistically postprocessed forecasts still do not outperform simple reference forecasts such as climatology or persistence. More elaborate statistical forecasts can hopefully lead to an improvement in the quality of precipitation forecasts above climatological or persistent ones. In this contribution, we concentrate on the potential of statistical forecasts to predict the occurrence of precipitation, while the prediction of the amount will be addressed in the future. Using increasingly sophisticated statistical models, we start with forecasts solely relying on the spatio-temporal information contained in precipitation observations. With the necessity of a full spatial coverage of precipitation observations in order to understand its spatio-temporal properties, we rely on Tropical Rainfall Measuring Mission (TRMM) observations and use accumulation periods of 1 to 5 days for the monsoon seasons from May to mid-October of the years 2007 to 2014. Especially for the full monsoon from the end of June to the end of September, the precipitation fields exhibit clear spatio-temporal information that is meteorologically interpretable and statistically meaningful. Using Markov models, we do in fact find an increased forecast quality for this period. While such forecasts already outperform persistent and climatological forecasts for the full monsoon, the forecast quality increases further and also covers the whole monsoon period from May to mid-October, when we add additional predictors. We find the activity of tropical waves such as Kelvin or African Easterly waves or the Madden-Julian Oscillation to be informative predictors and test for additional predictors closely linked to

  9. TU-F-BRF-08: Intensity Modulation Expanded to Spatio-Temporal Space; IMRT Combined with Optimal Fractionation Schedule

    Energy Technology Data Exchange (ETDEWEB)

    Kim, M; Saberian, F; Ghate, A [University of Washington, Seattle, WA (United States)

    2014-06-15

    Purpose: Past efforts to improve the therapeutic ratio have focused on a spatial approach where highly conformal radiation dose is given to tumors while minimizing dose to normal tissues, e.g., IMRT, VMAT, and IGRT. However, the fractionation schedule, i.e., a temporal approach to radiotherapy, has been largely overlooked so far in maximizing the therapeutic ratio. We establish a rigorous mathematical spatio-temporal approach to systematically investigate the feasibility and potential benefits of simultaneously optimizing radiation dose distribution in space and time. Methods: Stochastic control formalism is constructed to maximize the average tumor BED by choosing an optimal radiation dose distribution for an optimal number of fractions subject to normal tissue BED constraints. Three separate simulations are run on two groups of phantom cases; 5 cases with prostate cancer and 5 cases with head-and-neck cancer. (1) Conventional IMRT with 70Gy/35fx for head-and-neck, and 81Gy/45fx for prostate, (2) IMRT is done independently from the fractionation schedule optimization (S-model), (3) integrated spatio-temporal approach (I-model) where radiation intensities are simultaneously optimized for the first time ever in space and time. Final tumor BEDs from the three trials are compared in prostate and head-and-neck cases. Results: Numerical simulations show that final tumor BED from I-model is 20–90% larger than conventional IMRT, and 20-50% larger than S-model for head-and-neck cancer with α/β=10 and Tdouble=2–50 days. Final tumor BED from I-model is also 90–140% larger than conventional IMRT, and 20–30% larger than S-model for prostate cancer with α/β=2 and Tdouble=5–80 days. Conclusion: Our spatio-temporal optimization of radiotherapy allows an expansion of search space for the optimal treatment plans to include the temporal distribution of radiation dose in addition to the spatial distribution. Such spatio-temporal approach shows great potential to improve

  10. A biologically plausible embodied model of action discovery

    Directory of Open Access Journals (Sweden)

    Rufino eBolado-Gomez

    2013-03-01

    Full Text Available During development, animals can spontaneously discover action-outcomepairings enabling subsequent achievement of their goals. We present abiologically plausible embodied model addressing key aspects of thisprocess. The biomimetic model core comprises the basal ganglia and itsloops through cortex and thalamus. We incorporate reinforcementlearning with phasic dopamine supplying a sensory prediction error,signalling 'surprising' outcomes. Phasic dopamine is used in acorticostriatal learning rule which is consistent with recent data. Wealso hypothesised that objects associated with surprising outcomesacquire 'novelty salience' contingent on the predicability of theoutcome. To test this idea we used a simple model of predictiongoverning the dynamics of novelty salience and phasic dopamine. Thetask of the virtual robotic agent mimicked an in vivo counterpart(Gancarz et al., 2011 and involved interaction with a target objectwhich caused a light flash, or a control object which did not.Learning took place according to two schedules. In one, the phasicoutcome was delivered after interaction with the target in anunpredictable way which emulated the in vivo protocol. Without noveltysalience, the model was unable to account for the experimental data.In the other schedule, the phasic outcome was reliably delivered andthe agent showed a rapid increase in the number of interactions withthe target which then decreased over subsequent sessions. We arguethis is precisely the kind of change in behaviour required torepeatedly present representations of context, action and outcome, toneural networks responsible for learning action-outcome contingency.The model also showed corticostriatal plasticity consistent withlearning a new action in basal ganglia. We conclude that actionlearning is underpinned by a complex interplay of plasticity andstimulus salience, and that our model contains many of the elementsfor biological action discovery to take place.

  11. Spatio-Temporal Deforestation Measurement Using Automatic Clustering

    Directory of Open Access Journals (Sweden)

    Irene Erlyn Wina Rachmawan

    2016-06-01

    Full Text Available Deforestation is one of the crucial issues in Indonesia. In 2012, deforestation rate in Indonesia reached 0.84 million hectares, exceeding Brazil. According to the 2009 Guinness World Records, Indonesia's deforestation rate was 1.8 million hectares per year between 2000 and 2005. An interesting view is the fact that Indonesia government denied the deforestation rate in those years and said that the rate was only 1.08 million hectares per year in 2000 and 2005. The different problem is on the technique how to deal with the deforestation rate. In this paper, we proposed a new approach for automatically identifying the deforestation area and measuring the deforestation rate. This approach involves differential image processing for detecting Spatio-temporal nature changes of deforestation. It consists series of important features extracted from multiband satellite images which are considered as the dataset of the research. These data are proceeded through the following stages: (1 Automatic clustering for multiband satellite images, (2 Reinforcement Programming to optimize K-Means clustering, (3 Automatic interpretation for deforestation areas, and (4 Deforestation measurement adjusting with elevation of the satellite. For experimental study, we applied our proposed approach to analyze and measure the deforestation in Mendawai, South Borneo. We utilized Landsat 7 to obtain the multiband images for that area from the year 2001 to 2013. Our proposed approach is able to identify the deforestation area and measure the rate. The experiment with our proposed approach made a temporal measurement for the area and showed the increasing deforestation size of the area 1.80 hectares during those years.

  12. Simulating Future Changes in Spatio-temporal Precipitation by Identifying and Characterizing Individual Rainstorm Events

    Science.gov (United States)

    Chang, W.; Stein, M.; Wang, J.; Kotamarthi, V. R.; Moyer, E. J.

    2015-12-01

    A growing body of literature suggests that human-induced climate change may cause significant changes in precipitation patterns, which could in turn influence future flood levels and frequencies and water supply and management practices. Although climate models produce full three-dimensional simulations of precipitation, analyses of model precipitation have focused either on time-averaged distributions or on individual timeseries with no spatial information. We describe here a new approach based on identifying and characterizing individual rainstorms in either data or model output. Our approach enables us to readily characterize important spatio-temporal aspects of rainstorms including initiation location, intensity (mean and patterns), spatial extent, duration, and trajectory. We apply this technique to high-resolution precipitation over the continental U.S. both from radar-based observations (NCEP Stage IV QPE product, 1-hourly, 4 km spatial resolution) and from model runs with dynamical downscaling (WRF regional climate model, 3-hourly, 12 km spatial resolution). In the model studies we investigate the changes in storm characteristics under a business-as-usual warming scenario to 2100 (RCP 8.5). We find that in these model runs, rainstorm intensity increases as expected with rising temperatures (approximately 7%/K, following increased atmospheric moisture content), while total precipitation increases by a lesser amount (3%/K), consistent with other studies. We identify for the first time the necessary compensating mechanism: in these model runs, individual precipitation events become smaller. Other aspects are approximately unchanged in the warmer climate. Because these spatio-temporal changes in rainfall patterns would impact regional hydrology, it is important that they be accurately incorporated into any impacts assessment. For this purpose we have developed a methodology for producing scenarios of future precipitation that combine observational data and

  13. Big Data GPU-Driven Parallel Processing Spatial and Spatio-Temporal Clustering Algorithms

    Science.gov (United States)

    Konstantaras, Antonios; Skounakis, Emmanouil; Kilty, James-Alexander; Frantzeskakis, Theofanis; Maravelakis, Emmanuel

    2016-04-01

    Advances in graphics processing units' technology towards encompassing parallel architectures [1], comprised of thousands of cores and multiples of parallel threads, provide the foundation in terms of hardware for the rapid processing of various parallel applications regarding seismic big data analysis. Seismic data are normally stored as collections of vectors in massive matrices, growing rapidly in size as wider areas are covered, denser recording networks are being established and decades of data are being compiled together [2]. Yet, many processes regarding seismic data analysis are performed on each seismic event independently or as distinct tiles [3] of specific grouped seismic events within a much larger data set. Such processes, independent of one another can be performed in parallel narrowing down processing times drastically [1,3]. This research work presents the development and implementation of three parallel processing algorithms using Cuda C [4] for the investigation of potentially distinct seismic regions [5,6] present in the vicinity of the southern Hellenic seismic arc. The algorithms, programmed and executed in parallel comparatively, are the: fuzzy k-means clustering with expert knowledge [7] in assigning overall clusters' number; density-based clustering [8]; and a selves-developed spatio-temporal clustering algorithm encompassing expert [9] and empirical knowledge [10] for the specific area under investigation. Indexing terms: GPU parallel programming, Cuda C, heterogeneous processing, distinct seismic regions, parallel clustering algorithms, spatio-temporal clustering References [1] Kirk, D. and Hwu, W.: 'Programming massively parallel processors - A hands-on approach', 2nd Edition, Morgan Kaufman Publisher, 2013 [2] Konstantaras, A., Valianatos, F., Varley, M.R. and Makris, J.P.: 'Soft-Computing Modelling of Seismicity in the Southern Hellenic Arc', Geoscience and Remote Sensing Letters, vol. 5 (3), pp. 323-327, 2008 [3] Papadakis, S. and

  14. Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

    KAUST Repository

    Huang, Huang

    2017-07-16

    This thesis focuses on two topics, computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data. The first topic is motivated by the prohibitive computational burden in fitting Gaussian process models to large and irregularly spaced spatial datasets. Various approximation methods have been introduced to reduce the computational cost, but many rely on unrealistic assumptions about the process and retaining statistical efficiency remains an issue. We propose a new scheme to approximate the maximum likelihood estimator and the kriging predictor when the exact computation is infeasible. The proposed method provides different types of hierarchical low-rank approximations that are both computationally and statistically efficient. We explore the improvement of the approximation theoretically and investigate the performance by simulations. For real applications, we analyze a soil moisture dataset with 2 million measurements with the hierarchical low-rank approximation and apply the proposed fast kriging to fill gaps for satellite images. The second topic is motivated by rank-based outlier detection methods for functional data. Compared to magnitude outliers, it is more challenging to detect shape outliers as they are often masked among samples. We develop a new notion of functional data depth by taking the integration of a univariate depth function. Having a form of the integrated depth, it shares many desirable features. Furthermore, the novel formation leads to a useful decomposition for detecting both shape and magnitude outliers. Our simulation studies show the proposed outlier detection procedure outperforms competitors in various outlier models. We also illustrate our methodology using real datasets of curves, images, and video frames. Finally, we introduce the functional data ranking technique to spatio-temporal statistics for visualizing and assessing covariance properties, such as

  15. A spatio-temporal detective quantum efficiency and its application to fluoroscopic systems

    Energy Technology Data Exchange (ETDEWEB)

    Friedman, S. N.; Cunningham, I. A. [Sackler School of Medicine, Faculty of Medicine, Tel Aviv University, Ramat Aviv 69978 (Israel); Imaging Research Laboratories, Robarts Research Institute and Lawson Health Research Institute, 100 Perth Drive, London, Ontario N6A 5K8 (Canada) and Department of Medical Biophysics, University of Western Ontario, 1151 Richmond Street, London, Ontario N6A 5B8 (Canada)

    2010-11-15

    Purpose: Fluoroscopic x-ray imaging systems are used extensively in spatio-temporal detection tasks and require a spatio-temporal description of system performance. No accepted metric exists that describes spatio-temporal fluoroscopic performance. The detective quantum efficiency (DQE) is a metric widely used in radiography to quantify system performance and as a surrogate measure of patient ''dose efficiency.'' It has been applied previously to fluoroscopic systems with the introduction of a temporal correction factor. However, the use of a temporally-corrected DQE does not provide system temporal information and it is only valid under specific conditions, many of which are not likely to be satisfied by suboptimal systems. The authors propose a spatio-temporal DQE that describes performance in both space and time and is applicable to all spatio-temporal quantum-based imaging systems. Methods: The authors define a spatio-temporal DQE (two spatial-frequency axes and one temporal-frequency axis) in terms of a small-signal spatio-temporal modulation transfer function (MTF) and spatio-temporal noise power spectrum (NPS). Measurements were made on an x-ray image intensifier-based bench-top system using continuous fluoroscopy with an RQA-5 beam at 3.9 {mu}R/frame and hardened 50 kVp beam (0.8 mm Cu filtration added) at 1.9 {mu}R/frame. Results: A zero-frequency DQE value of 0.64 was measured under both conditions. Nonideal performance was noted at both larger spatial and temporal frequencies; DQE values decreased by {approx}50% at the cutoff temporal frequency of 15 Hz. Conclusions: The spatio-temporal DQE enables measurements of decreased temporal system performance at larger temporal frequencies analogous to previous measurements of decreased (spatial) performance. This marks the first time that system performance and dose efficiency in both space and time have been measured on a fluoroscopic system using DQE and is the first step toward the

  16. A spatio-temporal detective quantum efficiency and its application to fluoroscopic systems.

    Science.gov (United States)

    Friedman, S N; Cunningham, I A

    2010-11-01

    Fluoroscopic x-ray imaging systems are used extensively in spatio-temporal detection tasks and require a spatio-temporal description of system performance. No accepted metric exists that describes spatio-temporal fluoroscopic performance. The detective quantum efficiency (DQE) is a metric widely used in radiography to quantify system performance and as a surrogate measure of patient "dose efficiency". It has been applied previously to fluoroscopic systems with the introduction of a temporal correction factor. However, the use of a temporally-corrected DQE does not provide system temporal information and it is only valid under specific conditions, many of which are not likely to be satisfied by suboptimal systems. The authors propose a spatio-temporal DQE that describes performance in both space and time and is applicable to all spatio-temporal quantum-based imaging systems. The authors define a spatio-temporal DQE (two spatial-frequency axes and one temporal-frequency axis) in terms of a small-signal spatio-temporal modulation transfer function (MTF) and spatio-temporal noise power spectrum (NPS). Measurements were made on an x-ray image intensifier-based bench-top system using continuous fluoroscopy with an RQA-5 beam at 3.9 microR/frame and hardened 50 kVp beam (0.8 mm Cu filtration added) at 1.9 microR/frame. A zero-frequency DQE value of 0.64 was measured under both conditions. Nonideal performance was noted at both larger spatial and temporal frequencies; DQE values decreased by approximately 50% at the cutoff temporal frequency of 15 Hz. The spatio-temporal DQE enables measurements of decreased temporal system performance at larger temporal frequencies analogous to previous measurements of decreased (spatial) performance. This marks the first time that system performance and dose efficiency in both space and time have been measured on a fluoroscopic system using DQE and is the first step toward the generalized use of DQE on clinical fluoroscopic systems.

  17. Advanced spatio-temporal filtering techniques for photogrammetric image sequence analysis in civil engineering material testing

    Science.gov (United States)

    Liebold, F.; Maas, H.-G.

    2016-01-01

    The paper shows advanced spatial, temporal and spatio-temporal filtering techniques which may be used to reduce noise effects in photogrammetric image sequence analysis tasks and tools. As a practical example, the techniques are validated in a photogrammetric spatio-temporal crack detection and analysis tool applied in load tests in civil engineering material testing. The load test technique is based on monocular image sequences of a test object under varying load conditions. The first image of a sequence is defined as a reference image under zero load, wherein interest points are determined and connected in a triangular irregular network structure. For each epoch, these triangles are compared to the reference image triangles to search for deformations. The result of the feature point tracking and triangle comparison process is a spatio-temporally resolved strain value field, wherein cracks can be detected, located and measured via local discrepancies. The strains can be visualized as a color-coded map. In order to improve the measuring system and to reduce noise, the strain values of each triangle must be treated in a filtering process. The paper shows the results of various filter techniques in the spatial and in the temporal domain as well as spatio-temporal filtering techniques applied to these data. The best results were obtained by a bilateral filter in the spatial domain and by a spatio-temporal EOF (empirical orthogonal function) filtering technique.

  18. RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG.

    Science.gov (United States)

    Qi, Feifei; Li, Yuanqing; Wu, Wei

    2015-12-01

    Learning optimal spatio-temporal filters is a key to feature extraction for single-trial electroencephalogram (EEG) classification. The challenges are controlling the complexity of the learning algorithm so as to alleviate the curse of dimensionality and attaining computational efficiency to facilitate online applications, e.g., brain-computer interfaces (BCIs). To tackle these barriers, this paper presents a novel algorithm, termed regularized spatio-temporal filtering and classification (RSTFC), for single-trial EEG classification. RSTFC consists of two modules. In the feature extraction module, an l2 -regularized algorithm is developed for supervised spatio-temporal filtering of the EEG signals. Unlike the existing supervised spatio-temporal filter optimization algorithms, the developed algorithm can simultaneously optimize spatial and high-order temporal filters in an eigenvalue decomposition framework and thus be implemented highly efficiently. In the classification module, a convex optimization algorithm for sparse Fisher linear discriminant analysis is proposed for simultaneous feature selection and classification of the typically high-dimensional spatio-temporally filtered signals. The effectiveness of RSTFC is demonstrated by comparing it with several state-of-the-arts methods on three brain-computer interface (BCI) competition data sets collected from 17 subjects. Results indicate that RSTFC yields significantly higher classification accuracies than the competing methods. This paper also discusses the advantage of optimizing channel-specific temporal filters over optimizing a temporal filter common to all channels.

  19. Spatio-temporal tracking and phylodynamics of an urban dengue 3 outbreak in Sao Paulo, Brazil.

    Directory of Open Access Journals (Sweden)

    Adriano Mondini

    Full Text Available The dengue virus has a single-stranded positive-sense RNA genome of approximately 10.700 nucleotides with a single open reading frame that encodes three structural (C, prM, and E and seven nonstructural (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5 proteins. It possesses four antigenically distinct serotypes (DENV 1-4. Many phylogenetic studies address particularities of the different serotypes using convenience samples that are not conducive to a spatio-temporal analysis in a single urban setting. We describe the pattern of spread of distinct lineages of DENV-3 circulating in São José do Rio Preto, Brazil, during 2006. Blood samples from patients presenting dengue-like symptoms were collected for DENV testing. We performed M-N-PCR using primers based on NS5 for virus detection and identification. The fragments were purified from PCR mixtures and sequenced. The positive dengue cases were geo-coded. To type the sequenced samples, 52 reference sequences were aligned. The dataset generated was used for iterative phylogenetic reconstruction with the maximum likelihood criterion. The best demographic model, the rate of growth, rate of evolutionary change, and Time to Most Recent Common Ancestor (TMRCA were estimated. The basic reproductive rate during the epidemics was estimated. We obtained sequences from 82 patients among 174 blood samples. We were able to geo-code 46 sequences. The alignment generated a 399-nucleotide-long dataset with 134 taxa. The phylogenetic analysis indicated that all samples were of DENV-3 and related to strains circulating on the isle of Martinique in 2000-2001. Sixty DENV-3 from São José do Rio Preto formed a monophyletic group (lineage 1, closely related to the remaining 22 isolates (lineage 2. We assumed that these lineages appeared before 2006 in different occasions. By transforming the inferred exponential growth rates into the basic reproductive rate, we obtained values for lineage 1 of R(0 = 1.53 and values for

  20. Nutrients and toxin producing phytoplankton control algal blooms – a spatio-temporal study in a noisy environment

    Indian Academy of Sciences (India)

    Ram Rup Sarkar; Horst Malchow

    2005-12-01

    A phytoplankton-zooplankton prey-predator model has been investigated for temporal, spatial and spatio-temporal dissipative pattern formation in a deterministic and noisy environment, respectively. The overall carrying capacity for the phytoplankton population depends on the nutrient level. The role of nutrient concentrations and toxin producing phytoplankton for controlling the algal blooms has been discussed. The local analysis yields a number of stationary and/or oscillatory regimes and their combinations. Correspondingly interesting is the spatio-temporal behaviour, modelled by stochastic reaction-diffusion equations. The present study also reveals the fact that the rate of toxin production by toxin producing phytoplankton (TPP) plays an important role for controlling oscillations in the plankton system. We also observe that different mortality functions of zooplankton due to TPP have significant influence in controlling oscillations, coexistence, survival or extinction of the zooplankton population. External noise can enhance the survival and spread of zooplankton that would go extinct in the deterministic system due to a high rate of toxin production.

  1. SuperFly: a comparative database for quantified spatio-temporal gene expression patterns in early dipteran embryos.

    Science.gov (United States)

    Cicin-Sain, Damjan; Pulido, Antonio Hermoso; Crombach, Anton; Wotton, Karl R; Jiménez-Guri, Eva; Taly, Jean-François; Roma, Guglielmo; Jaeger, Johannes

    2015-01-01

    We present SuperFly (http://superfly.crg.eu), a relational database for quantified spatio-temporal expression data of segmentation genes during early development in different species of dipteran insects (flies, midges and mosquitoes). SuperFly has a special focus on emerging non-drosophilid model systems. The database currently includes data of high spatio-temporal resolution for three species: the vinegar fly Drosophila melanogaster, the scuttle fly Megaselia abdita and the moth midge Clogmia albipunctata. At this point, SuperFly covers up to 9 genes and 16 time points per species, with a total of 1823 individual embryos. It provides an intuitive web interface, enabling the user to query and access original embryo images, quantified expression profiles, extracted positions of expression boundaries and integrated datasets, plus metadata and intermediate processing steps. SuperFly is a valuable new resource for the quantitative comparative study of gene expression patterns across dipteran species. Moreover, it provides an interesting test set for systems biologists interested in fitting mathematical gene network models to data. Both of these aspects are essential ingredients for progress toward a more quantitative and mechanistic understanding of developmental evolution.

  2. Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever.

    Directory of Open Access Journals (Sweden)

    Ram K Raghavan

    Full Text Available This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C in the region, and the relevance of these factors in the context of climate-change impacts on tick-borne diseases are discussed.

  3. Video object tracking in the compressed domain using spatio-temporal Markov random fields.

    Science.gov (United States)

    Khatoonabadi, Sayed Hossein; Bajić, Ivan V

    2013-01-01

    Despite the recent progress in both pixel-domain and compressed-domain video object tracking, the need for a tracking framework with both reasonable accuracy and reasonable complexity still exists. This paper presents a method for tracking moving objects in H.264/AVC-compressed video sequences using a spatio-temporal Markov random field (ST-MRF) model. An ST-MRF model naturally integrates the spatial and temporal aspects of the object's motion. Built upon such a model, the proposed method works in the compressed domain and uses only the motion vectors (MVs) and block coding modes from the compressed bitstream to perform tracking. First, the MVs are preprocessed through intracoded block motion approximation and global motion compensation. At each frame, the decision of whether a particular block belongs to the object being tracked is made with the help of the ST-MRF model, which is updated from frame to frame in order to follow the changes in the object's motion. The proposed method is tested on a number of standard sequences, and the results demonstrate its advantages over some of the recent state-of-the-art methods.

  4. Spatio-temporal cerebral blood flow perfusion patterns in cortical spreading depression

    Science.gov (United States)

    Verisokin, Andrey Yu.; Verveyko, Darya V.; Postnov, Dmitry E.

    2017-04-01

    Cortical spreading depression (CSD) is an example of one of the most common abnormalities in biophysical brain functioning. Despite the fact that there are many mathematical models describing the cortical spreading depression (CSD), most of them do not take into consideration the role of redistribution of cerebral blood flow (CBF), that results in the formation of spatio-temporal patterns. The paper presents a mathematical model, which successfully explains the CBD role in the CSD process. Numerical study of this model has revealed the formation of stationary dissipative structures, visually analogous to Turing structures. However, the mechanism of their formation is not diffusion. We show these structures occur due to another type of spatial coupling, that is related to tissue perfusion rate. The proposed model predicts that at similar state of neurons the distribution of blood flow and oxygenation may by different. Currently, this effect is not taken into account when the Blood oxygen-level dependent (BOLD) contrast imaging used in functional magnetic resonance imaging (fMRI). Thus, the diagnosis on the BOLD signal can be ambiguous. We believe that our results can be used in the future for a more correct interpretation of the data obtained with fMRI, NIRS and other similar methods for research of the brain activity.

  5. Somatic growth dynamics of West Atlantic hawksbill sea turtles: a spatio-temporal perspective

    Science.gov (United States)

    Bjorndal, Karen A.; Chaloupka, Milani; Saba, Vincent S.; Diez, Carlos E.; van Dam, Robert P.; Krueger, Barry H.; Horrocks, Julia A.; Santos, Armando J. B.; Bellini, Cláudio; Marcovaldi, Maria A. G.; Nava, Mabel; Willis, Sue; Godley, Brendan J.; Gore, Shannon; Hawkes, Lucy A.; McGowan, Andrew; Witt, Matthew J.; Stringell, Thomas B.; Sanghera, Amdeep; Richardson, Peter B.; Broderick, Annette C.; Phillips, Quinton; Calosso, Marta C.; Claydon, John A. B.; Blumenthal, Janice; Moncada, Felix; Nodarse, Gonzalo; Medina, Yosvani; Dunbar, Stephen G.; Wood, Lawrence D.; Lagueux, Cynthia J.; Campbell, Cathi L.; Meylan, Anne B.; Meylan, Peter A.; Burns Perez, Virginia R.; Coleman, Robin A.; Strindberg, Samantha; Guzmán-H, Vicente; Hart, Kristen M.; Cherkiss, Michael S.; Hillis-Starr, Zandy; Lundgren, Ian; Boulon, Ralf H.; Connett, Stephen; Outerbridge, Mark E.; Bolten, Alan B.

    2016-01-01

    Somatic growth dynamics are an integrated response to environmental conditions. Hawksbill sea turtles (Eretmochelys imbricata) are long-lived, major consumers in coral reef habitats that move over broad geographic areas (hundreds to thousands of kilometers). We evaluated spatio-temporal effects on hawksbill growth dynamics over a 33-yr period and 24 study sites throughout the West Atlantic and explored relationships between growth dynamics and climate indices. We compiled the largest ever data set on somatic growth rates for hawksbills – 3541 growth increments from 1980 to 2013. Using generalized additive mixed model analyses, we evaluated 10 covariates, including spatial and temporal variation, that could affect growth rates. Growth rates throughout the region responded similarly over space and time. The lack of a spatial effect or spatio-temporal interaction and the very strong temporal effect reveal that growth rates in West Atlantic hawksbills are likely driven by region-wide forces. Between 1997 and 2013, mean growth rates declined significantly and steadily by 18%. Regional climate indices have significant relationships with annual growth rates with 0- or 1-yr lags: positive with the Multivariate El Niño Southern Oscillation Index (correlation = 0.99) and negative with Caribbean sea surface temperature (correlation = −0.85). Declines in growth rates between 1997 and 2013 throughout the West Atlantic most likely resulted from warming waters through indirect negative effects on foraging resources of hawksbills. These climatic influences are complex. With increasing temperatures, trajectories of decline of coral cover and availability in reef habitats of major prey species of hawksbills are not parallel. Knowledge of how choice of foraging habitats, prey selection, and prey abundance are affected by warming water temperatures is needed to understand how climate change will affect productivity of consumers that live in association with coral reefs. Main

  6. Dying like rabbits: general determinants of spatio-temporal variability in survival.

    Science.gov (United States)

    Tablado, Zulima; Revilla, Eloy; Palomares, Francisco

    2012-01-01

    1. Identifying general patterns of how and why survival rates vary across space and time is necessary to truly understand population dynamics of a species. However, this is not an easy task given the complexity and interactions of processes involved, and the interpopulation differences in main survival determinants. 2. Here, using European rabbits (Oryctolagus cuniculus) as a model and information from local studies, we investigated whether we could make inferences about trends and drivers of survival of a species that are generalizable to large spatio-temporal scales. To do this, we first focused on overall survival and then examined cause-specific mortalities, mainly predation and diseases, which may lead to those patterns. 3. Our results show that within the large-scale variability in rabbit survival, there exist general patterns that are explained by the integration of factors previously known to be important at the local level (i.e. age, climate, diseases, predation or density dependence). We found that both inter- and intrastudy survival rates increased in magnitude and decreased in variability as rabbits grow old, although this tendency was less pronounced in populations with epidemic diseases. Some causes leading to these higher mortalities in young rabbits could be the stronger effect of rainfall at those ages, as well as, other death sources like malnutrition or infanticide. 4. Predation is also greater for newborns and juveniles, especially in population without diseases. Apart from the effect of diseases, predation patterns also depended on factors, such as, density, season, and type and density of predators. Finally, we observed that infectious diseases also showed general relationships with climate, breeding (i.e. new susceptible rabbits) and age, although the association type varied between myxomatosis and rabbit haemorrhagic disease. 5. In conclusion, large-scale patterns of spatio-temporal variability in rabbit survival emerge from the combination

  7. Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling

    KAUST Repository

    Schillaci, Calogero

    2017-06-02

    SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0–0.3m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (RS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-R2 (0.63–0.69) and low uncertainty (s.d.<0.76gCkg−1 with RS, and <1.25gCkg−1 without RS). These outputs allowed depicting a time variation of SOC at 1arcsec. SOC estimation strongly depended on the soil texture, land use, rainfall and topographic indices related to erosion and deposition. RS indices captured one fifth of the total variance explained, slightly changed the ranking of variance explained by the non-RS predictors, and reduced the variability of the model replicates. During the study period, SOC decreased in the areas with relatively high initial SOC, and increased in the area with high temperature and low rainfall, dominated by arables. This was likely due to the compulsory application of some Good Agricultural and Environmental practices. These results confirm that the importance of texture and land use in short-term SOC variation is comparable to climate. The present results call for agronomic and policy intervention at the district level to maintain fertility and yield potential. In addition, the present results suggest that the application of RS covariates enhanced the modelling performance.

  8. Spatio-temporal animation of Army logistics, simulations, facilitating analysis of military deployments.

    Energy Technology Data Exchange (ETDEWEB)

    Love, R. J.; Horsthemke, W.; Macal, C. M.; Van Groningen, C.; Decision and Information Sciences

    2004-01-01

    Visualization techniques for simulations are often limited to statistical reports, graphs, and charts, but simulations can be enhanced through the use of animation. A spatio-temporal animation allows a viewer to observe a simulation operate, rather than deduce it from numerical output. The Route Viewer, developed by Argonne National Laboratory, is a two-dimensional animation model that animates the objects and events produced by a discrete event simulation. It operates in a playback mode, whereby a simulated scenario is animated after the simulation has completed. The Route Viewer is used to verify the simulation's processes and data, but it also benefits the simulation as an analytical tool by facilitating spatial and temporal analysis. By visualizing the events of a simulated scenario in two-dimensional space, it is possible to determine whether the scenario, or simulation model, is reasonable. Further, the Route Viewer provides an awareness of what happens in a scenario, when it happens, and the completeness and efficiency of the scenario and its processes. For Army deployments, it highlights utilization of resources and where bottlenecks are occurring. This paper discusses how the Route Viewer facilitates the analysis of military deployment simulation model results.

  9. Economic Development And Transfrontier Shipments Of Waste In Poland – Spatio-Temporal Analysis

    Directory of Open Access Journals (Sweden)

    Antczak Elżbieta

    2014-12-01

    Full Text Available The aim of the paper is to apply the spatio-temporal Environmental Kuznets Curve (SpEKC to test the relationship between economic growth and the amount of collected mixed municipal waste. The analysis was conducted at the level of sixty-six Polish sub-regions. The study contained selected environmental indicators. The dependent variable - the amount of municipal waste generated in kilograms per capita characterized the state of the environment. The GDP per capita in constant prices (as an explanatory variable presented the level of economic development of the sub-regions. In the empirical part of the research there were used spatial panel data models based on EKCs. It determined the levels of economic development, at which the amount of produced wastes has fallen or increased, depending on the wealth of the region. The application of different types of spatial weight matrices was an important element of this modelling. Data obtained the years 2005-2012. Models were estimated in the RCran package.

  10. Cluster Oriented Spatio Temporal Multidimensional Data Visualization of Earthquakes in Indonesia

    Directory of Open Access Journals (Sweden)

    Mohammad Nur Shodiq

    2016-03-01

    Full Text Available Spatio temporal data clustering is challenge task. The result of clustering data are utilized to investigate the seismic parameters. Seismic parameters are used to describe the characteristics of earthquake behavior. One of the effective technique to study multidimensional spatio temporal data is visualization. But, visualization of multidimensional data is complicated problem. Because, this analysis consists of observed data cluster and seismic parameters. In this paper, we propose a visualization system, called as IES (Indonesia Earthquake System, for cluster analysis, spatio temporal analysis, and visualize the multidimensional data of seismic parameters. We analyze the cluster analysis by using automatic clustering, that consists of get optimal number of cluster and Hierarchical K-means clustering. We explore the visual cluster and multidimensional data in low dimensional space visualization. We made experiment with observed data, that consists of seismic data around Indonesian archipelago during 2004 to 2014. Keywords: Clustering, visualization, multidimensional data, seismic parameters.

  11. Geovisualization Approaches for Spatio-temporal Crime Scene Analysis - Towards 4D Crime Mapping

    Science.gov (United States)

    Wolff, Markus; Asche, Hartmut

    This paper presents a set of methods and techniques for analysis and multidimensional visualisation of crime scenes in a German city. As a first step the approach implies spatio-temporal analysis of crime scenes. Against this background a GIS-based application is developed that facilitates discovering initial trends in spatio-temporal crime scene distributions even for a GIS untrained user. Based on these results further spatio-temporal analysis is conducted to detect variations of certain hotspots in space and time. In a next step these findings of crime scene analysis are integrated into a geovirtual environment. Behind this background the concept of the space-time cube is adopted to allow for visual analysis of repeat burglary victimisation. Since these procedures require incorporating temporal elements into virtual 3D environments, basic methods for 4D crime scene visualisation are outlined in this paper.

  12. Hippo signaling pathway reveals a spatio-temporal correlation with the size of primordial follicle pool in mice.

    Science.gov (United States)

    Xiang, Cheng; Li, Jia; Hu, Liaoliao; Huang, Jian; Luo, Tao; Zhong, Zhisheng; Zheng, Yuehui; Zheng, Liping

    2015-01-01

    The Hippo signaling pathway, a highly conserved cell signaling system, exists in most multicellular organisms and regulates cell proliferation, differentiation, and apoptosis. It has been reported that the members of Hippo signaling are expressed in mammalian ovaries, but the exact functions of this pathway in primordial follicle development remains unclear. To analyze the spatio-temporal correlation between the core component of Hippo pathway and the size of primordial follicle pool, Western blot, Real-time PCR and immunohistochemistry were used, and the expression and localization of MST1, LATS2 and YAP1 mRNA and protein were examined in 3 d, 1 m, 5 m, 16 m postnatal mice ovary and the culture model of mice primordial follicle in vitro. Both the protein and mRNA expression of the MST1 and LATS2 were decreased significantly as mouse age increased (p primordial follicles in 3 d postnatal mice ovaries, and these developed into primary follicles with the expression of PCNA increasing significantly (p primordial follicle activation in vitro. The primordial follicle activation may be related to the significant decrease of the ratio of pYAP1/YAP1. In conclusion, Hippo signaling pathway expressed in mice ovaries and have spatio-temporal correlation with the size of primordial follicle pool. © 2015 S. Karger AG, Basel

  13. Spatio-temporal PLC activation in parallel with intracellular Ca2+ wave propagation in mechanically stimulated single MDCK cells.

    Science.gov (United States)

    Tsukamoto, Akira; Hayashida, Yasunori; Furukawa, Katsuko S; Ushida, Takashi

    2010-03-01

    Intracellular Ca2+ transients are evoked either by the opening of Ca2+ channels on the plasma membrane or by phospholipase C (PLC) activation resulting in IP3 production. Ca2+ wave propagation is known to occur in mechanically stimulated cells; however, it remains uncertain whether and how PLC activation is involved in intracellular Ca2+ wave propagation in mechanically stimulated cells. To answer these questions, it is indispensable to clarify the spatio-temporal relations between intracellular Ca2+ wave propagation and PLC activation. Thus, we visualized both cytosolic Ca2+ and PLC activation using a real-time dual-imaging system in individual Mardin-Darby Canine Kidney (MDCK) cells. This system allowed us to simultaneously observe intracellular Ca2+ wave propagation and PLC activation in a spatio-temporal manner in a single mechanically stimulated MDCK cell. The results showed that PLC was activated not only in the mechanically stimulated region but also in other subcellular regions in parallel with intracellular Ca2+ wave propagation. These results support a model in which PLC is involved in Ca2+ signaling amplification in mechanically stimulated cells.

  14. Estimation of 3D cardiac deformation using spatio-temporal elastic registration of non-scanconverted ultrasound data

    Science.gov (United States)

    Elen, An; Loeckx, Dirk; Choi, Hon Fai; Gao, Hang; Claus, Piet; Maes, Frederik; Suetens, Paul; D'hooge, Jan

    2008-03-01

    Current ultrasound methods for measuring myocardial strain are often limited to measurements in one or two dimensions. Spatio-temporal elastic registration of 3D cardiac ultrasound data can however be used to estimate the 3D motion and full 3D strain tensor. In this work, the spatio-temporal elastic registration method was validated for both non-scanconverted and scanconverted images. This was done using simulated 3D pyramidal ultrasound data sets based on a thick-walled deforming ellipsoid and an adapted convolution model. A B-spline based frame-to-frame elastic registration method was applied to both the scanconverted and non-scanconverded data sets and the accuracy of the resulting deformation fields was quantified. The mean accuracy of the estimated displacement was very similar for the scanconverted and non-scanconverted data sets and thus, it was shown that 3D elastic registration to estimate the cardiac deformation from ultrasound images can be performed on non-scanconverted images, but that avoiding of the scanconversion step does not significantly improve the results of the displacement estimation.

  15. New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images

    Directory of Open Access Journals (Sweden)

    Lei Yang

    2015-09-01

    Full Text Available In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position. The dense spatio-temporal context (STC algorithm is then applied to track the head position and the distance from the head to floor plane is calculated in every following frame of the depth image. When the distance is lower than an adaptive threshold, the centroid height of the human will be used as the second judgment criteria to decide whether a fall incident happened. Lastly, four groups of experiments with different falling directions are performed. Experimental results show that the proposed method can detect fall incidents that occurred in different orientations, and they only need a low computation complexity.

  16. Unravelling spatio-temporal evapotranspiration patterns in topographically complex landscapes

    Science.gov (United States)

    Metzen, Daniel; Sheridan, Gary; Nyman, Petter; Lane, Patrick

    2016-04-01

    Vegetation co-evolves with soils and topography under a given long-term climatic forcing. Previous studies demonstrated a strong eco-hydrologic feedback between topography, vegetation and energy and water fluxes. Slope orientation (aspect and gradient) alter the magnitude of incoming solar radiation resulting in larger evaporative losses and less water availability on equator-facing slopes. Furthermore, non-local water inputs from upslope areas potentially contribute to available water at downslope positions. The combined effect of slope orientation and drainage position creates complex spatial patterns in biological productivity and pedogenesis, which in turn alter the local hydrology. In complex upland landscapes, topographic alteration of incoming radiation can cause substantial aridity index (ratio of potential evapotranspiration to precipitation) variations over small spatial extents. Most of the upland forests in south-east Australia are located in an aridity index (AI) range of 1-2, around the energy limited to water limited boundary, where forested systems are expected to be most sensitive to AI changes. In this research we aim to improve the fundamental understanding of spatio-temporal evolution of evapotranspiration (ET) patterns in complex terrain, accounting for local topographic effects on system properties (e.g. soil depth, sapwood area, leaf area) and variation in energy and water exchange processes due to slope orientation and drainage position. Six measurement plots were set-up in a mixed species eucalypt forest on a polar and equatorial-facing hillslope (AI ˜1.3 vs. 1.8) at varying drainage position (ridge, mid-slope, gully), while minimizing variations in other factors, e.g. geology and weather patterns. Sap flow, soil water content, incoming solar radiation and throughfall were continuously monitored at field sites spanning a wide range of soil depth (0.5 - >3m), maximum tree heights (17 - 51m) and LAI (1.2 - 4.6). Site-specific response curves

  17. Spatio-temporal Kinetics of Nontypeable Haemophilus influenzae(NTHi) Biofilms

    Science.gov (United States)

    Dhanji, Aleya; Rosas, Lucia; Ray, William; Jayaprakash, Ciriyam; Bakaletz, Lauren; Das, Jayajit

    2014-03-01

    Bacteria can form complex spatial structures known as biofilms. Biofilm formation is frequently associated with chronic infections due to the greatly enhanced antibiotic resistance of resident bacteria. However, our understanding of the role of basic processes, such as bacteria replication and resource consumption, in controlling the development and temporal change of the spatial structure remains rudimentary. Here, we examine the growth of cultured biofilms by the opportunistic pathogen NTHi. Through spatial information extracted from confocal microscopy images, we quantitatively characterize the biofilm structure as it evolves over time. We find that the equal-time height-height pair correlation function decreases with distance and scales with time for small length scales. Furthermore, both the surface roughness and the correlation length perpendicular to the surface growth direction increase with time initially and then decrease. We construct a spatially resolved agent based model beginning with the simplest possible case of a single bacteria species Fisher-Kolmogorov-Petrovsky-Piscounov equation. We show that it cannot describe the observed spatio-temporal behavior and suggest an improved two-species model that better captures the dynamics of the NTHi system. Supported by The Research Institute at Nationwide Children's Hospital.

  18. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

    Science.gov (United States)

    Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

    2016-11-01

    Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

  19. Spatio-temporal variability of CO and O3 in Hyderabad (17°N, 78°E, central India, based on MOZAIC and TES observations and WRF-Chem and MOZART-4 models

    Directory of Open Access Journals (Sweden)

    Varun Sheel

    2016-05-01

    Full Text Available This article is based on the study of the seasonal and interannual variability of carbon monoxide (CO and ozone (O3 at different altitudes of the troposphere over Hyderabad, India, during 2006–2010 using Measurement of OZone and water vapour by Airbus In-Service Aircraft (MOZAIC and observation from Tropospheric Emission Spectrometer (TES aboard NASA's Aura satellite. The MOZAIC observations show maximum seasonal variability in both CO and O3 during winter and pre-monsoon season, with CO in the range (100–200±13 ppbv and O3 in the range (50–70±9 ppbv. The time-series of MOZAIC data shows a significant increase of 4.2±1.3 % in the surface CO and 6.7±1.3 % in the surface O3 during 2006–2010 in Hyderabad. From MOZAIC observations, we identify CO and O3 profiles that are anomalous with respect to the monthly mean and compare those with Weather Research Forecast model coupled with Chemistry (WRF-Chem and Model for OZone and Related Tracers, version 4 profiles for the same day. The anomalous profiles of WRF-Chem are simulated using three convection schemes. The goodness of comparison depends on the convection scheme and the altitude region of the troposphere.

  20. Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM method

    Directory of Open Access Journals (Sweden)

    A. Kuentz

    2015-01-01

    Full Text Available Improving the understanding of past climatic or hydrologic variability has received a large attention in different fields of geosciences, such as glaciology, dendrochronology, sedimentology or hydrology. Based on different proxies, each research community produces different kind of climatic or hydrologic reanalyses, at different spatio-temporal scales and resolution. When considering climate or hydrology, numerous studies aim at characterising variability, trends or breaks using observed time-series of different regions or climate of world. However, in hydrology, these studies are usually limited to reduced temporal scale (mainly few decades, seldomly a century because they are limited to observed time-series, that suffers from a limited spatio-temporal density. This paper introduces a new model, ANATEM, based on a combination of local observations and large scale climatic informations (such as 20CR Reanalysis. This model allow to build long-term air temperature and precipitation time-series, with a high spatio-temporal resolution (daily time-step, few km2. ANATEM was tested on the air temperature and precipitation time-series of 22 watersheds situated on the Durance watershed, in the french Alps. Based on a multi-criteria and multi-scale diagnostic, the results show that ANATEM improves the performances of classical statistical models. ANATEM model have been validated on a regional level, improving spatial homogeneity of performances and on independent long-term time-series, being able to capture the regional low-frequency variabilities over more than a century (1883–2010.

  1. Spatio-temporal light springs: extended encoding of orbital angular momentum in ultrashort pulses.

    Science.gov (United States)

    Pariente, G; Quéré, F

    2015-05-01

    We introduce a new class of spatio-temporally coupled ultrashort laser beams, which are obtained by superimposing Laguerre-Gauss beams whose azimuthal mode index is correlated to their frequency. These beams are characterized by helical structures for their phase and intensity profiles, which both encode the orbital angular momentum carried by the light. They can easily be engineered in the optical range, and are naturally produced at shorter wavelengths when attosecond pulses are generated by intense femtosecond Laguerre-Gauss laser beams. These spatio-temporal "light springs" will allow for the transfer of the orbital angular momentum to matter by stimulated Raman scattering.

  2. A registration strategy for long spatio-temporal aerial remote sensing image sequence

    Science.gov (United States)

    Cao, Yutian; Yan, Dongmei; Li, Jianming; Wang, Gang

    2015-12-01

    A novel registration strategy for aerial image sequence is put forward to adapt to the long spatio-temporal span of the aerial remote sensing imaging. By setting keyframe, this strategy aligns all images in sequence to a unified datum with high registration sustainability and precision. The contrast experiment on different registration strategies is carried out based on SIFT feature matching of mid-infrared aerial sequences. The experiment results show that the proposed strategy performs well on long spatio-temporal sequences with different imaging resolutions and scenes.

  3. Spatio-Temporal Occurrence Modeling of Highly Pathogenic Avian Influenza Subtype H5N1: A Case Study in the Red River Delta, Vietnam

    Directory of Open Access Journals (Sweden)

    Chinh C. Tran

    2013-11-01

    Full Text Available Highly Pathogenic Avian Influenza (HPAI subtype H5N1 poses severe threats to both animals and humans. Investigating where, when and why the disease occurs is important to help animal health authorities develop effective control policies. This study takes into account spatial and temporal occurrence of HPAI H5N1 in the Red River Delta of Vietnam. A two-stage procedure was used: (1 logistic regression modeling to identify and quantify factors influencing the occurrence of HPAI H5N1; and (2 a geostatistical approach to develop monthly predictive maps. The results demonstrated that higher average monthly temperatures and poultry density in combination with lower average monthly precipitation, humidity in low elevation areas, roughly from November to January and April to June, contribute to the higher occurrence of HPAI H5N1. Provinces near the Gulf of Tonkin, including Hai Phong, Hai Duong, Thai Binh, Nam Dinh and Ninh Binh are areas with higher probability of occurrence of HPAI H5N1.

  4. Feature Selection, Flaring Size and Time-to-Flare Prediction Using Support Vector Regression, and Automated Prediction of Flaring Behavior Based on Spatio-Temporal Measures Using Hidden Markov Models

    Science.gov (United States)

    Al-Ghraibah, Amani

    error of approximately 3/4 a GOES class. We also consider thresholding the regressed flare size for the experiment containing both flaring and non-flaring regions and find a TPR. of 0.69 and a TNR of 0.86 for flare prediction, consistent with our previous studies of flare prediction using the same magnetic complexity features. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This conjecture is supported by our larger error rates of some 40 hours in the time-to-flare regression problem. The magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem. We also study the prediction of solar flare size and time-to-flare using two temporal features, namely the ▵- and ▵-▵-features, the same average size and time-to-flare regression error are found when these temporal features are used in size and time-to-flare prediction. In the third topic, we study the temporal evolution of active region magnetic fields using Hidden Markov Models (HMMs) which is one of the efficient temporal analyses found in literature. We extracted 38 features which describing the complexity of the photospheric magnetic field. These features are converted into a sequence of symbols using k-nearest neighbor search method. We study many parameters before prediction; like the length of the training window Wtrain which denotes to the number of history images use to train the flare and non-flare HMMs, and number of hidden states Q. In training phase, the model parameters of the HMM of each category are optimized so as to best describe the training symbol sequences. In testing phase, we use the best flare and non-flare models to predict/classify active regions as a flaring or non-flaring region

  5. Collective non-equilibrium dynamics at surfaces and the spatio-temporal edge

    Science.gov (United States)

    Marcuzzi, M.; Gambassi, A.; Pleimling, M.

    2012-11-01

    Symmetries represent a fundamental constraint for physical systems and relevant new phenomena often emerge as a consequence of their breaking. An important example is provided by space- and time-translational invariance in statistical systems, which hold at a coarse-grained scale in equilibrium and are broken by spatial and temporal boundaries, the former being implemented by surfaces —unavoidable in real samples— the latter by some initial condition for the dynamics which causes a non-equilibrium evolution. While the separate effects of these two boundaries are well understood, we demonstrate here that additional, unexpected features arise upon approaching the effective edge formed by their intersection. For this purpose, we focus on the classical semi-infinite Ising model with spin-flip dynamics evolving out of equilibrium at its critical point. Considering both subcritical and critical values of the coupling among surface spins, we present numerical evidence of a scaling regime with universal features which emerges upon approaching the spatio-temporal edge and we rationalise these findings within a field-theoretical approach.

  6. Mass spectrometric analysis of spatio-temporal dynamics of crustacean neuropeptides.

    Science.gov (United States)

    OuYang, Chuanzi; Liang, Zhidan; Li, Lingjun

    2015-07-01

    Neuropeptides represent one of the largest classes of signaling molecules used by nervous systems to regulate a wide range of physiological processes. Over the past several years, mass spectrometry (MS)-based strategies have revolutionized the discovery of neuropeptides in numerous model organisms, especially in decapod crustaceans. Here, we focus our discussion on recent advances in the use of MS-based techniques to map neuropeptides in the spatial domain and monitoring their dynamic changes in the temporal domain. These MS-enabled investigations provide valuable information about the distribution, secretion and potential function of neuropeptides with high molecular specificity and sensitivity. In situ MS imaging and in vivo microdialysis are highlighted as key technologies for probing spatio-temporal dynamics of neuropeptides in the crustacean nervous system. This review summarizes the latest advancement in MS-based methodologies for neuropeptide analysis including typical workflow and sample preparation strategies as well as major neuropeptide families discovered in decapod crustaceans. This article is part of a Special Issue entitled: Neuroproteomics: Applications in Neuroscience and Neurology.

  7. Disentangling multidimensional spatio-temporal data into their common and aberrant responses.

    Directory of Open Access Journals (Sweden)

    Young Hwan Chang

    Full Text Available With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. Since the wide range of experiments and unknown complexity of the underlying system contribute to the heterogeneity of biological data, we develop a new method by proposing an extension of Robust Principal Component Analysis (RPCA, which models common variations across multiple experiments as the lowrank component and anomalies across these experiments as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses. Thus, the proposed method provides us a new representation of these data sets which has the potential to help users acquire new insight from data.

  8. Spatio-temporal feature-extraction techniques for isolated gesture recognition in Arabic sign language.

    Science.gov (United States)

    Shanableh, Tamer; Assaleh, Khaled; Al-Rousan, M

    2007-06-01

    This paper presents various spatio-temporal feature-extraction techniques with applications to online and offline recognitions of isolated Arabic Sign Language gestures. The temporal features of a video-based gesture are extracted through forward, backward, and bidirectional predictions. The prediction errors are thresholded and accumulated into one image that represents the motion of the sequence. The motion representation is then followed by spatial-domain feature extractions. As such, the temporal dependencies are eliminated and the whole video sequence is represented by a few coefficients. The linear separability of the extracted features is assessed, and its suitability for both parametric and nonparametric classification techniques is elaborated upon. The proposed feature-extraction scheme was complemented by simple classification techniques, namely, K nearest neighbor (KNN) and Bayesian, i.e., likelihood ratio, classifiers. Experimental results showed classification performance ranging from 97% to 100% recognition rates. To validate our proposed technique, we have conducted a series of experiments using the classical way of classifying data with temporal dependencies, namely, hidden Markov models (HMMs). Experimental results revealed that the proposed feature-extraction scheme combined with simple KNN or Bayesian classification yields comparable results to the classical HMM-based scheme. Moreover, since the proposed scheme compresses the motion information of an image sequence into a single image, it allows for using simple classification techniques where the temporal dimension is eliminated. This is actually advantageous for both computational and storage requirements of the classifier.

  9. Permeability and permeability anisotropy in Crab Orchard sandstone: Experimental insights into spatio-temporal effects

    Science.gov (United States)

    Gehne, Stephan; Benson, Philip M.

    2017-08-01

    Permeability in tight crustal rocks is primarily controlled by the connected porosity, shape and orientation of microcracks, the preferred orientation of cross-bedding, and sedimentary features such as layering. This leads to a significant permeability anisotropy. Less well studied, however, are the effects of time and stress recovery on the evolution of the permeability hysteresis which is becoming increasingly important in areas ranging from fluid migration in ore-forming processes to enhanced resource extraction. Here, we report new data simulating spatio-temporal permeability changes induced using effective pressure, simulating burial depth, on a tight sandstone (Crab Orchard). We find an initially (measured at 5 MPa) anisotropy of 2.5% in P-wave velocity and 180% in permeability anisotropy is significantly affected by the direction of the effective pressure change and cyclicity; anisotropy values decrease to 1% and 10% respectively after 3 cycles to 90 MPa and back. Furthermore, we measure a steadily increasing recovery time (10-20 min) for flow parallel to cross-bedding, and a far slower recovery time (20-50 min) for flow normal to cross-bedding. These data are interpreted via strain anisotropy and accommodation models, similar to the ;seasoning; process often used in dynamic reservoir extraction.

  10. Spatio-temporal distribution of dengue fever under scenarios of climate change in the southern Taiwan

    Science.gov (United States)

    Lee, Chieh-Han; Yu, Hwa-Lung

    2014-05-01

    Dengue fever has been recognized as the most important widespread vector-borne infectious disease in recent decades. Over 40% of the world's population is risk from dengue and about 50-100 million people are infected world wide annually. Previous studies have found that dengue fever is highly correlated with climate covariates. Thus, the potential effects of global climate change on dengue fever are crucial to epidemic concern, in particular, the transmission of the disease. This present study investigated the nonlinearity of time-delayed impact of climate on spatio-temporal variations of dengue fever in the southern Taiwan during 1998 to 2011. A distributed lag nonlinear model (DLNM) is used to assess the nonlinear lagged effects of meteorology. The statistically significant meteorological factors are considered, including weekly minimum temperature and maximum 24-hour rainfall. The relative risk and the distribution of dengue fever then predict under various climate change scenarios. The result shows that the relative risk is similar for different scenarios. In addition, the impact of rainfall on the incidence risk is higher than temperature. Moreover, the incidence risk is associated to spatially population distribution. The results can be served as practical reference for environmental regulators for the epidemic prevention under climate change scenarios.

  11. A novel sensor to map auxin response and distribution at high spatio-temporal resolution.

    Science.gov (United States)

    Brunoud, Géraldine; Wells, Darren M; Oliva, Marina; Larrieu, Antoine; Mirabet, Vincent; Burrow, Amy H; Beeckman, Tom; Kepinski, Stefan; Traas, Jan; Bennett, Malcolm J; Vernoux, Teva

    2012-01-15

    Auxin is a key plant morphogenetic signal but tools to analyse dynamically its distribution and signalling during development are still limited. Auxin perception directly triggers the degradation of Aux/IAA repressor proteins. Here we describe a novel Aux/IAA-based auxin signalling sensor termed DII-VENUS that was engineered in the model plant Arabidopsis thaliana. The VENUS fast maturing form of yellow fluorescent protein was fused in-frame to the Aux/IAA auxin-interaction domain (termed domain II; DII) and expressed under a constitutive promoter. We initially show that DII-VENUS abundance is dependent on auxin, its TIR1/AFBs co-receptors and proteasome activities. Next, we demonstrate that DII-VENUS provides a map of relative auxin distribution at cellular resolution in different tissues. DII-VENUS is also rapidly degraded in response to auxin and we used it to visualize dynamic changes in cellular auxin distribution successfully during two developmental responses, the root gravitropic response and lateral organ production at the shoot apex. Our results illustrate the value of developing response input sensors such as DII-VENUS to provide high-resolution spatio-temporal information about hormone distribution and response during plant growth and development.

  12. Spatio-temporal remodeling of functional membrane microdomains organizes the signaling networks of a bacterium.

    Directory of Open Access Journals (Sweden)

    Johannes Schneider

    2015-04-01

    Full Text Available Lipid rafts are membrane microdomains specialized in the regulation of numerous cellular processes related to membrane organization, as diverse as signal transduction, protein sorting, membrane trafficking or pathogen invasion. It has been proposed that this functional diversity would require a heterogeneous population of raft domains with varying compositions. However, a mechanism for such diversification is not known. We recently discovered that bacterial membranes organize their signal transduction pathways in functional membrane microdomains (FMMs that are structurally and functionally similar to the eukaryotic lipid rafts. In this report, we took advantage of the tractability of the prokaryotic model Bacillus subtilis to provide evidence for the coexistence of two distinct families of FMMs in bacterial membranes, displaying a distinctive distribution of proteins specialized in different biological processes. One family of microdomains harbors the scaffolding flotillin protein FloA that selectively tethers proteins specialized in regulating cell envelope turnover and primary metabolism. A second population of microdomains containing the two scaffolding flotillins, FloA and FloT, arises exclusively at later stages of cell growth and specializes in adaptation of cells to stationary phase. Importantly, the diversification of membrane microdomains does not occur arbitrarily. We discovered that bacterial cells control the spatio-temporal remodeling of microdomains by restricting the activation of FloT expression to stationary phase. This regulation ensures a sequential assembly of functionally specialized membrane microdomains to strategically organize signaling networks at the right time during the lifespan of a bacterium.

  13. Spatio-temporal requirements for transposable element piRNA-mediated silencing during Drosophila oogenesis.

    Science.gov (United States)

    Dufourt, Jérémy; Dennis, Cynthia; Boivin, Antoine; Gueguen, Nathalie; Théron, Emmanuelle; Goriaux, Coline; Pouchin, Pierre; Ronsseray, Stéphane; Brasset, Emilie; Vaury, Chantal

    2014-02-01

    During Drosophila oogenesis, transposable element (TE) repression involves the Piwi-interacting RNA (piRNA) pathway which ensures genome integrity for the next generation. We developed a transgenic model to study repression of the Idefix retrotransposon in the germline. Using a candidate gene KD-approach, we identified differences in the spatio-temporal requirements of the piRNA pathway components for piRNA-mediated silencing. Some of them (Aub, Vasa, Spn-E) are necessary in very early stages of oogenesis within the germarium and appear to be less important for efficient TE silencing thereafter. Others (Piwi, Ago3, Mael) are required at all stages of oogenesis. Moreover, during early oogenesis, in the dividing cysts within the germarium, Idefix anti-sense transgenes escape host control, and this is associated with very low piwi expression. Silencing of P-element-based transgenes is also strongly weakened in these cysts. This region, termed the 'Piwiless pocket' or Pilp, may ensure that new TE insertions occur and are transmitted to the next generation, thereby contributing to genome dynamics. In contrast, piRNA-mediated silencing is strong in germline stem cells in which TE mobilization is tightly repressed ensuring the continued production of viable germline cysts.

  14. Spatio-temporal snowmelt variability across the headwaters of the Southern Rocky Mountains

    Science.gov (United States)

    Fassnacht, S. R.; López-Moreno, J. I.; Ma, C.; Weber, A. N.; Pfohl, A. K. D.; Kampf, S. K.; Kappas, M.

    2017-09-01

    Understanding the rate of snowmelt helps inform how water stored as snow will transform into streamflow. Data from 87 snow telemetry (SNOTEL) stations across the Southern Rocky Mountains were used to estimate spatio-temporal melt factors. Decreases in snow water equivalent were correlated to temperature at these monitoring stations for eight half-month periods from early March through late June. Time explained 70% of the variance in the computed snow melt factors. A residual linear correlation model was used to explain subsequent spatial variability. Longitude, slope, and land cover type explained further variance. For evergreen trees, canopy density was relevant to find enhanced melt rates; while for all other land cover types, denoted as non-evergreen, lower melt rates were found at high elevation, high latitude and north facing slopes, denoting that in cold environments melting is less effective than in milder sites. A change in the temperature sensor about mid-way through the time series (1990 to 2013) created a discontinuity in the temperature dataset. An adjustment to the time series yield larger computed melt factors.

  15. Spatio-temporal analysis of soil erosion risk and runoff using AnnAGNPS

    Science.gov (United States)

    Yeshaneh, Eleni; Wagner, Wolfgang; Blöschl, Günter

    2014-05-01

    Soil erosion is one form of land degradation in Ethiopia deteriorating the fertility and productivity of the land. This fact indicates the need to delineate high erosion risk areas for appropriate soil and conservation measures. Land use/cover change is one of the important factors in soil erosion. This study attempts test and implement AnnAGNPS model to estimate the spatio-temporal patterns of soil erosion and runoff associated with land use changes in the past 50 years in the 9900 ha upstream part of the Koga catchment. High erosion risk areas will then be delineated for simulation of the appropriate soil and water conservation measures that would reduce the soil loss. The study is based on two years high temporal resolution data on discharge, sediment, and rain fall accompanied by historical land use/cover data generated from satellite imagery. In addition, it uses several documented physical parameters of the study area. The Koga catchment is one of the agriculture dominated typical catchments in the North Western Ethiopian highlands with high population density that lead to increased pressure on natural resources.

  16. SafeBox: adaptable spatio-temporal generalization for location privacy protection

    Directory of Open Access Journals (Sweden)

    Sergio Mascetti

    2014-08-01

    Full Text Available Spatial and temporal generalization emerged in the literature as a common approach to preserve location privacy. However, existing solutions have two main shortcomings. First, spatiotemporal generalization can be used with different objectives: for example, to guarantee anonymity or to decrease the sensitivity of the location information. Hence, the strategy used to compute the generalization can follow different semantics often depending on the privacy threat, while most of the existing solutions are specifically designed for a single semantics. Second, existing techniques prevent the so-called inversion attack by adopting a top-down strategy that needs to acquire a large amount of information. This may not be feasible when this information is dynamic (e.g., position or properties of objects and needs to be acquired from external services (e.g., Google Maps. In this contribution we present a formal model of the problem that is compatible with most of the semantics proposed so far in the literature, and that supports new semantics as well. Our BottomUp algorithm for spatio-temporal generalization is compatible with the use of online services, it supports generalizations based on arbitrary semantics, and it is safe with respect to the inversion attack. By considering two datasets and two examples of semantics, we experimentally compare BottomUp with a more classical top-down algorithm, showing that BottomUp is efficient and guarantees better performance in terms of the average size (space and time of the generalized regions.

  17. Characterizing the spatio-temporal and energy-dependent response of riometer absorption to particle precipitation

    Science.gov (United States)

    Kellerman, Adam; Makarevich, Roman; Spanswick, Emma; Donovan, Eric; Shprits, Yuri

    2016-07-01

    Energetic electrons in the 10's of keV range precipitate to the upper D- and lower E-region ionosphere, and are responsible for enhanced ionization. The same particles are important in the inner magnetosphere, as they provide a source of energy for waves, and thus relate to relativistic electron enhancements in Earth's radiation belts.In situ observations of plasma populations and waves are usually limited to a single point, which complicates temporal and spatial analysis. Also, the lifespan of satellite missions is often limited to several years which does not allow one to infer long-term climatology of particle precipitation, important for affecting ionospheric conditions at high latitudes. Multi-point remote sensing of the ionospheric plasma conditions can provide a global view of both ionospheric and magnetospheric conditions, and the coupling between magnetospheric and ionospheric phenomena can be examined on time-scales that allow comprehensive statistical analysis. In this study we utilize multi-point riometer measurements in conjunction with in situ satellite data, and physics-based modeling to investigate the spatio-temporal and energy-dependent response of riometer absorption. Quantifying this relationship may be a key to future advancements in our understanding of the complex D-region ionosphere, and may lead to enhanced specification of auroral precipitation both during individual events and over climatological time-scales.

  18. Disentangling multidimensional spatio-temporal data into their common and aberrant responses.

    Science.gov (United States)

    Chang, Young Hwan; Korkola, James; Amin, Dhara N; Moasser, Mark M; Carmena, Jose M; Gray, Joe W; Tomlin, Claire J

    2015-01-01

    With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. Since the wide range of experiments and unknown complexity of the underlying system contribute to the heterogeneity of biological data, we develop a new method by proposing an extension of Robust Principal Component Analysis (RPCA), which models common variations across multiple experiments as the lowrank component and anomalies across these experiments as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses. Thus, the proposed method provides us a new representation of these data sets which has the potential to help users acquire new insight from data.

  19. Integrating spatio-temporal environmental models for planning ski runs

    NARCIS (Netherlands)

    Pfeffer, Karin

    2003-01-01

    The establishment of ski runs and ski lifts, the action of skiing and maintenance of ski runs may cause considerable environmental impact. Clearly, for improvements to be made in the planning of ski runs in alpine terrain a good understanding of the environmental system and the response of environme

  20. Statistical modelling of spatio-temporal dependencies in NGS data

    NARCIS (Netherlands)

    Ranciati, Saverio

    2016-01-01

    Next-generation sequencing (NGS) heeft zich snel gevestigd als de huidige standaard in de genetische analyse. Deze omschakeling van microarray naar NGS vereist nieuwe statistische strategieën om de onderzoeksvragen aan te pakken. Ten eerste, NGS data bestaat uit discrete waarnemingen, meestal gekenm

  1. Spatio-temporal analysis of forest modeling in Mexico

    Directory of Open Access Journals (Sweden)

    Saira Y. Martínez-Santiago

    2017-01-01

    Full Text Available Hay consenso de que las acciones antropogénicas están degradando los ecosistemas a un ritmo alarmante. La modelación y las nuevas tecnologías, como las tecnologías de la información y de la comunicación ( TIC, se utilizan en modo creciente para tomar decisiones sobre el manejo y la conservación de los recursos naturales. En este trabajo se analizaron la evolución temporal y la distribución espacial de la producción científica en modelación forestal en México. De 1980 a 2015, 454 autores participaron en la publicación de 259 artículos en 37 revistas (84 % mexicanas, de las cuales 28 están indizadas en el Journal Citation Reports (JCR. Los trabajos sobre manejo forestal han sido los más relevantes, aunque tienen una importancia relativa a la baja, mientras que los de servicios ambientales y distribución potencial van ganando importancia. Los autores pertenecen a 89 instituciones, de las cuales 65 % son mexicanas. Durante el periodo analizado, el número de autores (y las colaboraciones y publicaciones incrementaron 12 y nueve veces, respectivamente. Estos incrementos coinciden con la evolución de las políticas normativas y el establecimiento y apoyo del Sistema Nacional de Investigadores. Las colaboraciones en la red actual de modelación forestal aún tienen gran potencial de crecimiento.

  2. Joint Spatio-Temporal Filtering Methods for DOA and Fundamental Frequency Estimation

    DEFF Research Database (Denmark)

    Jensen, Jesper Rindom; Christensen, Mads Græsbøll; Benesty, Jacob

    2015-01-01

    In this paper, spatio-temporal filtering methods are proposed for estimating the direction-of-arrival (DOA) and fundamental frequency of periodic signals, like those produced by the speech production system and many musical instruments using microphone arrays. This topic has quite recently received...

  3. Spatio-temporal flow pattern observations using bio-inspired hair flow sensors

    NARCIS (Netherlands)

    Dagamseh, Ahmad; Hmeidi, Sarah; Krijnen, Gijs

    2015-01-01

    In nature, sensing is a fundamental property of virtually all living creatures. For many insects airflow patterns, as observed by means of their hair-sensors, carry highly valuable information exposing the sources of these flows. Flow-sensor arrays can be used to extract spatio-temporal flow fields

  4. Microscale spatio-temporal patterns of oxygen dynamics in permeable intertidal sediments (Skallingen, Denmark)

    DEFF Research Database (Denmark)

    Walpersdorf, Eva Christine; Andersen, Thorbjørn Joest; Elberling, Bo

    a range of spatial and temporal timescales are required to fully quantify the importance of such ecosystems for benthic autotrophic and heterotrophic activity, carbon mineralization and element cycling. Here, small scale spatio-temporal heterogeneity of oxygen (O2) dynamics (mm to cm, min to hours, day...

  5. Spatio-Temporal Layout of Human Actions for Improved Bag-of-Words Action Detection

    NARCIS (Netherlands)

    Burghouts, G.J.; Schutte, K.

    2013-01-01

    We investigate how human action recognition can be improved by considering spatio-temporal layout of actions. From literature, we adopt a pipeline consisting of STIP features, a random forest to quantize the features into histograms, and an SVM classifier. Our goal is to detect 48 human actions, ran

  6. Spatio-temporal second-order quantum correlations of surface plasmon polaritons

    CERN Document Server

    Berthel, Martin; Drezet, Aurélien

    2016-01-01

    We present an experimental methodology to observe spatio-temporal second-order quantum coherence of surface plasmon polaritons which are emitted by nitrogen vacancy color centers attached at the apex of an optical tip. The approach relies on leakage radiation microscopy in the Fourier space and we use this approach to test wave-particle duality for surface plasmon polaritons.

  7. The electromagnetic fields and the radiation of a spatio-temporally varying electric current loop

    CERN Document Server

    Lazar, Markus

    2013-01-01

    The electric and magnetic fields of a spatio-temporally varying electric current loop are calculated using the Jefimenko equations. The radiation and the nonradiation parts of the electromagnetic fields are derived in the framework of Maxwell's theory of electromagnetic fields. In this way, a new, exact, analytical solution of the Maxwell equation is found.

  8. BING3D: Fast Spatio-Temporal Proposals for Action Localization

    NARCIS (Netherlands)

    Gati, E.; Schavemaker, J.G.M.; van Gemert, J.C.

    2015-01-01

    The goal of this work is realistic action localization in video with the aid of spatio-temporal proposals. Current proposal generation methods are computationally demanding and are not practical for large-scale datasets. The main contribution of this work is a novel and fast alternative. Our method

  9. A FRAMEWORK FOR ONLINE SPATIO-TEMPORAL DATA VISUALIZATION BASED ON HTML5

    Directory of Open Access Journals (Sweden)

    B. Mao

    2012-07-01

    Full Text Available Web is entering a new phase – HTML5. New features of HTML5 should be studied for online spatio-temporal data visualization. In the proposed framework, spatio-temporal data is stored in the data server and is sent to user browsers with WebSocket. Public geo-data such as Internet digital map is integrated into the browsers. Then animation is implemented through the canvas object defined by the HTML5 specification. To simulate the spatio-temporal data source, we collected the daily location of 15 users with GPS tracker. The current positions of the users are collected every minute and are recorded in a file. Based on this file, we generate a real time spatio-temporal data source which sends out current user location every second.By enlarging the real time scales by 60 times, we can observe the movement clearly. The data transmitted with WebSocket is the coordinates of users' current positions, which will can be demonstrated in client browsers.

  10. Spatio-temporal tomography of the lower troposhere using GPS signals

    NARCIS (Netherlands)

    Flores, A.; Vilà-Guerau de Arellano, J.; Gradinarsky, L.P.; Rius, A.; Escudera, A.

    2001-01-01

    The obtaining of the spatio-temporal representation of the wet refractivity distribution in the lower troposphere using GPS has been a line of research that has recently achieved very promising results. We here present a review of the work done and discuss some aspects as well as trace some future l

  11. Cortical Spatio-Temporal Dynamics Underlying Phonological Target Detection in Humans

    Science.gov (United States)

    Chang, Edward F.; Edwards, Erik; Nagarajan, Srikantan S.; Fogelson, Noa; Dalal, Sarang S.; Canolty, Ryan T.; Kirsch, Heidi E.; Barbaro, Nicholas M.; Knight, Robert T.

    2011-01-01

    Selective processing of task-relevant stimuli is critical for goal-directed behavior. We used electrocorticography to assess the spatio-temporal dynamics of cortical activation during a simple phonological target detection task, in which subjects press a button when a prespecified target syllable sound is heard. Simultaneous surface potential…

  12. Spatio-temporal distribution and production of calanoid copepods in the central Baltic Sea

    DEFF Research Database (Denmark)

    Hansen, F.C.; Möllmann, Christian; Schutz, U.

    2006-01-01

    The aim of our study was the exploration of species-specific distribution and production patterns of dominant copepods in the Central Baltic Sea (Bornholm Basin). Spatio-temporal distribution, egg and secondary production were studied by means of net-sampling and egg production experiments from A...

  13. Spatio-temporal tomography of the lower troposhere using GPS signals

    NARCIS (Netherlands)

    Flores, A.; Vilà-Guerau de Arellano, J.; Gradinarsky, L.P.; Rius, A.; Escudera, A.

    2001-01-01

    The obtaining of the spatio-temporal representation of the wet refractivity distribution in the lower troposphere using GPS has been a line of research that has recently achieved very promising results. We here present a review of the work done and discuss some aspects as well as trace some future l

  14. Spatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery

    Directory of Open Access Journals (Sweden)

    Ömer Kücük

    2008-06-01

    Full Text Available Computing fire danger and fire risk on a spatio-temporal scale is of crucial importance in fire management planning, and in the simulation of fire growth and development across a landscape. However, due to the complex nature of forests, fire risk and danger potential maps are considered one of the most difficult thematic layers to build up. Remote sensing and digital terrain data have been introduced for efficient discrete classification of fire risk and fire danger potential. In this study, two time-series data of Landsat imagery were used for determining spatio-temporal change of fire risk and danger potential in Korudag forest planning unit in northwestern Turkey. The method comprised the following two steps: (1 creation of indices of the factors influencing fire risk and danger; (2 evaluation of spatio-temporal changes in fire risk and danger of given areas using remote sensing as a quick and inexpensive means and determining the pace of forest cover change. Fire risk and danger potential indices were based on species composition, stand crown closure, stand development stage, insolation, slope and, proximity of agricultural lands to forest and distance from settlement areas. Using the indices generated, fire risk and danger maps were produced for the years 1987 and 2000. Spatio-temporal analyses were then realized based on the maps produced. Results obtained from the study showed that the use of Landsat imagery provided a valuable characterization and mapping of vegetation structure and type with overall classification accuracy higher than 83%.

  15. Spatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery

    Science.gov (United States)

    Sağlam, Bülent; Bilgili, Ertuğrul; Durmaz, Bahar Dinç; Kadıoğulları, Ali İhsan; Küçük, Ömer

    2008-01-01

    Computing fire danger and fire risk on a spatio-temporal scale is of crucial importance in fire management planning, and in the simulation of fire growth and development across a landscape. However, due to the complex nature of forests, fire risk and danger potential maps are considered one of the most difficult thematic layers to build up. Remote sensing and digital terrain data have been introduced for efficient discrete classification of fire risk and fire danger potential. In this study, two time-series data of Landsat imagery were used for determining spatio-temporal change of fire risk and danger potential in Korudag forest planning unit in northwestern Turkey. The method comprised the following two steps: (1) creation of indices of the factors influencing fire risk and danger; (2) evaluation of spatio-temporal changes in fire risk and danger of given areas using remote sensing as a quick and inexpensive means and determining the pace of forest cover change. Fire risk and danger potential indices were based on species composition, stand crown closure, stand development stage, insolation, slope and, proximity of agricultural lands to forest and distance from settlement areas. Using the indices generated, fire risk and danger maps were produced for the years 1987 and 2000. Spatio-temporal analyses were then realized based on the maps produced. Results obtained from the study showed that the use of Landsat imagery provided a valuable characterization and mapping of vegetation structure and type with overall classification accuracy higher than 83%. PMID:27879918

  16. The Impact of Spatio-Temporal Constraints on Cursive Letter Handwriting in Children

    Science.gov (United States)

    Chartrel, Estelle; Vinter, Annie

    2008-01-01

    The study assessed the impact of spatial and temporal constraints on handwriting movements in young children. One hundred children of 5-7 years of age of both genders were given the task of copying isolated cursive letters under four conditions: normal, with temporal, spatial, or spatio-temporal constraints. The results showed that imposing…

  17. Developmental regulation of spatio-temporal patterns of cortical circuit activation

    Directory of Open Access Journals (Sweden)

    Trevor Charles Griffen

    2013-01-01

    Full Text Available Neural circuits are refined in an experience-dependent manner during early postnatal development. How development modulates the spatio-temporal propagation of activity through cortical circuits is poorly understood. Here we use voltage sensitive dye imaging (VSD to show that there are significant changes in the spatio-temporal patterns of intracortical signals in primary visual cortex from postnatal day 13 (P13, eye opening, to P28, the peak of the critical period for rodent visual cortical plasticity. Upon direct stimulation of layer 4 (L4, activity spreads to L2/3 and to L5 at all ages. However, while from eye opening to the peak of the critical period, the amplitude and persistence of the voltage signal decrease, peak activation is reached more quickly and the interlaminar gain increases with age. The lateral spread of activation within layers remains unchanged throughout the time window under analysis. These developmental changes in spatio-temporal patterns of intracortical circuit activation are mediated by differences in the contributions of excitatory and inhibitory synaptic components. Our results demonstrate that after eye opening the circuit in primary visual cortex is refined through a progression of changes that shape the spatio-temporal patterns of circuit activation. Signals become more efficiently propagated across layers through developmentally regulated changes in interlaminar gain.

  18. The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation

    National Research Council Canada - National Science Library

    Phillips, Lawrence; Pearl, Lisa

    2015-01-01

    The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility...

  19. The design and realization of a socio-economic statistical spatio-temporal database

    Science.gov (United States)

    Yang, Cankun; Li, Xiaojuan; Liu, Qiang; Zhao, Huimin; Zhang, Jia; Zhang, Haibo

    2010-11-01

    This paper aims to introduce a case of Socio-economic statistical Spatio-temporal Database. This database system services in the rural socio-economic statistical work, which is a combination of statistical tables, spatial data, search algorithm and maintenance interface. Administrative codes are the conjunction media of spatial data and attribute data, and also are the key words of database query processing. Through storing the changing information in the database, it could reflect the change of administrative divisions. As the main issues of database design, the studying of the approach to recording and querying these changes as well as the processing of statistical data by the rules of administrative divisions changes, requires a large amount of research work. To address these problems, a series of management analysis tools have been developed to deal with the processing of socio-economic statistical data with changes in the administrative division. A searching algorithm of spatio-temporal database is used to ensure the comparability of the results, which are acquired by the positive sequence and the anti-sequence temporal query under complex spatial changes in the administrative division. According to the spatial changes, searching algorithm of spatio-temporal database mainly translates temporal series statistical data into standard format data which is matched to the benchmark year. The searching algorithm controls the process of inquiry through recursion of the table of the administrative code changes, which are composed of multi-way tree structure and double linked list and record the relationship between upper and lower level administrative units. These search algorithms and meta-data storage structures constitute the spatio-temporal database, so as to serve the spatial analysis of statistical data. The comparability problem mentioned above was well solved by this approach. And a set of functions was provided by this system with spatio-temporal database

  20. Disease mapping and spatio-temporal analysis: importance of expected-case computation criteria

    Directory of Open Access Journals (Sweden)

    Gonzalo López-Abente

    2014-11-01

    Full Text Available The municipal, spatial pattern of male stomach cancer mortality in Spain, spanning the period 1989-2008, was studied, comparing the results of depicting mortality using different expected-case computation methods in a spatial and spa- tio-temporal modelling context. Expected cases for each municipality were first calculated by two methods: (i using refer- ence rates for each 5-year period; and (ii using average reference rates for the overall period. This was visualised by two types of models: (i independent maps for each period based on the model proposed by Besag, York and Mollié; and (ii a series of maps over time based on a model with spatio-temporal interaction terms. An additional model, based on mortality rate ratios as an alternative to the traditional use of standardised mortality ratios, was also fitted. Integrated nested Laplace approximations were used as the Bayesian inference tool. The results show that, in general, the geographical pattern was maintained across the study period, and that the maps differed appreciably according to the method used to obtain the expected number of cases. While the use of average reference rates appears to be the most suitable choice where the aim is to study time trends by area, it may nevertheless mask the spatial pattern in situations where the time trend is very marked and the study period is long. When it comes to studying changes in the spatial pattern of stomach cancer mortality, we feel that it is most useful to plot independent maps by period and use the “local” rates for each period as reference in the com- putation of expected cases.

  1. Combination of multi-mission altimetry data along the Mekong River with spatio-temporal kriging

    Science.gov (United States)

    Boergens, Eva; Buhl, Sven; Dettmering, Denise; Klüppelberg, Claudia; Seitz, Florian

    2016-12-01

    River water-level time series at fixed geographical locations, so-called virtual stations, have been computed from single altimeter crossings for many years. Their temporal resolution is limited by the repeat cycle of the individual altimetry missions. The combination of all altimetry measurements along a river enables computing a water-level time series with improved temporal and spatial resolutions. This study uses the geostatistical method of spatio-temporal ordinary kriging to link multi-mission altimetry data along the Mekong River. The required covariance models reflecting the water flow are estimated based on empirical covariance values between altimetry observations at various locations. In this study, two covariance models are developed and tested in the case of the Mekong River: a stationary and a non-stationary covariance model. The proposed approach predicts water-level time series at different locations along the Mekong River with a temporal resolution of 5 days. Validation is performed against in situ data from four gauging stations, yielding RMS differences between 0.82 and 1.29 m and squared correlation coefficients between 0.89 and 0.94. Both models produce comparable results when used for combining data from Envisat, Jason-1, and SARAL for the time period between 2002 and 2015. The quality of the predicted time series turns out to be robust against a possibly decreasing availability of altimetry mission data. This demonstrates that our method is able to close the data gap between the end of the Envisat and the launch of the SARAL mission with interpolated time series.

  2. Combination of multi-mission altimetry data along the Mekong River with spatio-temporal kriging

    Science.gov (United States)

    Boergens, Eva; Buhl, Sven; Dettmering, Denise; Klüppelberg, Claudia; Seitz, Florian

    2017-05-01

    River water-level time series at fixed geographical locations, so-called virtual stations, have been computed from single altimeter crossings for many years. Their temporal resolution is limited by the repeat c