WorldWideScience

Sample records for spatial plant-wrack model

  1. Spatial cluster modelling

    CERN Document Server

    Lawson, Andrew B

    2002-01-01

    Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

  2. Modeling for spatial multilevel structural data

    Science.gov (United States)

    Min, Suqin; He, Xiaoqun

    2013-03-01

    The traditional multilevel model assumed independence between groups. However, the datasets grouped by geographical units often has spatial dependence. The individual is influenced not only by its region but also by the adjacent regions, and level-2 residual distribution assumption of traditional multilevel model is violated. In order to deal with such spatial multilevel data, we introduce spatial statistics and spatial econometric models into multilevel model, and apply spatial parameters and adjacency matrix in traditional level-2 model to reflect the spatial autocorrelation. Spatial lag model express spatial effects. We build spatial multilevel model which consider both multilevel thinking and spatial correlation.

  3. Continuous Spatial Process Models for Spatial Extreme Values

    KAUST Repository

    Sang, Huiyan

    2010-01-28

    We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e. g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model. © 2009 International Biometric Society.

  4. Spatial Uncertainty Analysis of Ecological Models

    Energy Technology Data Exchange (ETDEWEB)

    Jager, H.I.; Ashwood, T.L.; Jackson, B.L.; King, A.W.

    2000-09-02

    The authors evaluated the sensitivity of a habitat model and a source-sink population model to spatial uncertainty in landscapes with different statistical properties and for hypothetical species with different habitat requirements. Sequential indicator simulation generated alternative landscapes from a source map. Their results showed that spatial uncertainty was highest for landscapes in which suitable habitat was rare and spatially uncorrelated. Although, they were able to exert some control over the degree of spatial uncertainty by varying the sampling density drawn from the source map, intrinsic spatial properties (i.e., average frequency and degree of spatial autocorrelation) played a dominant role in determining variation among realized maps. To evaluate the ecological significance of landscape variation, they compared the variation in predictions from a simple habitat model to variation among landscapes for three species types. Spatial uncertainty in predictions of the amount of source habitat depended on both the spatial life history characteristics of the species and the statistical attributes of the synthetic landscapes. Species differences were greatest when the landscape contained a high proportion of suitable habitat. The predicted amount of source habitat was greater for edge-dependent (interior) species in landscapes with spatially uncorrelated(correlated) suitable habitat. A source-sink model demonstrated that, although variation among landscapes resulted in relatively little variation in overall population growth rate, this spatial uncertainty was sufficient in some situations, to produce qualitatively different predictions about population viability (i.e., population decline vs. increase).

  5. Dynamic spatial panels : models, methods, and inferences

    NARCIS (Netherlands)

    Elhorst, J. Paul

    This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent

  6. Location Aggregation of Spatial Population CTMC Models

    Directory of Open Access Journals (Sweden)

    Luca Bortolussi

    2016-10-01

    Full Text Available In this paper we focus on spatial Markov population models, describing the stochastic evolution of populations of agents, explicitly modelling their spatial distribution, representing space as a discrete, finite graph. More specifically, we present a heuristic approach to aggregating spatial locations, which is designed to preserve the dynamical behaviour of the model whilst reducing the computational cost of analysis. Our approach combines stochastic approximation ideas (moment closure, linear noise, with computational statistics (spectral clustering to obtain an efficient aggregation, which is experimentally shown to be reasonably accurate on two case studies: an instance of epidemic spreading and a London bike sharing scenario.

  7. Hierarchical modeling and analysis for spatial data

    CERN Document Server

    Banerjee, Sudipto; Gelfand, Alan E

    2003-01-01

    Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and dat

  8. Spatial Allocator for air quality modeling

    Science.gov (United States)

    The Spatial Allocator is a set of tools that helps users manipulate and generate data files related to emissions and air quality modeling without requiring the use of a commercial Geographic Information System.

  9. Evaluating spatial patterns in hydrological modelling

    DEFF Research Database (Denmark)

    Koch, Julian

    is not fully exploited by current modelling frameworks due to the lack of suitable spatial performance metrics. Furthermore, the traditional model evaluation using discharge is found unsuitable to lay confidence on the predicted catchment inherent spatial variability of hydrological processes in a fully...... the contiguous United Sates (10^6 km2). To this end, the thesis at hand applies a set of spatial performance metrics on various hydrological variables, namely land-surface-temperature (LST), evapotranspiration (ET) and soil moisture. The inspiration for the applied metrics is found in related fields...

  10. Crime Modeling using Spatial Regression Approach

    Science.gov (United States)

    Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.

    2018-01-01

    Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.

  11. Using Spatial Gradients to Model Localization Phenomena

    Energy Technology Data Exchange (ETDEWEB)

    D.J.Bammann; D.Mosher; D.A.Hughes; N.R.Moody; P.R.Dawson

    1999-07-01

    We present the final report on a Laboratory-Directed Research and Development project, Using Spatial Gradients to Model Localization Phenomena, performed during the fiscal years 1996 through 1998. The project focused on including spatial gradients in the temporal evolution equations of the state variables that describe hardening in metal plasticity models. The motivation was to investigate the numerical aspects associated with post-bifurcation mesh dependent finite element solutions in problems involving damage or crack propagation as well as problems in which strain Localizations occur. The addition of the spatial gradients introduces a mathematical length scale that eliminates the mesh dependency of the solution. In addition, new experimental techniques were developed to identify the physical mechanism associated with the numerical length scale.

  12. Landscape Modelling and Simulation Using Spatial Data

    Directory of Open Access Journals (Sweden)

    Amjed Naser Mohsin AL-Hameedawi

    2017-08-01

    Full Text Available In this paper a procedure was performed for engendering spatial model of landscape acclimated to reality simulation. This procedure based on combining spatial data and field measurements with computer graphics reproduced using Blender software. Thereafter that we are possible to form a 3D simulation based on VIS ALL packages. The objective was to make a model utilising GIS, including inputs to the feature attribute data. The objective of these efforts concentrated on coordinating a tolerable spatial prototype, circumscribing facilitation scheme and outlining the intended framework. Thus; the eventual result was utilized in simulation form. The performed procedure contains not only data gathering, fieldwork and paradigm providing, but extended to supply a new method necessary to provide the respective 3D simulation mapping production, which authorises the decision makers as well as investors to achieve permanent acceptance an independent navigation system for Geoscience applications.

  13. Spatial Modeling for Resources Framework (SMRF)

    Science.gov (United States)

    Spatial Modeling for Resources Framework (SMRF) was developed by Dr. Scott Havens at the USDA Agricultural Research Service (ARS) in Boise, ID. SMRF was designed to increase the flexibility of taking measured weather data and distributing the point measurements across a watershed. SMRF was developed...

  14. Testing spatial heterogeneity with stock assessment models

    DEFF Research Database (Denmark)

    Jardim, Ernesto; Eero, Margit; Silva, Alexandra

    2018-01-01

    This paper describes a methodology that combines meta-population theory and stock assessment models to gain insights about spatial heterogeneity of the meta-population in an operational time frame. The methodology was tested with stochastic simulations for different degrees of connectivity betwee...

  15. The 3-D global spatial data model foundation of the spatial data infrastructure

    CERN Document Server

    Burkholder, Earl F

    2008-01-01

    Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements. Modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Foundation of the Spatial Data Infrastructure offers a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This groundbreaking spatial model incorporates both a functional model and a stochastic model to connect the physical world to the ECEF rectangular system. Combining horizontal and vertical data into a single, three-dimensional database, this authoritative monograph provides a logical development of theoretical concepts and practical tools that can be used to handle spatial data mo...

  16. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    . Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...

  17. Spatially explicit modeling in ecology: A review

    Science.gov (United States)

    DeAngelis, Donald L.; Yurek, Simeon

    2017-01-01

    The use of spatially explicit models (SEMs) in ecology has grown enormously in the past two decades. One major advancement has been that fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology. Many areas of modeling have shifted toward a focus on capturing these fine-scale details, to improve mechanistic understanding of ecosystems. However, spatially implicit models (SIMs) have played a dominant role in ecology, and arguments have been made that SIMs, which account for the effects of space without specifying spatial positions, have an advantage of being simpler and more broadly applicable, perhaps contributing more to understanding. We address this debate by comparing SEMs and SIMs in examples from the past few decades of modeling research. We argue that, although SIMs have been the dominant approach in the incorporation of space in theoretical ecology, SEMs have unique advantages for addressing pragmatic questions concerning species populations or communities in specific places, because local conditions, such as spatial heterogeneities, organism behaviors, and other contingencies, produce dynamics and patterns that usually cannot be incorporated into simpler SIMs. SEMs are also able to describe mechanisms at the local scale that can create amplifying positive feedbacks at that scale, creating emergent patterns at larger scales, and therefore are important to basic ecological theory. We review the use of SEMs at the level of populations, interacting populations, food webs, and ecosystems and argue that SEMs are not only essential in pragmatic issues, but must play a role in the understanding of causal relationships on landscapes.

  18. Linking spatial and dynamic models for traffic maneuvers

    DEFF Research Database (Denmark)

    Olderog, Ernst-Rüdiger; Ravn, Anders Peter; Wisniewski, Rafal

    2015-01-01

    For traffic maneuvers of multiple vehicles on highways we build an abstract spatial and a concrete dynamic model. In the spatial model we show the safety (collision freedom) of lane-change maneuvers. By linking the spatial and dynamic model via suitable refinements of the spatial atoms to distance...

  19. Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel

    2016-12-19

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  20. A Spatially Extended Model for Residential Segregation

    Directory of Open Access Journals (Sweden)

    Antonio Aguilera

    2007-01-01

    Full Text Available We analyze urban spatial segregation phenomenon in terms of the income distribution over a population, and an inflationary parameter weighting the evolution of housing prices. For this, we develop a discrete spatially extended model based on a multiagent approach. In our model, the mobility of socioeconomic agents is driven only by the housing prices. Agents exchange location in order to fit their status to the cost of their housing. On the other hand, the price of a particular house depends on the status of its tenant, and on the neighborhood mean lodging cost weighted by a control parameter. The agent's dynamics converges to a spatially organized configuration, whose regularity is measured by using an entropy-like indicator. This simple model provides a dynamical process organizing the virtual city, in a way that the population inequality and the inflationary parameter determine the degree of residential segregation in the final stage of the process, in agreement with the segregation-inequality thesis put forward by Douglas Massey.

  1. Indoorgml - a Standard for Indoor Spatial Modeling

    Science.gov (United States)

    Li, Ki-Joune

    2016-06-01

    With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.

  2. Spatially explicit non-Mendelian diploid model

    OpenAIRE

    Lanchier, N.; Neuhauser, C.

    2009-01-01

    We introduce a spatially explicit model for the competition between type $a$ and type $b$ alleles. Each vertex of the $d$-dimensional integer lattice is occupied by a diploid individual, which is in one of three possible states or genotypes: $aa$, $ab$ or $bb$. We are interested in the long-term behavior of the gene frequencies when Mendel's law of segregation does not hold. This results in a voter type model depending on four parameters; each of these parameters measures the strength of comp...

  3. The quantitative modelling of human spatial habitability

    Science.gov (United States)

    Wise, J. A.

    1985-01-01

    A model for the quantitative assessment of human spatial habitability is presented in the space station context. The visual aspect assesses how interior spaces appear to the inhabitants. This aspect concerns criteria such as sensed spaciousness and the affective (emotional) connotations of settings' appearances. The kinesthetic aspect evaluates the available space in terms of its suitability to accommodate human movement patterns, as well as the postural and anthrometric changes due to microgravity. Finally, social logic concerns how the volume and geometry of available space either affirms or contravenes established social and organizational expectations for spatial arrangements. Here, the criteria include privacy, status, social power, and proxemics (the uses of space as a medium of social communication).

  4. Modeling mental spatial reasoning about cardinal directions.

    Science.gov (United States)

    Schultheis, Holger; Bertel, Sven; Barkowsky, Thomas

    2014-01-01

    This article presents research into human mental spatial reasoning with orientation knowledge. In particular, we look at reasoning problems about cardinal directions that possess multiple valid solutions (i.e., are spatially underdetermined), at human preferences for some of these solutions, and at representational and procedural factors that lead to such preferences. The article presents, first, a discussion of existing, related conceptual and computational approaches; second, results of empirical research into the solution preferences that human reasoners actually have; and, third, a novel computational model that relies on a parsimonious and flexible spatio-analogical knowledge representation structure to robustly reproduce the behavior observed with human reasoners. Copyright © 2014 Cognitive Science Society, Inc.

  5. Spatial Database Modeling for Indoor Navigation Systems

    Science.gov (United States)

    Gotlib, Dariusz; Gnat, Miłosz

    2013-12-01

    For many years, cartographers are involved in designing GIS and navigation systems. Most GIS applications use the outdoor data. Increasingly, similar applications are used inside buildings. Therefore it is important to find the proper model of indoor spatial database. The development of indoor navigation systems should utilize advanced teleinformation, geoinformatics, geodetic and cartographical knowledge. The authors present the fundamental requirements for the indoor data model for navigation purposes. Presenting some of the solutions adopted in the world they emphasize that navigation applications require specific data to present the navigation routes in the right way. There is presented original solution for indoor data model created by authors on the basis of BISDM model. Its purpose is to expand the opportunities for use in indoor navigation.

  6. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species......When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number...... of different factors influence the time development of the system. This often makes it challenging to construct a mathematical model from which to draw conclusions. One traditional way of capturing the dynamics in a mathematical model is to formulate a set of coupled differential equations for the essential...

  7. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  8. A Computational Model of Spatial Development

    Science.gov (United States)

    Hiraki, Kazuo; Sashima, Akio; Phillips, Steven

    Psychological experiments on children's development of spatial knowledge suggest experience at self-locomotion with visual tracking as important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to mentally track a target object (i.e., maintaining a representation of an object's position when outside the field-of-view) as a model for spatial development. Mental tracking is considered as prediction of an object's position given the previous environmental state and motor commands, and the current environment state resulting from movement. Following Jordan & Rumelhart's (1992) forward modeling architecture the system consists of two components: an inverse model of sensory input to desired motor commands; and a forward model of motor commands to desired sensory input (goals). The robot was tested on the `three cups' paradigm (where children are required to select the cup containing the hidden object under various movement conditions). Consistent with child development, without the capacity for self-locomotion the robot's errors are self-center based. When given the ability of self-locomotion the robot responds allocentrically.

  9. Spherical Process Models for Global Spatial Statistics

    KAUST Repository

    Jeong, Jaehong

    2017-11-28

    Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture the spatial and temporal behavior of these global data sets. Though the geodesic distance is the most natural metric for measuring distance on the surface of a sphere, mathematical limitations have compelled statisticians to use the chordal distance to compute the covariance matrix in many applications instead, which may cause physically unrealistic distortions. Therefore, covariance functions directly defined on a sphere using the geodesic distance are needed. We discuss the issues that arise when dealing with spherical data sets on a global scale and provide references to recent literature. We review the current approaches to building process models on spheres, including the differential operator, the stochastic partial differential equation, the kernel convolution, and the deformation approaches. We illustrate realizations obtained from Gaussian processes with different covariance structures and the use of isotropic and nonstationary covariance models through deformations and geographical indicators for global surface temperature data. To assess the suitability of each method, we compare their log-likelihood values and prediction scores, and we end with a discussion of related research problems.

  10. Latent spatial models and sampling design for landscape genetics

    Science.gov (United States)

    Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

    2016-01-01

    We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

  11. Spatial Stochastic Point Models for Reservoir Characterization

    Energy Technology Data Exchange (ETDEWEB)

    Syversveen, Anne Randi

    1997-12-31

    The main part of this thesis discusses stochastic modelling of geology in petroleum reservoirs. A marked point model is defined for objects against a background in a two-dimensional vertical cross section of the reservoir. The model handles conditioning on observations from more than one well for each object and contains interaction between objects, and the objects have the correct length distribution when penetrated by wells. The model is developed in a Bayesian setting. The model and the simulation algorithm are demonstrated by means of an example with simulated data. The thesis also deals with object recognition in image analysis, in a Bayesian framework, and with a special type of spatial Cox processes called log-Gaussian Cox processes. In these processes, the logarithm of the intensity function is a Gaussian process. The class of log-Gaussian Cox processes provides flexible models for clustering. The distribution of such a process is completely characterized by the intensity and the pair correlation function of the Cox process. 170 refs., 37 figs., 5 tabs.

  12. Theoretical aspects of spatial-temporal modeling

    CERN Document Server

    Matsui, Tomoko

    2015-01-01

    This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alph...

  13. Models and Inference for Multivariate Spatial Extremes

    KAUST Repository

    Vettori, Sabrina

    2017-12-07

    The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical

  14. Spatially explicit modelling of cholera epidemics

    Science.gov (United States)

    Finger, F.; Bertuzzo, E.; Mari, L.; Knox, A. C.; Gatto, M.; Rinaldo, A.

    2013-12-01

    Epidemiological models can provide crucial understanding about the dynamics of infectious diseases. Possible applications range from real-time forecasting and allocation of health care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. We apply a spatially explicit model to the cholera epidemic that struck Haiti in October 2010 and is still ongoing. The dynamics of susceptibles as well as symptomatic and asymptomatic infectives are modelled at the scale of local human communities. Dissemination of Vibrio cholerae through hydrological transport and human mobility along the road network is explicitly taken into account, as well as the effect of rainfall as a driver of increasing disease incidence. The model is calibrated using a dataset of reported cholera cases. We further model the long term impact of several types of interventions on the disease dynamics by varying parameters appropriately. Key epidemiological mechanisms and parameters which affect the efficiency of treatments such as antibiotics are identified. Our results lead to conclusions about the influence of different intervention strategies on the overall epidemiological dynamics.

  15. Modeling strategic investment decisions in spatial markets

    International Nuclear Information System (INIS)

    Lorenczik, Stefan; Malischek, Raimund

    2014-01-01

    Markets for natural resources and commodities are often oligopolistic. In these markets, production capacities are key for strategic interaction between the oligopolists. We analyze how different market structures influence oligopolistic capacity investments and thereby affect supply, prices and rents in spatial natural resource markets using mathematical programing models. The models comprise an investment period and a supply period in which players compete in quantities. We compare three models, one perfect competition and two Cournot models, in which the product is either traded through long-term contracts or on spot markets in the supply period. Tractability and practicality of the approach are demonstrated in an application to the international metallurgical coal market. Results may vary substantially between the different models. The metallurgical coal market has recently made progress in moving away from long-term contracts and more towards spot market-based trade. Based on our results, we conclude that this regime switch is likely to raise consumer rents but lower producer rents. The total welfare differs only negligibly.

  16. The Role of Visuo-Spatial Abilities in Recall of Spatial Descriptions: A Mediation Model

    Science.gov (United States)

    Meneghetti, Chiara; De Beni, Rossana; Pazzaglia, Francesca; Gyselinck, Valerie

    2011-01-01

    This research investigates how visuo-spatial abilities (such as mental rotation--MR--and visuo-spatial working memory--VSWM--) work together to influence the recall of environmental descriptions. We tested a mediation model in which VSWM was assumed to mediate the relationship between MR and spatial text recall. First, 120 participants were…

  17. Spatial Situation Models and Text Comprehension.

    Science.gov (United States)

    Haenggi, Dieter; And Others

    1995-01-01

    Reports findings from three experiments designed to show how readers inferred spatial information relevant to a story character's movements through a previously memorized layout of a fictional building. Examines how inference measures are related to spatial imagery. (HB)

  18. A physically based analytical spatial air temperature and humidity model

    Science.gov (United States)

    Yang Yang; Theodore A. Endreny; David J. Nowak

    2013-01-01

    Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat...

  19. Spectral Modelling for Spatial Network Analysis

    NARCIS (Netherlands)

    Nourian, P.; Rezvani, S.; Sariyildiz, I.S.; van der Hoeven, F.D.; Attar, Ramtin; Chronis, Angelos; Hanna, Sean; Turrin, Michela

    2016-01-01

    Spatial Networks represent the connectivity structure between units of space as a weighted graph whose links are weighted as to the strength of connections. In case of urban spatial networks, the units of space correspond closely to streets and in architectural spatial networks the units correspond

  20. Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

    Science.gov (United States)

    Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

    2010-12-01

    Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies

  1. Spatial Data Web Services Pricing Model Infrastructure

    Science.gov (United States)

    Ozmus, L.; Erkek, B.; Colak, S.; Cankurt, I.; Bakıcı, S.

    2013-08-01

    most important law with related NSDI is the establishment of General Directorate of Geographic Information System under the Ministry of Environment and Urbanism. due to; to do or to have do works and activities with related to the establishment of National Geographic Information Systems (NGIS), usage of NGIS and improvements of NGIS. Outputs of these projects are served to not only public administration but also to Turkish society. Today for example, TAKBIS data (cadastre services) are shared more than 50 institutions by Web services, Tusaga-Aktif system has more than 3800 users who are having real-time GPS data correction, Orthophoto WMS services has been started for two years as a charge of free. Today there is great discussion about data pricing among the institutions. Some of them think that the pricing is storage of the data. Some of them think that the pricing is value of data itself. There is no certain rule about pricing. On this paper firstly, pricing of data storage and later on spatial data pricing models in different countries are investigated to improve institutional understanding in Turkey.

  2. Reducing Spatial Data Complexity for Classification Models

    International Nuclear Information System (INIS)

    Ruta, Dymitr; Gabrys, Bogdan

    2007-01-01

    Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which further hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the

  3. Spatial regression-based model specifications for exogenous and endogenous spatial interaction

    OpenAIRE

    Manfred M Fischer; James P. LeSage

    2014-01-01

    Spatial interaction models represent a class of models that are used for modelling origin-destination flow data. The focus of this paper is on the log-normal version of the model. In this context, we consider spatial econometric specifications that can be used to accommodate two types of dependence scenarios, one involving endogenous interaction and the other exogenous interaction. These model specifications replace the conventional assumption of independence between origin-destination flows ...

  4. Panchromatic SED modelling of spatially resolved galaxies

    Science.gov (United States)

    Smith, Daniel J. B.; Hayward, Christopher C.

    2018-05-01

    We test the efficacy of the energy-balance spectral energy distribution (SED) fitting code MAGPHYS for recovering the spatially resolved properties of a simulated isolated disc galaxy, for which it was not designed. We perform 226 950 MAGPHYS SED fits to regions between 0.2 and 25 kpc in size across the galaxy's disc, viewed from three different sight-lines, to probe how well MAGPHYS can recover key galaxy properties based on 21 bands of UV-far-infrared model photometry. MAGPHYS yields statistically acceptable fits to >99 per cent of the pixels within the r-band effective radius and between 59 and 77 percent of pixels within 20 kpc of the nucleus. MAGPHYS is able to recover the distribution of stellar mass, star formation rate (SFR), specific SFR, dust luminosity, dust mass, and V-band attenuation reasonably well, especially when the pixel size is ≳ 1 kpc, whereas non-standard outputs (stellar metallicity and mass-weighted age) are recovered less well. Accurate recovery is more challenging in the smallest sub-regions of the disc (pixel scale ≲ 1 kpc), where the energy balance criterion becomes increasingly incorrect. Estimating integrated galaxy properties by summing the recovered pixel values, the true integrated values of all parameters considered except metallicity and age are well recovered at all spatial resolutions, ranging from 0.2 kpc to integrating across the disc, albeit with some evidence for resolution-dependent biases. These results must be considered when attempting to analyse the structure of real galaxies with actual observational data, for which the `ground truth' is unknown.

  5. Mathematical Modeling of spatial disease variables by Spatial Fuzzy Logic for Spatial Decision Support Systems

    Science.gov (United States)

    Platz, M.; Rapp, J.; Groessler, M.; Niehaus, E.; Babu, A.; Soman, B.

    2014-11-01

    A Spatial Decision Support System (SDSS) provides support for decision makers and should not be viewed as replacing human intelligence with machines. Therefore it is reasonable that decision makers are able to use a feature to analyze the provided spatial decision support in detail to crosscheck the digital support of the SDSS with their own expertise. Spatial decision support is based on risk and resource maps in a Geographic Information System (GIS) with relevant layers e.g. environmental, health and socio-economic data. Spatial fuzzy logic allows the representation of spatial properties with a value of truth in the range between 0 and 1. Decision makers can refer to the visualization of the spatial truth of single risk variables of a disease. Spatial fuzzy logic rules that support the allocation of limited resources according to risk can be evaluated with measure theory on topological spaces, which allows to visualize the applicability of this rules as well in a map. Our paper is based on the concept of a spatial fuzzy logic on topological spaces that contributes to the development of an adaptive Early Warning And Response System (EWARS) providing decision support for the current or future spatial distribution of a disease. It supports the decision maker in testing interventions based on available resources and apply risk mitigation strategies and provide guidance tailored to the geo-location of the user via mobile devices. The software component of the system would be based on open source software and the software developed during this project will also be in the open source domain, so that an open community can build on the results and tailor further work to regional or international requirements and constraints. A freely available EWARS Spatial Fuzzy Logic Demo was developed wich enables a user to visualize risk and resource maps based on individual data in several data formats.

  6. Spatial Econometric data analysis: moving beyond traditional models

    NARCIS (Netherlands)

    Florax, R.J.G.M.; Vlist, van der A.J.

    2003-01-01

    This article appraises recent advances in the spatial econometric literature. It serves as the introduction too collection of new papers on spatial econometric data analysis brought together in this special issue, dealing specifically with new extensions to the spatial econometric modeling

  7. Spatially explicit fate modelling of nanomaterials in natural waters

    NARCIS (Netherlands)

    Quik, J.T.K.; Klein, de J.J.M.; Koelmans, A.A.

    2015-01-01

    Site specific exposure assessments for engineered nanoparticles (ENPs) require spatially explicit fate models, which however are not yet available. Here we present an ENP fate model (NanoDUFLOW) that links ENP specific process descriptions to a spatially explicit hydrological model. The link enables

  8. Modeling spatial variation in avian survival and residency probabilities

    Science.gov (United States)

    Saracco, James F.; Royle, J. Andrew; DeSante, David F.; Gardner, Beth

    2010-01-01

    The importance of understanding spatial variation in processes driving animal population dynamics is widely recognized. Yet little attention has been paid to spatial modeling of vital rates. Here we describe a hierarchical spatial autoregressive model to provide spatially explicit year-specific estimates of apparent survival (phi) and residency (pi) probabilities from capture-recapture data. We apply the model to data collected on a declining bird species, Wood Thrush (Hylocichla mustelina), as part of a broad-scale bird-banding network, the Monitoring Avian Productivity and Survivorship (MAPS) program. The Wood Thrush analysis showed variability in both phi and pi among years and across space. Spatial heterogeneity in residency probability was particularly striking, suggesting the importance of understanding the role of transients in local populations. We found broad-scale spatial patterning in Wood Thrush phi and pi that lend insight into population trends and can direct conservation and research. The spatial model developed here represents a significant advance over approaches to investigating spatial pattern in vital rates that aggregate data at coarse spatial scales and do not explicitly incorporate spatial information in the model. Further development and application of hierarchical capture-recapture models offers the opportunity to more fully investigate spatiotemporal variation in the processes that drive population changes.

  9. Spatial data modelling and maximum entropy theory

    Czech Academy of Sciences Publication Activity Database

    Klimešová, Dana; Ocelíková, E.

    2005-01-01

    Roč. 51, č. 2 (2005), s. 80-83 ISSN 0139-570X Institutional research plan: CEZ:AV0Z10750506 Keywords : spatial data classification * distribution function * error distribution Subject RIV: BD - Theory of Information

  10. Adaptive Gaussian Predictive Process Models for Large Spatial Datasets

    Science.gov (United States)

    Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.

    2011-01-01

    Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is determined by a judicious choice of “knots” or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial-temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. PMID:22298952

  11. Topological models and frameworks for 3D spatial objects

    Science.gov (United States)

    Zlatanova, Siyka; Rahman, Alias Abdul; Shi, Wenzhong

    2004-05-01

    Topology is one of the mechanisms to describe relationships between spatial objects. Thus, it is the basis for many spatial operations. Models utilizing the topological properties of spatial objects are usually called topological models, and are considered by many researchers as the best suited for complex spatial analysis (i.e., the shortest path search). A number of topological models for two-dimensional and 2.5D spatial objects have been implemented (or are under consideration) by GIS and DBMS vendors. However, when we move to one more dimension (i.e., three-dimensions), the complexity of the relationships increases, and this requires new approaches, rules and representations. This paper aims to give an overview of the 3D topological models presented in the literature, and to discuss generic issues related to 3D modeling. The paper also considers models in object-oriented (OO) environments. Finally, future trends for research and development in this area are highlighted.

  12. Uncertainties in spatially aggregated predictions from a logistic regression model

    NARCIS (Netherlands)

    Horssen, P.W. van; Pebesma, E.J.; Schot, P.P.

    2002-01-01

    This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The

  13. Recent developments in spatial analysis spatial statistics, behavioural modelling, and computational intelligence

    CERN Document Server

    Getis, Arthur

    1997-01-01

    In recent years, spatial analysis has become an increasingly active field, as evidenced by the establishment of educational and research programs at many universities. Its popularity is due mainly to new technologies and the development of spatial data infrastructures. This book illustrates some recent developments in spatial analysis, behavioural modelling, and computational intelligence. World renown spatial analysts explain and demonstrate their new and insightful models and methods. The applications are in areas of societal interest such as the spread of infectious diseases, migration behaviour, and retail and agricultural location strategies. In addition, there is emphasis on the uses of new technologoies for the analysis of spatial data through the application of neural network concepts.

  14. Practical likelihood analysis for spatial generalized linear mixed models

    DEFF Research Database (Denmark)

    Bonat, W. H.; Ribeiro, Paulo Justiniano

    2016-01-01

    , respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...

  15. Spatial and Temporal Low-Dimensional Models for Fluid Flow

    Science.gov (United States)

    Kalb, Virginia

    2008-01-01

    A document discusses work that obtains a low-dimensional model that captures both temporal and spatial flow by constructing spatial and temporal four-mode models for two classic flow problems. The models are based on the proper orthogonal decomposition at two reference Reynolds numbers. Model predictions are made at an intermediate Reynolds number and compared with direct numerical simulation results at the new Reynolds number.

  16. Spatial modeling of potential woody biomass flow

    Science.gov (United States)

    Woodam Chung; Nathaniel Anderson

    2012-01-01

    The flow of woody biomass to end users is determined by economic factors, especially the amount available across a landscape and delivery costs of bioenergy facilities. The objective of this study develop methodology to quantify landscape-level stocks and potential biomass flows using the currently available spatial database road network analysis tool. We applied this...

  17. Free-streaming radiation in cosmological models with spatial curvature

    Science.gov (United States)

    Wilson, M. L.

    1982-01-01

    The effects of spatial curvature on radiation anisotropy are examined for the standard Friedmann-Robertson-Walker model universes. The effect of curvature is found to be very important when considering fluctuations with wavelengths comparable to the horizon. It is concluded that the behavior of radiation fluctuations in models with spatial curvature is quite different from that in spatially flat models, and that models with negative curvature are most strikingly different. It is therefore necessary to take the curvature into account in careful studies of the anisotropy of the microwave background.

  18. Spatial-Temporal Correlation Properties of the 3GPP Spatial Channel Model and the Kronecker MIMO Channel Model

    Directory of Open Access Journals (Sweden)

    Cheng-Xiang Wang

    2007-02-01

    Full Text Available The performance of multiple-input multiple-output (MIMO systems is greatly influenced by the spatial-temporal correlation properties of the underlying MIMO channels. This paper investigates the spatial-temporal correlation characteristics of the spatial channel model (SCM in the Third Generation Partnership Project (3GPP and the Kronecker-based stochastic model (KBSM at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-temporal separability at all the three levels. The spatial-temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA and angle of departure (AoD. The KBSM with the Gaussian-shaped power azimuth spectrum (PAS is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.

  19. Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor

    Directory of Open Access Journals (Sweden)

    Jae-Han Park

    2012-06-01

    Full Text Available This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

  20. Spatial uncertainty model for visual features using a Kinect™ sensor.

    Science.gov (United States)

    Park, Jae-Han; Shin, Yong-Deuk; Bae, Ji-Hun; Baeg, Moon-Hong

    2012-01-01

    This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

  1. Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model

    Directory of Open Access Journals (Sweden)

    Chengcheng Xu

    2017-08-01

    Full Text Available Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.

  2. FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

    Science.gov (United States)

    Russell A. Parsons

    2006-01-01

    Efforts to quantitatively evaluate the effectiveness of fuels treatments are hampered by inconsistencies between the spatial scale at which fuel treatments are implemented and the spatial scale, and detail, with which we model fire and fuel interactions. Central to this scale inconsistency is the resolution at which variability within the fuel bed is considered. Crown...

  3. Stochastic Spatial Models in Ecology: A Statistical Physics Approach

    Science.gov (United States)

    Pigolotti, Simone; Cencini, Massimo; Molina, Daniel; Muñoz, Miguel A.

    2017-11-01

    Ecosystems display a complex spatial organization. Ecologists have long tried to characterize them by looking at how different measures of biodiversity change across spatial scales. Ecological neutral theory has provided simple predictions accounting for general empirical patterns in communities of competing species. However, while neutral theory in well-mixed ecosystems is mathematically well understood, spatial models still present several open problems, limiting the quantitative understanding of spatial biodiversity. In this review, we discuss the state of the art in spatial neutral theory. We emphasize the connection between spatial ecological models and the physics of non-equilibrium phase transitions and how concepts developed in statistical physics translate in population dynamics, and vice versa. We focus on non-trivial scaling laws arising at the critical dimension D = 2 of spatial neutral models, and their relevance for biological populations inhabiting two-dimensional environments. We conclude by discussing models incorporating non-neutral effects in the form of spatial and temporal disorder, and analyze how their predictions deviate from those of purely neutral theories.

  4. Updates to the Demographic and Spatial Allocation Models to ...

    Science.gov (United States)

    EPA announced the availability of the draft report, Updates to the Demographic and Spatial Allocation Models to Produce Integrated Climate and Land Use Scenarios (ICLUS) for a 30-day public comment period. The ICLUS version 2 (v2) modeling tool furthered land change modeling by providing nationwide housing development scenarios up to 2100. ICLUS V2 includes updated population and land use data sets and addressing limitations identified in ICLUS v1 in both the migration and spatial allocation models. The companion user guide describes the development of ICLUS v2 and the updates that were made to the original data sets and the demographic and spatial allocation models. [2017 UPDATE] Get the latest version of ICLUS and stay up-to-date by signing up to the ICLUS mailing list. The GIS tool enables users to run SERGoM with the population projections developed for the ICLUS project and allows users to modify the spatial allocation housing density across the landscape.

  5. An API for Integrating Spatial Context Models with Spatial Reasoning Algorithms

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun

    2006-01-01

    The integration of context-aware applications with spatial context models is often done using a common query language. However, algorithms that estimate and reason about spatial context information can benefit from a tighter integration. An object-oriented API makes such integration possible...... and can help reduce the complexity of algorithms making them easier to maintain and develop. This paper propose an object-oriented API for context models of the physical environment and extensions to a location modeling approach called geometric space trees for it to provide adequate support for location...... modeling. The utility of the API is evaluated in several real-world cases from an indoor location system, and spans several types of spatial reasoning algorithms....

  6. Applications of spatial statistical network models to stream data

    Science.gov (United States)

    Isaak, Daniel J.; Peterson, Erin E.; Ver Hoef, Jay M.; Wenger, Seth J.; Falke, Jeffrey A.; Torgersen, Christian E.; Sowder, Colin; Steel, E. Ashley; Fortin, Marie-Josée; Jordan, Chris E.; Ruesch, Aaron S.; Som, Nicholas; Monestiez, Pascal

    2014-01-01

    Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for terrestrial applications and are not optimized for streams. A new class of spatial statistical model, based on valid covariance structures for stream networks, can be used with many common types of stream data (e.g., water quality attributes, habitat conditions, biological surveys) through application of appropriate distributions (e.g., Gaussian, binomial, Poisson). The spatial statistical network models account for spatial autocorrelation (i.e., nonindependence) among measurements, which allows their application to databases with clustered measurement locations. Large amounts of stream data exist in many areas where spatial statistical analyses could be used to develop novel insights, improve predictions at unsampled sites, and aid in the design of efficient monitoring strategies at relatively low cost. We review the topic of spatial autocorrelation and its effects on statistical inference, demonstrate the use of spatial statistics with stream datasets relevant to common research and management questions, and discuss additional applications and development potential for spatial statistics on stream networks. Free software for implementing the spatial statistical network models has been developed that enables custom applications with many stream databases.

  7. Bayesian disease mapping: hierarchical modeling in spatial epidemiology

    National Research Council Canada - National Science Library

    Lawson, Andrew

    2013-01-01

    .... Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications...

  8. Bayesian disease mapping: hierarchical modeling in spatial epidemiology

    National Research Council Canada - National Science Library

    Lawson, Andrew

    2013-01-01

    Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas...

  9. A latent parameter node-centric model for spatial networks.

    Directory of Open Access Journals (Sweden)

    Nicholas D Larusso

    Full Text Available Spatial networks, in which nodes and edges are embedded in space, play a vital role in the study of complex systems. For example, many social networks attach geo-location information to each user, allowing the study of not only topological interactions between users, but spatial interactions as well. The defining property of spatial networks is that edge distances are associated with a cost, which may subtly influence the topology of the network. However, the cost function over distance is rarely known, thus developing a model of connections in spatial networks is a difficult task. In this paper, we introduce a novel model for capturing the interaction between spatial effects and network structure. Our approach represents a unique combination of ideas from latent variable statistical models and spatial network modeling. In contrast to previous work, we view the ability to form long/short-distance connections to be dependent on the individual nodes involved. For example, a node's specific surroundings (e.g. network structure and node density may make it more likely to form a long distance link than other nodes with the same degree. To capture this information, we attach a latent variable to each node which represents a node's spatial reach. These variables are inferred from the network structure using a Markov Chain Monte Carlo algorithm. We experimentally evaluate our proposed model on 4 different types of real-world spatial networks (e.g. transportation, biological, infrastructure, and social. We apply our model to the task of link prediction and achieve up to a 35% improvement over previous approaches in terms of the area under the ROC curve. Additionally, we show that our model is particularly helpful for predicting links between nodes with low degrees. In these cases, we see much larger improvements over previous models.

  10. Spatial pattern of diarrhea based on regional economic and environment by spatial autoregressive model

    Science.gov (United States)

    Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy

    2014-10-01

    The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.

  11. From spatial ecology to spatial epidemiology: modeling spatial distributions of different cancer types with principal coordinates of neighbor matrices.

    Science.gov (United States)

    Voutilainen, Ari; Tolppanen, Anna-Maija; Vehviläinen-Julkunen, Katri; Sherwood, Paula R

    2014-01-01

    Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n = 320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010. PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165(th) PCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13 - 58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet's criterion. The spatial variation of prostate cancer was best captured (adjusted r (2) = 0.579). PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of

  12. How does spatial study design influence density estimates from spatial capture-recapture models?

    Directory of Open Access Journals (Sweden)

    Rahel Sollmann

    Full Text Available When estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (SCR models present an advance over non-spatial models by accounting for individual movement. While these models should be more robust to changes in trapping designs, they have not been well tested. Here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for SCR models. We analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions. To see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the South of the array. Additionally, we simulated and analysed data under a suite of trap designs and home range sizes. In the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. Black bear density was approximately 10 individuals per 100 km(2. Our simulation study showed that SCR models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. SCR models performed well across a range of spatial trap setups and animal movements. Contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. This renders SCR models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species.

  13. Spatial Modeling Tools for Cell Biology

    Science.gov (United States)

    2006-10-01

    of the cells total volume. The cytosol contains thousands of enzymes that are responsible for the catalyzation of glycolysis and gluconeogenesis ... dog , swine and pig models [Pantely, 1990, 1991; Stanley 1992]. In these studies, blood flow through the left anterior descending (LAD) coronary...perfusion. In conclusion, even thought our model falls within the (rather large) error bounds of experimental dog , pig and swine models, the

  14. Spatial modelling with R-INLA: A review

    KAUST Repository

    Bakka, Haakon

    2018-02-18

    Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R-INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Mat\\\\\\'ern Gaussian random fields. In this review, we discuss the large success of spatial modelling with R-INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight-forward approaches for non-stationary spatial models and non-separable space-time models.

  15. Modeling structural change in spatial system dynamics: A Daisyworld example.

    Science.gov (United States)

    Neuwirth, C; Peck, A; Simonović, S P

    2015-03-01

    System dynamics (SD) is an effective approach for helping reveal the temporal behavior of complex systems. Although there have been recent developments in expanding SD to include systems' spatial dependencies, most applications have been restricted to the simulation of diffusion processes; this is especially true for models on structural change (e.g. LULC modeling). To address this shortcoming, a Python program is proposed to tightly couple SD software to a Geographic Information System (GIS). The approach provides the required capacities for handling bidirectional and synchronized interactions of operations between SD and GIS. In order to illustrate the concept and the techniques proposed for simulating structural changes, a fictitious environment called Daisyworld has been recreated in a spatial system dynamics (SSD) environment. The comparison of spatial and non-spatial simulations emphasizes the importance of considering spatio-temporal feedbacks. Finally, practical applications of structural change models in agriculture and disaster management are proposed.

  16. Spatial emission modelling for residential wood combustion in Denmark

    DEFF Research Database (Denmark)

    Plejdrup, Marlene Schmidt; Nielsen, Ole-Kenneth; Brandt, Jørgen

    2016-01-01

    model with the developed weighting factors (76 ton PM2.5) is in good agreement with the case study (95 ton PM2.5), and that the new model has improved the spatial emission distribution significantly compared to the previous model (284 ton PM2.5). Additionally, a sensitivity analysis was done...

  17. Can spatial statistical river temperature models be transferred between catchments?

    Science.gov (United States)

    Jackson, Faye L.; Fryer, Robert J.; Hannah, David M.; Malcolm, Iain A.

    2017-09-01

    There has been increasing use of spatial statistical models to understand and predict river temperature (Tw) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses Tw data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum Tw (Twmax) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (Ta) metric (LS_Ta models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of Twmax. However, the predictions were biased when mean Twmax differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_Ta models transferred less well, with particularly poor performance when the relationship with the Ta metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between Twmax and the Ta metric, indicating that the Ta metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting Tw at unmonitored locations across

  18. Was Thebes Necessary? Contingency in Spatial Modelling

    OpenAIRE

    Evans, Tim S.; Rivers, Ray J.

    2016-01-01

    When data are poor, we resort to theory modeling. This is a two-step process. We have first to identify the appropriate type of model for the system under consideration and then to tailor it to the specifics of the case. To understand settlement formation, which is the concern of this article, this involves choosing not only input parameter values such as site separations but also input functions that characterizes the ease of travel between sites. Although the generic behavior of the model i...

  19. Sensor placement for calibration of spatially varying model parameters

    Science.gov (United States)

    Nath, Paromita; Hu, Zhen; Mahadevan, Sankaran

    2017-08-01

    This paper presents a sensor placement optimization framework for the calibration of spatially varying model parameters. To account for the randomness of the calibration parameters over space and across specimens, the spatially varying parameter is represented as a random field. Based on this representation, Bayesian calibration of spatially varying parameter is investigated. To reduce the required computational effort during Bayesian calibration, the original computer simulation model is substituted with Kriging surrogate models based on the singular value decomposition (SVD) of the model response and the Karhunen-Loeve expansion (KLE) of the spatially varying parameters. A sensor placement optimization problem is then formulated based on the Bayesian calibration to maximize the expected information gain measured by the expected Kullback-Leibler (K-L) divergence. The optimization problem needs to evaluate the expected K-L divergence repeatedly which requires repeated calibration of the spatially varying parameter, and this significantly increases the computational effort of solving the optimization problem. To overcome this challenge, an approximation for the posterior distribution is employed within the optimization problem to facilitate the identification of the optimal sensor locations using the simulated annealing algorithm. A heat transfer problem with spatially varying thermal conductivity is used to demonstrate the effectiveness of the proposed method.

  20. Empirical spatial econometric modelling of small scale neighbourhood

    Science.gov (United States)

    Gerkman, Linda

    2012-07-01

    The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.

  1. An Evolutionary Model of Spatial Competition

    DEFF Research Database (Denmark)

    Knudsen, Thorbjørn; Winter, Sidney G.

    to environmental change.  Formally, the model builds on the NK framework for organizational analysis, with firm policy choices and environmental conditions represented by segments of a string of N bits; it joins this structure to an abstract representation of space based on the idea of a cellular automaton......  This paper sets forth an evolutionary model in which diverse businesses, with diverse offerings, compete in a stylized physical space.  When a business firm attempts to expand its activity, so as to profit further from the capabilities it has developed, it necessarily does so in a "new location......" - sometimes close-by existing activity, but often not. The model representation reflects the fact that the physical space in which economic activity takes place is far from homogeneous. The firm then confronts both the challenge of replicating its routines and the hazard that existing routines may not work...

  2. On spatial mutation-selection models

    Energy Technology Data Exchange (ETDEWEB)

    Kondratiev, Yuri, E-mail: kondrat@math.uni-bielefeld.de [Fakultät für Mathematik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld (Germany); Kutoviy, Oleksandr, E-mail: kutoviy@math.uni-bielefeld.de, E-mail: kutovyi@mit.edu [Fakultät für Mathematik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld (Germany); Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); Minlos, Robert, E-mail: minl@iitp.ru; Pirogov, Sergey, E-mail: pirogov@proc.ru [IITP, RAS, Bolshoi Karetnyi 19, Moscow (Russian Federation)

    2013-11-15

    We discuss the selection procedure in the framework of mutation models. We study the regulation for stochastically developing systems based on a transformation of the initial Markov process which includes a cost functional. The transformation of initial Markov process by cost functional has an analytic realization in terms of a Kimura-Maruyama type equation for the time evolution of states or in terms of the corresponding Feynman-Kac formula on the path space. The state evolution of the system including the limiting behavior is studied for two types of mutation-selection models.

  3. SPATIAL MODELLING FOR DESCRIBING SPATIAL VARIABILITY OF SOIL PHYSICAL PROPERTIES IN EASTERN CROATIA

    Directory of Open Access Journals (Sweden)

    Igor Bogunović

    2016-06-01

    Full Text Available The objectives of this study were to characterize the field-scale spatial variability and test several interpolation methods to identify the best spatial predictor of penetration resistance (PR, bulk density (BD and gravimetric water content (GWC in the silty loam soil in Eastern Croatia. The measurements were made on a 25 x 25-m grid which created 40 individual grid cells. Soil properties were measured at the center of the grid cell deep 0-10 cm and 10-20 cm. Results demonstrated that PR and GWC displayed strong spatial dependence at 0-10 cm BD, while there was moderate and weak spatial dependence of PR, BD and GWC at depth of 10-20 cm. Semi-variogram analysis suggests that future sampling intervals for investigated parameters can be increased to 35 m in order to reduce research costs. Additionally, interpolation models recorded similar root mean square values with high predictive accuracy. Results suggest that investigated properties do not have uniform interpolation method implying the need for spatial modelling in the evaluation of these soil properties in Eastern Croatia.

  4. Properties of spatial Cox process models

    DEFF Research Database (Denmark)

    Møller, Jesper

    Probabilistic properties of Cox processes of relevance for statistical modelling and inference are studied. Particularly, we study the most important classes of Cox processes, including log Gaussian Cox processes, shot noise Cox processes, and permanent Cox processes. We consider moment properties...... and point process operations such as thinning, displacements, and superpositioning. We also discuss how to simulate specific Cox processes....

  5. Modelling spatial density using continuous wavelet transforms

    Indian Academy of Sciences (India)

    Space debris; wavelets; Mexican hat; Laplace distribution; random search; parameter estimation. ... Author Affiliations. D Sudheer Reddy1 N Gopal Reddy2 A K Anilkumar3. Digital Mapping and Modelling Division, Advanced Data Processing Research Institute, Secunderabad 500 009, India; Department of Mathematics, ...

  6. Modelling spatial density using continuous wavelet transforms

    Indian Academy of Sciences (India)

    A K ANILKUMAR3. 1Digital Mapping and Modelling Division, Advanced Data Processing Research .... probability of conjunction is very high and the miss distance between active satellite and debri object is less ... particularly helpful in tackling problems involving signal identification and detection of hidden transients (hard ...

  7. A random spatial network model based on elementary postulates

    Science.gov (United States)

    Karlinger, Michael R.; Troutman, Brent M.

    1989-01-01

    A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

  8. Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

    Science.gov (United States)

    Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

    2017-09-11

    Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

  9. Uncertainty in a spatial evacuation model

    Science.gov (United States)

    Mohd Ibrahim, Azhar; Venkat, Ibrahim; Wilde, Philippe De

    2017-08-01

    Pedestrian movements in crowd motion can be perceived in terms of agents who basically exhibit patient or impatient behavior. We model crowd motion subject to exit congestion under uncertainty conditions in a continuous space and compare the proposed model via simulations with the classical social force model. During a typical emergency evacuation scenario, agents might not be able to perceive with certainty the strategies of opponents (other agents) owing to the dynamic changes entailed by the neighborhood of opponents. In such uncertain scenarios, agents will try to update their strategy based on their own rules or their intrinsic behavior. We study risk seeking, risk averse and risk neutral behaviors of such agents via certain game theory notions. We found that risk averse agents tend to achieve faster evacuation time whenever the time delay in conflicts appears to be longer. The results of our simulations also comply with previous work and conform to the fact that evacuation time of agents becomes shorter once mutual cooperation among agents is achieved. Although the impatient strategy appears to be the rational strategy that might lead to faster evacuation times, our study scientifically shows that the more the agents are impatient, the slower is the egress time.

  10. A Unified 3D Spatial Data Model for Surface and Subsurface Spatial ...

    African Journals Online (AJOL)

    A simulation of the above, on and below 3D spatial models for man-made constructions at differ-ent LoDs is presented. A simulation of this with regards to mining and cadastre is also presented. The model presented can be adopted in realising 3D GIS for mining and 3D cadastre can be realised in Ghana. Further work is ...

  11. Stochastic Dynamics on Hypergraphs and the Spatial Majority Rule Model

    Science.gov (United States)

    Lanchier, N.; Neufer, J.

    2013-04-01

    This article starts by introducing a new theoretical framework to model spatial systems which is obtained from the framework of interacting particle systems by replacing the traditional graphical structure that defines the network of interactions with a structure of hypergraph. This new perspective is more appropriate to define stochastic spatial processes in which large blocks of vertices may flip simultaneously, which is then applied to define a spatial version of the Galam's majority rule model. In our spatial model, each vertex of the lattice has one of two possible competing opinions, say opinion 0 and opinion 1, as in the popular voter model. Hyperedges are updated at rate one, which results in all the vertices in the hyperedge changing simultaneously their opinion to the majority opinion of the hyperedge. In the case of a tie in hyperedges with even size, a bias is introduced in favor of type 1, which is motivated by the principle of social inertia. Our analytical results along with simulations and heuristic arguments suggest that, in any spatial dimensions and when the set of hyperedges consists of the collection of all n×⋯× n blocks of the lattice, opinion 1 wins when n is even while the system clusters when n is odd, which contrasts with results about the voter model in high dimensions for which opinions coexist. This is fully proved in one dimension while the rest of our analysis focuses on the cases when n=2 and n=3 in two dimensions.

  12. Appropriatie spatial scales to achieve model output uncertainty goals

    NARCIS (Netherlands)

    Booij, Martijn J.; Melching, Charles S.; Chen, Xiaohong; Chen, Yongqin; Xia, Jun; Zhang, Hailun

    2008-01-01

    Appropriate spatial scales of hydrological variables were determined using an existing methodology based on a balance in uncertainties from model inputs and parameters extended with a criterion based on a maximum model output uncertainty. The original methodology uses different relationships between

  13. Spatially adaptive mixture modeling for analysis of FMRI time series.

    Science.gov (United States)

    Vincent, Thomas; Risser, Laurent; Ciuciu, Philippe

    2010-04-01

    Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM

  14. Distributed multi-criteria model evaluation and spatial association analysis

    Science.gov (United States)

    Scherer, Laura; Pfister, Stephan

    2015-04-01

    Model performance, if evaluated, is often communicated by a single indicator and at an aggregated level; however, it does not embrace the trade-offs between different indicators and the inherent spatial heterogeneity of model efficiency. In this study, we simulated the water balance of the Mississippi watershed using the Soil and Water Assessment Tool (SWAT). The model was calibrated against monthly river discharge at 131 measurement stations. Its time series were bisected to allow for subsequent validation at the same gauges. Furthermore, the model was validated against evapotranspiration which was available as a continuous raster based on remote sensing. The model performance was evaluated for each of the 451 sub-watersheds using four different criteria: 1) Nash-Sutcliffe efficiency (NSE), 2) percent bias (PBIAS), 3) root mean square error (RMSE) normalized to standard deviation (RSR), as well as 4) a combined indicator of the squared correlation coefficient and the linear regression slope (bR2). Conditions that might lead to a poor model performance include aridity, a very flat and steep relief, snowfall and dams, as indicated by previous research. In an attempt to explain spatial differences in model efficiency, the goodness of the model was spatially compared to these four phenomena by means of a bivariate spatial association measure which combines Pearson's correlation coefficient and Moran's index for spatial autocorrelation. In order to assess the model performance of the Mississippi watershed as a whole, three different averages of the sub-watershed results were computed by 1) applying equal weights, 2) weighting by the mean observed river discharge, 3) weighting by the upstream catchment area and the square root of the time series length. Ratings of model performance differed significantly in space and according to efficiency criterion. The model performed much better in the humid Eastern region than in the arid Western region which was confirmed by the

  15. A Statistical Toolbox For Mining And Modeling Spatial Data

    Directory of Open Access Journals (Sweden)

    D’Aubigny Gérard

    2016-12-01

    Full Text Available Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP, valuable in exploratory spatial data analysis.

  16. A spatial model of mosquito host-seeking behavior.

    Directory of Open Access Journals (Sweden)

    Bree Cummins

    Full Text Available Mosquito host-seeking behavior and heterogeneity in host distribution are important factors in predicting the transmission dynamics of mosquito-borne infections such as dengue fever, malaria, chikungunya, and West Nile virus. We develop and analyze a new mathematical model to describe the effect of spatial heterogeneity on the contact rate between mosquito vectors and hosts. The model includes odor plumes generated by spatially distributed hosts, wind velocity, and mosquito behavior based on both the prevailing wind and the odor plume. On a spatial scale of meters and a time scale of minutes, we compare the effectiveness of different plume-finding and plume-tracking strategies that mosquitoes could use to locate a host. The results show that two different models of chemotaxis are capable of producing comparable results given appropriate parameter choices and that host finding is optimized by a strategy of flying across the wind until the odor plume is intercepted. We also assess the impact of changing the level of host aggregation on mosquito host-finding success near the end of the host-seeking flight. When clusters of hosts are more tightly associated on smaller patches, the odor plume is narrower and the biting rate per host is decreased. For two host groups of unequal number but equal spatial density, the biting rate per host is lower in the group with more individuals, indicative of an attack abatement effect of host aggregation. We discuss how this approach could assist parameter choices in compartmental models that do not explicitly model the spatial arrangement of individuals and how the model could address larger spatial scales and other probability models for mosquito behavior, such as Lévy distributions.

  17. Toward micro-scale spatial modeling of gentrification

    Science.gov (United States)

    O'Sullivan, David

    A simple preliminary model of gentrification is presented. The model is based on an irregular cellular automaton architecture drawing on the concept of proximal space, which is well suited to the spatial externalities present in housing markets at the local scale. The rent gap hypothesis on which the model's cell transition rules are based is discussed. The model's transition rules are described in detail. Practical difficulties in configuring and initializing the model are described and its typical behavior reported. Prospects for further development of the model are discussed. The current model structure, while inadequate, is well suited to further elaboration and the incorporation of other interesting and relevant effects.

  18. Modeling Urban Spatial Growth in Mountainous Regions of Western China

    Directory of Open Access Journals (Sweden)

    Guoping Huang

    2017-08-01

    Full Text Available The scale and speed of urbanization in the mountainous regions of western China have received little attention from researchers. These cities are facing rapid population growth and severe environmental degradation. This study analyzed historical urban growth trends in this mountainous region to better understand the interaction between the spatial growth pattern and the mountainous topography. Three major factors—slope, accessibility, and land use type—were studied in light of their relationships with urban spatial growth. With the analysis of historical data as the basis, a conceptual urban spatial growth model was devised. In this model, slope, accessibility, and land use type together create resistance to urban growth, while accessibility controls the sequence of urban development. The model was tested and evaluated using historical data. It serves as a potential tool for planners to envision and assess future urban growth scenarios and their potential environmental impacts to make informed decisions.

  19. Spatial distribution of emissions to air – the SPREAD model

    DEFF Research Database (Denmark)

    Plejdrup, Marlene Schmidt; Gyldenkærne, Steen

    to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously......The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark’s obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long......-range transboundary air pollution, CLRTAP. NERI has developed a model to distribute emissions from the national emission inventories on a 1x1 km grid covering the Danish land and sea territory. The new spatial high resolution distribution model for emissions to air (SPREAD) has been developed according...

  20. Image categorization based on spatial visual vocabulary model

    Science.gov (United States)

    Wang, Yuxin; He, Changqin; Guo, He; Feng, Zhen; Jia, Qi

    2010-08-01

    In this paper, we propose an approach to recognize scene categories by means of a novel method named spatial visual vocabulary. Firstly, we hierarchically divide images into sub regions and construct the spatial visual vocabulary by grouping the low-level features collected from every corresponding spatial sub region into a specified number of clusters using k-means algorithm. To recognize the category of a scene, the visual vocabulary distributions of all spatial sub regions are concatenated to form a global feature vector. The classification is obtained using LIBSVM, a support vector machine classifier. Our goal is to find a universal framework which is applicable to various types of features, so two kinds of features are used in the experiments: "V1-like" filters and PACT features. In almost all experimental cases, the proposed model achieves superior results. Source codes are available by email.

  1. Spatial capture-recapture models for search-encounter data

    Science.gov (United States)

    Royle, J. Andrew; Kery, Marc; Guelat, Jerome

    2011-01-01

    1. Spatial capture–recapture models make use of auxiliary data on capture location to provide density estimates for animal populations. Previously, models have been developed primarily for fixed trap arrays which define the observable locations of individuals by a set of discrete points. 2. Here, we develop a class of models for 'search-encounter' data, i.e. for detections of recognizable individuals in continuous space, not restricted to trap locations. In our hierarchical model, detection probability is related to the average distance between individual location and the survey path. The locations are allowed to change over time owing to movements of individuals, and individual locations are related formally by a model describing individual activity or home range centre which is itself regarded as a latent variable in the model. We provide a Bayesian analysis of the model in WinBUGS, and develop a custom MCMC algorithm in the R language. 3. The model is applied to simulated data and to territory mapping data for the Willow Tit from the Swiss Breeding Bird Survey MHB. While the observed density was 15 territories per nominal 1 km2 plot of unknown effective sample area, the model produced a density estimate of 21∙12 territories per square km (95% posterior interval: 17–26). 4. Spatial capture–recapture models are relevant to virtually all animal population studies that seek to estimate population size or density, yet existing models have been proposed mainly for conventional sampling using arrays of traps. Our model for search-encounter data, where the spatial pattern of searching can be arbitrary and may change over occasions, greatly expands the scope and utility of spatial capture–recapture models.

  2. Analysing earthquake slip models with the spatial prediction comparison test

    KAUST Repository

    Zhang, L.

    2014-11-10

    Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.

  3. Gaussian Process Regression Model in Spatial Logistic Regression

    Science.gov (United States)

    Sofro, A.; Oktaviarina, A.

    2018-01-01

    Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

  4. Spatial and Temporal Clustering in a Simple Earthquake Asperity Model

    Science.gov (United States)

    Tiampo, K. F.; Kazemian, J.; Dominguez, R.; Klein, W.

    2016-12-01

    Natural earthquake fault systems are highly heterogeneous in space, the result of inhomogeneities that are a function of the variety of materials of different strengths. However, despite their inhomogeneous nature, real faults are often modeled as spatially homogeneous systems. Here we present a simple earthquake fault model based on the Olami-Feder-Christensen (OFC) and Rundle-Jackson-Brown (RJB) cellular automata models with long-range interactions that incorporates asperities, or stronger sites, into the lattice (Rundle and Jackson, 1977; Olami et al., 1992). These asperity cells are significantly stronger than the surrounding lattice sites but eventually rupture when the applied stress reaches their higher threshold stress. The introduction of these spatial heterogeneities results in spatial and temporal clustering in the model similar to that seen in natural fault systems. We observe sequences of activity that begin with a gradually accelerating number of larger events, or foreshocks, prior to a large event, followed by a tail of decreasing activity, or aftershocks. These recurrent large events occur at regular intervals and the characteristic time between events and their magnitude are a function of the stress dissipation parameter. The relative length of the foreshock to aftershock sequence depends on the amount of stress dissipation in the system. This work provides further evidence that the spatial and temporal patterns observed in natural seismicity are strongly influenced by the underlying physical properties and are not solely the result of a simple cascade mechanism. We find that the scaling depends not only on the amount of damage, but also on the spatial distribution of that damage (Dominguez et al., 2011; Kazemian et al., 2014). Here we compare the modeled sequences to those of natural earthquake sequences from California and around the world in order to investigate the interplay between cascade dynamics and spatial structure.

  5. An image-computable psychophysical spatial vision model.

    Science.gov (United States)

    Schütt, Heiko H; Wichmann, Felix A

    2017-10-01

    A large part of classical visual psychophysics was concerned with the fundamental question of how pattern information is initially encoded in the human visual system. From these studies a relatively standard model of early spatial vision emerged, based on spatial frequency and orientation-specific channels followed by an accelerating nonlinearity and divisive normalization: contrast gain-control. Here we implement such a model in an image-computable way, allowing it to take arbitrary luminance images as input. Testing our implementation on classical psychophysical data, we find that it explains contrast detection data including the ModelFest data, contrast discrimination data, and oblique masking data, using a single set of parameters. Leveraging the advantage of an image-computable model, we test our model against a recent dataset using natural images as masks. We find that the model explains these data reasonably well, too. To explain data obtained at different presentation durations, our model requires different parameters to achieve an acceptable fit. In addition, we show that contrast gain-control with the fitted parameters results in a very sparse encoding of luminance information, in line with notions from efficient coding. Translating the standard early spatial vision model to be image-computable resulted in two further insights: First, the nonlinear processing requires a denser sampling of spatial frequency and orientation than optimal coding suggests. Second, the normalization needs to be fairly local in space to fit the data obtained with natural image masks. Finally, our image-computable model can serve as tool in future quantitative analyses: It allows optimized stimuli to be used to test the model and variants of it, with potential applications as an image-quality metric. In addition, it may serve as a building block for models of higher level processing.

  6. Multivariate Receptor Models for Spatially Correlated Multipollutant Data

    KAUST Repository

    Jun, Mikyoung

    2013-08-01

    The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.

  7. ALADYN - a spatially explicit, allelic model for simulating adaptive dynamics.

    Science.gov (United States)

    Schiffers, Katja H; Travis, Justin Mj

    2014-12-01

    ALADYN is a freely available cross-platform C++ modeling framework for stochastic simulation of joint allelic and demographic dynamics of spatially-structured populations. Juvenile survival is linked to the degree of match between an individual's phenotype and the local phenotypic optimum. There is considerable flexibility provided for the demography of the considered species and the genetic architecture of the traits under selection. ALADYN facilitates the investigation of adaptive processes to spatially and/or temporally changing conditions and the resulting niche and range dynamics. To our knowledge ALADYN is so far the only model that allows a continuous resolution of individuals' locations in a spatially explicit landscape together with the associated patterns of selection.

  8. A Non-Gaussian Spatial Generalized Linear Latent Variable Model

    KAUST Repository

    Irincheeva, Irina

    2012-08-03

    We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

  9. A spatial and temporal continuous surface-subsurface hydrologic model

    Science.gov (United States)

    Xiao, Qing-Fu; Ustin, Susan L.; Wallender, Wesley W.

    1996-12-01

    A hydrologic model integrating surface-subsurface processes was developed based on spatial and temporal continuity theory. The raster-based mass balance hydrologic model consists of several submodels which determine spatial and temporal patterns in precipitation, surface flow, infiltration, subsurface flow, and the linkages between these submodels. Model parameters and variables are derived directly or indirectly from satellite remote sensing data, topographic maps, soil maps, literature, and weather station data and are stored in a Geographic Information System (GIS) database used for visualization. Surface resolution of cells in the model is 20 m by 20 m (pixel resolution of the Systeme Probatoire d'Observation de la Terre (SPOT) satellite image) over a 2511 km2 study area around the Crazy Mountains, Alaska, a watershed on the Arctic Circle draining into the Yukon River. The outputs from this model illustrate the interaction of physical and biologic factors on the partitioning of hydrologic components in a complex landscape.

  10. A spatial mark–resight model augmented with telemetry data

    Science.gov (United States)

    Sollmann, Rachel; Gardner, Beth; Parsons, Arielle W.; Stocking, Jessica J.; McClintock, Brett T.; Simons, Theodore R.; Pollock, Kenneth H.; O’Connell, Allan F.

    2013-01-01

    Abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. Mark-resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark-recapture. However, density estimation using mark-resight is difficult because the area sampled must be explicitly defined, historically using ad-hoc approaches. We develop a spatial mark-resight model for estimating population density that combines spatial resighting data and telemetry data. Incorporating telemetry data allows us to inform model parameters related to movement and individual location. Our model also allows 2. The model presented here will have widespread utility in future applications, especially for species that are not naturally marked.

  11. Multidimensional Big Spatial Data Modeling Through A Case Study: Lte Rf Subsystem Power Consumption Modeling

    DEFF Research Database (Denmark)

    Antón Castro, Francesc/François; Musiige, Deogratius; Mioc, Darka

    2016-01-01

    This paper presents a case study for comparing different multidimensional mathematical modeling methodologies used in multidimensional spatial big data modeling and proposing a new technique. An analysis of multidimensional modeling approaches (neural networks, polynomial interpolation and homoto...

  12. Function modeling improves the efficiency of spatial modeling using big data from remote sensing

    Science.gov (United States)

    John Hogland; Nathaniel Anderson

    2017-01-01

    Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have...

  13. Design of spatial experiments: Model fitting and prediction

    Energy Technology Data Exchange (ETDEWEB)

    Fedorov, V.V.

    1996-03-01

    The main objective of the paper is to describe and develop model oriented methods and algorithms for the design of spatial experiments. Unlike many other publications in this area, the approach proposed here is essentially based on the ideas of convex design theory.

  14. Lateral specialization in unilateral spatial neglect: a cognitive robotics model.

    Science.gov (United States)

    Conti, Daniela; Di Nuovo, Santo; Cangelosi, Angelo; Di Nuovo, Alessandro

    2016-08-01

    In this paper, we present the experimental results of an embodied cognitive robotic approach for modelling the human cognitive deficit known as unilateral spatial neglect (USN). To this end, we introduce an artificial neural network architecture designed and trained to control the spatial attentional focus of the iCub robotic platform. Like the human brain, the architecture is divided into two hemispheres and it incorporates bio-inspired plasticity mechanisms, which allow the development of the phenomenon of the specialization of the right hemisphere for spatial attention. In this study, we validate the model by replicating a previous experiment with human patients affected by the USN and numerical results show that the robot mimics the behaviours previously exhibited by humans. We also simulated recovery after the damage to compare the performance of each of the two hemispheres as additional validation of the model. Finally, we highlight some possible advantages of modelling cognitive dysfunctions of the human brain by means of robotic platforms, which can supplement traditional approaches for studying spatial impairments in humans.

  15. Rockfall hazard analysis using LiDAR and spatial modeling

    Science.gov (United States)

    Lan, Hengxing; Martin, C. Derek; Zhou, Chenghu; Lim, Chang Ho

    2010-05-01

    Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a high-resolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy.

  16. New advances in spatial network modelling: towards evolutionary algorithms

    NARCIS (Netherlands)

    Reggiani, A; Nijkamp, P.; Sabella, E.

    2001-01-01

    This paper discusses analytical advances in evolutionary methods with a view towards their possible applications in the space-economy. For this purpose, we present a brief overview and illustration of models actually available in the spatial sciences which attempt to map the complex patterns of

  17. Classifying and comparing spatial models of fire dynamics

    Science.gov (United States)

    Geoffrey J. Cary; Robert E. Keane; Mike D. Flannigan

    2007-01-01

    Wildland fire is a significant disturbance in many ecosystems worldwide and the interaction of fire with climate and vegetation over long time spans has major effects on vegetation dynamics, ecosystem carbon budgets, and patterns of biodiversity. Landscape-Fire-Succession Models (LFSMs) that simulate the linked processes of fire and vegetation development in a spatial...

  18. Modelling spatial anisotropy of gold concentration data using GIS ...

    Indian Academy of Sciences (India)

    linear trends are interpreted to represent major fault zones that exerted a prinicipal control on gold mineralization and therefore ... concentration data are particularly useful in the field of mineral exploration. Keywords. Structural control .... the variogram is the most com- monly used tool for modelling spatial structure and.

  19. Individual based model of slug population and spatial dynamics

    NARCIS (Netherlands)

    Choi, Y.H.; Bohan, D.A.; Potting, R.P.J.; Semenov, M.A.; Glen, D.M.

    2006-01-01

    The slug, Deroceras reticulatum, is one of the most important pests of agricultural and horticultural crops in UK and Europe. In this paper, a spatially explicit individual based model (IbM) is developed to study the dynamics of a population of D. reticulatum. The IbM establishes a virtual field

  20. Spatial modeling on the nutrient retention of an estuary wetland

    NARCIS (Netherlands)

    Li, X.; Xiao, D.; Jongman, R.H.G.; Harms, W.B.; Bregt, A.K.

    2003-01-01

    There is a great potential to use the estuary wetland as a final filter for nutrient enriched river water, and reduce the possibility of coastal water eutrophication. Based upon field data, spatial models were designed on a stepwise basis to simulate the nutrient reduction function of the wetland in

  1. Testing for spatial error dependence in probit models

    NARCIS (Netherlands)

    Amaral, P. V.; Anselin, L.; Arribas-Bel, D.

    2013-01-01

    In this note, we compare three test statistics that have been suggested to assess the presence of spatial error autocorrelation in probit models. We highlight the differences between the tests proposed by Pinkse and Slade (J Econom 85(1):125-254, 1998), Pinkse (Asymptotics of the Moran test and a

  2. Spatial variability and parametric uncertainty in performance assessment models

    International Nuclear Information System (INIS)

    Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo

    2011-01-01

    The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)

  3. Landform classification using a sub-pixel spatial attraction model to increase spatial resolution of digital elevation model (DEM

    Directory of Open Access Journals (Sweden)

    Marzieh Mokarrama

    2018-04-01

    Full Text Available The purpose of the present study is preparing a landform classification by using digital elevation model (DEM which has a high spatial resolution. To reach the mentioned aim, a sub-pixel spatial attraction model was used as a novel method for preparing DEM with a high spatial resolution in the north of Darab, Fars province, Iran. The sub-pixel attraction models convert the pixel into sub-pixels based on the neighboring pixels fraction values, which can only be attracted by a central pixel. Based on this approach, a mere maximum of eight neighboring pixels can be selected for calculating of the attraction value. In the mentioned model, other pixels are supposed to be far from the central pixel to receive any attraction. In the present study by using a sub-pixel attraction model, the spatial resolution of a DEM was increased. The design of the algorithm is accomplished by using a DEM with a spatial resolution of 30 m (the Advanced Space borne Thermal Emission and Reflection Radiometer; (ASTER and a 90 m (the Shuttle Radar Topography Mission; (SRTM. In the attraction model, scale factors of (S = 2, S = 3, and S = 4 with two neighboring methods of touching (T = 1 and quadrant (T = 2 are applied to the DEMs by using MATLAB software. The algorithm is evaluated by taking the best advantages of 487 sample points, which are measured by surveyors. The spatial attraction model with scale factor of (S = 2 gives better results compared to those scale factors which are greater than 2. Besides, the touching neighborhood method is turned to be more accurate than the quadrant method. In fact, dividing each pixel into more than two sub-pixels decreases the accuracy of the resulted DEM. On the other hand, in these cases DEM, is itself in charge of increasing the value of root-mean-square error (RMSE and shows that attraction models could not be used for S which is greater than 2. Thus considering results, the proposed model is highly capable of

  4. Modern methodology and applications in spatial-temporal modeling

    CERN Document Server

    Matsui, Tomoko

    2015-01-01

    This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component an...

  5. Spatial Development Modeling Methodology Application Possibilities in Vilnius

    Directory of Open Access Journals (Sweden)

    Lina Panavaitė

    2017-05-01

    Full Text Available In order to control the continued development of high-rise buildings and their irreversible visual impact on the overall silhouette of the city, the great cities of the world introduced new methodological principles to city’s spatial development models. These methodologies and spatial planning guidelines are focused not only on the controlled development of high-rise buildings, but on the spatial modelling of the whole city by defining main development criteria and estimating possible consequences. Vilnius city is no exception, however the re-establishment of independence of Lithuania caused uncontrolled urbanization process, so most of the city development regulations emerged as a consequence of unmanaged processes of investors’ expectations legalization. The importance of consistent urban fabric as well as conservation and representation of city’s most important objects gained attention only when an actual threat of overshadowing them with new architecture along with unmanaged urbanization in the city center or urban sprawl at suburbia, caused by land-use projects, had emerged. Current Vilnius’ spatial planning documents clearly define urban structure and key development principles, however the definitions are relatively abstract, causing uniform building coverage requirements for territories with distinct qualities and simplifying planar designs which do not meet quality standards. The overall quality of urban architecture is not regulated. The article deals with current spatial modeling methods, their individual parts, principles, the criteria for quality assessment and their applicability in Vilnius. The text contains an outline of possible building coverage regulations and impact assessment criteria for new development. The article contains a compendium of requirements for high-quality spatial planning and building design.

  6. Spatial Temporal Modelling of Particulate Matter for Health Effects Studies

    Science.gov (United States)

    Hamm, N. A. S.

    2016-10-01

    Epidemiological studies of the health effects of air pollution require estimation of individual exposure. It is not possible to obtain measurements at all relevant locations so it is necessary to predict at these space-time locations, either on the basis of dispersion from emission sources or by interpolating observations. This study used data obtained from a low-cost sensor network of 32 air quality monitoring stations in the Dutch city of Eindhoven, which make up the ILM (innovative air (quality) measurement system). These stations currently provide PM10 and PM2.5 (particulate matter less than 10 and 2.5 m in diameter), aggregated to hourly means. The data provide an unprecedented level of spatial and temporal detail for a city of this size. Despite these benefits the time series of measurements is characterized by missing values and noisy values. In this paper a space-time analysis is presented that is based on a dynamic model for the temporal component and a Gaussian process geostatistical for the spatial component. Spatial-temporal variability was dominated by the temporal component, although the spatial variability was also substantial. The model delivered accurate predictions for both isolated missing values and 24-hour periods of missing values (RMSE = 1.4 μg m-3 and 1.8 μg m-3 respectively). Outliers could be detected by comparison to the 95% prediction interval. The model shows promise for predicting missing values, outlier detection and for mapping to support health impact studies.

  7. Modelling the Spatial Distribution of Wind Energy Resources in Latvia

    Science.gov (United States)

    Aniskevich, S.; Bezrukovs, V.; Zandovskis, U.; Bezrukovs, D.

    2017-12-01

    The paper studies spatial wind energy flow distribution in Latvia based on wind speed measurements carried out at an altitude of 10 m over a period of two years, from 2015 to 2016. The measurements, with 1 min increments, were carried out using certified measuring instruments installed at 22 observation stations of the Latvian National Hydrometeorological and Climatological Service of the Latvian Environment, Geology and Meteorology Centre (LEGMC). The models of the spatial distribution of averaged wind speed and wind energy density were developed using the method of spatial interpolation based on the historical measurement results and presented in the form of colour contour maps with a 1×1 km resolution. The paper also provides the results of wind speed spatial distribution modelling using a climatological reanalysis ERA5 at the altitudes of 10, 54, 100 and 136 m with a 31×31 km resolution. The analysis includes the comparison of actual wind speed measurement results with the outcomes of ERA5 modelling for meteorological observation stations in Ainazi, Daugavpils, Priekuli, Saldus and Ventspils.

  8. Spatial Linear Mixed Models with Covariate Measurement Errors.

    Science.gov (United States)

    Li, Yi; Tang, Haicheng; Lin, Xihong

    2009-01-01

    Spatial data with covariate measurement errors have been commonly observed in public health studies. Existing work mainly concentrates on parameter estimation using Gibbs sampling, and no work has been conducted to understand and quantify the theoretical impact of ignoring measurement error on spatial data analysis in the form of the asymptotic biases in regression coefficients and variance components when measurement error is ignored. Plausible implementations, from frequentist perspectives, of maximum likelihood estimation in spatial covariate measurement error models are also elusive. In this paper, we propose a new class of linear mixed models for spatial data in the presence of covariate measurement errors. We show that the naive estimators of the regression coefficients are attenuated while the naive estimators of the variance components are inflated, if measurement error is ignored. We further develop a structural modeling approach to obtaining the maximum likelihood estimator by accounting for the measurement error. We study the large sample properties of the proposed maximum likelihood estimator, and propose an EM algorithm to draw inference. All the asymptotic properties are shown under the increasing-domain asymptotic framework. We illustrate the method by analyzing the Scottish lip cancer data, and evaluate its performance through a simulation study, all of which elucidate the importance of adjusting for covariate measurement errors.

  9. Spatial modelling and ecology of Echinococcus multilocularis transmission in China.

    Science.gov (United States)

    Danson, F Mark; Giraudoux, Patrick; Craig, Philip S

    2006-01-01

    Recent research in central China has suggested that the most likely transmission mechanism for Echinococcus multilocularis to humans is via domestic dogs which are allowed to roam freely and hunt (infected) small mammals within areas close to villages or in areas of tented pasture. This assertion has led to the hypothesis that there is a landscape control on transmission risk since the proximity of suitable habitat for susceptible small mammals appears to be the key. We have tested this hypothesis in a number of endemic areas in China, notably south Gansu Province and the Tibetan region of western Sichuan Province. The fundamental landscape control is its effect at a regional scale on small mammal species assemblages (susceptible species are not ubiquitous) and, at a local scale, the spatial distributions of small mammal populations. To date the research has examined relationships between landscape composition and patterns of human infection, landscape and small mammal distributions and recently the relationships between landscape and dog infection rates. The key tool to characterize landscape is satellite remote sensing and these data are used as inputs to drive spatial models of transmission risk. This paper reviews the progress that has been made so far in spatial modeling of the ecology of E. multilocularis with particular reference to China, outlines current research issues, and describes a framework for building a spatial-temporal model of transmission ecology.

  10. Scaling-up spatially-explicit ecological models using graphics processors

    OpenAIRE

    Koppel, Johan van de; Gupta, Rohit; Vuik, Cornelis

    2011-01-01

    How the properties of ecosystems relate to spatial scale is a prominent topic in current ecosystem research. Despite this, spatially explicit models typically include only a limited range of spatial scales, mostly because of computing limitations. Here, we describe the use of graphics processors to efficiently solve spatially explicit ecological models at large spatial scale using the CUDA language extension. We explain this technique by implementing three classical models of spatial self-org...

  11. Space in multi-agent systems modelling spatial processes

    Directory of Open Access Journals (Sweden)

    Petr Rapant

    2007-06-01

    Full Text Available Need for modelling of spatial processes arise in the spehere of geoinformation systems in the last time. Some processes (espetially natural ones can be modeled by means of using external tools, e. g. for modelling of contaminant transport in the environment. But in the case of socio-economic processes suitable tools interconnected with GIS are still in quest of reserch and development. One of the candidate technologies are so called multi-agent systems. Their theory is developed quite well, but they lack suitable means for dealing with space. This article deals with this problem and proposes solution for the field of a road transport modelling.

  12. Exploring regional economic convergence in Romania. A spatial modeling approach

    Directory of Open Access Journals (Sweden)

    Zizi GOSCHIN

    2017-12-01

    Full Text Available This paper explores spatial economic convergence in Romania, from the perspective of real GDP/capita, and examines how the shock of the recent economic crisis has affected the convergence process. Given the presence of spatial autocorrelation in the values of GDP per capita, we address the question of convergence in terms of both classic and spatial regression models, thus filling a gap in the Romanian literature on this topic. The empirical results seem to provide support for both absolute and relative beta divergence in GDP/capita, as well as sigma divergence among Romanian counties on the long run. This is the consequence of the two-speed regional development, with the capital region and some large cities thriving by attracting human capital and FDIs, while the lagging regions are systematically left behind. Failing to validate the neoclassical approach on convergence, our results rather support the new divergence theory based on polarization and centre-periphery inequality.

  13. Modelling spatial patterns of urban growth in Africa

    Science.gov (United States)

    Linard, Catherine; Tatem, Andrew J.; Gilbert, Marius

    2013-01-01

    The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers. PMID:25152552

  14. Neuromorphic model of magnocellular and parvocellular visual paths: spatial resolution

    International Nuclear Information System (INIS)

    Aguirre, Rolando C; Felice, Carmelo J; Colombo, Elisa M

    2007-01-01

    Physiological studies of the human retina show the existence of at least two visual information processing channels, the magnocellular and the parvocellular ones. Both have different spatial, temporal and chromatic features. This paper focuses on the different spatial resolution of these two channels. We propose a neuromorphic model, so that they match the retina's physiology. Considering the Deutsch and Deutsch model (1992), we propose two configurations (one for each visual channel) of the connection between the retina's different cell layers. The responses of the proposed model have similar behaviour to those of the visual cells: each channel has an optimum response corresponding to a given stimulus size which decreases for larger or smaller stimuli. This size is bigger for the magno path than for the parvo path and, in the end, both channels produce a magnifying of the borders of a stimulus

  15. Single Canonical Model of Reflexive Memory and Spatial Attention.

    Science.gov (United States)

    Patel, Saumil S; Red, Stuart; Lin, Eric; Sereno, Anne B

    2015-10-23

    Many neurons in the dorsal and ventral visual stream have the property that after a brief visual stimulus presentation in their receptive field, the spiking activity in these neurons persists above their baseline levels for several seconds. This maintained activity is not always correlated with the monkey's task and its origin is unknown. We have previously proposed a simple neural network model, based on shape selective neurons in monkey lateral intraparietal cortex, which predicts the valence and time course of reflexive (bottom-up) spatial attention. In the same simple model, we demonstrate here that passive maintained activity or short-term memory of specific visual events can result without need for an external or top-down modulatory signal. Mutual inhibition and neuronal adaptation play distinct roles in reflexive attention and memory. This modest 4-cell model provides the first simple and unified physiologically plausible mechanism of reflexive spatial attention and passive short-term memory processes.

  16. Analytical model of reactive transport processes with spatially variable coefficients.

    Science.gov (United States)

    Simpson, Matthew J; Morrow, Liam C

    2015-05-01

    Analytical solutions of partial differential equation (PDE) models describing reactive transport phenomena in saturated porous media are often used as screening tools to provide insight into contaminant fate and transport processes. While many practical modelling scenarios involve spatially variable coefficients, such as spatially variable flow velocity, v(x), or spatially variable decay rate, k(x), most analytical models deal with constant coefficients. Here we present a framework for constructing exact solutions of PDE models of reactive transport. Our approach is relevant for advection-dominant problems, and is based on a regular perturbation technique. We present a description of the solution technique for a range of one-dimensional scenarios involving constant and variable coefficients, and we show that the solutions compare well with numerical approximations. Our general approach applies to a range of initial conditions and various forms of v(x) and k(x). Instead of simply documenting specific solutions for particular cases, we present a symbolic worksheet, as supplementary material, which enables the solution to be evaluated for different choices of the initial condition, v(x) and k(x). We also discuss how the technique generalizes to apply to models of coupled multispecies reactive transport as well as higher dimensional problems.

  17. Modified Spatial Channel Model for MIMO Wireless Systems

    Directory of Open Access Journals (Sweden)

    Pekka Kyösti

    2007-12-01

    Full Text Available The third generation partnership Project's (3GPP spatial channel model (SCM is a stochastic channel model for MIMO systems. Due to fixed subpath power levels and angular directions, the SCM model does not show the degree of variation which is encountered in real channels. In this paper, we propose a modified SCM model which has random subpath powers and directions and still produces Laplace shape angular power spectrum. Simulation results on outage MIMO capacity with basic and modified SCM models show that the modified SCM model gives constantly smaller capacity values. Accordingly, it seems that the basic SCM gives too small correlation between MIMO antennas. Moreover, the variance in capacity values is larger using the proposed SCM model. Simulation results were supported by the outage capacity results from a measurement campaign conducted in the city centre of Oulu, Finland.

  18. Sparse modeling of spatial environmental variables associated with asthma.

    Science.gov (United States)

    Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

    2015-02-01

    Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. A general modeling framework for describing spatially structured population dynamics

    Science.gov (United States)

    Sample, Christine; Fryxell, John; Bieri, Joanna; Federico, Paula; Earl, Julia; Wiederholt, Ruscena; Mattsson, Brady; Flockhart, Tyler; Nicol, Sam; Diffendorfer, James E.; Thogmartin, Wayne E.; Erickson, Richard A.; Norris, D. Ryan

    2017-01-01

    Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance

  20. A general modeling framework for describing spatially structured population dynamics.

    Science.gov (United States)

    Sample, Christine; Fryxell, John M; Bieri, Joanna A; Federico, Paula; Earl, Julia E; Wiederholt, Ruscena; Mattsson, Brady J; Flockhart, D T Tyler; Nicol, Sam; Diffendorfer, Jay E; Thogmartin, Wayne E; Erickson, Richard A; Norris, D Ryan

    2018-01-01

    Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance

  1. Spatial distribution of emissions to air - the SPREAD model

    Energy Technology Data Exchange (ETDEWEB)

    Plejdrup, M.S.; Gyldenkaerne, S.

    2011-04-15

    The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark's obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long-range transboundary air pollution, CLRTAP. NERI has developed a model to distribute emissions from the national emission inventories on a 1x1 km grid covering the Danish land and sea territory. The new spatial high resolution distribution model for emissions to air (SPREAD) has been developed according to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously, a distribution on the 17x17 km EMEP grid has been set up and used in research projects combined with detailed distributions for a few sectors or sub-sectors e.g. a distribution for emissions from road traffic on 1x1 km resolution. SPREAD is developed to generate improved spatial emission data for e.g. air quality modelling in exposure studies. SPREAD includes emission distributions for each sector in the Danish inventory system; stationary combustion, mobile sources, fugitive emissions from fuels, industrial processes, solvents and other product use, agriculture and waste. This model enables generation of distributions for single sectors and for a number of sub-sectors and single sources as well. This report documents the methodologies in this first version of SPREAD and presents selected results. Further, a number of potential improvements for later versions of SPREAD are addressed and discussed. (Author)

  2. Tapered composite likelihood for spatial max-stable models

    KAUST Repository

    Sang, Huiyan

    2014-05-01

    Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.

  3. Database modeling to integrate macrobenthos data in Spatial Data Infrastructure

    Directory of Open Access Journals (Sweden)

    José Alberto Quintanilha

    2012-08-01

    Full Text Available Coastal zones are complex areas that include marine and terrestrial environments. Besides its huge environmental wealth, they also attracts humans because provides food, recreation, business, and transportation, among others. Some difficulties to manage these areas are related with their complexity, diversity of interests and the absence of standardization to collect and share data to scientific community, public agencies, among others. The idea to organize, standardize and share this information based on Web Atlas is essential to support planning and decision making issues. The construction of a spatial database integrating the environmental business, to be used on Spatial Data Infrastructure (SDI is illustrated by a bioindicator that indicates the quality of the sediments. The models show the phases required to build Macrobenthos spatial database based on Santos Metropolitan Region as a reference. It is concluded that, when working with environmental data the structuring of knowledge in a conceptual model is essential for their subsequent integration into the SDI. During the modeling process it can be noticed that methodological issues related to the collection process may obstruct or prejudice the integration of data from different studies of the same area. The development of a database model, as presented in this study, can be used as a reference for further research with similar goals.

  4. Supplementary Material for: Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel

    2016-01-01

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  5. Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models

    Science.gov (United States)

    Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea

    2014-05-01

    Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.

  6. An alternative to the standard spatial econometric approaches in hedonic house price models

    DEFF Research Database (Denmark)

    Veie, Kathrine Lausted; Panduro, Toke Emil

    Hedonic models are subject to spatially correlated errors which are a symptom of omitted spatial variables, mis-specification or mismeasurement. Methods have been developed to address this problem through the use of spatial econometrics or spatial fixed effects. However, often spatial correlation...

  7. Approximate Bayesian computation for spatial SEIR(S) epidemic models.

    Science.gov (United States)

    Brown, Grant D; Porter, Aaron T; Oleson, Jacob J; Hinman, Jessica A

    2018-02-01

    Approximate Bayesia n Computation (ABC) provides an attractive approach to estimation in complex Bayesian inferential problems for which evaluation of the kernel of the posterior distribution is impossible or computationally expensive. These highly parallelizable techniques have been successfully applied to many fields, particularly in cases where more traditional approaches such as Markov chain Monte Carlo (MCMC) are impractical. In this work, we demonstrate the application of approximate Bayesian inference to spatially heterogeneous Susceptible-Exposed-Infectious-Removed (SEIR) stochastic epidemic models. These models have a tractable posterior distribution, however MCMC techniques nevertheless become computationally infeasible for moderately sized problems. We discuss the practical implementation of these techniques via the open source ABSEIR package for R. The performance of ABC relative to traditional MCMC methods in a small problem is explored under simulation, as well as in the spatially heterogeneous context of the 2014 epidemic of Chikungunya in the Americas. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Blangiardo, Marta; Cameletti, Michela; Baio, Gianluca; Rue, Håvard

    2013-12-01

    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 very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method. In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.

  9. Representing spatial information in a computational model for network management

    Science.gov (United States)

    Blaisdell, James H.; Brownfield, Thomas F.

    1994-01-01

    While currently available relational database management systems (RDBMS) allow inclusion of spatial information in a data model, they lack tools for presenting this information in an easily comprehensible form. Computer-aided design (CAD) software packages provide adequate functions to produce drawings, but still require manual placement of symbols and features. This project has demonstrated a bridge between the data model of an RDBMS and the graphic display of a CAD system. It is shown that the CAD system can be used to control the selection of data with spatial components from the database and then quickly plot that data on a map display. It is shown that the CAD system can be used to extract data from a drawing and then control the insertion of that data into the database. These demonstrations were successful in a test environment that incorporated many features of known working environments, suggesting that the techniques developed could be adapted for practical use.

  10. Unsupervised Posture Modeling Based on Spatial-Temporal Movement Features

    Science.gov (United States)

    Yan, Chunjuan

    Traditional posture modeling for human action recognition is based on silhouette segmentation, which is subject to the noise from illumination variation and posture occlusions and shadow interruptions. In this paper, we extract spatial temporal movement features from human actions and adopt unsupervised clustering method for salient posture learning. First, spatial-temporal interest points (STIPs) were extracted according to the properties of human movement, and then, histogram of gradient was built to describe the distribution of STIPs in each frame for a single pose. In addition, the training samples were clustered by non-supervised classification method. Moreover, the salient postures were modeled with GMM according to Expectation Maximization (EM) estimation. The experiment results proved that our method can effectively and accurately recognize human's action postures.

  11. Spatial models of Northern Bobwhite populations for conservation planning

    Science.gov (United States)

    Twedt, Daniel J.; Wilson, R. Randy; Keister, Amy S.

    2007-01-01

    Since 1980, northern bobwhite (Colinus virginianus) range-wide populations declined 3.9% annually. Within the West Gulf Coastal Plain Bird Conservation Region in the south-central United States, populations of this quail species have declined 6.8% annually. These declines sparked calls for land use change and prompted implementation of various conservation practices. However, to effectively reverse these declines and restore northern bobwhite to their former population levels, habitat conservation and management efforts must target establishment and maintenance of sustainable populations. To provide guidance for conservation and restoration of habitat capable of supporting sustainable northern bobwhite populations in the West Gulf Coastal Plain, we modeled their spatial distribution using landscape characteristics derived from 1992 National Land Cover Data and bird detections, from 1990 to 1994, along 10-stop Breeding Bird Survey route segments. Four landscape metrics influenced detections of northern bobwhite: detections were greater in areas with more grassland and increased aggregation of agricultural lands, but detections were reduced in areas with increased density of land cover edge and grassland edge. Using these landscape metrics, we projected the abundance and spatial distribution of northern bobwhite populations across the entire West Gulf Coastal Plain. Predicted populations closely approximated abundance estimates from a different cadre of concurrently collected data but model predictions did not accurately reflect bobwhite detections along species-specific call-count routes in Arkansas and Louisiana. Using similar methods, we also projected northern bobwhite population distribution circa 1980 based on Land Use Land Cover data and bird survey data from 1976 to 1984. We compared our 1980 spatial projections with our spatial estimate of 1992 populations to identify areas of population change. Additionally, we used our projection of the spatial

  12. An exactly solvable, spatial model of mutation accumulation in cancer

    Science.gov (United States)

    Paterson, Chay; Nowak, Martin A.; Waclaw, Bartlomiej

    2016-12-01

    One of the hallmarks of cancer is the accumulation of driver mutations which increase the net reproductive rate of cancer cells and allow them to spread. This process has been studied in mathematical models of well mixed populations, and in computer simulations of three-dimensional spatial models. But the computational complexity of these more realistic, spatial models makes it difficult to simulate realistically large and clinically detectable solid tumours. Here we describe an exactly solvable mathematical model of a tumour featuring replication, mutation and local migration of cancer cells. The model predicts a quasi-exponential growth of large tumours, even if different fragments of the tumour grow sub-exponentially due to nutrient and space limitations. The model reproduces clinically observed tumour growth times using biologically plausible rates for cell birth, death, and migration rates. We also show that the expected number of accumulated driver mutations increases exponentially in time if the average fitness gain per driver is constant, and that it reaches a plateau if the gains decrease over time. We discuss the realism of the underlying assumptions and possible extensions of the model.

  13. Spatial Model of Deforestation in Kalimantan from 2000 to 2013

    OpenAIRE

    Judin Purwanto; Teddy Rusolono; Lilik Budi Prasetyo

    2015-01-01

    Forestry sector is the biggest carbon emission contributor in Indonesia which is mainly caused by deforestation. A significant area of forest cover still can be found in Kalimantan Island (one of the largest island in Indonesia) although an alarming rates deforestation is also exist. This study was purposed to established spatial model of deforestation in Kalimantan islands. This information is expected to provide options to develop sustainable forest management in Kalimantan trou...

  14. A modal approach to modeling spatially distributed vibration energy dissipation.

    Energy Technology Data Exchange (ETDEWEB)

    Segalman, Daniel Joseph

    2010-08-01

    The nonlinear behavior of mechanical joints is a confounding element in modeling the dynamic response of structures. Though there has been some progress in recent years in modeling individual joints, modeling the full structure with myriad frictional interfaces has remained an obstinate challenge. A strategy is suggested for structural dynamics modeling that can account for the combined effect of interface friction distributed spatially about the structure. This approach accommodates the following observations: (1) At small to modest amplitudes, the nonlinearity of jointed structures is manifest primarily in the energy dissipation - visible as vibration damping; (2) Correspondingly, measured vibration modes do not change significantly with amplitude; and (3) Significant coupling among the modes does not appear to result at modest amplitudes. The mathematical approach presented here postulates the preservation of linear modes and invests all the nonlinearity in the evolution of the modal coordinates. The constitutive form selected is one that works well in modeling spatially discrete joints. When compared against a mathematical truth model, the distributed dissipation approximation performs well.

  15. Spatial interpolation schemes of daily precipitation for hydrologic modeling

    Science.gov (United States)

    Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

    2012-01-01

    Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

  16. Modeling the spatial structure of hog production in Denmark

    DEFF Research Database (Denmark)

    Larue, Solène; Abildtrup, Jens; Schmitt, Bertrand

    , the interaction between the location of hog production and slaughterhouses. It is the assumption that the location of slaughterhouses is influenced by the location of the primary producers, implying that this variable is endogenous, whereas the location of primary producers is independent of the location...... of slaughterhouses. This is due to the fact that transportation costs of pigs are paid by the cooperatives owning the slaughterhouses. This assumption is tested applying a spatial econometric model. The model is estimated for 1989, 1999 and 2004. In the latter period, it is the hypothesis that the demand for export...

  17. The spatial impact of neighbouring on the exports activities of COMESA countries by using spatial panel models

    Science.gov (United States)

    Hamzalouh, L.; Ismail, M. T.; Rahman, R. A.

    2017-09-01

    In this paper, spatial panel models were used and the method for selecting the best model amongst the spatial fixed effects model and the spatial random effects model to estimate the fitting model by using the robust Hausman test for analysis of the exports pattern of the Common Market for Eastern and Southern African (COMESA) countries. And examine the effects of the interactions of the economic statistic of explanatory variables on the exports of the COMESA. Results indicated that the spatial Durbin model with fixed effects specification should be tested and considered in most cases of this study. After that, the direct and indirect effects among COMESA regions were assessed, and the role of indirect spatial effects in estimating exports was empirically demonstrated. Regarding originality and research value, and to the best of the authors’ knowledge, this is the first attempt to examine exports between COMESA and its member countries through spatial panel models using XSMLE, which is a new command for spatial analysis using STATA.

  18. A theory and a computational model of spatial reasoning with preferred mental models.

    Science.gov (United States)

    Ragni, Marco; Knauff, Markus

    2013-07-01

    Inferences about spatial arrangements and relations like "The Porsche is parked to the left of the Dodge and the Ferrari is parked to the right of the Dodge, thus, the Porsche is parked to the left of the Ferrari," are ubiquitous. However, spatial descriptions are often interpretable in many different ways and compatible with several alternative mental models. This article suggests that individuals tackle such indeterminate multiple-model problems by constructing a single, simple, and typical mental model but neglect other possible models. The model that first comes to reasoners' minds is the preferred mental model. It helps save cognitive resources but also leads to reasoning errors and illusory inferences. The article presents a preferred model theory and an instantiation of this theory in the form of a computational model, preferred inferences in reasoning with spatial mental models (PRISM). PRISM can be used to simulate and explain how preferred models are constructed, inspected, and varied in a spatial array that functions as if it were a spatial working memory. A spatial focus inserts tokens into the array, inspects the array to find new spatial relations, and relocates tokens in the array to generate alternative models of the problem description, if necessary. The article also introduces a general measure of difficulty based on the number of necessary focus operations (rather than the number of models). A comparison with results from psychological experiments shows that the theory can explain preferences, errors, and the difficulty of spatial reasoning problems. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  19. Modeling spatial processes with unknown extremal dependence class

    KAUST Repository

    Huser, Raphaël G.

    2017-03-17

    Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as it can have a large impact upon extrapolation, i.e., the estimated probabilities of events more extreme than those observed. In this work, we present a single spatial model that is able to capture both dependence classes in a parsimonious manner, and with a smooth transition between the two cases. The model covers a wide range of possibilities from asymptotic independence through to complete dependence, and permits weakening dependence of extremes even under asymptotic dependence. Censored likelihood-based inference for the implied copula is feasible in moderate dimensions due to closed-form margins. The model is applied to oceanographic datasets with ambiguous true limiting dependence structure.

  20. Spatially balanced topological interaction grants optimal cohesion in flocking models.

    Science.gov (United States)

    Camperi, Marcelo; Cavagna, Andrea; Giardina, Irene; Parisi, Giorgio; Silvestri, Edmondo

    2012-12-06

    Models of self-propelled particles (SPPs) are an indispensable tool to investigate collective animal behaviour. Originally, SPP models were proposed with metric interactions, where each individual coordinates with neighbours within a fixed metric radius. However, recent experiments on bird flocks indicate that interactions are topological: each individual interacts with a fixed number of neighbours, irrespective of their distance. It has been argued that topological interactions are more robust than metric ones against external perturbations, a significant evolutionary advantage for systems under constant predatory pressure. Here, we test this hypothesis by comparing the stability of metric versus topological SPP models in three dimensions. We show that topological models are more stable than metric ones. We also show that a significantly better stability is achieved when neighbours are selected according to a spatially balanced topological rule, namely when interacting neighbours are evenly distributed in angle around the focal individual. Finally, we find that the minimal number of interacting neighbours needed to achieve fully stable cohesion in a spatially balanced model is compatible with the value observed in field experiments on starling flocks.

  1. Dynamic Optimization of Ecosystem Services: A Comparative Analysis of Non-Spatial and Spatially-Explicit Models

    OpenAIRE

    Yun, Seong Do; Gramig, Benjamin M.

    2014-01-01

    This study develops and solves a stochastic, multi-year, discrete space-time model that allows the comparative analysis between non-spatial and spatially explicit models. The solution to this model implies the Stochastic Space-Time Natural Enemy-adjusted Economic Threshold (SST-NEET) to guide the choice of the optimal level of a pest that warrants management intervention. Using numerical simulation experiments over a generated synthetic geography, we derive three major conclusions. First, a u...

  2. 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

  3. Spatial succession modeling of biological communities: a multi-model approach.

    Science.gov (United States)

    Zhang, WenJun; Wei, Wu

    2009-11-01

    Strong spatial correlation may exist in the spatial succession of biological communities, and the spatial succession can be mathematically described. It was confirmed by our study on spatial succession of both plant and arthropod communities along a linear transect of natural grassland. Both auto-correlation and cross-correlation analyses revealed that the succession of plant and arthropod communities exhibited a significant spatial correlation, and the spatial correlation for plant community succession was stronger than arthropod community succession. Theoretically it should be reasonable to infer a site's community composition from the last site in the linear transect. An artificial neural network for state space modeling (ANNSSM) was developed in present study. An algorithm (i.e., Importance Detection Method (IDM)) for determining the relative importance of input variables was proposed. The relative importance for plant families Gramineae, Compositae and Leguminosae, and arthropod orders Homoptera, Diptera and Orthoptera, were detected and analyzed using IDM. ANNSSM performed better than multivariate linear regression and ordinary differential equation, while ordinary differential equation exhibited the worst performance in the simulation and prediction of spatial succession of biological communities. A state transition probability model (STPM) was proposed to simulate the state transition process of biological communities. STPM performed better than multinomial logistic regression in the state transition modeling. We suggested a novel multi-model framework, i.e., the joint use of ANNSSM and STPM, to predict the spatial succession of biological communities. In this framework, ANNSSM and STPM can be separately used to simulate the continuous and discrete dynamics.

  4. Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

    Directory of Open Access Journals (Sweden)

    Hongqiang Liu

    2017-01-01

    Full Text Available A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.

  5. GIS-Based Analytical Tools for Transport Planning: Spatial Regression Models for Transportation Demand Forecast

    Directory of Open Access Journals (Sweden)

    Simone Becker Lopes

    2014-04-01

    Full Text Available Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included. This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.

  6. Modeling temporal and spatial variability of crop yield

    Science.gov (United States)

    Bonetti, S.; Manoli, G.; Scudiero, E.; Morari, F.; Putti, M.; Teatini, P.

    2014-12-01

    In a world of increasing food insecurity the development of modeling tools capable of supporting on-farm decision making processes is highly needed to formulate sustainable irrigation practices in order to preserve water resources while maintaining adequate crop yield. The design of these practices starts from the accurate modeling of soil-plant-atmosphere interaction. We present an innovative 3D Soil-Plant model that couples 3D hydrological soil dynamics with a mechanistic description of plant transpiration and photosynthesis, including a crop growth module. Because of its intrinsically three dimensional nature, the model is able to capture spatial and temporal patterns of crop yield over large scales and under various climate and environmental factors. The model is applied to a 25 ha corn field in the Venice coastland, Italy, that has been continuously monitored over the years 2010 and 2012 in terms of both hydrological dynamics and yield mapping. The model results satisfactorily reproduce the large variability observed in maize yield (from 2 to 15 ton/ha). This variability is shown to be connected to the spatial heterogeneities of the farmland, which is characterized by several sandy paleo-channels crossing organic-rich silty soils. Salt contamination of soils and groundwater in a large portion of the area strongly affects the crop yield, especially outside the paleo-channels, where measured salt concentrations are lower than the surroundings. The developed model includes a simplified description of the effects of salt concentration in soil water on transpiration. The results seem to capture accurately the effects of salt concentration and the variability of the climatic conditions occurred during the three years of measurements. This innovative modeling framework paves the way to future large scale simulations of farmland dynamics.

  7. Assessing fit in Bayesian models for spatial processes

    KAUST Repository

    Jun, M.

    2014-09-16

    © 2014 John Wiley & Sons, Ltd. Gaussian random fields are frequently used to model spatial and spatial-temporal data, particularly in geostatistical settings. As much of the attention of the statistics community has been focused on defining and estimating the mean and covariance functions of these processes, little effort has been devoted to developing goodness-of-fit tests to allow users to assess the models\\' adequacy. We describe a general goodness-of-fit test and related graphical diagnostics for assessing the fit of Bayesian Gaussian process models using pivotal discrepancy measures. Our method is applicable for both regularly and irregularly spaced observation locations on planar and spherical domains. The essential idea behind our method is to evaluate pivotal quantities defined for a realization of a Gaussian random field at parameter values drawn from the posterior distribution. Because the nominal distribution of the resulting pivotal discrepancy measures is known, it is possible to quantitatively assess model fit directly from the output of Markov chain Monte Carlo algorithms used to sample from the posterior distribution on the parameter space. We illustrate our method in a simulation study and in two applications.

  8. Sustainable Street Vendors Spatial Zoning Models in Surakarta

    Science.gov (United States)

    Rahayu, M. J.; Putri, R. A.; Rini, E. F.

    2018-02-01

    Various strategies that have been carried out by Surakarta’s government to organize street vendors have not achieved the goal of street vendors’ arrangement comprehensively. The street vendors arrangement strategy consists of physical (spatial) and non-physical. One of the physical arrangements is to define the street vendor’s zoning. Based on the street vendors’ characteristics, there are two alternative locations of stabilization (as one kind of street vendors’ arrangement) that can be used. The aim of this study is to examine those alternative locations to set the street vendor’s zoning models. Quatitative method is used to formulate the spatial zoning model. The street vendor’s zoning models are formulated based on two approaches, which are the distance to their residences and previous trading locations. Geographic information system is used to indicate all street vendors’ residences and trading locations based on their type of goods. Through proximity point distance tool on ArcGIS, we find the closeness of residential location and previous trading location with the alternative location of street vendors’ stabilization. The result shows that the location was chosen by the street vendors to sell their goods mainly consider the proximity to their homes. It also shows street vendor’s zoning models which based on the type of street vendor’s goods.

  9. Towards Quantitative Spatial Models of Seabed Sediment Composition.

    Directory of Open Access Journals (Sweden)

    David Stephens

    Full Text Available There is a need for fit-for-purpose maps for accurately depicting the types of seabed substrate and habitat and the properties of the seabed for the benefits of research, resource management, conservation and spatial planning. The aim of this study is to determine whether it is possible to predict substrate composition across a large area of seabed using legacy grain-size data and environmental predictors. The study area includes the North Sea up to approximately 58.44°N and the United Kingdom's parts of the English Channel and the Celtic Seas. The analysis combines outputs from hydrodynamic models as well as optical remote sensing data from satellite platforms and bathymetric variables, which are mainly derived from acoustic remote sensing. We build a statistical regression model to make quantitative predictions of sediment composition (fractions of mud, sand and gravel using the random forest algorithm. The compositional data is analysed on the additive log-ratio scale. An independent test set indicates that approximately 66% and 71% of the variability of the two log-ratio variables are explained by the predictive models. A EUNIS substrate model, derived from the predicted sediment composition, achieved an overall accuracy of 83% and a kappa coefficient of 0.60. We demonstrate that it is feasible to spatially predict the seabed sediment composition across a large area of continental shelf in a repeatable and validated way. We also highlight the potential for further improvements to the method.

  10. Spatial modelling and mapping of female genital mutilation in Kenya

    Science.gov (United States)

    2014-01-01

    Background Female genital mutilation/cutting (FGM/C) is still prevalent in several communities in Kenya and other areas in Africa, as well as being practiced by some migrants from African countries living in other parts of the world. This study aimed at detecting clustering of FGM/C in Kenya, and identifying those areas within the country where women still intend to continue the practice. A broader goal of the study was to identify geographical areas where the practice continues unabated and where broad intervention strategies need to be introduced. Methods The prevalence of FGM/C was investigated using the 2008 Kenya Demographic and Health Survey (KDHS) data. The 2008 KDHS used a multistage stratified random sampling plan to select women of reproductive age (15–49 years) and asked questions concerning their FGM/C status and their support for the continuation of FGM/C. A spatial scan statistical analysis was carried out using SaTScan™ to test for statistically significant clustering of the practice of FGM/C in the country. The risk of FGM/C was also modelled and mapped using a hierarchical spatial model under the Integrated Nested Laplace approximation approach using the INLA library in R. Results The prevalence of FGM/C stood at 28.2% and an estimated 10.3% of the women interviewed indicated that they supported the continuation of FGM. On the basis of the Deviance Information Criterion (DIC), hierarchical spatial models with spatially structured random effects were found to best fit the data for both response variables considered. Age, region, rural–urban classification, education, marital status, religion, socioeconomic status and media exposure were found to be significantly associated with FGM/C. The current FGM/C status of a woman was also a significant predictor of support for the continuation of FGM/C. Spatial scan statistics confirm FGM clusters in the North-Eastern and South-Western regions of Kenya (p < 0.001). Conclusion This suggests that the

  11. A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models.

    Science.gov (United States)

    Whittington, Jesse; Sawaya, Michael A

    2015-01-01

    Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071) for females, 0.844 (0.703-0.975) for males, and 0.882 (0.779-0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024) for females, 0.825 (0.700-0.948) for males, and 0.863 (0.771-0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth

  12. Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models.

    Science.gov (United States)

    Shen, Yuan; Mayhew, Stephen D; Kourtzi, Zoe; Tiňo, Peter

    2014-01-01

    Previous work investigated a range of spatio-temporal constraints for fMRI data analysis to provide robust detection of neural activation. We present a mixture-based method for the spatio-temporal modelling of fMRI data. This approach assumes that fMRI time series are generated by a probabilistic superposition of a small set of spatio-temporal prototypes (mixture components). Each prototype comprises a temporal model that explains fMRI signals on a single voxel and the model's "region of influence" through a spatial prior over the voxel space. As the key ingredient of our temporal model, the Hidden Process Model (HPM) framework proposed in Hutchinson et al. (2009) is adopted to infer the overlapping cognitive processes triggered by stimuli. Unlike the original HPM framework, we use a parametric model of Haemodynamic Response Function (HRF) so that biological constraints are naturally incorporated in the HRF estimation. The spatial priors are defined in terms of a parameterised distribution. Thus, the total number of parameters in the model does not depend on the number of voxels. The resulting model provides a conceptually principled and computationally efficient approach to identify spatio-temporal patterns of neural activation from fMRI data, in contrast to most conventional approaches in the literature focusing on the detection of spatial patterns. We first verify the proposed model in a controlled experimental setting using synthetic data. The model is further validated on real fMRI data obtained from a rapid event-related visual recognition experiment (Mayhew et al., 2012). Our model enables us to evaluate in a principled manner the variability of neural activations within individual regions of interest (ROIs). The results strongly suggest that, compared with occipitotemporal regions, the frontal ones are less homogeneous, requiring two HPM prototypes per region. Despite the rapid event-related experimental design, the model is capable of disentangling the

  13. A Spatial Model of the Biomass to Energy Cycle

    DEFF Research Database (Denmark)

    Möller, Bernd

    2003-01-01

    by location. This paper aims to contribute to the development of a biomass to energy evaluation and mapping system, using geographical information systems (GIS). A GIS-based in-forest residue model considers forest growth and choice of harvest method. Data from a sawmill survey is used to assess sawmill resi......-dues. For both sources the costs of road transportation have been modelled using spatial cost allocation. As emphasis has been on using public data, the model is still a rough es-timate, which could be improved using forest industry data and refined algorithms. As a first result, the cost distribution...... and the costs of accumulated amounts of wood residues can now be calculated almost instantly for each location in the country. It is assumed that this approach will facilitate the assessment of future biomass markets....

  14. Modelling spatial-temporal and coordinative parameters in swimming.

    Science.gov (United States)

    Seifert, L; Chollet, D

    2009-07-01

    This study modelled the changes in spatial-temporal and coordinative parameters through race paces in the four swimming strokes. The arm and leg phases in simultaneous strokes (butterfly and breaststroke) and the inter-arm phases in alternating strokes (crawl and backstroke) were identified by video analysis to calculate the time gaps between propulsive phases. The relationships among velocity, stroke rate, stroke length and coordination were modelled by polynomial regression. Twelve elite male swimmers swam at four race paces. Quadratic regression modelled the changes in spatial-temporal and coordinative parameters with velocity increases for all four strokes. First, the quadratic regression between coordination and velocity showed changes common to all four strokes. Notably, the time gaps between the key points defining the beginning and end of the stroke phases decreased with increases in velocity, which led to decreases in glide times and increases in the continuity between propulsive phases. Conjointly, the quadratic regression among stroke rate, stroke length and velocity was similar to the changes in coordination, suggesting that these parameters may influence coordination. The main practical application for coaches and scientists is that ineffective time gaps can be distinguished from those that simply reflect an individual swimmer's profile by monitoring the glide times within a stroke cycle. In the case of ineffective time gaps, targeted training could improve the swimmer's management of glide time.

  15. Spatial air pollution modelling for a West-African town

    Directory of Open Access Journals (Sweden)

    Sirak Zenebe Gebreab

    2015-11-01

    Full Text Available Land use regression (LUR modelling is a common approach used in European and Northern American epidemiological studies to assess urban and traffic related air pollution exposures. Studies applying LUR in Africa are lacking. A need exists to understand if this approach holds for an African setting, where urban features, pollutant exposures and data availability differ considerably from other continents. We developed a parsimonious regression model based on 48-hour nitrogen dioxide (NO2 concentrations measured at 40 sites in Kaédi, a medium sized West-African town, and variables generated in a geographic information system (GIS. Road variables and settlement land use characteristics were found to be important predictors of 48-hour NO2 concentration in the model. About 68% of concentration variability in the town was explained by the model. The model was internally validated by leave-one-out cross-validation and it was found to perform moderately well. Furthermore, its parameters were robust to sampling variation. We applied the model at 100 m pixels to create a map describing the broad spatial pattern of NO2 across Kaédi. In this research, we demonstrated the potential for LUR as a valid, cost-effective approach for air pollution modelling and mapping in an African town. If the methodology were to be adopted by environmental and public health authorities in these regions, it could provide a quick assessment of the local air pollution burden and potentially support air pollution policies and guidelines.

  16. Spatial memory impairments in a prediabetic rat model.

    Science.gov (United States)

    Soares, E; Prediger, R D; Nunes, S; Castro, A A; Viana, S D; Lemos, C; De Souza, C M; Agostinho, P; Cunha, R A; Carvalho, E; Fontes Ribeiro, C A; Reis, F; Pereira, F C

    2013-10-10

    Diabetes is associated with an increased risk for brain disorders, namely cognitive impairments associated with hippocampal dysfunction underlying diabetic encephalopathy. However, the impact of a prediabetic state on cognitive function is unknown. Therefore, we now investigated whether spatial learning and memory deficits and the underlying hippocampal dysfunction were already present in a prediabetic animal model. Adult Wistar rats drinking high-sucrose (HSu) diet (35% sucrose solution during 9 weeks) were compared to controls' drinking water. HSu rats exhibited fasting normoglycemia accompanied by hyperinsulinemia and hypertriglyceridemia in the fed state, and insulin resistance with impaired glucose tolerance confirming them as a prediabetic rodent model. HSu rats displayed a poorer performance in hippocampal-dependent short- and long-term spatial memory performance, assessed with the modified Y-maze and Morris water maze tasks, respectively; this was accompanied by a reduction of insulin receptor-β density with normal levels of insulin receptor substrate-1 pSer636/639, and decreased hippocampal glucocorticoid receptor levels without changes of the plasma corticosterone levels. Importantly, HSu animals exhibited increased hippocampal levels of AMPA and NMDA receptor subunits GluA1 and GLUN1, respectively, whereas the levels of protein markers related to nerve terminals (synaptophysin) and oxidative stress/inflammation (HNE, RAGE, TNF-α) remained unaltered. These findings indicate that 9 weeks of sucrose consumption resulted in a metabolic condition suggestive of a prediabetic state, which translated into short- and long-term spatial memory deficits accompanied by alterations in hippocampal glutamatergic neurotransmission and abnormal glucocorticoid signaling. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  17. An alternative to the standard spatial econometric approaches in hedonic house price models

    DEFF Research Database (Denmark)

    von Graevenitz, Kathrine; Panduro, Toke Emil

    2015-01-01

    Omitted, misspecified, or mismeasured spatially varying characteristics are a cause for concern in hedonic house price models. Spatial econometrics or spatial fixed effects have become popular ways of addressing these concerns. We discuss the limitations of standard spatial approaches to hedonic...

  18. Hierarchical spatial capture-recapture models: Modeling population density from stratified populations

    Science.gov (United States)

    Royle, J. Andrew; Converse, Sarah J.

    2014-01-01

    Capture–recapture studies are often conducted on populations that are stratified by space, time or other factors. In this paper, we develop a Bayesian spatial capture–recapture (SCR) modelling framework for stratified populations – when sampling occurs within multiple distinct spatial and temporal strata.We describe a hierarchical model that integrates distinct models for both the spatial encounter history data from capture–recapture sampling, and also for modelling variation in density among strata. We use an implementation of data augmentation to parameterize the model in terms of a latent categorical stratum or group membership variable, which provides a convenient implementation in popular BUGS software packages.We provide an example application to an experimental study involving small-mammal sampling on multiple trapping grids over multiple years, where the main interest is in modelling a treatment effect on population density among the trapping grids.Many capture–recapture studies involve some aspect of spatial or temporal replication that requires some attention to modelling variation among groups or strata. We propose a hierarchical model that allows explicit modelling of group or strata effects. Because the model is formulated for individual encounter histories and is easily implemented in the BUGS language and other free software, it also provides a general framework for modelling individual effects, such as are present in SCR models.

  19. A Biophysical Neural Model To Describe Spatial Visual Attention

    International Nuclear Information System (INIS)

    Hugues, Etienne; Jose, Jorge V.

    2008-01-01

    Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations

  20. Spatially-explicit models of global tree density

    Science.gov (United States)

    Glick, Henry B.; Bettigole, Charlie; Maynard, Daniel S.; Covey, Kristofer R.; Smith, Jeffrey R.; Crowther, Thomas W.

    2016-01-01

    Remote sensing and geographic analysis of woody vegetation provide means of evaluating the distribution of natural resources, patterns of biodiversity and ecosystem structure, and socio-economic drivers of resource utilization. While these methods bring geographic datasets with global coverage into our day-to-day analytic spheres, many of the studies that rely on these strategies do not capitalize on the extensive collection of existing field data. We present the methods and maps associated with the first spatially-explicit models of global tree density, which relied on over 420,000 forest inventory field plots from around the world. This research is the result of a collaborative effort engaging over 20 scientists and institutions, and capitalizes on an array of analytical strategies. Our spatial data products offer precise estimates of the number of trees at global and biome scales, but should not be used for local-level estimation. At larger scales, these datasets can contribute valuable insight into resource management, ecological modelling efforts, and the quantification of ecosystem services. PMID:27529613

  1. Spatial Model of Sky Brightness Magnitude in Langkawi Island, Malaysia

    Science.gov (United States)

    Redzuan Tahar, Mohammad; Kamarudin, Farahana; Umar, Roslan; Khairul Amri Kamarudin, Mohd; Sabri, Nor Hazmin; Ahmad, Karzaman; Rahim, Sobri Abdul; Sharul Aikal Baharim, Mohd

    2017-03-01

    Sky brightness is an essential topic in the field of astronomy, especially for optical astronomical observations that need very clear and dark sky conditions. This study presents the spatial model of sky brightness magnitude in Langkawi Island, Malaysia. Two types of Sky Quality Meter (SQM) manufactured by Unihedron are used to measure the sky brightness on a moonless night (or when the Moon is below the horizon), when the sky is cloudless and the locations are at least 100 m from the nearest light source. The selected locations are marked by their GPS coordinates. The sky brightness data obtained in this study were interpolated and analyzed using a Geographic Information System (GIS), thus producing a spatial model of sky brightness that clearly shows the dark and bright sky areas in Langkawi Island. Surprisingly, our results show the existence of a few dark sites nearby areas of high human activity. The sky brightness of 21.45 mag arcsec{}-2 in the Johnson-Cousins V-band, as the average of sky brightness equivalent to 2.8 × {10}-4{cd} {{{m}}}-2 over the entire island, is an indication that the island is, overall, still relatively dark. However, the amount of development taking place might reduce the number in the near future as the island is famous as a holiday destination.

  2. Spatial assignment of emissions using a new locomotive emissions model.

    Science.gov (United States)

    Gould, Gregory M; Niemeier, Deb A

    2011-07-01

    Estimates of fuel use and air pollutant emissions from freight rail currently rely highly on aggregate methods and largely obsolete data which offer little insight into contemporary air quality problems. Because the freight industry is for the most part privately held and data are closely guarded for competitive reasons, the challenge is to produce robust estimates using current reporting requirements, while accurately portraying the spatial nature of freight rail impacts. This research presents a new spatially resolved model for estimating air pollutant emissions (hydrocarbons, carbon monoxide, nitrogen oxides, particulate matter less than 10 μm in diameter, sulfur dioxide, and carbon dioxide) from locomotives. Emission estimates are based on track segment level data including track grade, type of train traffic (bulk, intermodal, or manifest) and the local locomotive fleet (EPA tier certification level and fuel efficiency). We model the California Class I freight rail system and compare our results to regional estimates from the California Air Resources Board and to estimates following U.S. Environmental Protection Agency guidance. We find that our results vary considerably from the other methods depending on the region or corridor analyzed. We also find large differences in fuel and emission intensity for individual rail corridors.

  3. A spatially structured metapopulation model within a stochastic environment.

    Science.gov (United States)

    Smith, Andrew G

    2017-09-01

    Populations often exist, either by choice or by external pressure, in a fragmented way, referred to as a metapopulation. Typically, the dynamics accounted for within metapopulation models are assumed to be static. For example, patch occupancy models often assume that the colonisation and extinction rates do not change, while spatially structured models often assume that the rates of births, deaths and migrations do not depend on time. While some progress has been made when these dynamics are changing deterministically, less is known when the changes are stochastic. It can be quite common that the environment a population inhabits determines how these dynamics change over time. Changes to this environment can have a large impact on the survival probability of a population and such changes will often be stochastic. The typical metapopulation model allows for catastrophes that could eradicate most, if not all, individuals on an entire patch. It is this type of phenomenon that this article addresses. A Markov process is developed that models the number of individuals on each patch within a metapopulation. An approximation for the original model is presented in the form of a piecewise-deterministic Markov process and the approximation is analysed to present conditions for extinction. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Parameterizing the Spatial Markov Model From Breakthrough Curve Data Alone

    Science.gov (United States)

    Sherman, Thomas; Fakhari, Abbas; Miller, Savannah; Singha, Kamini; Bolster, Diogo

    2017-12-01

    The spatial Markov model (SMM) is an upscaled Lagrangian model that effectively captures anomalous transport across a diverse range of hydrologic systems. The distinct feature of the SMM relative to other random walk models is that successive steps are correlated. To date, with some notable exceptions, the model has primarily been applied to data from high-resolution numerical simulations and correlation effects have been measured from simulated particle trajectories. In real systems such knowledge is practically unattainable and the best one might hope for is breakthrough curves (BTCs) at successive downstream locations. We introduce a novel methodology to quantify velocity correlation from BTC data alone. By discretizing two measured BTCs into a set of arrival times and developing an inverse model, we estimate velocity correlation, thereby enabling parameterization of the SMM in studies where detailed Lagrangian velocity statistics are unavailable. The proposed methodology is applied to two synthetic numerical problems, where we measure all details and thus test the veracity of the approach by comparison of estimated parameters with known simulated values. Our results suggest that our estimated transition probabilities agree with simulated values and using the SMM with this estimated parameterization accurately predicts BTCs downstream. Our methodology naturally allows for estimates of uncertainty by calculating lower and upper bounds of velocity correlation, enabling prediction of a range of BTCs. The measured BTCs fall within the range of predicted BTCs. This novel method to parameterize the SMM from BTC data alone is quite parsimonious, thereby widening the SMM's practical applicability.

  5. Is a matrix exponential specification suitable for the modeling of spatial correlation structures?

    Science.gov (United States)

    Strauß, Magdalena E; Mezzetti, Maura; Leorato, Samantha

    2017-05-01

    This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an alternative to the widely used spatial autoregressive models (SAR). To provide as complete a picture as possible, we extend the analysis to all the main spatial models governed by matrix exponentials comparing them with their spatial autoregressive counterparts. We propose a new implementation of Bayesian parameter estimation for the MESS model with vague prior distributions, which is shown to be precise and computationally efficient. Our implementations also account for spatially lagged regressors. We further allow for location-specific heterogeneity, which we model by including spatial splines. We conclude by comparing the performances of the different model specifications in applications to a real data set and by running simulations. Both the applications and the simulations suggest that the spatial splines are a flexible and efficient way to account for spatial heterogeneities governed by unknown mechanisms.

  6. Infection dynamics on spatial small-world network models

    Science.gov (United States)

    Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario

    2017-11-01

    The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.

  7. Modelling Spatial and Temporal Fault Zone Evolution in Basement Rocks

    Science.gov (United States)

    Lunn, R. J.; Willson, J. P.; Shipton, Z. K.

    2006-12-01

    There is considerable industrial interest in assessing the permeability of faults for the purpose of oil and gas production, deep well injection of waste liquids, underground storage of natural gas and disposal of radioactive waste. Prior estimation of fault hydraulic properties is highly error prone. Faults zones are formed through a complex interaction of mechanical, hydraulic and chemical processes and their permeability varies considerably over both space and time. Algorithms for predicting fault seal potential using throw and host rock property data exist for clay-rich fault seals but are contentious. In the case of crystalline rocks and sand-sand contacts, no such algorithms exist. In any case, the study of fault growth processes does not suggest that there is a clear or simple relationship between fault throw and the fault zone permeability. To improve estimates of fault zone permeability, it is important to understand the underlying hydro-mechanical processes of fault zone formation. In this research, we explore the spatial and temporal evolution of fault zones through development and application of a 2D hydro-mechanical finite element model. The development of fault zone damage is simulated perpendicular to the main slip surface using a fully coupled solution of Navier's equation for mechanical deformation and Darcy's Law/conservation of fluid mass for subsurface fluid flow. The model is applied to study development of fault zones in basement rocks, based on the conceptual model of S. J. Martell, J. Struct. Geol. 12(7):869-882, 1990. We simulate the evolution of fault zones from pre-existing joints and explore controls on the growth rate and locations of multiple splay fractures which link-up to form complex damage zones. We are the first researchers to successfully simulate the temporal and spatial evolution of multiple wing cracks, tertiary fracturing, antithetic fractures propagating into the compressive region, infill fracturing between faults and

  8. Spatial Modeling of Risk in Natural Resource Management

    Directory of Open Access Journals (Sweden)

    Peter Jones

    2002-01-01

    Full Text Available Making decisions in natural resource management involves an understanding of the risk and uncertainty of the outcomes, such as crop failure or cattle starvation, and of the normal spread of the expected production. Hedging against poor outcomes often means lack of investment and slow adoption of new methods. At the household level, production instability can have serious effects on income and food security. At the national level, it can have social and economic impacts that may affect all sectors of society. Crop models such as CERES-Maize are excellent tools for assessing weather-related production variability. WATBAL is a water balance model that can provide robust estimates of the potential growing days for a pasture. These models require large quantities of daily weather data that are rarely available. MarkSim is an application for generating synthetic daily weather files by estimating the third-order Markov model parameters from interpolated climate surfaces. The models can then be run for each distinct point on the map. This paper examines the growth of maize and pasture in dryland agriculture in southern Africa. Weather simulators produce independent estimates for each point on the map; however, we know that a spatial coherence of weather exists. We investigated a method of incorporating spatial coherence into MarkSim and show that it increases the variance of production. This means that all of the farmers in a coherent area share poor yields, with important consequences for food security, markets, transport, and shared grazing lands. The long-term aspects of risk are associated with global climate change. We used the results of a Global Circulation Model to extrapolate to the year 2055. We found that low maize yields would become more likely in the marginal areas, whereas they may actually increase in some areas. The same trend was found with pasture growth. We outline areas where further work is required before these tools and methods

  9. Evaluating water erosion prediction project model using Cesium-137-derived spatial soil redistribution data

    Science.gov (United States)

    The lack of spatial soil erosion data has been a major constraint on the refinement and application of physically based erosion models. Spatially distributed models can only be thoroughly validated with distributed erosion data. The fallout cesium-137 has been widely used to generate spatial soil re...

  10. Lessons learned for spatial modelling of ecosystem services in support of ecosystem accounting

    NARCIS (Netherlands)

    Schroter, M.; Remme, R.P.; Sumarga, E.; Barton, D.N.; Hein, L.G.

    2015-01-01

    Assessment of ecosystem services through spatial modelling plays a key role in ecosystem accounting. Spatial models for ecosystem services try to capture spatial heterogeneity with high accuracy. This endeavour, however, faces several practical constraints. In this article we analyse the trade-offs

  11. Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data for snow modeling in mountain basins

    Science.gov (United States)

    Havens, Scott; Marks, Danny; Kormos, Patrick; Hedrick, Andrew

    2017-12-01

    In the Western US and many mountainous regions of the world, critical water resources and climate conditions are difficult to monitor because the observation network is generally very sparse. The critical resource from the mountain snowpack is water flowing into streams and reservoirs that will provide for irrigation, flood control, power generation, and ecosystem services. Water supply forecasting in a rapidly changing climate has become increasingly difficult because of non-stationary conditions. In response, operational water supply managers have begun to move from statistical techniques towards the use of physically based models. As we begin to transition physically based models from research to operational use, we must address the most difficult and time-consuming aspect of model initiation: the need for robust methods to develop and distribute the input forcing data. In this paper, we present a new open source framework, the Spatial Modeling for Resources Framework (SMRF), which automates and simplifies the common forcing data distribution methods. It is computationally efficient and can be implemented for both research and operational applications. We present an example of how SMRF is able to generate all of the forcing data required to a run physically based snow model at 50-100 m resolution over regions of 1000-7000 km2. The approach has been successfully applied in real time and historical applications for both the Boise River Basin in Idaho, USA and the Tuolumne River Basin in California, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of a physically based snow model possible.

  12. Spatial Extent Models for Natural Language Phrases Involving Directional Containment

    NARCIS (Netherlands)

    Singh, G.; de By, R.A.

    2015-01-01

    We study the problem of assigning a spatial extent to a text phrase such as central northern California', with the objective of allowing spatial interpretations of natural language, and consistency testing of complex utterances that involve multiple phrases from which spatial extent can be derived.

  13. Spatial Fleming-Viot models with selection and mutation

    CERN Document Server

    Dawson, Donald A

    2014-01-01

    This book constructs a rigorous framework for analysing selected phenomena in evolutionary theory of populations arising due to the combined effects of migration, selection and mutation in a spatial stochastic population model, namely the evolution towards fitter and fitter types through punctuated equilibria. The discussion is based on a number of new methods, in particular multiple scale analysis, nonlinear Markov processes and their entrance laws, atomic measure-valued evolutions and new forms of duality (for state-dependent mutation and multitype selection) which are used to prove ergodic theorems in this context and are applicable for many other questions and renormalization analysis for a variety of phenomena (stasis, punctuated equilibrium, failure of naive branching approximations, biodiversity) which occur due to the combination of rare mutation, mutation, resampling, migration and selection and make it necessary to mathematically bridge the gap (in the limit) between time and space scales.

  14. Spatial Segregation, Redistribution and Welfare: A Theoretical Model

    Directory of Open Access Journals (Sweden)

    Tommaso Gabrieli

    2016-03-01

    Full Text Available This paper develops a theoretical model focusing on the effect that different neighborhood compositions can have on the formation of individual beliefs about economic opportunities. Specifically we highlight two effects that spatial segregation may have: (1 it can efficiently separate the individual effort choices of highly and low productive individuals, (2 it may imply that the median voter imposes a level of redistribution that is inefficient from the aggregate point of view. The trade-off implies that segregated and non-segregated cities may present very similar levels of aggregate welfare. We employ this framework to discuss how the structure of cities can play a role in the determination of US-type and Europe-type politico-economic equilibria and the implications for planning policies.

  15. Unemployment estimation: Spatial point referenced methods and models

    KAUST Repository

    Pereira, Soraia

    2017-06-26

    Portuguese Labor force survey, from 4th quarter of 2014 onwards, started geo-referencing the sampling units, namely the dwellings in which the surveys are carried. This opens new possibilities in analysing and estimating unemployment and its spatial distribution across any region. The labor force survey choose, according to an preestablished sampling criteria, a certain number of dwellings across the nation and survey the number of unemployed in these dwellings. Based on this survey, the National Statistical Institute of Portugal presently uses direct estimation methods to estimate the national unemployment figures. Recently, there has been increased interest in estimating these figures in smaller areas. Direct estimation methods, due to reduced sampling sizes in small areas, tend to produce fairly large sampling variations therefore model based methods, which tend to

  16. The backbone of a City Information Model (CIM) : Implementing a spatial data model for urban design

    NARCIS (Netherlands)

    Gil, J.A.; Almeida, J.; Duarte, J.P.

    2011-01-01

    We have been witnessing an increased interest in a more holistic approach to urban design practice and education. In this paper we present a spatial data model for urban design that proposes the combination of urban environment feature classes with design process feature classes. This data model is

  17. The formulation and estimation of a spatial skew-normal generalized ordered-response model.

    Science.gov (United States)

    2016-06-01

    This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an : associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal : distribut...

  18. Spatial Welfare Economics versus Ecological Footprint: Modeling Agglomeration, Externalities and Trade

    NARCIS (Netherlands)

    Grazi, F.; van den Bergh, J.C.J.M.; Rietveld, P.

    2007-01-01

    A welfare framework for the analysis of the spatial dimensions of sustainability is developed. It covers agglomeration effects, interregional trade, negative environmental externalities, and various land use categories. The model is used to compare rankings of spatial configurations according to

  19. SPATIAL MOTION OF THE MAGELLANIC CLOUDS: TIDAL MODELS RULED OUT?

    International Nuclear Information System (INIS)

    Ruzicka, Adam; Palous, Jan; Theis, Christian

    2009-01-01

    Recently, Kallivayalil et al. derived new values of the proper motion for the Large and Small Magellanic Clouds (LMC and SMC, respectively). The spatial velocities of both Clouds are unexpectedly higher than their previous values resulting from agreement between the available theoretical models of the Magellanic System and the observations of neutral hydrogen (H I) associated with the LMC and the SMC. Such proper motion estimates are likely to be at odds with the scenarios for creation of the large-scale structures in the Magellanic System suggested so far. We investigated this hypothesis for the pure tidal models, as they were the first ones devised to explain the evolution of the Magellanic System, and the tidal stripping is intrinsically involved in every model assuming the gravitational interaction. The parameter space for the Milky Way (MW)-LMC-SMC interaction was analyzed by a robust search algorithm (genetic algorithm) combined with a fast, restricted N-body model of the interaction. Our method extended the known variety of evolutionary scenarios satisfying the observed kinematics and morphology of the Magellanic large-scale structures. Nevertheless, assuming the tidal interaction, no satisfactory reproduction of the H I data available for the Magellanic Clouds was achieved with the new proper motions. We conclude that for the proper motion data by Kallivayalil et al., within their 1σ errors, the dynamical evolution of the Magellanic System with the currently accepted total mass of the MW cannot be explained in the framework of pure tidal models. The optimal value for the western component of the LMC proper motion was found to be μ W lmc ∼> -1.3 mas yr -1 in case of tidal models. It corresponds to the reduction of the Kallivayalil et al. value for μ W lmc by ∼ 40% in its magnitude.

  20. Pattern formation through spatial interactions in a modified Daisyworld model

    Science.gov (United States)

    Alberti, Tommaso; Primavera, Leonardo; Lepreti, Fabio; Vecchio, Antonio; Carbone, Vincenzo

    2015-04-01

    The Daisyworld model is based on a hypothetical planet, like the Earth, which receives the radiant energy coming from a Sun-like star, and populated by two kinds of identical plants differing by their colour: white daisies reflecting light and black daisies absorbing light. The interactions and feedbacks between the collective biota of the planet and the incoming radiation form a self-regulating system where the conditions for life are maintained. We investigate a modified version of the Daisyworld model where a spatial dependency on latitude is introduced, and both a variable heat diffusivity along latitude and a simple greenhouse model are included. We show that the spatial interactions between the variables of the system can generate some equilibrium patterns which can locally stabilize the coexistence of the two vegetation types. The feedback on albedo is able to generate new equilibrium solutions which can efficiently self-regulate the planet climate, even for values of the solar luminosity relatively far from the current Earth conditions. The extension to spatial Daisyworld gives room to the possibility of inhomogeneous solar forcing in a curved planet, with explicit differences between poles and equator and the direct use of the heat diffusion equation. As a first approach, to describe a spherical planet, we consider the temperature T(θ,t) and the surface coverage as depending only on time and on latitude θ (-90° ≤ θ ≤ 90°). A second step is the introduction of the greenhouse effect in the model, the process by which outgoing infrared radiation is partly screened by greenhouse gases. This effect can be described by relaxing the black-body radiation hypothesis and by introducing a grayness function g(T) in the heat equation. As a third step, we consider a latitude dependence of the Earth's conductivity, χ = χ(θ). Considering these terms, using spherical coordinates and symmetry with respect to θ, the modified Daisyworld equations reduce to the

  1. Spatial and Temporal Self-Calibration of a Hydroeconomic Model

    Science.gov (United States)

    Howitt, R. E.; Hansen, K. M.

    2008-12-01

    across key nodes on the network and to annual carryover storage at ground and surface water storage facilities. To our knowledge, this is the first hydroeconomic model to perform spatial and temporal calibration simultaneously. The base for the LFN model is CALVIN, a hydroeconomic optimization model of the California water system developed at the University of California, Davis (Draper, et al. 2003). The LFN model, programmed in GAMS, is nonlinear, which permits incorporation of dynamic groundwater pumping costs that reflect head elevation. Hydropower production, also nonlinear in storage levels, could be added in the future. In this paper, we describe model implementation and performance over a sequence of water years drawn from the historical hydrologic record in California. Preliminary findings indicate that calibration occurs within acceptable limits and simulations replicate base case results well. Cai, X., and Wang, D. 2006. "Calibrating Holistic Water Resources-Economic Models." Journal of Water Resources Planning and Management November-December. Draper, A.J., M.W. Jenkins, K.W. Kirby, J.R. Lund, and R.E. Howitt. 2003. "Economic-Engineering Optimization for California Water Management." Journal of Water Resources Planning and Management 129(3):155-164. Howitt, R.E. 1995. "Positive Mathematical Programming." American Journal of Agricultural Economics 77:329-342. Howitt, R.E. 1998. "Self-Calibrating Network Flow Models." Working Paper, Department of Agricultural and Resource Economics, University of California, Davis. October 1998. class="ab'>

  2. Accounting for spatial effects in land use regression for urban air pollution modeling.

    Science.gov (United States)

    Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G

    2015-01-01

    In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  3. A spatial stochastic programming model for timber and core area management under risk of fires

    Science.gov (United States)

    Yu Wei; Michael Bevers; Dung Nguyen; Erin Belval

    2014-01-01

    Previous stochastic models in harvest scheduling seldom address explicit spatial management concerns under the influence of natural disturbances. We employ multistage stochastic programming models to explore the challenges and advantages of building spatial optimization models that account for the influences of random stand-replacing fires. Our exploratory test models...

  4. Panchromatic SED modelling of spatially-resolved galaxies

    Science.gov (United States)

    Smith, Daniel J. B.; Hayward, Christopher C.

    2018-02-01

    We test the efficacy of the energy-balance spectral energy distribution (SED) fitting code MAGPHYS for recovering the spatially-resolved properties of a simulated isolated disc galaxy, for which it was not designed. We perform 226,950 MAGPHYS SED fits to regions between 0.2 kpc and 25 kpc in size across the galaxy's disc, viewed from three different sight-lines, to probe how well MAGPHYS can recover key galaxy properties based on 21 bands of UV-far-infrared model photometry. MAGPHYS yields statistically acceptable fits to >99 per cent of the pixels within the r-band effective radius and between 59 and 77 percent of pixels within 20 kpc of the nucleus. MAGPHYS is able to recover the distribution of stellar mass, star formation rate (SFR), specific SFR, dust luminosity, dust mass, and V-band attenuation reasonably well, especially when the pixel size is ≳ 1 kpc, whereas non-standard outputs (stellar metallicity and mass-weighted age) are recovered less well. Accurate recovery is more challenging in the smallest sub-regions of the disc (pixel scale ≲ 1 kpc), where the energy balance criterion becomes increasingly incorrect. Estimating integrated galaxy properties by summing the recovered pixel values, the true integrated values of all parameters considered except metallicity and age are well recovered at all spatial resolutions, ranging from 0.2 kpc to integrating across the disc, albeit with some evidence for resolution-dependent biases. These results must be considered when attempting to analyse the structure of real galaxies with actual observational data, for which the `ground truth' is unknown.

  5. Multiresolution Network Temporal and Spatial Scheduling Model of Scenic Spot

    Directory of Open Access Journals (Sweden)

    Peng Ge

    2013-01-01

    Full Text Available Tourism is one of pillar industries of the world economy. Low-carbon tourism will be the mainstream direction of the scenic spots' development, and the ω path of low-carbon tourism development is to develop economy and protect environment simultaneously. However, as the tourists' quantity is increasing, the loads of scenic spots are out of control. And the instantaneous overload in some spots caused the image phenomenon of full capacity of the whole scenic spot. Therefore, realizing the real-time schedule becomes the primary purpose of scenic spot’s management. This paper divides the tourism distribution system into several logically related subsystems and constructs a temporal and spatial multiresolution network scheduling model according to the regularity of scenic spots’ overload phenomenon in time and space. It also defines dynamic distribution probability and equivalent dynamic demand to realize the real-time prediction. We define gravitational function between fields and takes it as the utility of schedule, after resolving the transportation model of each resolution, it achieves hierarchical balance between demand and capacity of the system. The last part of the paper analyzes the time complexity of constructing a multiresolution distribution system.

  6. Working models for spatial distribution and level of Mars' seismicity

    Science.gov (United States)

    Knapmeyer, M.; Oberst, J.; Hauber, E.; Wählisch, M.; Deuchler, C.; Wagner, R.

    2006-11-01

    We present synthetic catalogs of Mars quakes, intended to be used for performance assessments of future seismic networks on the planet. We have compiled a new inventory of compressional and extensional tectonic faults for the planet Mars, comprising 8500 faults with a total length of 680,000 km. The faults were mapped on the basis of Mars Orbiting Laser Altimeter (MOLA) shaded relief. Hence we expect to have assembled a homogeneous data set, not biased by illumination and viewing conditions of image data. Updated models of Martian crater statistics and geological maps were used to assign new maximum ages to all faults. On the basis of the fault catalog, spatial distributions of seismicity were simulated, using assumptions on the available annual seismic moment budget, the moment-frequency relationship, and a relation between rupture length and released moment. We have constructed five different models of Martian seismicity, predicting an annual moment release between 3.42 × 1016 Nm and 4.78 × 1018 Nm and up to 572 events with magnitudes greater than 4 per year as upper limit end-member case. Most events are expected on the Tharsis shield, but minor seismic centers are expected south of Hellas and north of Utopia Planitia.

  7. Spatial Preference Modelling for equitable infrastructure provision: an application of Sen's Capability Approach

    Science.gov (United States)

    Wismadi, Arif; Zuidgeest, Mark; Brussel, Mark; van Maarseveen, Martin

    2014-01-01

    To determine whether the inclusion of spatial neighbourhood comparison factors in Preference Modelling allows spatial decision support systems (SDSSs) to better address spatial equity, we introduce Spatial Preference Modelling (SPM). To evaluate the effectiveness of this model in addressing equity, various standardisation functions in both Non-Spatial Preference Modelling and SPM are compared. The evaluation involves applying the model to a resource location-allocation problem for transport infrastructure in the Special Province of Yogyakarta in Indonesia. We apply Amartya Sen's Capability Approach to define opportunity to mobility as a non-income indicator. Using the extended Moran's I interpretation for spatial equity, we evaluate the distribution output regarding, first, `the spatial distribution patterns of priority targeting for allocation' (SPT) and, second, `the effect of new distribution patterns after location-allocation' (ELA). The Moran's I index of the initial map and its comparison with six patterns for SPT as well as ELA consistently indicates that the SPM is more effective for addressing spatial equity. We conclude that the inclusion of spatial neighbourhood comparison factors in Preference Modelling improves the capability of SDSS to address spatial equity. This study thus proposes a new formal method for SDSS with specific attention on resource location-allocation to address spatial equity.

  8. A spatially-averaged mathematical model of kidney branching morphogenesis

    KAUST Repository

    Zubkov, V.S.

    2015-08-01

    © 2015 Published by Elsevier Ltd. Kidney development is initiated by the outgrowth of an epithelial ureteric bud into a population of mesenchymal cells. Reciprocal morphogenetic responses between these two populations generate a highly branched epithelial ureteric tree with the mesenchyme differentiating into nephrons, the functional units of the kidney. While we understand some of the mechanisms involved, current knowledge fails to explain the variability of organ sizes and nephron endowment in mice and humans. Here we present a spatially-averaged mathematical model of kidney morphogenesis in which the growth of the two key populations is described by a system of time-dependant ordinary differential equations. We assume that branching is symmetric and is invoked when the number of epithelial cells per tip reaches a threshold value. This process continues until the number of mesenchymal cells falls below a critical value that triggers cessation of branching. The mathematical model and its predictions are validated against experimentally quantified C57Bl6 mouse embryonic kidneys. Numerical simulations are performed to determine how the final number of branches changes as key system parameters are varied (such as the growth rate of tip cells, mesenchyme cells, or component cell population exit rate). Our results predict that the developing kidney responds differently to loss of cap and tip cells. They also indicate that the final number of kidney branches is less sensitive to changes in the growth rate of the ureteric tip cells than to changes in the growth rate of the mesenchymal cells. By inference, increasing the growth rate of mesenchymal cells should maximise branch number. Our model also provides a framework for predicting the branching outcome when ureteric tip or mesenchyme cells change behaviour in response to different genetic or environmental developmental stresses.

  9. Scaling-up spatially-explicit ecological models using graphics processors

    NARCIS (Netherlands)

    Koppel, Johan van de; Gupta, Rohit; Vuik, Cornelis

    2011-01-01

    How the properties of ecosystems relate to spatial scale is a prominent topic in current ecosystem research. Despite this, spatially explicit models typically include only a limited range of spatial scales, mostly because of computing limitations. Here, we describe the use of graphics processors to

  10. Spatial object modeling in fuzzy topological spaces: with applications to land cover change

    NARCIS (Netherlands)

    Tang, Xinming; Tang, Xinming

    2004-01-01

    The central topic of this thesis focuses on the accommodation of fuzzy spatial objects in a GIS. Several issues are discussed theoretically and practically, including the definition of fuzzy spatial objects, the topological relations between them, the modeling of fuzzy spatial objects, the

  11. Including spatial data in nutrient balance modelling on dairy farms

    Science.gov (United States)

    van Leeuwen, Maricke; van Middelaar, Corina; Stoof, Cathelijne; Oenema, Jouke; Stoorvogel, Jetse; de Boer, Imke

    2017-04-01

    The Annual Nutrient Cycle Assessment (ANCA) calculates the nitrogen (N) and phosphorus (P) balance at a dairy farm, while taking into account the subsequent nutrient cycles of the herd, manure, soil and crop components. Since January 2016, Dutch dairy farmers are required to use ANCA in order to increase understanding of nutrient flows and to minimize nutrient losses to the environment. A nutrient balance calculates the difference between nutrient inputs and outputs. Nutrients enter the farm via purchased feed, fertilizers, deposition and fixation by legumes (nitrogen), and leave the farm via milk, livestock, manure, and roughages. A positive balance indicates to which extent N and/or P are lost to the environment via gaseous emissions (N), leaching, run-off and accumulation in soil. A negative balance indicates that N and/or P are depleted from soil. ANCA was designed to calculate average nutrient flows on farm level (for the herd, manure, soil and crop components). ANCA was not designed to perform calculations of nutrient flows at the field level, as it uses averaged nutrient inputs and outputs across all fields, and it does not include field specific soil characteristics. Land management decisions, however, such as the level of N and P application, are typically taken at the field level given the specific crop and soil characteristics. Therefore the information that ANCA provides is likely not sufficient to support farmers' decisions on land management to minimize nutrient losses to the environment. This is particularly a problem when land management and soils vary between fields. For an accurate estimate of nutrient flows in a given farming system that can be used to optimize land management, the spatial scale of nutrient inputs and outputs (and thus the effect of land management and soil variation) could be essential. Our aim was to determine the effect of the spatial scale of nutrient inputs and outputs on modelled nutrient flows and nutrient use efficiencies

  12. MODELING SPATIAL TREE PATTERNS IN THE TAPAJÓS FOREST USING INTERFEROMETRIC HEIGHT

    Directory of Open Access Journals (Sweden)

    João R. dos Santos

    2005-04-01

    Full Text Available The spatial distribution of very large trees in primary Amazon forest is extracted from a digital model of interferometric forest height by an approach of local maximum filtering. The spatial point patterns of very large trees are modeled by a series of Markov point process models. Spatial distribution is regular, and interaction decreases with distance; very large trees are shown to exert repulsive interaction with their neighboring very large trees.

  13. Environmental Impacts of Large Scale Biochar Application Through Spatial Modeling

    Science.gov (United States)

    Huber, I.; Archontoulis, S.

    2017-12-01

    In an effort to study the environmental (emissions, soil quality) and production (yield) impacts of biochar application at regional scales we coupled the APSIM-Biochar model with the pSIMS parallel platform. So far the majority of biochar research has been concentrated on lab to field studies to advance scientific knowledge. Regional scale assessments are highly needed to assist decision making. The overall objective of this simulation study was to identify areas in the USA that have the most gain environmentally from biochar's application, as well as areas which our model predicts a notable yield increase due to the addition of biochar. We present the modifications in both APSIM biochar and pSIMS components that were necessary to facilitate these large scale model runs across several regions in the United States at a resolution of 5 arcminutes. This study uses the AgMERRA global climate data set (1980-2010) and the Global Soil Dataset for Earth Systems modeling as a basis for creating its simulations, as well as local management operations for maize and soybean cropping systems and different biochar application rates. The regional scale simulation analysis is in progress. Preliminary results showed that the model predicts that high quality soils (particularly those common to Iowa cropping systems) do not receive much, if any, production benefit from biochar. However, soils with low soil organic matter ( 0.5%) do get a noteworthy yield increase of around 5-10% in the best cases. We also found N2O emissions to be spatial and temporal specific; increase in some areas and decrease in some other areas due to biochar application. In contrast, we found increases in soil organic carbon and plant available water in all soils (top 30 cm) due to biochar application. The magnitude of these increases (% change from the control) were larger in soil with low organic matter (below 1.5%) and smaller in soils with high organic matter (above 3%) and also dependent on biochar

  14. Spatial Statistical Network Models for Stream and River Temperature in the Chesapeake Bay Watershed, USA

    Science.gov (United States)

    Regional temperature models are needed for characterizing and mapping stream thermal regimes, establishing reference conditions, predicting future impacts and identifying critical thermal refugia. Spatial statistical models have been developed to improve regression modeling techn...

  15. Spatial Double Generalized Beta Regression Models: Extensions and Application to Study Quality of Education in Colombia

    Science.gov (United States)

    Cepeda-Cuervo, Edilberto; Núñez-Antón, Vicente

    2013-01-01

    In this article, a proposed Bayesian extension of the generalized beta spatial regression models is applied to the analysis of the quality of education in Colombia. We briefly revise the beta distribution and describe the joint modeling approach for the mean and dispersion parameters in the spatial regression models' setting. Finally, we motivate…

  16. Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models

    Directory of Open Access Journals (Sweden)

    Kostas Alexandridis

    2013-06-01

    Full Text Available Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of 1963–1990. The artificial neural network-based Land Transformation Model (LTM predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process.

  17. A novel spatial performance metric for robust pattern optimization of distributed hydrological models

    Science.gov (United States)

    Stisen, S.; Demirel, C.; Koch, J.

    2017-12-01

    Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing

  18. Modeling Spatial and Temporal Fault Zone Evolution in Basement Rocks

    Science.gov (United States)

    Lunn, R. J.; Moir, H.; Shipton, Z. K.; Willson, J. P.

    2007-05-01

    There is considerable industrial interest in assessing the permeability of faults for the purpose of oil and gas production, deep well injection of waste liquids, underground storage of natural gas and disposal of radioactive waste. Deterministic prior estimation of fault hydraulic properties is highly error prone. Faults zones are formed through a complex interaction of mechanical, hydraulic and chemical processes and their permeability varies considerably over both space and time. Algorithms for predicting fault seal potential using throw and host rock property data exist for clay-rich fault seals but are contentious. In the case of crystalline rocks and sand-sand contacts, no such algorithms exist. In any case, the study of fault growth processes does not suggest that there is a clear or simple relationship between fault throw and the fault zone permeability. To improve estimates of fault zone permeability, it is important to understand the underlying hydro-mechanical processes of fault zone formation. In this research, we explore the spatial and temporal evolution of fault zones through development and application of a 2D hydro-mechanical finite element model. The temporal development of fault zone damage is simulated perpendicular to the main slip surface using Navier's equation for mechanical deformation. The model is applied to study development of fault zones in basement rocks. We simulate the evolution of fault zones from pre-existing joints and explore controls on the growth rate and locations of multiple splay fractures which link-up to form complex damage zones. We explore the temporal evolution of the stress field surrounding the fault tip for both propagation of isolated small faults and for fault linkage Results from these simulations have been validated using outcrop data.

  19. Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference.

    Science.gov (United States)

    Duncan, Earl W; White, Nicole M; Mengersen, Kerrie

    2017-12-16

    When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main specifications of the spatial weights matrix found in the literature, and together with some new and less common specifications, compare the effect that they have on smoothing and model performance. The popular BYM model is described, and a simple solution for addressing the identifiability issue among the spatial random effects is provided. Seventeen different definitions of the spatial weights matrix are defined, which are classified into four classes: adjacency-based weights, and weights based on geographic distance, distance between covariate values, and a hybrid of geographic and covariate distances. These last two definitions embody the main novelty of this research. Three synthetic data sets are generated, each representing a different underlying spatial structure. These data sets together with a real spatial data set from the literature are analysed using the models. The models are evaluated using the deviance information criterion and Moran's I statistic. The deviance information criterion indicated that the model which uses binary, first-order adjacency weights to perform spatial smoothing is generally an optimal choice for achieving a good model fit. Distance-based weights also generally perform quite well and offer similar parameter interpretations. The less commonly explored options for performing spatial smoothing generally provided a worse model fit than models with more traditional approaches to smoothing, but usually outperformed the benchmark model which did not conduct spatial smoothing. The specification of the spatial weights matrix can have a colossal impact on model

  20. Modeling Spatial Data within Object Relational-Databases

    Directory of Open Access Journals (Sweden)

    Iuliana BOTHA

    2011-03-01

    Full Text Available Spatial data can refer to elements that help place a certain object in a certain area. These elements are latitude, longitude, points, geometric figures represented by points, etc. However, when translating these elements into data that can be stored in a computer, it all comes down to numbers. The interesting part that requires attention is how to memorize them in order to obtain fast and various spatial queries. This part is where the DBMS (Data Base Management System that contains the database acts in. In this paper, we analyzed and compared two object-relational DBMS that work with spatial data: Oracle and PostgreSQL.

  1. The dynamic and indirect spatial effects of neighborhood conditions on land value, spatial panel dynamic econometrics model

    Science.gov (United States)

    Fitriani, Rahma; Sumarminingsih, Eni; Astutik, Suci

    2017-05-01

    Land value is the product of past decision of its use leading to its value, as well as the value of the surrounded land. It is also affected by the local characteristic and the spillover development demand of the previous time period. The effect of each factor on land value will have dynamic and spatial virtues. Thus, a spatial panel dynamic model is used to estimate the particular effects. The model will be useful for predicting the future land value or the effect of implemented policy on land value. The objective of this paper is to derive the dynamic and indirect spatial marginal effects of the land characteristic and the spillover development demand on land value. Each effect is the partial derivative of the expected land value based on the spatial dynamic model with respect to each variable, by considering different time period and different location. The results indicate that the instant change of local or neighborhood characteristics on land value affect the local and the immediate neighborhood land value. However, the longer the change take place, the effect will spread further, not only on the immediate neighborhood.

  2. Modelling of the education quality of a high schools in Sumenep Regency using spatial structural equation modelling

    Science.gov (United States)

    Anekawati, Anik; Widjanarko Otok, Bambang; Purhadi; Sutikno

    2017-09-01

    In some cases, education research often involves the latent variables that have a causal relationship as well as a spatial effect. Therefore, it requires a statistical analysis technique called spatial structural equation modelling (spatial SEM). In this research, a spatial SEM was developed to model the quality of education in high schools in Sumenep Regency. This model was improved after the evaluation of an outer and inner model of the model scheme centroid, factor and path since some indicators were not valid. The path scheme model showed better results compared to the other schemes since all of its indicators were valid and its value of R-square increased. Furthermore, only the model of path scheme was tested for spatial effects. The result of the identification test of spatial effects on the inner model using a robust Lagrange multiplier test (using queen contiguity) showed that the education quality model leads to a spatial autoregressive model (SAR in SEM) with a significance level α of 5%, while the model of school infrastructure has no significant spatial effects. The improved model of SAR in SEM, the R2 value obtained was 47.33%, so that it is clear that data variation can be explained by the model of SAR in SEM for the quality of education in high schools.

  3. MODEL OF SPATIAL EVALUATION FOR TOURISM ECO-RENT

    Directory of Open Access Journals (Sweden)

    Maja Fredotović

    2011-02-01

    Full Text Available Tourism is extremely interacted with the environment. Taking into account that tourism uses the space and related resources, it seems right to pay for the damages caused to the environment. This is the basis of the tourist spatial eco rent. The paper evaluates the space and resources used by tourism as the basis for the introduction of the tourism eco-rent in the area of Makarska Riviera, a traditional tourism destination. It is divided into three main spatial units: urban areas, bathing zone (beaches, Biokovo Park of Nature. According to natural and geographical reasoning, a number of zones with different spatial values within each spatial unit has been identified. Each unit, i.e. zone was evaluated according to various criteria relevant to the evaluation of space for tourism and tourism development purposes. Having ranked zones within each unit, using the multiriteria ranking method PROMETHEE II, comparative analysis of the obtained results was carried out as well.

  4. Disaggregation, aggregation and spatial scaling in hydrological modelling

    Science.gov (United States)

    Becker, Alfred; Braun, Peter

    1999-04-01

    A typical feature of the land surface is its heterogeneity in terms of the spatial variability of land surface characteristics and parameters controlling physical/hydrological, biological, and other related processes. Different forms and degrees of heterogeneity need to be taken into account in hydrological modelling. The first part of the article concerns the conditions under which a disaggregation of the land surface into subareas of uniform or "quasihomogeneous" behaviour (hydrotopes or hydrological response units - HRUs) is indispensable. In a case study in northern Germany, it is shown that forests in contrast to arable land, areas with shallow groundwater in contrast to those with deep, water surfaces and sealed areas should generally be distinguished (disaggregated) in modelling, whereas internal heterogeneities within these hydrotopes can be assessed statistically, e.g., by areal distribution functions (soil water holding capacity, hydraulic conductivity, etc.). Models with hydrotope-specific parameters can be applied to calculate the "vertical" processes (fluxes, storages, etc.), and this, moreover, for hydrotopes of different area, and even for groups of distributed hydrotopes in a reference area (hydrotope classes), provided that the meteorological conditions are similar. Thus, a scaling problem does not really exist in this process domain. The primary domain for the application of scaling laws is that of lateral flows in landscapes and river basins. This is illustrated in the second part of the article, where results of a case study in Bavaria/Germany are presented and discussed. It is shown that scaling laws can be applied efficiently for the determination of the Instantaneous Unit Hydrograph (IUH) of the surface runoff system in river basins: simple scaling for basins larger than 43 km 2, and multiple scaling for smaller basins. Surprisingly, only two parameters were identified as important in the derived relations: the drainage area and, in some

  5. Collaborative spatial analysis and modelling in a research environment

    CSIR Research Space (South Africa)

    Naudé, A

    2006-02-01

    Full Text Available of an open-source geoportal and geospatial content management framework (adapted for low-bandwidth environments), customisable spatial analysis workbenches (providing guidance and tools for geoprocesses such as spatial disaggregation) and the formulation... resources and processes. In these two sections, the concept of a knowledge geoportal is introduced. A knowledge geoportal includes the notion of customisable workbenches, aimed at addressing the other key problems seen in Figure 1. Further...

  6. Exploring neighborhood inequality in female breast cancer incidence in Tehran using Bayesian spatial models and a spatial scan statistic

    Directory of Open Access Journals (Sweden)

    Erfan Ayubi

    2017-05-01

    Full Text Available OBJECTIVES The aim of this study was to explore the spatial pattern of female breast cancer (BC incidence at the neighborhood level in Tehran, Iran. METHODS The present study included all registered incident cases of female BC from March 2008 to March 2011. The raw standardized incidence ratio (SIR of BC for each neighborhood was estimated by comparing observed cases relative to expected cases. The estimated raw SIRs were smoothed by a Besag, York, and Mollie spatial model and the spatial empirical Bayesian method. The purely spatial scan statistic was used to identify spatial clusters. RESULTS There were 4,175 incident BC cases in the study area from 2008 to 2011, of which 3,080 were successfully geocoded to the neighborhood level. Higher than expected rates of BC were found in neighborhoods located in northern and central Tehran, whereas lower rates appeared in southern areas. The most likely cluster of higher than expected BC incidence involved neighborhoods in districts 3 and 6, with an observed-to-expected ratio of 3.92 (p<0.001, whereas the most likely cluster of lower than expected rates involved neighborhoods in districts 17, 18, and 19, with an observed-to-expected ratio of 0.05 (p<0.001. CONCLUSIONS Neighborhood-level inequality in the incidence of BC exists in Tehran. These findings can serve as a basis for resource allocation and preventive strategies in at-risk areas.

  7. Advanced spatial metrics analysis in cellular automata land use and cover change modeling

    International Nuclear Information System (INIS)

    Zamyatin, Alexander; Cabral, Pedro

    2011-01-01

    This paper proposes an approach for a more effective definition of cellular automata transition rules for landscape change modeling using an advanced spatial metrics analysis. This approach considers a four-stage methodology based on: (i) the search for the appropriate spatial metrics with minimal correlations; (ii) the selection of the appropriate neighborhood size; (iii) the selection of the appropriate technique for spatial metrics application; and (iv) the analysis of the contribution level of each spatial metric for joint use. The case study uses an initial set of 7 spatial metrics of which 4 are selected for modeling. Results show a better model performance when compared to modeling without any spatial metrics or with the initial set of 7 metrics.

  8. Bayesian spatial modeling of HIV mortality via zero-inflated Poisson models.

    Science.gov (United States)

    Musal, Muzaffer; Aktekin, Tevfik

    2013-01-30

    In this paper, we investigate the effects of poverty and inequality on the number of HIV-related deaths in 62 New York counties via Bayesian zero-inflated Poisson models that exhibit spatial dependence. We quantify inequality via the Theil index and poverty via the ratios of two Census 2000 variables, the number of people under the poverty line and the number of people for whom poverty status is determined, in each Zip Code Tabulation Area. The purpose of this study was to investigate the effects of inequality and poverty in addition to spatial dependence between neighboring regions on HIV mortality rate, which can lead to improved health resource allocation decisions. In modeling county-specific HIV counts, we propose Bayesian zero-inflated Poisson models whose rates are functions of both covariate and spatial/random effects. To show how the proposed models work, we used three different publicly available data sets: TIGER Shapefiles, Census 2000, and mortality index files. In addition, we introduce parameter estimation issues of Bayesian zero-inflated Poisson models and discuss MCMC method implications. Copyright © 2012 John Wiley & Sons, Ltd.

  9. Modelling malaria treatment practices in Bangladesh using spatial statistics

    Directory of Open Access Journals (Sweden)

    Haque Ubydul

    2012-03-01

    Full Text Available Abstract Background Malaria treatment-seeking practices vary worldwide and Bangladesh is no exception. Individuals from 88 villages in Rajasthali were asked about their treatment-seeking practices. A portion of these households preferred malaria treatment from the National Control Programme, but still a large number of households continued to use drug vendors and approximately one fourth of the individuals surveyed relied exclusively on non-control programme treatments. The risks of low-control programme usage include incomplete malaria treatment, possible misuse of anti-malarial drugs, and an increased potential for drug resistance. Methods The spatial patterns of treatment-seeking practices were first examined using hot-spot analysis (Local Getis-Ord Gi statistic and then modelled using regression. Ordinary least squares (OLS regression identified key factors explaining more than 80% of the variation in control programme and vendor treatment preferences. Geographically weighted regression (GWR was then used to assess where each factor was a strong predictor of treatment-seeking preferences. Results Several factors including tribal affiliation, housing materials, household densities, education levels, and proximity to the regional urban centre, were found to be effective predictors of malaria treatment-seeking preferences. The predictive strength of each of these factors, however, varied across the study area. While education, for example, was a strong predictor in some villages, it was less important for predicting treatment-seeking outcomes in other villages. Conclusion Understanding where each factor is a strong predictor of treatment-seeking outcomes may help in planning targeted interventions aimed at increasing control programme usage. Suggested strategies include providing additional training for the Building Resources across Communities (BRAC health workers, implementing educational programmes, and addressing economic factors.

  10. Spatial pattern evaluation of a calibrated national hydrological model - a remote-sensing-based diagnostic approach

    Science.gov (United States)

    Mendiguren, Gorka; Koch, Julian; Stisen, Simon

    2017-11-01

    Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds

  11. A spatial error model with continuous random effects and an application to growth convergence

    Science.gov (United States)

    Laurini, Márcio Poletti

    2017-10-01

    We propose a spatial error model with continuous random effects based on Matérn covariance functions and apply this model for the analysis of income convergence processes (β -convergence). The use of a model with continuous random effects permits a clearer visualization and interpretation of the spatial dependency patterns, avoids the problems of defining neighborhoods in spatial econometrics models, and allows projecting the spatial effects for every possible location in the continuous space, circumventing the existing aggregations in discrete lattice representations. We apply this model approach to analyze the economic growth of Brazilian municipalities between 1991 and 2010 using unconditional and conditional formulations and a spatiotemporal model of convergence. The results indicate that the estimated spatial random effects are consistent with the existence of income convergence clubs for Brazilian municipalities in this period.

  12. Impact of climate change on river flooding assessed with different spatial model resolutions

    NARCIS (Netherlands)

    Booij, Martijn J.

    2005-01-01

    The impact of climate change on flooding in the river Meuse is assessed on a daily basis using spatially and temporally changed climate patterns and a hydrological model with three different spatial resolutions. This is achieved by selecting a hydrological modelling framework and implementing

  13. Computer Games versus Maps before Reading Stories: Priming Readers' Spatial Situation Models

    Science.gov (United States)

    Smith, Glenn Gordon; Majchrzak, Dan; Hayes, Shelley; Drobisz, Jack

    2011-01-01

    The current study investigated how computer games and maps compare as preparation for readers to comprehend and retain spatial relations in text narratives. Readers create situation models of five dimensions: spatial, temporal, causal, goal, and protagonist (Zwaan, Langston, & Graesser 1995). Of these five, readers mentally model the spatial…

  14. Model predictive control for optimal treatment in a spatial cancer game

    NARCIS (Netherlands)

    Javier Muros, Francisco; M. Maestre, Jose; You, Li; Stankova, Katerina

    2018-01-01

    This work focuses on modeling tumorigenesis as a spatial evolutionary game and on finding optimal cancer treatment using a model predictive control approach. Extending a nonspatial cancer game from the literature into a spatial setting, we consider a solid tumor composed of cells of two different

  15. A spatial-dynamic value transfer model of economic losses from a biological invasion

    Science.gov (United States)

    Thomas P. Holmes; Andrew M. Liebhold; Kent F. Kovacs; Betsy. Von Holle

    2010-01-01

    Rigorous assessments of the economic impacts of introduced species at broad spatial scales are required to provide credible information to policy makers. We propose that economic models of aggregate damages induced by biological invasions need to link microeconomic analyses of site-specific economic damages with spatial-dynamic models of value change associated with...

  16. A spatial- and age-structured assessment model to estimate the ...

    African Journals Online (AJOL)

    , thereby indirectly negatively impacting juvenile abalone which rely on the urchins for shelter. A model is developed for abalone that is an extension of more standard age-structured assessment models because it explicitly takes spatial effects ...

  17. Improving the spatial representation of basin hydrology and flow processes in the SWAT model

    OpenAIRE

    Rathjens, Hendrik

    2014-01-01

    This dissertation aims at improving the spatial representation of basin hydrology and flow processes in the SWAT model. Die vorliegende Dissertation stellt die methodischen Grundlage zur räumlich differenzierten Modellierung mit dem Modell SWAT dar.

  18. Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.

    Directory of Open Access Journals (Sweden)

    Xiaodong Huang

    Full Text Available Nitrous oxide (N2O is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2 field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.

  19. Modelling malaria incidence by an autoregressive distributed lag model with spatial component.

    Science.gov (United States)

    Laguna, Francisco; Grillet, María Eugenia; León, José R; Ludeña, Carenne

    2017-08-01

    The influence of climatic variables on the dynamics of human malaria has been widely highlighted. Also, it is known that this mosquito-borne infection varies in space and time. However, when the data is spatially incomplete most popular spatio-temporal methods of analysis cannot be applied directly. In this paper, we develop a two step methodology to model the spatio-temporal dependence of malaria incidence on local rainfall, temperature, and humidity as well as the regional sea surface temperatures (SST) in the northern coast of Venezuela. First, we fit an autoregressive distributed lag model (ARDL) to the weekly data, and then, we adjust a linear separable spacial vectorial autoregressive model (VAR) to the residuals of the ARDL. Finally, the model parameters are tuned using a Markov Chain Monte Carlo (MCMC) procedure derived from the Metropolis-Hastings algorithm. Our results show that the best model to account for the variations of malaria incidence from 2001 to 2008 in 10 endemic Municipalities in North-Eastern Venezuela is a logit model that included the accumulated local precipitation in combination with the local maximum temperature of the preceding month as positive regressors. Additionally, we show that although malaria dynamics is highly heterogeneous in space, a detailed analysis of the estimated spatial parameters in our model yield important insights regarding the joint behavior of the disease incidence across the different counties in our study. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Spatial Inequalities in the Incidence of Colorectal Cancer and Associated Factors in the Neighborhoods of Tehran, Iran: Bayesian Spatial Models.

    Science.gov (United States)

    Mansori, Kamyar; Solaymani-Dodaran, Masoud; Mosavi-Jarrahi, Alireza; Motlagh, Ali Ganbary; Salehi, Masoud; Delavari, Alireza; Asadi-Lari, Mohsen

    2018-01-01

    The aim of this study was to determine the factors associated with the spatial distribution of the incidence of colorectal cancer (CRC) in the neighborhoods of Tehran, Iran using Bayesian spatial models. This ecological study was implemented in Tehran on the neighborhood level. Socioeconomic variables, risk factors, and health costs were extracted from the Equity Assessment Study conducted in Tehran. The data on CRC incidence were extracted from the Iranian population-based cancer registry. The Besag-York-Mollié (BYM) model was used to identify factors associated with the spatial distribution of CRC incidence. The software programs OpenBUGS version 3.2.3, ArcGIS 10.3, and GeoDa were used for the analysis. The Moran index was statistically significant for all the variables studied (p<0.05). The BYM model showed that having a women head of household (median standardized incidence ratio [SIR], 1.63; 95% confidence interval [CI], 1.06 to 2.53), living in a rental house (median SIR, 0.82; 95% CI, 0.71 to 0.96), not consuming milk daily (median SIR, 0.71; 95% CI, 0.55 to 0.94) and having greater household health expenditures (median SIR, 1.34; 95% CI, 1.06 to 1.68) were associated with a statistically significant elevation in the SIR of CRC. The median (interquartile range) and mean (standard deviation) values of the SIR of CRC, with the inclusion of all the variables studied in the model, were 0.57 (1.01) and 1.05 (1.31), respectively. Inequality was found in the spatial distribution of CRC incidence in Tehran on the neighborhood level. Paying attention to this inequality and the factors associated with it may be useful for resource allocation and developing preventive strategies in atrisk areas.

  1. Spatial Inequalities in the Incidence of Colorectal Cancer and Associated Factors in the Neighborhoods of Tehran, Iran: Bayesian Spatial Models

    Directory of Open Access Journals (Sweden)

    Kamyar Mansori

    2018-01-01

    Full Text Available Objectives The aim of this study was to determine the factors associated with the spatial distribution of the incidence of colorectal cancer (CRC in the neighborhoods of Tehran, Iran using Bayesian spatial models. Methods This ecological study was implemented in Tehran on the neighborhood level. Socioeconomic variables, risk factors, and health costs were extracted from the Equity Assessment Study conducted in Tehran. The data on CRC incidence were extracted from the Iranian population-based cancer registry. The Besag-York-Mollié (BYM model was used to identify factors associated with the spatial distribution of CRC incidence. The software programs OpenBUGS version 3.2.3, ArcGIS 10.3, and GeoDa were used for the analysis. Results The Moran index was statistically significant for all the variables studied (p<0.05. The BYM model showed that having a women head of household (median standardized incidence ratio [SIR], 1.63; 95% confidence interval [CI], 1.06 to 2.53, living in a rental house (median SIR, 0.82; 95% CI, 0.71 to 0.96, not consuming milk daily (median SIR, 0.71; 95% CI, 0.55 to 0.94 and having greater household health expenditures (median SIR, 1.34; 95% CI, 1.06 to 1.68 were associated with a statistically significant elevation in the SIR of CRC. The median (interquartile range and mean (standard deviation values of the SIR of CRC, with the inclusion of all the variables studied in the model, were 0.57 (1.01 and 1.05 (1.31, respectively. Conclusions Inequality was found in the spatial distribution of CRC incidence in Tehran on the neighborhood level. Paying attention to this inequality and the factors associated with it may be useful for resource allocation and developing preventive strategies in atrisk areas.

  2. Spatial Mapping of Agricultural Water Productivity Using the SWAT Model

    Science.gov (United States)

    Thokal, Rajesh Tulshiram; Gorantiwar, S. D.; Kothari, Mahesh; Bhakar, S. R.; Nandwana, B. P.

    2015-03-01

    The Sina river basin is facing both episodic and chronic water shortages due to intensive irrigation development. The main objective of this study was to characterize the hydrologic processes of the Sina river basin and assess crop water productivity using the distributed hydrologic model, SWAT. In the simulation year (1998-1999), the inflow to reservoir from upstream side was the major contributor to the reservoir accounting for 92 % of the total required water release for irrigation purpose (119.5 Mm3), while precipitation accounted for 4.1 Mm3. Annual release of water for irrigation was 119.5 Mm3 out of which 54 % water was diverted for irrigation purpose, 26 % was wasted as conveyance loss, average discharge at the command outlet was estimated as 4 % and annual average ground-water recharge coefficient was in the range of 13-17 %. Various scenarios involving water allocation rule were tested with the goal of increasing economic water productivity values in the Sina Irrigation Scheme. Out of those, only most benefited allocation rule is analyzed in this paper. Crop yield varied from 1.98 to 25.9 t/ha, with the majority of the area between 2.14 and 2.78 t/ha. Yield and WP declined significantly in loamy soils of the irrigation command. Crop productivity in the basin was found in the lower range when compared with potential and global values. The findings suggested that there was a potential to improve further. Spatial variations in yield and WP were found to be very high for the crops grown during rabi season, while those were low for the crops grown during kharif season. The crop yields and WP during kharif season were more in the lower reach of the irrigation commands, where loamy soil is more concentrated. Sorghum in both seasons was most profitable. Sorghum fetched net income fivefold that of sunflower, two and half fold of pearl millet and one and half fold of mung beans as far as crop during kharif season were concerned and it fetched fourfold that of

  3. From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions

    Science.gov (United States)

    Fenicia, Fabrizio; Kavetski, Dmitri; Savenije, Hubert H. G.; Pfister, Laurent

    2016-02-01

    This paper explores the development and application of distributed hydrological models, focusing on the key decisions of how to discretize the landscape, which model structures to use in each landscape element, and how to link model parameters across multiple landscape elements. The case study considers the Attert catchment in Luxembourg—a 300 km2 mesoscale catchment with 10 nested subcatchments that exhibit clearly different streamflow dynamics. The research questions are investigated using conceptual models applied at hydrologic response unit (HRU) scales (1-4 HRUs) on 6 hourly time steps. Multiple model structures are hypothesized and implemented using the SUPERFLEX framework. Following calibration, space/time model transferability is tested using a split-sample approach, with evaluation criteria including streamflow prediction error metrics and hydrological signatures. Our results suggest that: (1) models using geology-based HRUs are more robust and capture the spatial variability of streamflow time series and signatures better than models using topography-based HRUs; this finding supports the hypothesis that, in the Attert, geology exerts a stronger control than topography on streamflow generation, (2) streamflow dynamics of different HRUs can be represented using distinct and remarkably simple model structures, which can be interpreted in terms of the perceived dominant hydrologic processes in each geology type, and (3) the same maximum root zone storage can be used across the three dominant geological units with no loss in model transferability; this finding suggests that the partitioning of water between streamflow and evaporation in the study area is largely independent of geology and can be used to improve model parsimony. The modeling methodology introduced in this study is general and can be used to advance our broader understanding and prediction of hydrological behavior, including the landscape characteristics that control hydrologic response, the

  4. Spatial autocorrelation method using AR model; Kukan jiko sokanho eno AR model no tekiyo

    Energy Technology Data Exchange (ETDEWEB)

    Yamamoto, H.; Obuchi, T.; Saito, T. [Iwate University, Iwate (Japan). Faculty of Engineering

    1996-05-01

    Examination was made about the applicability of the AR model to the spatial autocorrelation (SAC) method, which analyzes the surface wave phase velocity in a microtremor, for the estimation of the underground structure. In this examination, microtremor data recorded in Morioka City, Iwate Prefecture, was used. In the SAC method, a spatial autocorrelation function with the frequency as a variable is determined from microtremor data observed by circular arrays. Then, the Bessel function is adapted to the spatial autocorrelation coefficient with the distance between seismographs as a variable for the determination of the phase velocity. The result of the AR model application in this study and the results of the conventional BPF and FFT method were compared. It was then found that the phase velocities obtained by the BPF and FFT methods were more dispersed than the same obtained by the AR model. The dispersion in the BPF method is attributed to the bandwidth used in the band-pass filter and, in the FFT method, to the impact of the bandwidth on the smoothing of the cross spectrum. 2 refs., 7 figs.

  5. Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times

    DEFF Research Database (Denmark)

    Rasmussen, Jakob Gulddahl; Møller, Jesper

    2007-01-01

    , and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has previously been analysed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared......Summary. We consider statistical and computational aspects of simulation-based Bayesian inference for a spatial-temporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice...

  6. A participatory GIS approach to spatial modeling for slum upgrading ...

    African Journals Online (AJOL)

    The most prominent problem of rapid urbanism in Harare is the development of slums and Epworth is a notable example. The quality of planning and decision making in the participatory slum upgrading initiative can be sustainably improved by well managed processes of spatial and socio-economic data collection. More so ...

  7. Modelling the spatial distribution of linear landscape elements in Europe

    NARCIS (Netherlands)

    Zanden, van der E.H.; Verburg, P.H.; Mücher, C.A.

    2013-01-01

    Linear landscape elements, such as ditches, hedgerows, lines of trees and field margins, provide important habitats and ecosystem services and function as ecological infrastructure for species within agricultural landscapes. Spatial maps of the distribution of these elements are needed to better

  8. Selecting one among many referents in spatial situation models

    NARCIS (Netherlands)

    Bower, G.H.; Rinck, M.

    2001-01-01

    Five experiments related to anaphor resolution to a classic memory variable, namely, interference created by multiple uses of a given object-concept, and by spatial distance of the referent from the reader's focus of attention. Participants memorized a diagram of a building with rooms containing

  9. Social dynamics interest groups in a model of spatial competition

    NARCIS (Netherlands)

    Tuinstra, J.; Sadiraj, V.; van Winden, F.A.A.M.

    2000-01-01

    A well-known result in spatial voting theory is that, for a one-dimensional issue space and under certain mild conditions, political parties choose platforms coinciding with the median voter's position. This result does not carry over to multi-dimensional issue spaces however, since then an

  10. Prediction of water temperature metrics using spatial modelling in ...

    African Journals Online (AJOL)

    Water temperature regime dynamics should be viewed regionally, where regional divisions have an inherent underpinning by an understanding of natural thermal variability. The aim of this research was to link key water temperature metrics to readily-mapped environmental surrogates, and to produce spatial images of ...

  11. Using 3D Geometric Models to Teach Spatial Geometry Concepts.

    Science.gov (United States)

    Bertoline, Gary R.

    1991-01-01

    An explanation of 3-D Computer Aided Design (CAD) usage to teach spatial geometry concepts using nontraditional techniques is presented. The software packages CADKEY and AutoCAD are described as well as their usefulness in solving space geometry problems. (KR)

  12. Modeling spatial pattern of deforestation using GIS and logistic ...

    African Journals Online (AJOL)

    This study aimed to predict spatial distribution of deforestation and detects factors influencing forest degradation of Northern forests of Ilam province. For this purpose, effects of six factors including distance from road and settlement areas, forest fragmentation index, elevation, slope and distance from the forest edge on the ...

  13. A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation

    Directory of Open Access Journals (Sweden)

    T. Skaugen

    2016-09-01

    Full Text Available Snow is an important and complicated element in hydrological modelling. The traditional catchment hydrological model with its many free calibration parameters, also in snow sub-models, is not a well-suited tool for predicting conditions for which it has not been calibrated. Such conditions include prediction in ungauged basins and assessing hydrological effects of climate change. In this study, a new model for the spatial distribution of snow water equivalent (SWE, parameterized solely from observed spatial variability of precipitation, is compared with the current snow distribution model used in the operational flood forecasting models in Norway. The former model uses a dynamic gamma distribution and is called Snow Distribution_Gamma, (SD_G, whereas the latter model has a fixed, calibrated coefficient of variation, which parameterizes a log-normal model for snow distribution and is called Snow Distribution_Log-Normal (SD_LN. The two models are implemented in the parameter parsimonious rainfall–runoff model Distance Distribution Dynamics (DDD, and their capability for predicting runoff, SWE and snow-covered area (SCA is tested and compared for 71 Norwegian catchments. The calibration period is 1985–2000 and validation period is 2000–2014. Results show that SD_G better simulates SCA when compared with MODIS satellite-derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" and giving spurious positive trends in SWE, typical for SD_LN, is prevented. The precision of runoff simulations using SD_G is slightly inferior, with a reduction in Nash–Sutcliffe and Kling–Gupta efficiency criterion of 0.01, but it is shown that the high precision in runoff prediction using SD_LN is accompanied with erroneous simulations of SWE.

  14. Spatial variability in compartmental fate modelling : Linking fugacity models and GIS.

    Science.gov (United States)

    Wania, F

    1996-03-01

    A new approach is presented which is designed to address the spatial heterogeneity of the environment in compartmental mass balance models of chemical fate in the environment. It rests on the assumption of chemical equilibration within one phase despite prevailing environmental heterogeneity. Composite D- and Z-values are derived from sub-unit specific environmental parameters and are used to solve mass balance equations which can be adopted essentially unchanged from existing compartmental fugacity models. With the resulting common fugacity value for each compartment, sub-unit specific concentrations and process rates can be calculated. The approach is illustrated using the QWASI lake model to calculate the fate of hexachlorobenzene in a hypothetical lake sub-divided in four distinct sub-units. The approach allows the subdivision of each compartment in a large number of sub-units with distinct environmental characteristics without substantially increasing model complexity. This is a necessary condition for linking fugacity models to geographical information systems.

  15. A Unified 3D Spatial Data Model for Surface and Subsurface Spatial ...

    African Journals Online (AJOL)

    user

    surface. LoD maps for surface and subsurface integration exist for most city centres but the 3D component is lacking and this ... the integration of surface and subsurface models are discussed and a geometric, topological 3D object oriented model is sug- gested. .... dimensional (3D) continuous geological stratigraphy,.

  16. Remote sensing inputs to landscape models which predict future spatial land use patterns for hydrologic models

    Science.gov (United States)

    Miller, L. D.; Tom, C.; Nualchawee, K.

    1977-01-01

    A tropical forest area of Northern Thailand provided a test case of the application of the approach in more natural surroundings. Remote sensing imagery subjected to proper computer analysis has been shown to be a very useful means of collecting spatial data for the science of hydrology. Remote sensing products provide direct input to hydrologic models and practical data bases for planning large and small-scale hydrologic developments. Combining the available remote sensing imagery together with available map information in the landscape model provides a basis for substantial improvements in these applications.

  17. Spatial Multiplication Model as an alternative to the Point Model in Neutron Multiplicity Counting

    Energy Technology Data Exchange (ETDEWEB)

    Hauck, Danielle K. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Henzl, Vladimir [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2014-03-26

    The point model is commonly used in neutron multiplicity counting to relate the correlated neutron detection rates (singles, doubles, triples) to item properties (mass, (α,n) reaction rate and neutron multiplication). The point model assumes that the probability that a neutron will induce fission is a constant across the physical extent of the item. However, in reality, neutrons near the center of an item have a greater probability of inducing fission then items near the edges. As a result, the neutron multiplication has a spatial distribution.

  18. Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models

    OpenAIRE

    Fang, Chuanglin; Liu, Haimeng; Li, Guangdong; Sun, Dongqi; Miao, Zhuang

    2015-01-01

    Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI) values at the city level, and employed Ordinary Least Squares (OLS), Spatial Lag Model (SAR), and Geographically Weighted Regression (GWR) to quantitatively estimate the comprehensive impact and spatial variati...

  19. A sequential point process model and Bayesian inference for spatial point patterns with linear structures

    DEFF Research Database (Denmark)

    Møller, Jesper; Rasmussen, Jakob Gulddahl

    We introduce a flexible spatial point process model for spatial point patterns exhibiting linear structures, without incorporating a latent line process. The model is given by an underlying sequential point process model, i.e. each new point is generated given the previous points. Under this model...... points is such that the dependent cluster point is likely to occur closely to a previous cluster point. We demonstrate the flexibility of the model for producing point patterns with linear structures, and propose to use the model as the likelihood in a Bayesian setting when analyzing a spatial point...... pattern exhibiting linear structures but where the exact mechanism responsible for the formations of lines is unknown. We illustrate this methodology by analyzing two spatial point pattern data sets (locations of bronze age graves in Denmark and locations of mountain tops in Spain) without knowing which...

  20. Sparse modeling of spatial environmental variables associated with asthma

    OpenAIRE

    Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

    2014-01-01

    Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s ...

  1. Modeling Spatial Maps Inspired by the Hippocampal System

    Science.gov (United States)

    2015-08-24

    landmark cues and path integration based on self-motion ( dead - reckoning ). The path integration system is probably separate from the megamap itself but...system is known to use two types of information for determining spatial location, namely, landmark cues and path integration based on self-motion ( dead ... reckoning ). The path integration system is probably separate from the megamap itself but provides an input to the map. One key requirement for

  2. Spatial modeling of bicycle activity at signalized intersections

    OpenAIRE

    Strauss, Jillian; Miranda-Moreno, Luis F.

    2013-01-01

    This paper presents a methodology to investigate the link between bicycle activity and built environment, road and transit network characteristics, and bicycle facilities while also accounting for spatial autocorrelation between intersections. The methodology includes the normalization of manual cyclist counts to average seasonal daily volumes (ASDV), taking into account temporal variations and using hourly, daily, and monthly expansion factors obtained from automatic bicycle count data. To c...

  3. Climate Change and Agricultural Productivity in Sub-Saharan Africa: A Spatial Sample Selection Model

    NARCIS (Netherlands)

    Ward, P.S.; Florax, R.J.G.M.; Flores-Lagunes, A.

    2014-01-01

    Using spatially explicit data, we estimate a cereal yield response function using a recently developed estimator for spatial error models when endogenous sample selection is of concern. Our results suggest that yields across Sub-Saharan Africa will decline with projected climatic changes, and that

  4. Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior

    Science.gov (United States)

    Russell A. Parsons; William E. Mell; Peter McCauley

    2011-01-01

    Crownfire endangers fire fighters and can have severe ecological consequences. Prediction of fire behavior in tree crowns is essential to informed decisions in fire management. Current methods used in fire management do not address variability in crown fuels. New mechanistic physics-based fire models address convective heat transfer with computational fluid dynamics (...

  5. Predictive spatio-temporal model for spatially sparse global solar radiation data

    International Nuclear Information System (INIS)

    André, Maïna; Dabo-Niang, Sophie; Soubdhan, Ted; Ould-Baba, Hanany

    2016-01-01

    This paper introduces a new approach for the forecasting of solar radiation series at a located station for very short time scale. We built a multivariate model in using few stations (3 stations) separated with irregular distances from 26 km to 56 km. The proposed model is a spatio temporal vector autoregressive VAR model specifically designed for the analysis of spatially sparse spatio-temporal data. This model differs from classic linear models in using spatial and temporal parameters where the available predictors are the lagged values at each station. A spatial structure of stations is defined by the sequential introduction of predictors in the model. Moreover, an iterative strategy in the process of our model will select the necessary stations removing the uninteresting predictors and also selecting the optimal p-order. We studied the performance of this model. The metric error, the relative root mean squared error (rRMSE), is presented at different short time scales. Moreover, we compared the results of our model to simple and well known persistence model and those found in literature. - Highlights: • A spatio-temporal VAR forecast model is used for spatially sparse data solar. • Lags and locations are selected by an optimization strategy. • Definition of spatial ordering of predictors influences forecasting results. • The model shows a better performance predictive at 30 min ahead in our context. • Benchmarking study shows a more accurate forecast at 1 h ahead with spatio-temporal VAR.

  6. Developing a modelling for the spatial data infrastructure

    CSIR Research Space (South Africa)

    Hjelmager, J

    2005-07-01

    Full Text Available . The models cannot be seen as a final result, but more as a small step towards a model that defines the previously mentioned overall model of the SDI and its technical characteristics. During the model development process, the roles of the different actors...

  7. Spatial dynamics of a periodic population model with dispersal

    International Nuclear Information System (INIS)

    Jin Yu; Zhao Xiaoqiang

    2009-01-01

    This paper is devoted to the study of spatial dynamics of a class of periodic integro-differential equations which describe the population dispersal process via a dispersal kernel. By appealing to the theory of asymptotic speeds of spread and travelling waves for monotonic periodic semiflows, we establish the existence of the spreading speed c * and the nonexistence of continuous periodic travelling wave solutions with wave speed c * . We also prove the existence of left-continuous periodic travelling waves with wave speed c ≥ c * . In the autonomous case, the continuity of monotonic wave profiles with wave speed c ≥ c * is obtained

  8. Comparative analysis of elements and models of implementation in local-level spatial plans in Serbia

    Directory of Open Access Journals (Sweden)

    Stefanović Nebojša

    2017-01-01

    Full Text Available Implementation of local-level spatial plans is of paramount importance to the development of the local community. This paper aims to demonstrate the importance of and offer further directions for research into the implementation of spatial plans by presenting the results of a study on models of implementation. The paper describes the basic theoretical postulates of a model for implementing spatial plans. A comparative analysis of the application of elements and models of implementation of plans in practice was conducted based on the spatial plans for the local municipalities of Arilje, Lazarevac and Sremska Mitrovica. The analysis includes four models of implementation: the strategy and policy of spatial development; spatial protection; the implementation of planning solutions of a technical nature; and the implementation of rules of use, arrangement and construction of spaces. The main results of the analysis are presented and used to give recommendations for improving the elements and models of implementation. Final deliberations show that models of implementation are generally used in practice and combined in spatial plans. Based on the analysis of how models of implementation are applied in practice, a general conclusion concerning the complex character of the local level of planning is presented and elaborated. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. TR 36035: Spatial, Environmental, Energy and Social Aspects of Developing Settlements and Climate Change - Mutual Impacts and Grant no. III 47014: The Role and Implementation of the National Spatial Plan and Regional Development Documents in Renewal of Strategic Research, Thinking and Governance in Serbia

  9. Revealing spatially heterogeneous relaxation in a model nanocomposite

    Energy Technology Data Exchange (ETDEWEB)

    Cheng, Shiwang; Bocharova, Vera [Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Mirigian, Stephen; Schweizer, Kenneth S. [Department of Materials Science and Chemistry, Frederick Seitz Materials Research Laboratory, University of Illinois, Urbana, Illinois 61801 (United States); Carrillo, Jan-Michael Y.; Sumpter, Bobby G. [Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Sokolov, Alexei P., E-mail: sokolov@utk.edu [Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Department of Chemistry, Department of Physics and Astronomy, University of Tennessee, Knoxville, Tennessee 37996 (United States)

    2015-11-21

    The detailed nature of spatially heterogeneous dynamics of glycerol-silica nanocomposites is unraveled by combining dielectric spectroscopy with atomistic simulation and statistical mechanical theory. Analysis of the spatial mobility gradient shows no “glassy” layer, but the α-relaxation time near the nanoparticle grows with cooling faster than the α-relaxation time in the bulk and is ∼20 times longer at low temperatures. The interfacial layer thickness increases from ∼1.8 nm at higher temperatures to ∼3.5 nm upon cooling to near bulk T{sub g}. A real space microscopic description of the mobility gradient is constructed by synergistically combining high temperature atomistic simulation with theory. Our analysis suggests that the interfacial slowing down arises mainly due to an increase of the local cage scale barrier for activated hopping induced by enhanced packing and densification near the nanoparticle surface. The theory is employed to predict how local surface densification can be manipulated to control layer dynamics and shear rigidity over a wide temperature range.

  10. Temporal and spatial distribution characteristics of water resources in Guangdong Province based on a cloud model

    Directory of Open Access Journals (Sweden)

    Qi Zhou

    2015-10-01

    Full Text Available With a focus on the difficulty of quantitatively describing the degree of nonuniformity of temporal and spatial distributions of water resources, quantitative research was carried out on the temporal and spatial distribution characteristics of water resources in Guangdong Province from 1956 to 2000 based on a cloud model. The spatial variation of the temporal distribution characteristics and the temporal variation of the spatial distribution characteristics were both analyzed. In addition, the relationships between the numerical characteristics of the cloud model of temporal and spatial distributions of water resources and precipitation were also studied. The results show that, using a cloud model, it is possible to intuitively describe the temporal and spatial distribution characteristics of water resources in cloud images. Water resources in Guangdong Province and their temporal and spatial distribution characteristics are differentiated by their geographic locations. Downstream and coastal areas have a larger amount of water resources with greater uniformity and stronger stability in terms of temporal distribution. Regions with more precipitation possess larger amounts of water resources, and years with more precipitation show greater nonuniformity in the spatial distribution of water resources. The correlation between the nonuniformity of the temporal distribution and local precipitation is small, and no correlation is found between the stability of the nonuniformity of the temporal and spatial distributions of water resources and precipitation. The amount of water resources in Guangdong Province shows an increasing trend from 1956 to 2000, the nonuniformity of the spatial distribution of water resources declines, and the stability of the nonuniformity of the spatial distribution of water resources is enhanced.

  11. Can inducible resistance in plants cause herbivore aggregations? Spatial patterns in an inducible plant/herbivore model

    OpenAIRE

    Anderson, KE; Inouye, BD; Underwood, N

    2015-01-01

    © 2015 by the Ecological Society of America. Many theories regarding the evolution of inducible resistance in plants have an implicit spatial component, but most relevant population dynamic studies ignore spatial dynamics. We examined a spatially explicit model of plant inducible resistance and herbivore population dynamics to explore how realistic features of resistance and herbivore responses influence spatial patterning. Both transient and persistent spatial patterns developed in all model...

  12. Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

    Directory of Open Access Journals (Sweden)

    David W Redding

    Full Text Available Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT, to a spatial Bayesian SDM method (fitted using R-INLA, when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account

  13. Housing price prediction: parametric versus semi-parametric spatial hedonic models

    Science.gov (United States)

    Montero, José-María; Mínguez, Román; Fernández-Avilés, Gema

    2018-01-01

    House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric and semi-parametric spatial hedonic model variants that account for spatial autocorrelation, spatial heterogeneity and (smooth and nonparametrically specified) nonlinearities using penalized splines methodology. The models are represented as a mixed model that allow for the estimation of the smoothing parameters along with the other parameters of the model. To assess the out-of-sample performance of the models, the paper uses a database containing the price and characteristics of 10,512 homes in Madrid, Spain (Q1 2010). The results obtained suggest that the nonlinear models accounting for spatial heterogeneity and flexible nonlinear relationships between some of the individual or areal characteristics of the houses and their prices are the best strategies for house price prediction.

  14. Predictability of locomotion: Effects on updating of spatial situation models during narrative comprehension

    NARCIS (Netherlands)

    Dutke, S.; Rinck, M.

    2006-01-01

    We investigated how the updating of spatial situation models during narrative comprehension depends on the interaction of cognitive abilities and text characteristics. Participants with low verbal and visuospatial abilities and participants with high abilities read narratives in which the

  15. Impact of precipitation spatial resolution on the hydrological response of an integrated distributed water resources model

    DEFF Research Database (Denmark)

    Fu, Suhua; Sonnenborg, Torben; Jensen, Karsten Høgh

    2011-01-01

    Precipitation is a key input variable to hydrological models, and the spatial variability of the input is expected to impact the hydrological response predicted by a distributed model. In this study, the effect of spatial resolution of precipitation on runoff , recharge and groundwater head...... was analyzed in the Alergaarde catchment in Denmark. Six different precipitation spatial resolutions were used as inputs to a physically based, distributed hydrological model, the MIKE SHE model. The results showed that the resolution of precipitation input had no apparent effect on annual water balance...... of the total catchment and runoff discharge hydrograph at watershed outlet. On the other hand, groundwater recharge and groundwater head were both aff ected. The impact of the spatial resolution of precipitation input is reduced with increasing catchment size. The effect on stream discharge is relatively low...

  16. Spatial modeling on the upperstream of the Citarum watershed: An application of geoinformatics

    Science.gov (United States)

    Ningrum, Windy Setia; Widyaningsih, Yekti; Indra, Tito Latif

    2017-03-01

    The Citarum watershed is the longest and the largest watershed in West Java, Indonesia, located at 106°51'36''-107°51' E and 7°19'-6°24'S across 10 districts, and serves as the water supply for over 15 million people. In this area, the water criticality index is concerned to reach the balance between water supply and water demand, so that in the dry season, the watershed is still able to meet the water needs of the society along the Citarum river. The objective of this research is to evaluate the water criticality index of Citarum watershed area using spatial model to overcome the spatial dependencies in the data. The result of Lagrange multiplier diagnostics for spatial dependence results are LM-err = 34.6 (p-value = 4.1e-09) and LM-lag = 8.05 (p-value = 0.005), then modeling using Spatial Lag Model (SLM) and Spatial Error Model (SEM) were conducted. The likelihood ratio test show that both of SLM dan SEM model is better than OLS model in modeling water criticality index in Citarum watershed. The AIC value of SLM and SEM model are 78.9 and 51.4, then the SEM model is better than SLM model in predicting water criticality index in Citarum watershed.

  17. Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

    Science.gov (United States)

    Wilson, T.L.; Odei, J.B.; Hooten, M.B.; Edwards, T.C.

    2010-01-01

    Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.

  18. Integrated Modeling of Spatial and Temporal Heterogeneities and Decisions Induced by Catastrophic Events

    OpenAIRE

    Ermolieva, T.Y.; Fischer, G.; Obersteiner, M.

    2003-01-01

    This paper discusses an integrated model capable of dealing with spatial and temporal heterogeneities induced by extreme events, in particular weather related catastrophes. The model can be used for quite different problems which take explicitly into account the specifics of catastrophic risks: highly mutually dependent losses, inherent capacity of information, the need for long-term perspectives (temporal heterogeneity) and geographically explicit analyses (spatial heterogeneity) with respec...

  19. Impact imaging of aircraft composite structure based on a model-independent spatial-wavenumber filter.

    Science.gov (United States)

    Qiu, Lei; Liu, Bin; Yuan, Shenfang; Su, Zhongqing

    2016-01-01

    The spatial-wavenumber filtering technique is an effective approach to distinguish the propagating direction and wave mode of Lamb wave in spatial-wavenumber domain. Therefore, it has been gradually studied for damage evaluation in recent years. But for on-line impact monitoring in practical application, the main problem is how to realize the spatial-wavenumber filtering of impact signal when the wavenumber of high spatial resolution cannot be measured or the accurate wavenumber curve cannot be modeled. In this paper, a new model-independent spatial-wavenumber filter based impact imaging method is proposed. In this method, a 2D cross-shaped array constructed by two linear piezoelectric (PZT) sensor arrays is used to acquire impact signal on-line. The continuous complex Shannon wavelet transform is adopted to extract the frequency narrowband signals from the frequency wideband impact response signals of the PZT sensors. A model-independent spatial-wavenumber filter is designed based on the spatial-wavenumber filtering technique. Based on the designed filter, a wavenumber searching and best match mechanism is proposed to implement the spatial-wavenumber filtering of the frequency narrowband signals without modeling, which can be used to obtain a wavenumber-time image of the impact relative to a linear PZT sensor array. By using the two wavenumber-time images of the 2D cross-shaped array, the impact direction can be estimated without blind angle. The impact distance relative to the 2D cross-shaped array can be calculated by using the difference of time-of-flight between the frequency narrowband signals of two different central frequencies and the corresponding group velocities. The validations performed on a carbon fiber composite laminate plate and an aircraft composite oil tank show a good impact localization accuracy of the model-independent spatial-wavenumber filter based impact imaging method. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

    Science.gov (United States)

    Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

    2015-01-01

    Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

  1. Cosmological backreaction within the Szekeres model and emergence of spatial curvature

    Science.gov (United States)

    Bolejko, Krzysztof

    2017-06-01

    This paper discusses the phenomenon of backreaction within the Szekeres model. Cosmological backreaction describes how the mean global evolution of the Universe deviates from the Friedmannian evolution. The analysis is based on models of a single cosmological environment and the global ensemble of the Szekeres models (of the Swiss-Cheese-type and Styrofoam-type). The obtained results show that non-linear growth of cosmic structures is associated with the growth of the spatial curvature ΩScript R (in the FLRW limit ΩScript R → Ωk). If averaged over global scales the result depends on the assumed global model of the Universe. Within the Swiss-Cheese model, which does have a fixed background, the volume average follows the evolution of the background, and the global spatial curvature averages out to zero (the background model is the ΛCDM model, which is spatially flat). In the Styrofoam-type model, which does not have a fixed background, the mean evolution deviates from the spatially flat ΛCDM model, and the mean spatial curvature evolves from ΩScript R =0 at the CMB to ΩScript R ~ 0.1 at 0z =. If the Styrofoam-type model correctly captures evolutionary features of the real Universe then one should expect that in our Universe, the spatial curvature should build up (local growth of cosmic structures) and its mean global average should deviate from zero (backreaction). As a result, this paper predicts that the low-redshift Universe should not be spatially flat (i.e. Ωk ≠ 0, even if in the early Universe Ωk = 0) and therefore when analysing low-z cosmological data one should keep Ωk as a free parameter and independent from the CMB constraints.

  2. Cosmological backreaction within the Szekeres model and emergence of spatial curvature

    Energy Technology Data Exchange (ETDEWEB)

    Bolejko, Krzysztof, E-mail: krzysztof.bolejko@sydney.edu.au [Sydney Institute for Astronomy, School of Physics A28, The University of Sydney, Sydney, NSW, 2006 (Australia)

    2017-06-01

    This paper discusses the phenomenon of backreaction within the Szekeres model. Cosmological backreaction describes how the mean global evolution of the Universe deviates from the Friedmannian evolution. The analysis is based on models of a single cosmological environment and the global ensemble of the Szekeres models (of the Swiss-Cheese-type and Styrofoam-type). The obtained results show that non-linear growth of cosmic structures is associated with the growth of the spatial curvature Ω{sub R} (in the FLRW limit Ω{sub R} → Ω {sub k} ). If averaged over global scales the result depends on the assumed global model of the Universe. Within the Swiss-Cheese model, which does have a fixed background, the volume average follows the evolution of the background, and the global spatial curvature averages out to zero (the background model is the ΛCDM model, which is spatially flat). In the Styrofoam-type model, which does not have a fixed background, the mean evolution deviates from the spatially flat ΛCDM model, and the mean spatial curvature evolves from Ω{sub R} =0 at the CMB to Ω{sub R} ∼ 0.1 at 0 z =. If the Styrofoam-type model correctly captures evolutionary features of the real Universe then one should expect that in our Universe, the spatial curvature should build up (local growth of cosmic structures) and its mean global average should deviate from zero (backreaction). As a result, this paper predicts that the low-redshift Universe should not be spatially flat (i.e. Ω {sub k} ≠ 0, even if in the early Universe Ω {sub k} = 0) and therefore when analysing low- z cosmological data one should keep Ω {sub k} as a free parameter and independent from the CMB constraints.

  3. A Spatial Model of Erosion and Sedimentation on Continental Margins

    National Research Council Canada - National Science Library

    Pratson, Lincoln

    1999-01-01

    .... A computer model that simulates the evolution of continental slope morphology under the interaction of sedimentation, slope failure, and sediment flow erosion has been constructed and validated...

  4. BAYESIAN SPATIAL-TEMPORAL MODELING OF ECOLOGICAL ZERO-INFLATED COUNT DATA.

    Science.gov (United States)

    Wang, Xia; Chen, Ming-Hui; Kuo, Rita C; Dey, Dipak K

    2015-01-01

    A Bayesian hierarchical model is developed for count data with spatial and temporal correlations as well as excessive zeros, uneven sampling intensities, and inference on missing spots. Our contribution is to develop a model on zero-inflated count data that provides flexibility in modeling spatial patterns in a dynamic manner and also improves the computational efficiency via dimension reduction. The proposed methodology is of particular importance for studying species presence and abundance in the field of ecological sciences. The proposed model is employed in the analysis of the survey data by the Northeast Fisheries Sciences Center (NEFSC) for estimation and prediction of the Atlantic cod in the Gulf of Maine - Georges Bank region. Model comparisons based on the deviance information criterion and the log predictive score show the improvement by the proposed spatial-temporal model.

  5. Program SPACECAP: software for estimating animal density using spatially explicit capture-recapture models

    Science.gov (United States)

    Gopalaswamy, Arjun M.; Royle, J. Andrew; Hines, James E.; Singh, Pallavi; Jathanna, Devcharan; Kumar, N. Samba; Karanth, K. Ullas

    2012-01-01

    1. The advent of spatially explicit capture-recapture models is changing the way ecologists analyse capture-recapture data. However, the advantages offered by these new models are not fully exploited because they can be difficult to implement. 2. To address this need, we developed a user-friendly software package, created within the R programming environment, called SPACECAP. This package implements Bayesian spatially explicit hierarchical models to analyse spatial capture-recapture data. 3. Given that a large number of field biologists prefer software with graphical user interfaces for analysing their data, SPACECAP is particularly useful as a tool to increase the adoption of Bayesian spatially explicit capture-recapture methods in practice.

  6. Spatial uncertainty modeling of fuzzy information in images for pattern classification.

    Directory of Open Access Journals (Sweden)

    Tuan D Pham

    Full Text Available The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.

  7. A spatial and nonstationary model for the frequency of extreme rainfall events

    DEFF Research Database (Denmark)

    Gregersen, Ida Bülow; Madsen, Henrik; Rosbjerg, Dan

    2013-01-01

    of extreme rainfall events, a statistical model is tested for this purpose. The model is built on the theory of generalized linear models and uses Poisson regression solved by generalized estimation equations. Spatial and temporal explanatory variables can be included simultaneously, and their relative...

  8. Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

    Science.gov (United States)

    Romañach, Stephanie; Watling, James I.; Fletcher, Robert J.; Speroterra, Carolina; Bucklin, David N.; Brandt, Laura A.; Pearlstine, Leonard G.; Escribano, Yesenia; Mazzotti, Frank J.

    2014-01-01

    Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

  9. Spatial landscape model to characterize biological diversity using R statistical computing environment.

    Science.gov (United States)

    Singh, Hariom; Garg, R D; Karnatak, Harish C; Roy, Arijit

    2018-01-15

    Due to urbanization and population growth, the degradation of natural forests and associated biodiversity are now widely recognized as a global environmental concern. Hence, there is an urgent need for rapid assessment and monitoring of biodiversity on priority using state-of-art tools and technologies. The main purpose of this research article is to develop and implement a new methodological approach to characterize biological diversity using spatial model developed during the study viz. Spatial Biodiversity Model (SBM). The developed model is scale, resolution and location independent solution for spatial biodiversity richness modelling. The platform-independent computation model is based on parallel computation. The biodiversity model based on open-source software has been implemented on R statistical computing platform. It provides information on high disturbance and high biological richness areas through different landscape indices and site specific information (e.g. forest fragmentation (FR), disturbance index (DI) etc.). The model has been developed based on the case study of Indian landscape; however it can be implemented in any part of the world. As a case study, SBM has been tested for Uttarakhand state in India. Inputs for landscape ecology are derived through multi-criteria decision making (MCDM) techniques in an interactive command line environment. MCDM with sensitivity analysis in spatial domain has been carried out to illustrate the model stability and robustness. Furthermore, spatial regression analysis has been made for the validation of the output. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piauí State, Brazil

    Directory of Open Access Journals (Sweden)

    Werneck Guilherme L.

    2002-01-01

    Full Text Available Most ecologic studies use geographical areas as units of observation. Because data from areas close to one another tend to be more alike than those from distant areas, estimation of effect size and confidence intervals should consider spatial autocorrelation of measurements. In this report we demonstrate a method for modeling spatial autocorrelation within a mixed model framework, using data on environmental and socioeconomic determinants of the incidence of visceral leishmaniasis (VL in the city of Teresina, Piauí, Brazil. A model with a spherical covariance structure indicated significant spatial autocorrelation in the data and yielded a better fit than one assuming independent observations. While both models showed a positive association between VL incidence and residence in a favela (slum or in areas with green vegetation, values for the fixed effects and standard errors differed substantially between the models. Exploration of the data's spatial correlation structure through the semivariogram should precede the use of these models. Our findings support the hypothesis of spatial dependence of VL rates and indicate that it might be useful to model spatial correlation in order to obtain more accurate point and standard error estimates.

  11. Chaos induced by breakup of waves in a spatial epidemic model with nonlinear incidence rate

    International Nuclear Information System (INIS)

    Sun, Gui-Quan; Jin, Zhen; Liu, Quan-Xing; Li, Li

    2008-01-01

    Spatial epidemiology is the study of spatial variation in disease risk or incidence, including the spatial patterns of the population. The spread of diseases in human populations can exhibit large scale patterns, underlining the need for spatially explicit approaches. In this paper, the spatiotemporal complexity of a spatial epidemic model with nonlinear incidence rate, which includes the behavioral changes and crowding effect of the infective individuals, is investigated. Based on both theoretical analysis and computer simulations, we find out when, under the parameters which can guarantee a stable limit cycle in the non-spatial model, spiral and target waves can emerge. Moreover, two different kinds of breakup of waves are shown. Specifically, the breakup of spiral waves is from the core and the breakup of target waves is from the far-field, and both kinds of waves become irregular patterns at last. Our results reveal that the spatiotemporal chaos is induced by the breakup of waves. The results obtained confirm that diffusion can form spiral waves, target waves or spatial chaos of high population density, which enrich the findings of spatiotemporal dynamics in the epidemic model

  12. A review of techniques for spatial modeling in geographical, conservation and landscape genetics.

    Science.gov (United States)

    Diniz-Filho, José Alexandre Felizola; Nabout, João Carlos; de Campos Telles, Mariana Pires; Soares, Thannya Nascimento; Rangel, Thiago Fernando L V B

    2009-04-01

    Most evolutionary processes occur in a spatial context and several spatial analysis techniques have been employed in an exploratory context. However, the existence of autocorrelation can also perturb significance tests when data is analyzed using standard correlation and regression techniques on modeling genetic data as a function of explanatory variables. In this case, more complex models incorporating the effects of autocorrelation must be used. Here we review those models and compared their relative performances in a simple simulation, in which spatial patterns in allele frequencies were generated by a balance between random variation within populations and spatially-structured gene flow. Notwithstanding the somewhat idiosyncratic behavior of the techniques evaluated, it is clear that spatial autocorrelation affects Type I errors and that standard linear regression does not provide minimum variance estimators. Due to its flexibility, we stress that principal coordinate of neighbor matrices (PCNM) and related eigenvector mapping techniques seem to be the best approaches to spatial regression. In general, we hope that our review of commonly used spatial regression techniques in biology and ecology may aid population geneticists towards providing better explanations for population structures dealing with more complex regression problems throughout geographic space.

  13. A Poisson regression approach for modelling spatial autocorrelation between geographically referenced observations.

    Science.gov (United States)

    Mohebbi, Mohammadreza; Wolfe, Rory; Jolley, Damien

    2011-10-03

    Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. We used age standardised incidence ratios (SIRs) of esophageal cancer (EC) from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1) Poisson regression with agglomeration-specific nonspatial random effects; (2) Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC), Akaike's information criterion (AIC) and adjusted pseudo R2, were used for model comparison. A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.

  14. A poisson regression approach for modelling spatial autocorrelation between geographically referenced observations

    Directory of Open Access Journals (Sweden)

    Jolley Damien

    2011-10-01

    Full Text Available Abstract Background Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. Methods We used age standardised incidence ratios (SIRs of esophageal cancer (EC from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1 Poisson regression with agglomeration-specific nonspatial random effects; (2 Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC, Akaike's information criterion (AIC and adjusted pseudo R2, were used for model comparison. Results A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. Conclusions The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.

  15. Trap configuration and spacing influences parameter estimates in spatial capture-recapture models.

    Directory of Open Access Journals (Sweden)

    Catherine C Sun

    Full Text Available An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation. We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

  16. Trap configuration and spacing influences parameter estimates in spatial capture-recapture models.

    Science.gov (United States)

    Sun, Catherine C; Fuller, Angela K; Royle, J Andrew

    2014-01-01

    An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

  17. Usage of Fuzzy Spatial Theory for Modelling of Terrain Passability

    Directory of Open Access Journals (Sweden)

    Alois Hofmann

    2013-01-01

    Full Text Available Geographic support of decision-making processes is based on various geographic products, usually in digital form, which come from various foundations and sources. Each product can be characterized by its quality or by its utility value for the given type of task or group of tasks, for which the product is used. They also usually have different characteristics and thus can very significantly influence the resulting analytical material. The aim of the paper is to contribute to the solution of the question of how it is possible to work with diverse spatial geographic information so that the user has an idea about the resulting product. The concept of fuzzy sets is used for representation of classes, whose boundaries are not clearly (not sharply set, namely, the fuzzy approach in overlaying operations realized in ESRI ArcGIS environment. The paper is based on a research project which is being solved at the Faculty of Military Technologies of the University of Defence. The research deals with the influence of geographic and climatic factors on the activity of armed forces and the Integrated Rescue System.

  18. Spatial Modelling of Land Price in The Semarang City

    Science.gov (United States)

    Widjonarko, W.

    2018-02-01

    Land has a very important role in supporting the population activity in both urban and rural areas. Demand for land tends to increase due to the increase in population, on the other hand the availability of land is limited. The increasing demand of land also occurred in the city of Semarang due to population growth and economic activity growth. The increasing demand for land in Semarang City has caused a shift in spatial demand patterns. The shift in land demand is due to limited supply of land in the area near to the city center, and the price become unaffordable for some residents of Semarang City. Due to the limitation of land supply in the city center has affected to the increasing demand of land in the suburbs. This phenomenon causes an increase in the price of land in the periphery of Semarang, and forms a land price pattern that resembles a circus tent, especially at a new center of activity on the periphery.

  19. Spatially Resolved Spectral Powder Analysis: Experiments and Modeling.

    Science.gov (United States)

    Scheibelhofer, Otto; Wahl, Patrick R; Larchevêque, Boris; Chauchard, Fabien; Khinast, Johannes G

    2018-01-01

    Understanding the behavior of light in granular media is necessary for determining the sample size, shape, and weight when probing using fiber optic setups. This is required for a correct estimate of the active pharmaceutical ingredient content in a pharmaceutical blend via near-infrared spectroscopy. Several strategies to describe the behavior of light in granular and turbid media exist. A common approach is the Monte-Carlo simulation of individual photons and their description using mean free path lengths for scattering and absorption. In this work, we chose a complementary method by approximating these parameters via real physical counterparts, i.e., the particle size, shape, and density and the resulting chord lengths. Additionally, the wavelength dependence of refractive indices is incorporated. The obtained results were compared with those obtained in an experimental setup that included the SAM-Spec Felin probe head by Indatech for detecting spatially resolved spectra of samples. Our method facilitates the interpretation of the acquired experimental results by contrasting the optical response, the physical particle attributes, and the simulation results.

  20. Stochastic ecological network occupancy (SENO) models: a new tool for modeling ecological networks across spatial scales

    Science.gov (United States)

    Lafferty, Kevin D.; Dunne, Jennifer A.

    2010-01-01

    Stochastic ecological network occupancy (SENO) models predict the probability that species will occur in a sample of an ecological network. In this review, we introduce SENO models as a means to fill a gap in the theoretical toolkit of ecologists. As input, SENO models use a topological interaction network and rates of colonization and extinction (including consumer effects) for each species. A SENO model then simulates the ecological network over time, resulting in a series of sub-networks that can be used to identify commonly encountered community modules. The proportion of time a species is present in a patch gives its expected probability of occurrence, whose sum across species gives expected species richness. To illustrate their utility, we provide simple examples of how SENO models can be used to investigate how topological complexity, species interactions, species traits, and spatial scale affect communities in space and time. They can categorize species as biodiversity facilitators, contributors, or inhibitors, making this approach promising for ecosystem-based management of invasive, threatened, or exploited species.

  1. The importance of spatial models for estimating the strength of density dependence

    DEFF Research Database (Denmark)

    Thorson, James T.; Skaug, Hans J.; Kristensen, Kasper

    2014-01-01

    for an entire population. However, it is increasingly recognized that spatial heterogeneity in population densities has implications for population and community dynamics. We therefore adapt the Gompertz model to approximate local densities over continuous space instead of population-wide abundance...... the California Coast. In this case, the nonspatial model estimates implausible oscillatory dynamics on an annual time scale, while the spatial model estimates strong autocorrelation and is supported by model selection tools. We conclude by discussing the importance of improved data archiving techniques, so...

  2. Can spatial data substitute temporal data in phenological modelling? A survey using birch flowering.

    Science.gov (United States)

    Jochner, Susanne; Caffarra, Amelia; Menzel, Annette

    2013-12-01

    In addition to the evaluation of long-term series, the analysis of spatial gradients, such as urbanization gradients, may be helpful in assessing phenological responses to global warming. But are phenological responses of birch (Betula pendula Roth) assessed by temperature variations comparable over time and space and can spatially calibrated models predict long-term phenological data adequately? We calibrated and tested linear regression models and the process-based DORMPHOT model on phenological and temperature data sampled along an urbanization gradient in 2010 and 2011 in the German cities Munich and Ingolstadt (spatial data). Additionally, we analysed data from the German Meteorological Service for the period 1991-2010 (long-term data). The model comparison showed that the DORMPHOT model performed better than the linear model. Therefore, the importance of forcing and chilling sums as well as photoperiod, factors which were all considered in the DORMPHOT model, was evident. Models calibrated on spatial data produced good predictions of spatial data, but they were less adequate for predicting long-term data. Therefore, a time-for-space substitution might not always be appropriate. This finding was also confirmed by a comparison of temperature response rates. The rate of change in the spatial data (-4.4 days °C(-1)) did not match the changes observed in the long-term data (-1.9 days °C(-1)). Consequently, it is important not to generalize results derived from one specific study method, but their inherent methodological, spatial and temporal peculiarities have to be considered.

  3. APPLICATION OF SPATIAL MODELLING APPROACHES, SAMPLING STRATEGIES AND 3S TECHNOLOGY WITHIN AN ECOLGOCIAL FRAMWORK

    Directory of Open Access Journals (Sweden)

    H.-C. Chen

    2012-07-01

    Full Text Available How to effectively describe ecological patterns in nature over broader spatial scales and build a modeling ecological framework has become an important issue in ecological research. We test four modeling methods (MAXENT, DOMAIN, GLM and ANN to predict the potential habitat of Schima superba (Chinese guger tree, CGT with different spatial scale in the Huisun study area in Taiwan. Then we created three sampling design (from small to large scales for model development and validation by different combinations of CGT samples from aforementioned three sites (Tong-Feng watershed, Yo-Shan Mountain, and Kuan-Dau watershed. These models combine points of known occurrence and topographic variables to infer CGT potential spatial distribution. Our assessment revealed that the method performance from highest to lowest was: MAXENT, DOMAIN, GLM and ANN on small spatial scale. The MAXENT and DOMAIN two models were the most capable for predicting the tree's potential habitat. However, the outcome clearly indicated that the models merely based on topographic variables performed poorly on large spatial extrapolation from Tong-Feng to Kuan-Dau because the humidity and sun illumination of the two watersheds are affected by their microterrains and are quite different from each other. Thus, the models developed from topographic variables can only be applied within a limited geographical extent without a significant error. Future studies will attempt to use variables involving spectral information associated with species extracted from high spatial, spectral resolution remotely sensed data, especially hyperspectral image data, for building a model so that it can be applied on a large spatial scale.

  4. Visual spatial localization and the two-process model

    OpenAIRE

    Uddin, Muhammad Kamal

    2006-01-01

    This review paper begins with a brief history of research on localization followed by its definition and classification. It also presents important parameters of localization and factors that affect localization. The paper gives an overview of the two-process model and highlights its limitations. A careful review exposed inadequacies in the model in particular and in localization research in general warranting a clear need for further investigations. Here the author reports findings of his se...

  5. Evaluation of spatial models to predict vulnerability of forest birds to brood parasitism by cowbirds

    Science.gov (United States)

    Gustafson, E.J.; Knutson, M.G.; Niemi, G.J.; Friberg, M.

    2002-01-01

    We constructed alternative spatial models at two scales to predict Brown-headed Cowbird (Molothrus ater) parasitism rates from land cover maps. The local-scale models tested competing hypotheses about the relationship between cowbird parasitism and distance of host nests from a forest edge (forest-nonforest boundary). The landscape models tested competing hypotheses about how landscape features (e.g., forests, agricultural fields) interact to determine rates of cowbird parasitism. The models incorporate spatial neighborhoods with a radius of 2.5 km in their formulation, reflecting the scale of the majority of cowbird commuting activity. Field data on parasitism by cowbirds (parasitism rate and number of cowbird eggs per nest) were collected at 28 sites in the Driftless Area Ecoregion of Wisconsin, Minnesota, and Iowa and were compared to the predictions of the alternative models. At the local scale, there was a significant positive relationship between cowbird parasitism and mean distance of nest sites from the forest edge. At the landscape scale, the best fitting models were the forest-dependent and forest-fragmentation-dependent models, in which more heavily forested and less fragmented landscapes had higher parasitism rates. However, much of the explanatory power of these models results from the inclusion of the local-scale relationship in these models. We found lower rates of cowbird parasitism than did most Midwestern studies, and we identified landscape patterns of cowbird parasitism that are opposite to those reported in several other studies of Midwestern songbirds. We caution that cowbird parasitism patterns can be unpredictable, depending upon ecoregional location and the spatial extent, and that our models should be tested in other ecoregions before they are applied there. Our study confirms that cowbird biology has a strong spatial component, and that improved spatial models applied at multiple spatial scales will be required to predict the effects of

  6. Calibration of a distributed hydrologic model using observed spatial patterns from MODIS data

    Science.gov (United States)

    Demirel, Mehmet C.; González, Gorka M.; Mai, Juliane; Stisen, Simon

    2016-04-01

    Distributed hydrologic models are typically calibrated against streamflow observations at the outlet of the basin. Along with these observations from gauging stations, satellite based estimates offer independent evaluation data such as remotely sensed actual evapotranspiration (aET) and land surface temperature. The primary objective of the study is to compare model calibrations against traditional downstream discharge measurements with calibrations against simulated spatial patterns and combinations of both types of observations. While the discharge based model calibration typically improves the temporal dynamics of the model, it seems to give rise to minimum improvement of the simulated spatial patterns. In contrast, objective functions specifically targeting the spatial pattern performance could potentially increase the spatial model performance. However, most modeling studies, including the model formulations and parameterization, are not designed to actually change the simulated spatial pattern during calibration. This study investigates the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale hydrologic model (mHM). This model is selected as it allows for a change in the spatial distribution of key soil parameters through the optimization of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) values directly as input. In addition the simulated aET can be estimated at a spatial resolution suitable for comparison to the spatial patterns observed with MODIS data. To increase our control on spatial calibration we introduced three additional parameters to the model. These new parameters are part of an empirical equation to the calculate crop coefficient (Kc) from daily LAI maps and used to update potential evapotranspiration (PET) as model inputs. This is done instead of correcting/updating PET with just a uniform (or aspect driven) factor used in the mHM model

  7. Extending Spatial Interaction Models with Agents for Understanding Relationships in a Dynamic Retail Market

    Directory of Open Access Journals (Sweden)

    Mark Birkin

    2011-01-01

    Full Text Available For many years, effective model-based representations of the dynamics and evolution of urban spatial structure have proved elusive. While some progress has been made through the deployment of spatial interaction models, these approaches have been limited by the difficulty of representing behavioural mechanisms and processes. In this paper, it is demonstrated that evolutionary models grounded in the principles of spatial interaction are compatible with the more novel approaches of agent-based modelling. The incorporation of agents provides a much more flexible means for the representation of behavioural mechanisms. The paper illustrates the way in which three more complicated situations can be handled through the fusion of spatial interaction and agent modelling perspectives. These situations comprise discontinuous evolution (in which structural adjustment takes place in discrete steps, and not as a continuously smooth process; nonequilibrium dynamics (in which the underlying system parameters continue to evolve through time; the incorporation of new decision variables (which we illustrate through the addition of land rents into the model. The conclusion of the paper is that the combination of spatial interaction and agent-based modelling methods provides encouraging prospects for the social simulation of real urban systems.

  8. Accounting for and predicting the influence of spatial autocorrelation in water quality modeling

    Science.gov (United States)

    Miralha, L.; Kim, D.

    2017-12-01

    Although many studies have attempted to investigate the spatial trends of water quality, more attention is yet to be paid to the consequences of considering and ignoring the spatial autocorrelation (SAC) that exists in water quality parameters. Several studies have mentioned the importance of accounting for SAC in water quality modeling, as well as the differences in outcomes between models that account for and ignore SAC. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC inherently possessed by a response variable (i.e., water quality parameter) influences the outcomes of spatial modeling. We evaluated whether the level of inherent SAC is associated with changes in R-Squared, Akaike Information Criterion (AIC), and residual SAC (rSAC), after accounting for SAC during modeling procedure. The main objective was to analyze if water quality parameters with higher Moran's I values (inherent SAC measure) undergo a greater increase in R² and a greater reduction in both AIC and rSAC. We compared a non-spatial model (OLS) to two spatial regression approaches (spatial lag and error models). Predictor variables were the principal components of topographic (elevation and slope), land cover, and hydrological soil group variables. We acquired these data from federal online sources (e.g. USGS). Ten watersheds were selected, each in a different state of the USA. Results revealed that water quality parameters with higher inherent SAC showed substantial increase in R² and decrease in rSAC after performing spatial regressions. However, AIC values did not show significant changes. Overall, the higher the level of inherent SAC in water quality variables, the greater improvement of model performance. This indicates a linear and direct relationship between the spatial model outcomes (R² and rSAC) and the degree of SAC in each water quality variable. Therefore, our study suggests that the inherent level of

  9. Development of a Discrete Spatial-Temporal SEIR Simulator for Modeling Infectious Diseases

    Energy Technology Data Exchange (ETDEWEB)

    McKenna, S.A.

    2000-11-01

    Multiple techniques have been developed to model the temporal evolution of infectious diseases. Some of these techniques have also been adapted to model the spatial evolution of the disease. This report examines the application of one such technique, the SEIR model, to the spatial and temporal evolution of disease. Applications of the SEIR model are reviewed briefly and an adaptation to the traditional SEIR model is presented. This adaptation allows for modeling the spatial evolution of the disease stages at the individual level. The transmission of the disease between individuals is modeled explicitly through the use of exposure likelihood functions rather than the global transmission rate applied to populations in the traditional implementation of the SEIR model. These adaptations allow for the consideration of spatially variable (heterogeneous) susceptibility and immunity within the population. The adaptations also allow for modeling both contagious and non-contagious diseases. The results of a number of numerical experiments to explore the effect of model parameters on the spread of an example disease are presented.

  10. A dynamic phase transition model for spatial agglomeration processes.

    Science.gov (United States)

    Weidlich, W; Haag, G

    1987-11-01

    A nonlinear model of population migration is presented in order to provide a dynamic explanation for the formation of metropolitan areas. "In Section 2 the model is introduced in terms of the rate equations for the mean values of the regional population numbers with specifically chosen individual transition rates. Section 3 gives a survey of concepts and results for the convenience of the reader not interested in the details of the mathematical derivations. Section 4 derives the stationary solutions of the rate equations, that is, the equilibria of the system. Section 5 treats the time dependent solutions of the model equations focussing on the exact analytic solutions along so-called symmetry paths. Section 6 analyzes the dynamic stability of the symmetry path solutions and decides which stationary states are unstable and which are stable equilibrium states." excerpt

  11. Influence of Regional Climate Model spatial resolution on wind climates

    Science.gov (United States)

    Pryor, S. C.; Barthelmie, R. J.; Nikulin, G.; Jones, C.

    2010-12-01

    Global and regional climate models are being run at increasingly fine horizontal and vertical resolution with the goal of increased skill. However, relatively few studies have quantified the change in modeled wind climates that derives from applying a Regional Climate Model (RCM) at varying resolutions, and the response to varying resolution may be highly non-linear since most models run in climate mode are hydrostatic. Thus, herein we examine the influence of grid-resolution on modelled wind speeds and gusts and derived extremes thereof over southern Scandinavia using output from the Rossby Centre (RCA3) RCM run at four different resolutions from 50 x 50 km to 6 x 6 km, and with two different vertical grid-spacings. Domain averaged fifty-year return period wind speeds and wind gusts derived using the method of moments approach to compute the Gumbel parameters, increase with resolution (Table 1), though the change is strongly mediated by the model grid-cell surface characteristics. Power spectra of the 3-hourly model time-step ‘instantaneous’ wind speeds and daily wind gusts at all four resolutions show clear peaks in the variance associated with bi-annual, annual, seasonal and synoptic frequencies. The variance associated with these peaks is enhanced with increased resolution, though not in a monotonic fashion, and is more marked in wind gusts than wind speeds. Relative to in situ observations, the model generally underestimates the variance, particularly associated with the synoptic time scale, even for the highest resolution simulations. There is some evidence to suggest that the change in the power spectra with horizontal resolution is less marked in the transition from 12.5 km to 6.25 km, than from 50 to 25 km, or 25 km to 12.5 km.Table 1. Domain averaged mean annual wind speed (U), 50-year return period extreme wind speed (U50yr) and wind gust (Gust50yr) (m/s) from the four RCA3 simulations at different resolution based on output from 1987-2008. The

  12. Modeling urban growth and spatial structure in Nanjing, China with GIS and remote sensing

    Science.gov (United States)

    Luo, Jun

    This research focuses on the use of GIS, remote sensing and spatial modeling for studies on urban growth and spatial structure. Previous studies on urban growth modeling have not elaborated the spatial heterogeneity of urban growth pattern, which, however, is well recognized. The census population data is widely used for investigating urban spatial structure, but it has inherent various problems which can lead to biased analysis results. Studies on urban growth and spatial structure of Chinese cities remain limited due to the data availability and methodology development. In this dissertation, I initiate a new analysis framework and a new method to address these critical issues through a case study of Nanjing, China. The study first set up urban land expansion models for Nanjing in the period of 1988-2000. Landsat imageries are processed and classified to provide land use data in 1988 and 2000. GIS data are used to provide spatial variables inputs for the land use conversion models. A combined land use data sampling is conducted to obtain land use sample points for the proposed models. Classic logistic regression is used to reveal the urban land expansion from a global view. Furthermore, a logistic geographically weighted regression (GWR) model is set up to reveal the local variations of influence of spatial factors on urban land expansion. The study finds that the logistic GWR significantly improved the global logistic regression model and verifies that the influences of explanatory variables of urban growth are spatially varying. An urban growth probability surface is then generated based on the variable and parameter surfaces. This new framework for analyzing urban growth pattern may open a new direction for urban growth modeling. Second, the dissertation develops a new method, which utilizes detailed urban land parcel and building data to generate population surface of Nanjing in 2000. With this method, populations of small areas at intraurban level can be

  13. A statistical model for spatial patterns of Buruli ulcer in the Amansie West district, Ghana

    Science.gov (United States)

    Duker, Alfred A.; Stein, Alfred; Hale, Martin

    2006-06-01

    Buruli ulcer (BU), a skin ulceration caused by Mycobacterium ulcerans (MU), is the second most widespread mycobacterium infection in Ghana. Its infection pathway is possibly related to the potable and agricultural water supply. This study aims to identify environmental factors that influence infection in a part of Ghana. It examines the significance of contaminated surface drainage channels and groundwater using conditional autoregressive (CAR) statistical modelling. This type of modelling implies that the spatial pattern of BU incidence in one community depends on the influence of the environment in neighbouring communities. Covariates were included to assess the spatial relationship between environmental risk factors and BU incidence in the study area. The study reveals an association between (a) the mean As content of soil and spatial distribution of BU and (b) the distance to sites of gold mining and spatial distribution of BU. We conclude that both arsenic in the natural environment and gold mining influence BU infection.

  14. The spatial spread of schistosomiasis: A multidimensional network model applied to Saint-Louis region, Senegal

    Science.gov (United States)

    Ciddio, Manuela; Mari, Lorenzo; Sokolow, Susanne H.; De Leo, Giulio A.; Casagrandi, Renato; Gatto, Marino

    2017-10-01

    Schistosomiasis is a parasitic, water-related disease that is prevalent in tropical and subtropical areas of the world, causing severe and chronic consequences especially among children. Here we study the spatial spread of this disease within a network of connected villages in the endemic region of the Lower Basin of the Senegal River, in Senegal. The analysis is performed by means of a spatially explicit metapopulation model that couples local-scale eco-epidemiological dynamics with spatial mechanisms related to human mobility (estimated from anonymized mobile phone records), snail dispersal and hydrological transport of schistosome larvae along the main water bodies of the region. Results show that the model produces epidemiological patterns consistent with field observations, and point out the key role of spatial connectivity on the spread of the disease. These findings underline the importance of considering different transport pathways in order to elaborate disease control strategies that can be effective within a network of connected populations.

  15. Competition for marine space: modelling the Baltic Sea fisheries and effort displacement under spatial restrictions

    DEFF Research Database (Denmark)

    Bastardie, Francois; Nielsen, J. Rasmus; Eigaard, Ole Ritzau

    2015-01-01

    to fishery and from vessel to vessel. The impact assessment of new spatial plans involving fisheries should be based on quantitative bioeconomic analyses that take into account individual vessel decisions, and trade-offs in cross-sector conflicting interests.Weuse a vessel-oriented decision-support tool (the...... DISPLACE model) to combine stochastic variations in spatial fishing activities with harvested resource dynamics in scenario projections. The assessment computes economic and stock status indicators by modelling the activity of Danish, Swedish, and German vessels (.12 m) in the international western Baltic...... Sea commercial fishery, together with the underlying size-based distribution dynamics of the main fishery resources of sprat, herring, and cod. The outcomes of alternative scenarios for spatial effort displacement are exemplified by evaluating the fishers’s abilities to adapt to spatial plans under...

  16. Modeling spatial-temporal operations with context-dependent associative memories.

    Science.gov (United States)

    Mizraji, Eduardo; Lin, Juan

    2015-10-01

    We organize our behavior and store structured information with many procedures that require the coding of spatial and temporal order in specific neural modules. In the simplest cases, spatial and temporal relations are condensed in prepositions like "below" and "above", "behind" and "in front of", or "before" and "after", etc. Neural operators lie beneath these words, sharing some similarities with logical gates that compute spatial and temporal asymmetric relations. We show how these operators can be modeled by means of neural matrix memories acting on Kronecker tensor products of vectors. The complexity of these memories is further enhanced by their ability to store episodes unfolding in space and time. How does the brain scale up from the raw plasticity of contingent episodic memories to the apparent stable connectivity of large neural networks? We clarify this transition by analyzing a model that flexibly codes episodic spatial and temporal structures into contextual markers capable of linking different memory modules.

  17. Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease (HFMD) in Mainland China

    Science.gov (United States)

    2014-01-01

    Background There have been large-scale outbreaks of hand, foot and mouth disease (HFMD) in Mainland China over the last decade. These events varied greatly across the country. It is necessary to identify the spatial risk factors and spatial distribution patterns of HFMD for public health control and prevention. Climate risk factors associated with HFMD occurrence have been recognized. However, few studies discussed the socio-economic determinants of HFMD risk at a space scale. Methods HFMD records in Mainland China in May 2008 were collected. Both climate and socio-economic factors were selected as potential risk exposures of HFMD. Odds ratio (OR) was used to identify the spatial risk factors. A spatial autologistic regression model was employed to get OR values of each exposures and model the spatial distribution patterns of HFMD risk. Results Results showed that both climate and socio-economic variables were spatial risk factors for HFMD transmission in Mainland China. The statistically significant risk factors are monthly average precipitation (OR = 1.4354), monthly average temperature (OR = 1.379), monthly average wind speed (OR = 1.186), the number of industrial enterprises above designated size (OR = 17.699), the population density (OR = 1.953), and the proportion of student population (OR = 1.286). The spatial autologistic regression model has a good goodness of fit (ROC = 0.817) and prediction accuracy (Correct ratio = 78.45%) of HFMD occurrence. The autologistic regression model also reduces the contribution of the residual term in the ordinary logistic regression model significantly, from 17.25 to 1.25 for the odds ratio. Based on the prediction results of the spatial model, we obtained a map of the probability of HFMD occurrence that shows the spatial distribution pattern and local epidemic risk over Mainland China. Conclusions The autologistic regression model was used to identify spatial risk factors and model spatial risk patterns of HFMD. HFMD

  18. Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease (HFMD) in Mainland China.

    Science.gov (United States)

    Bo, Yan-Chen; Song, Chao; Wang, Jin-Feng; Li, Xiao-Wen

    2014-04-14

    There have been large-scale outbreaks of hand, foot and mouth disease (HFMD) in Mainland China over the last decade. These events varied greatly across the country. It is necessary to identify the spatial risk factors and spatial distribution patterns of HFMD for public health control and prevention. Climate risk factors associated with HFMD occurrence have been recognized. However, few studies discussed the socio-economic determinants of HFMD risk at a space scale. HFMD records in Mainland China in May 2008 were collected. Both climate and socio-economic factors were selected as potential risk exposures of HFMD. Odds ratio (OR) was used to identify the spatial risk factors. A spatial autologistic regression model was employed to get OR values of each exposures and model the spatial distribution patterns of HFMD risk. Results showed that both climate and socio-economic variables were spatial risk factors for HFMD transmission in Mainland China. The statistically significant risk factors are monthly average precipitation (OR = 1.4354), monthly average temperature (OR = 1.379), monthly average wind speed (OR = 1.186), the number of industrial enterprises above designated size (OR = 17.699), the population density (OR = 1.953), and the proportion of student population (OR = 1.286). The spatial autologistic regression model has a good goodness of fit (ROC = 0.817) and prediction accuracy (Correct ratio = 78.45%) of HFMD occurrence. The autologistic regression model also reduces the contribution of the residual term in the ordinary logistic regression model significantly, from 17.25 to 1.25 for the odds ratio. Based on the prediction results of the spatial model, we obtained a map of the probability of HFMD occurrence that shows the spatial distribution pattern and local epidemic risk over Mainland China. The autologistic regression model was used to identify spatial risk factors and model spatial risk patterns of HFMD. HFMD occurrences were found to be spatially

  19. Spreading speed and travelling waves for a spatially discrete SIS epidemic model

    International Nuclear Information System (INIS)

    Zhang, Kate Fang; Zhao Xiaoqiang

    2008-01-01

    This paper is devoted to the study of the asymptotic speed of spread and travelling waves for a spatially discrete SIS epidemic model. By appealing to the theory of spreading speeds and travelling waves for monotonic semiflows, we establish the existence of asymptotic speed of spread and show that it coincides with the minimal wave speed for monotonic travelling waves. This also gives an affirmative answer to an open problem presented by Rass and Radcliffe (2003 Spatial Deterministic Epidemics (Mathematical Surveys and Monographs vol 102) (Providence, RI: American Mathematical Society)) in the case of discrete spatial habitat

  20. Spatial modeling of geographic inequalities in infant and child mortality across Nepal.

    Science.gov (United States)

    Chin, Brian; Montana, Livia; Basagaña, Xavier

    2011-07-01

    A survival regression model that allows for spatially correlated random effects is used to predict the hazard of dying among 12,714 children born between 1996 and 2006 in Nepal. The maps of fitted hazard rates show that even after accounting for individual and community-level covariates, a residual spatial pattern in infant mortality remains, with higher mortality concentrated in parts of Nepal's Far-Western and Mid-Western development regions. Results suggest a need to consider health policies and programs that reach children in spatially concentrated high-mortality areas. Copyright © 2011 Elsevier Ltd. All rights reserved.

  1. Validation of elk resource selection models with spatially independent data

    Science.gov (United States)

    Priscilla K. Coe; Bruce K. Johnson; Michael J. Wisdom; John G. Cook; Marty Vavra; Ryan M. Nielson

    2011-01-01

    Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely...

  2. Linear models for multivariate, time series, and spatial data

    CERN Document Server

    Christensen, Ronald

    1991-01-01

    This is a companion volume to Plane Answers to Complex Questions: The Theory 0/ Linear Models. It consists of six additional chapters written in the same spirit as the last six chapters of the earlier book. Brief introductions are given to topics related to linear model theory. No attempt is made to give a comprehensive treatment of the topics. Such an effort would be futile. Each chapter is on a topic so broad that an in depth discussion would require a book-Iength treatment. People need to impose structure on the world in order to understand it. There is a limit to the number of unrelated facts that anyone can remem­ ber. If ideas can be put within a broad, sophisticatedly simple structure, not only are they easier to remember but often new insights become avail­ able. In fact, sophisticatedly simple models of the world may be the only ones that work. I have often heard Arnold Zellner say that, to the best of his knowledge, this is true in econometrics. The process of modeling is fundamental to understand...

  3. Modeling the Spatial Dynamics of International Tuna Fleets.

    Directory of Open Access Journals (Sweden)

    Jenny Sun

    Full Text Available We developed an iterative sequential random utility model to investigate the social and environmental determinants of the spatiotemporal decision process of tuna purse-seine fishery fishing effort in the eastern Pacific Ocean. Operations of the fishing gear mark checkpoints in a continuous complex decision-making process. Individual fisher behavior is modeled by identifying diversified choices over decision-space for an entire fishing trip, which allows inclusion of prior and current vessel locations and conditions among the explanatory variables. Among these factors are vessel capacity; departure and arrival port; duration of the fishing trip; daily and cumulative distance travelled, which provides a proxy for operation costs; expected revenue; oceanographic conditions; and tons of fish on board. The model uses a two-step decision process to capture the probability of a vessel choosing a specific fishing region for the first set and the probability of switching to (or staying in a specific region to fish before returning to its landing port. The model provides a means to anticipate the success of marine resource management, and it can be used to evaluate fleet diversity in fisher behavior, the impact of climate variability, and the stability and resilience of complex coupled human and natural systems.

  4. Multiphase modelling of vascular tumour growth in two spatial dimensions

    KAUST Repository

    Hubbard, M.E.

    2013-01-01

    In this paper we present a continuum mathematical model of vascular tumour growth which is based on a multiphase framework in which the tissue is decomposed into four distinct phases and the principles of conservation of mass and momentum are applied to the normal/healthy cells, tumour cells, blood vessels and extracellular material. The inclusion of a diffusible nutrient, supplied by the blood vessels, allows the vasculature to have a nonlocal influence on the other phases. Two-dimensional computational simulations are carried out on unstructured, triangular meshes to allow a natural treatment of irregular geometries, and the tumour boundary is captured as a diffuse interface on this mesh, thereby obviating the need to explicitly track the (potentially highly irregular and ill-defined) tumour boundary. A hybrid finite volume/finite element algorithm is used to discretise the continuum model: the application of a conservative, upwind, finite volume scheme to the hyperbolic mass balance equations and a finite element scheme with a stable element pair to the generalised Stokes equations derived from momentum balance, leads to a robust algorithm which does not use any form of artificial stabilisation. The use of a matrix-free Newton iteration with a finite element scheme for the nutrient reaction-diffusion equations allows full nonlinearity in the source terms of the mathematical model.Numerical simulations reveal that this four-phase model reproduces the characteristic pattern of tumour growth in which a necrotic core forms behind an expanding rim of well-vascularised proliferating tumour cells. The simulations consistently predict linear tumour growth rates. The dependence of both the speed with which the tumour grows and the irregularity of the invading tumour front on the model parameters is investigated. © 2012 Elsevier Ltd.

  5. A multivariate conditional model for streamflow prediction and spatial precipitation refinement

    Science.gov (United States)

    Liu, Zhiyong; Zhou, Ping; Chen, Xiuzhi; Guan, Yinghui

    2015-10-01

    The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine-based model is compared with that of traditional data-driven models such as the support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.

  6. Application of the Multitype Strauss Point Model for Characterizing the Spatial Distribution of Landslides

    Directory of Open Access Journals (Sweden)

    Iswar Das

    2016-01-01

    Full Text Available Landslides are common but complex natural hazards. They occur on the Earth’s surface following a mass movement process. This study applies the multitype Strauss point process model to analyze the spatial distributions of small and large landslides along with geoenvironmental covariates. It addresses landslides as a set of irregularly distributed point-type locations within a spatial region. Their intensity and spatial interactions are analyzed by means of the distance correlation functions, model fitting, and simulation. We use as a dataset the landslide occurrences for 28 years from a landslide prone road corridor in the Indian Himalayas. The landslides are investigated for their spatial character, that is, whether they show inhibition or occur as a regular or a clustered point pattern, and for their interaction with landslides in the neighbourhood. Results show that the covariates lithology, land cover, road buffer, drainage density, and terrain units significantly improved model fitting. A comparison of the output made with logistic regression model output showed a superior prediction performance for the multitype Strauss model. We compared results of this model with the multitype/hard core Strauss point process model that further improved the modeling. Results from the study can be used to generate landslide susceptibility scenarios. The paper concludes that a multitype Strauss point process model enriches the set of statistical tools that can comprehensively analyze landslide data.

  7. Spatial enhancement of ECG using diagnostic similarity score based lead selective multi-scale linear model.

    Science.gov (United States)

    Nallikuzhy, Jiss J; Dandapat, S

    2017-06-01

    In this work, a new patient-specific approach to enhance the spatial resolution of ECG is proposed and evaluated. The proposed model transforms a three-lead ECG into a standard twelve-lead ECG thereby enhancing its spatial resolution. The three leads used for prediction are obtained from the standard twelve-lead ECG. The proposed model takes advantage of the improved inter-lead correlation in wavelet domain. Since the model is patient-specific, it also selects the optimal predictor leads for a given patient using a lead selection algorithm. The lead selection algorithm is based on a new diagnostic similarity score which computes the diagnostic closeness between the original and the spatially enhanced leads. Standard closeness measures are used to assess the performance of the model. The similarity in diagnostic information between the original and the spatially enhanced leads are evaluated using various diagnostic measures. Repeatability and diagnosability are performed to quantify the applicability of the model. A comparison of the proposed model is performed with existing models that transform a subset of standard twelve-lead ECG into the standard twelve-lead ECG. From the analysis of the results, it is evident that the proposed model preserves diagnostic information better compared to other models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

    Directory of Open Access Journals (Sweden)

    Sungwon Kim

    2015-06-01

    Full Text Available The objective of this study is to develop artificial neural network (ANN models, including multilayer perceptron (MLP and Kohonen self-organizing feature map (KSOFM, for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop and 11-3-1 (Levenberg-Marquardt were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop and 1-3-11 (Levenberg–Marquardt, which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

  9. Multilevel Modelling with Spatial Interaction Effects with Application to an Emerging Land Market in Beijing, China.

    Directory of Open Access Journals (Sweden)

    Guanpeng Dong

    Full Text Available This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership structure--for example, census zones into regions but the actual locations of the units and the distances between them are not directly considered: what matters is the groupings but not how close together any two units are within those groupings. As a consequence, spatial interaction effects are neither modelled nor measured, confounding group effects (understood as some sort of contextual effect that acts 'top down' upon members of a group with proximity effects (some sort of joint dependency that emerges between neighbours. To deal with this, we incorporate spatial simultaneous autoregressive processes into both the outcome variable and the higher level residuals. To assess the performance of the proposed method and the classic multilevel model, a series of Monte Carlo simulations are conducted. The results show that the proposed method performs well in retrieving the true model parameters whereas the classic multilevel model provides biased and inefficient parameter estimation in the presence of spatial interactions. An important implication of the study is to be cautious of an apparent neighbourhood effect in terms of both its magnitude and statistical significance if spatial interaction effects at a lower level are suspected. Applying the new approach to a two-level land price data set for Beijing, China, we find significant spatial interactions at both the land parcel and district levels.

  10. The Spatial Mechanism and Drive Mechanism Study of Chinese Urban Efficiency - Based on the Spatial Panel Data Model

    Directory of Open Access Journals (Sweden)

    Yuan Xiaoling

    2016-08-01

    Full Text Available In this article, the urban efficiency factors of 285 Chinese prefecture-level cities in the period from 2003 to 2012 are analyzed by using the spatial econometric model. The result shows that the development of urban efficiency between the cities positively correlates with space. And we conclude that the Industrial Structure, Openness and the Infrastructure can promote the development of such urban efficiency. The Urban Agglomeration Scale, Government Control, Fixed Asset Investment and other factors can inhibit the development of urban efficiency to a certain degree. Therefore, we come to a conclusion that, in the new urbanization construction process, the cities need to achieve cross-regional coordination from the perspective of urban agglomerations and metropolitan development. The efficiency of the city together with the scientific and rational flow of the factors should also be improved.

  11. Spatial and frequency domain ring source models for the single muscle fiber action potential

    DEFF Research Database (Denmark)

    Henneberg, Kaj-åge; R., Plonsey

    1994-01-01

    In the paper, single-fibre models for the extracellular action potential are developed that will allow the potential to the evaluated at an arbitrary field point in the extracellular space. Fourier-domain models are restricted in that they evaluate potentials at equidistant points along a line...... parallel to the fibre axis. Consequently, they cannot easily evaluate the potential at the boundary nodes of a boundary-element electrode model. The Fourier-domain models employ axial-symmetric ring source models, and thereby provide higher accuracy that the line source model, where the source is lumped...... including anisotropy show that the spatial models require extreme care in the integration procedure owing to the singularity in the weighting functions. With adequate sampling, the spatial models can evaluate extracellular potentials with high accuracy....

  12. [On selection criteria in spatially distributed models of competition].

    Science.gov (United States)

    Il'ichev, V G; Il'icheva, O A

    2014-01-01

    Discrete models of competitors (initial population and mutants) are considered in which reproduction is set by increasing and concave function, and migration in the space consisting of a set of areas, is described by a Markov matrix. This allows the use of the theory of monotonous operators to study problems of selection, coexistence and stability. It is shown that the higher is the number of areas, more and more severe constraints of selective advantage to initial population are required.

  13. Model for Atmospheric Propagation of Spatially Combined Laser Beams

    Science.gov (United States)

    2016-09-01

    pp. 432–445, Aug. 2002. [10] D. R. Williams. (2016, May.). Earth fact sheet. NASA Goddard Space Flight Center . [Online]. Available: http...propagation model by generating WaveTrain results for all my test cases. I would also like to thank my wife, Amy, for supporting me throughout this journey ...The LaWS project started in 2010 [4] and was awarded to Kratos Defense and Security solutions by the Naval Surface Warfare Center . LaWS was designed

  14. An Epidemiological Model of Rift Valley Fever with Spatial Dynamics

    Directory of Open Access Journals (Sweden)

    Tianchan Niu

    2012-01-01

    Full Text Available As a category A agent in the Center for Disease Control bioterrorism list, Rift Valley fever (RVF is considered a major threat to the United States (USA. Should the pathogen be intentionally or unintentionally introduced to the continental USA, there is tremendous potential for economic damages due to loss of livestock, trade restrictions, and subsequent food supply chain disruptions. We have incorporated the effects of space into a mathematical model of RVF in order to study the dynamics of the pathogen spread as affected by the movement of humans, livestock, and mosquitoes. The model accounts for the horizontal transmission of Rift Valley fever virus (RVFV between two mosquito and one livestock species, and mother-to-offspring transmission of virus in one of the mosquito species. Space effects are introduced by dividing geographic space into smaller patches and considering the patch-to-patch movement of species. For each patch, a system of ordinary differential equations models fractions of populations susceptible to, incubating, infectious with, or immune to RVFV. The main contribution of this work is a methodology for analyzing the likelihood of pathogen establishment should an introduction occur into an area devoid of RVF. Examples are provided for general and specific cases to illustrate the methodology.

  15. Spatial-temporal data model and fractal analysis of transportation network in GIS environment

    Science.gov (United States)

    Feng, Yongjiu; Tong, Xiaohua; Li, Yangdong

    2008-10-01

    How to organize transportation data characterized by multi-time, multi-scale, multi-resolution and multi-source is one of the fundamental problems of GIS-T development. A spatial-temporal data model for GIS-T is proposed based on Spatial-temporal- Object Model. Transportation network data is systemically managed using dynamic segmentation technologies. And then a spatial-temporal database is built to integrally store geographical data of multi-time for transportation. Based on the spatial-temporal database, functions of spatial analysis of GIS-T are substantively extended. Fractal module is developed to improve the analyzing in intensity, density, structure and connectivity of transportation network based on the validation and evaluation of topologic relation. Integrated fractal with GIS-T strengthens the functions of spatial analysis and enriches the approaches of data mining and knowledge discovery of transportation network. Finally, the feasibility of the model and methods are tested thorough Guangdong Geographical Information Platform for Highway Project.

  16. A Deep Similarity Metric Learning Model for Matching Text Chunks to Spatial Entities

    Science.gov (United States)

    Ma, K.; Wu, L.; Tao, L.; Li, W.; Xie, Z.

    2017-12-01

    The matching of spatial entities with related text is a long-standing research topic that has received considerable attention over the years. This task aims at enrich the contents of spatial entity, and attach the spatial location information to the text chunk. In the data fusion field, matching spatial entities with the corresponding describing text chunks has a big range of significance. However, the most traditional matching methods often rely fully on manually designed, task-specific linguistic features. This work proposes a Deep Similarity Metric Learning Model (DSMLM) based on Siamese Neural Network to learn similarity metric directly from the textural attributes of spatial entity and text chunk. The low-dimensional feature representation of the space entity and the text chunk can be learned separately. By employing the Cosine distance to measure the matching degree between the vectors, the model can make the matching pair vectors as close as possible. Mearnwhile, it makes the mismatching as far apart as possible through supervised learning. In addition, extensive experiments and analysis on geological survey data sets show that our DSMLM model can effectively capture the matching characteristics between the text chunk and the spatial entity, and achieve state-of-the-art performance.

  17. Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models

    Directory of Open Access Journals (Sweden)

    Chuanglin Fang

    2015-11-01

    Full Text Available Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI values at the city level, and employed Ordinary Least Squares (OLS, Spatial Lag Model (SAR, and Geographically Weighted Regression (GWR to quantitatively estimate the comprehensive impact and spatial variations of China’s urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality.

  18. A model of tumor architecture and spatial interactions with tumor microenvironment in breast carcinoma

    Science.gov (United States)

    Ben Cheikh, Bassem; Bor-Angelier, Catherine; Racoceanu, Daniel

    2017-03-01

    Breast carcinomas are cancers that arise from the epithelial cells of the breast, which are the cells that line the lobules and the lactiferous ducts. Breast carcinoma is the most common type of breast cancer and can be divided into different subtypes based on architectural features and growth patterns, recognized during a histopathological examination. Tumor microenvironment (TME) is the cellular environment in which tumor cells develop. Being composed of various cell types having different biological roles, TME is recognized as playing an important role in the progression of the disease. The architectural heterogeneity in breast carcinomas and the spatial interactions with TME are, to date, not well understood. Developing a spatial model of tumor architecture and spatial interactions with TME can advance our understanding of tumor heterogeneity. Furthermore, generating histological synthetic datasets can contribute to validating, and comparing analytical methods that are used in digital pathology. In this work, we propose a modeling method that applies to different breast carcinoma subtypes and TME spatial distributions based on mathematical morphology. The model is based on a few morphological parameters that give access to a large spectrum of breast tumor architectures and are able to differentiate in-situ ductal carcinomas (DCIS) and histological subtypes of invasive carcinomas such as ductal (IDC) and lobular carcinoma (ILC). In addition, a part of the parameters of the model controls the spatial distribution of TME relative to the tumor. The validation of the model has been performed by comparing morphological features between real and simulated images.

  19. Implementation of object-oriented GIS data model with topological relations between spatial objects

    Science.gov (United States)

    Chen, Youliang; Wang, Zhaoru; Chen, Zhicheng

    2013-03-01

    Traditional GIS (Geographical Information System) data models are focused on the description of the data organizational structure and constraints from single aspect, which are the absence or little relevance of the hierarchy and connotation of data objects. These data models do not match the natural concept of humans about geographical spatial data and also failed to fully consider the spatial relationships and arithmetic operations with the relationship between geographic objects. Object-oriented technology mimics the human way of thinking as much as possible, it appears to overcome the shortcomings of traditional software design methods and improve the stability and reusability of the software systems. Based on the existing GIS data model, this paper introduces the basic idea of object-oriented GIS data model combined the object-oriented methods and the vectorial expression of spatial entities, and describes the definition and implementation of the object-oriented GIS data model. Furthermore, the topological operations between spatial objects have been defined in the paper for the importance of topological relationships among spatial relationships. The topological operations are directly defined in geometry classes by means of methods, and they include Touch, Disjoint, Cross, Contains, Overlaps, and Intersects and so on.

  20. A national fine spatial scale land-use regression model for ozone

    NARCIS (Netherlands)

    Kerckhoffs, Jules|info:eu-repo/dai/nl/411260502; Wang, Meng|info:eu-repo/dai/nl/345480279; Meliefste, Kees; Malmqvist, Ebba; Fischer, Paul; Janssen, Nicole A H; Beelen, Rob|info:eu-repo/dai/nl/30483100X; Hoek, Gerard|info:eu-repo/dai/nl/069553475

    Uncertainty about health effects of long-term ozone exposure remains. Land use regression (LUR) models have been used successfully for modeling fine scale spatial variation of primary pollutants but very limited for ozone. Our objective was to assess the feasibility of developing a national LUR

  1. Bayesian prediction of spatial count data using generalized linear mixed models

    DEFF Research Database (Denmark)

    Christensen, Ole Fredslund; Waagepetersen, Rasmus Plenge

    2002-01-01

    Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, ...

  2. Modeling hydraulic conductivity of a complex confining layer at various spatial scales

    NARCIS (Netherlands)

    Bierkens, M.F.P.

    1996-01-01

    The spatial variation of the hydraulic conductivity of a complex confining layer in the Netherlands was modelled on four scales: sediment cores, numerical model blocks, and local and regional scales. Two stochastic methods were combined: conditional simulation of texture classes to obtain

  3. Evaluating critical uncertainty thresholds in a spatial model of forest pest invasion risk

    Science.gov (United States)

    Frank H. Koch; Denys Yemshanov; Daniel W. McKenney; William D. Smith

    2009-01-01

    Pest risk maps can provide useful decision support in invasive species management, but most do not adequately consider the uncertainty associated with predicted risk values. This study explores how increased uncertainty in a risk model’s numeric assumptions might affect the resultant risk map. We used a spatial stochastic model, integrating components for...

  4. Bayesian prediction of spatial count data using generalized linear mixed models

    DEFF Research Database (Denmark)

    Christensen, Ole Fredslund; Waagepetersen, Rasmus Plenge

    2002-01-01

    Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, we...

  5. Modelling spatial patterns of erosion in the West Usambara Mountains of Tanzania

    NARCIS (Netherlands)

    Vigiak, O.

    2005-01-01

    Prompt location of sources and sinks of sediment within a catchment would allow more effective Soil and Water Conservation (SWC) planning. Distributed erosion models are valuable tools for watershed planning, but the quality of spatially distributed model predictions is seriously hampered by the

  6. GLOBOX : A spatially differentiated global fate, intake and effect model for toxicity assessment in LCA

    NARCIS (Netherlands)

    Wegener Sleeswijk, Anneke; Heijungs, Reinout

    GLOBOX is a model for the calculation of spatially differentiated LCA toxicity characterisation factors on a global scale. It can also be used for human and environmental risk assessment. The GLOBOX model contains equations for the calculation of fate, intake and effect factors, and equations for

  7. Fundamental Frequency and Model Order Estimation Using Spatial Filtering

    DEFF Research Database (Denmark)

    Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll

    2014-01-01

    In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal...... extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment...... on a trumpet signal show the applicability on real signals....

  8. Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador

    Science.gov (United States)

    Chicaiza, E. G.; Leiva, C. A.; Arranz, J. J.; Buenańo, X. E.

    2017-06-01

    Geostatistics is a discipline that deals with the statistical analysis of regionalized variables. In this case study, geostatistics is used to estimate geoid undulation in the rural area of Guayaquil town in Ecuador. The geostatistical approach was chosen because the estimation error of prediction map is getting. Open source statistical software R and mainly geoR, gstat and RGeostats libraries were used. Exploratory data analysis (EDA), trend and structural analysis were carried out. An automatic model fitting by Iterative Least Squares and other fitting procedures were employed to fit the variogram. Finally, Kriging using gravity anomaly of Bouguer as external drift and Universal Kriging were used to get a detailed map of geoid undulation. The estimation uncertainty was reached in the interval [-0.5; +0.5] m for errors and a maximum estimation standard deviation of 2 mm in relation with the method of interpolation applied. The error distribution of the geoid undulation map obtained in this study provides a better result than Earth gravitational models publicly available for the study area according the comparison with independent validation points. The main goal of this paper is to confirm the feasibility to use geoid undulations from Global Navigation Satellite Systems and leveling field measurements and geostatistical techniques methods in order to use them in high-accuracy engineering projects.

  9. Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador

    Directory of Open Access Journals (Sweden)

    Chicaiza E.G.

    2017-06-01

    Full Text Available Geostatistics is a discipline that deals with the statistical analysis of regionalized variables. In this case study, geostatistics is used to estimate geoid undulation in the rural area of Guayaquil town in Ecuador. The geostatistical approach was chosen because the estimation error of prediction map is getting. Open source statistical software R and mainly geoR, gstat and RGeostats libraries were used. Exploratory data analysis (EDA, trend and structural analysis were carried out. An automatic model fitting by Iterative Least Squares and other fitting procedures were employed to fit the variogram. Finally, Kriging using gravity anomaly of Bouguer as external drift and Universal Kriging were used to get a detailed map of geoid undulation. The estimation uncertainty was reached in the interval [-0.5; +0.5] m for errors and a maximum estimation standard deviation of 2 mm in relation with the method of interpolation applied. The error distribution of the geoid undulation map obtained in this study provides a better result than Earth gravitational models publicly available for the study area according the comparison with independent validation points. The main goal of this paper is to confirm the feasibility to use geoid undulations from Global Navigation Satellite Systems and leveling field measurements and geostatistical techniques methods in order to use them in high-accuracy engineering projects.

  10. Evaluation of Spatial Uncertainties In Modeling of Cadastral Systems

    Science.gov (United States)

    Fathi, Morteza; Teymurian, Farideh

    2013-04-01

    Cadastre plays an essential role in sustainable development especially in developing countries like Iran. A well-developed Cadastre results in transparency of estates tax system, transparency of data of estate, reduction of action before the courts and effective management of estates and natural sources and environment. Multipurpose Cadastre through gathering of other related data has a vital role in civil, economic and social programs and projects. Iran is being performed Cadastre for many years but success in this program is subject to correct geometric and descriptive data of estates. Since there are various sources of data with different accuracy and precision in Iran, some difficulties and uncertainties are existed in modeling of geometric part of Cadastre such as inconsistency between data in deeds and Cadastral map which cause some troubles in execution of cadastre and result in losing national and natural source, rights of nation. Now there is no uniform and effective technical method for resolving such conflicts. This article describes various aspects of such conflicts in geometric part of cadastre and suggests a solution through some modeling tools of GIS.

  11. Predictive spatial modelling for mapping soil salinity at continental scale

    Science.gov (United States)

    Bui, Elisabeth; Wilford, John; de Caritat, Patrice

    2017-04-01

    Soil salinity is a serious limitation to agriculture and one of the main causes of land degradation. Soil is considered saline if its electrical conductivity (EC) is > 4 dS/m. Maps of saline soil distribution are essential for appropriate land development. Previous attempts to map soil salinity over extensive areas have relied on satellite imagery, aerial electromagnetic (EM) and/or proximally sensed EM data; other environmental (climate, topographic, geologic or soil) datasets are generally not used. Having successfully modelled and mapped calcium carbonate distribution over the 0-80 cm depth in Australian soils using machine learning with point samples from the National Geochemical Survey of Australia (NGSA), we took a similar approach to map soil salinity at 90-m resolution over the continent. The input data were the EC1:5 measurements on the soil and lithology classes, geophysics, and MODIS vegetation indices extracted at the same locations which were used as predictors in decision tree models. The machine learning software 'Cubist' (www.rulequest.com) was used as the inference engine for the modelling, a 90:10 training:test set data split was used to validate results, and 100 randomly sampled trees were built using the training data. The results were good with an average internal correlation (r) of 0.88 between predicted and measured logEC1:5 (training data), an average external correlation of 0.48 (test subset), and a Lin's concordance correlation coefficient (which evaluates the 1:1 fit) of 0.61. Therefore, the rules derived were mapped and the mean prediction for each 90-m pixel was used for the final logEC1:5 map. This is the most detailed picture of soil salinity over Australia since the 2001 National Land and Water Resources Audit and is generally consistent with it. Our map will be useful as a baseline salinity map circa 2008, when the NGSA samples were collected, for future State of the Environment reports.

  12. A spatial mean-variance MIP model for energy market risk analysis

    International Nuclear Information System (INIS)

    Yu, Zuwei

    2003-01-01

    The paper presents a short-term market risk model based on the Markowitz mean-variance method for spatial electricity markets. The spatial nature is captured using the correlation of geographically separated markets and the consideration of wheeling administration. The model also includes transaction costs and other practical constraints, resulting in a mixed integer programming (MIP) model. The incorporation of those practical constraints makes the model more attractive than the traditional Markowitz portfolio model with continuity. A case study is used to illustrate the practical application of the model. The results show that the MIP portfolio efficient frontier is neither smooth nor concave. The paper also considers the possible extension of the model to other energy markets, including natural gas and oil markets

  13. A spatial mean-variance MIP model for energy market risk analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zuwei Yu [Purdue University, West Lafayette, IN (United States). Indiana State Utility Forecasting Group and School of Industrial Engineering

    2003-05-01

    The paper presents a short-term market risk model based on the Markowitz mean-variance method for spatial electricity markets. The spatial nature is captured using the correlation of geographically separated markets and the consideration of wheeling administration. The model also includes transaction costs and other practical constraints, resulting in a mixed integer programming (MIP) model. The incorporation of those practical constraints makes the model more attractive than the traditional Markowitz portfolio model with continuity. A case study is used to illustrate the practical application of the model. The results show that the MIP portfolio efficient frontier is neither smooth nor concave. The paper also considers the possible extension of the model to other energy markets, including natural gas and oil markets. (author)

  14. A spatial mean-variance MIP model for energy market risk analysis

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Zuwei [Indiana State Utility Forecasting Group and School of Industrial Engineering, Purdue University, Room 334, 1293 A.A. Potter, West Lafayette, IN 47907 (United States)

    2003-05-01

    The paper presents a short-term market risk model based on the Markowitz mean-variance method for spatial electricity markets. The spatial nature is captured using the correlation of geographically separated markets and the consideration of wheeling administration. The model also includes transaction costs and other practical constraints, resulting in a mixed integer programming (MIP) model. The incorporation of those practical constraints makes the model more attractive than the traditional Markowitz portfolio model with continuity. A case study is used to illustrate the practical application of the model. The results show that the MIP portfolio efficient frontier is neither smooth nor concave. The paper also considers the possible extension of the model to other energy markets, including natural gas and oil markets.

  15. A spatial mean-variance MIP model for energy market risk analysis

    International Nuclear Information System (INIS)

    Zuwei Yu

    2003-01-01

    The paper presents a short-term market risk model based on the Markowitz mean-variance method for spatial electricity markets. The spatial nature is captured using the correlation of geographically separated markets and the consideration of wheeling administration. The model also includes transaction costs and other practical constraints, resulting in a mixed integer programming (MIP) model. The incorporation of those practical constraints makes the model more attractive than the traditional Markowitz portfolio model with continuity. A case study is used to illustrate the practical application of the model. The results show that the MIP portfolio efficient frontier is neither smooth nor concave. The paper also considers the possible extension of the model to other energy markets, including natural gas and oil markets. (author)

  16. Computation of spatial significance of mountain objects extracted from multiscale digital elevation models

    International Nuclear Information System (INIS)

    Sathyamoorthy, Dinesh

    2014-01-01

    The derivation of spatial significance is an important aspect of geospatial analysis and hence, various methods have been proposed to compute the spatial significance of entities based on spatial distances with other entities within the cluster. This paper is aimed at studying the spatial significance of mountain objects extracted from multiscale digital elevation models (DEMs). At each scale, the value of spatial significance index SSI of a mountain object is the minimum number of morphological dilation iterations required to occupy all the other mountain objects in the terrain. The mountain object with the lowest value of SSI is the spatially most significant mountain object, indicating that it has the shortest distance to the other mountain objects. It is observed that as the area of the mountain objects reduce with increasing scale, the distances between the mountain objects increase, resulting in increasing values of SSI. The results obtained indicate that the strategic location of a mountain object at the centre of the terrain is more important than its size in determining its reach to other mountain objects and thus, its spatial significance

  17. Fourier decomposition of spatial localization errors reveals an idiotropic dominance of an internal model of gravity.

    Science.gov (United States)

    De Sá Teixeira, Nuno Alexandre

    2014-12-01

    Given its conspicuous nature, gravity has been acknowledged by several research lines as a prime factor in structuring the spatial perception of one's environment. One such line of enquiry has focused on errors in spatial localization aimed at the vanishing location of moving objects - it has been systematically reported that humans mislocalize spatial positions forward, in the direction of motion (representational momentum) and downward in the direction of gravity (representational gravity). Moreover, spatial localization errors were found to evolve dynamically with time in a pattern congruent with an anticipated trajectory (representational trajectory). The present study attempts to ascertain the degree to which vestibular information plays a role in these phenomena. Human observers performed a spatial localization task while tilted to varying degrees and referring to the vanishing locations of targets moving along several directions. A Fourier decomposition of the obtained spatial localization errors revealed that although spatial errors were increased "downward" mainly along the body's longitudinal axis (idiotropic dominance), the degree of misalignment between the latter and physical gravity modulated the time course of the localization responses. This pattern is surmised to reflect increased uncertainty about the internal model when faced with conflicting cues regarding the perceived "downward" direction.

  18. Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges.

    Science.gov (United States)

    White, Lauren A; Forester, James D; Craft, Meggan E

    2017-09-25

    Individual differences in contact rate can arise from host, group and landscape heterogeneity and can result in different patterns of spatial spread for diseases in wildlife populations with concomitant implications for disease control in wildlife of conservation concern, livestock and humans. While dynamic disease models can provide a better understanding of the drivers of spatial spread, the effects of landscape heterogeneity have only been modelled in a few well-studied wildlife systems such as rabies and bovine tuberculosis. Such spatial models tend to be either purely theoretical with intrinsic limiting assumptions or individual-based models that are often highly species- and system-specific, limiting the breadth of their utility. Our goal was to review studies that have utilized dynamic, spatial models to answer questions about pathogen transmission in wildlife and identify key gaps in the literature. We begin by providing an overview of the main types of dynamic, spatial models (e.g., metapopulation, network, lattice, cellular automata, individual-based and continuous-space) and their relation to each other. We investigate different types of ecological questions that these models have been used to explore: pathogen invasion dynamics and range expansion, spatial heterogeneity and pathogen persistence, the implications of management and intervention strategies and the role of evolution in host-pathogen dynamics. We reviewed 168 studies that consider pathogen transmission in free-ranging wildlife and classify them by the model type employed, the focal host-pathogen system, and their overall research themes and motivation. We observed a significant focus on mammalian hosts, a few well-studied or purely theoretical pathogen systems, and a lack of studies occurring at the wildlife-public health or wildlife-livestock interfaces. Finally, we discuss challenges and future directions in the context of unprecedented human-mediated environmental change. Spatial models

  19. Spatial interaction models facility location using game theory

    CERN Document Server

    D'Amato, Egidio; Pardalos, Panos

    2017-01-01

    Facility location theory develops the idea of locating one or more facilities by optimizing suitable criteria such as minimizing transportation cost, or capturing the largest market share. The contributions in this book focus an approach to facility location theory through game theoretical tools highlighting situations where a location decision is faced by several decision makers and leading to a game theoretical framework in non-cooperative and cooperative methods. Models and methods regarding the facility location via game theory are explored and applications are illustrated through economics, engineering, and physics. Mathematicians, engineers, economists and computer scientists working in theory, applications and computational aspects of facility location problems using game theory will find this book useful.

  20. Fuzzy modeling with spatial information for geographic problems

    CERN Document Server

    Petry, Frederick E; Cobb, Maria A

    2005-01-01

    The capabilities of modern technology are rapidly increasing, spurred on to a large extent by the tremendous advances in communications and computing. Automated vehicles and global wireless connections are some examples of these advances. In order to take advantage of such enhanced capabilities, our need to model and manipulate our knowledge of the geophysical world, using compatible representations, is also rapidly increasing. In response to this one fundamental issue of great concern in modern geographical research is how to most effectively capture the physical world around us in systems like geographical information systems (GIS). Making this task even more challenging is the fact that uncertainty plays a pervasive role in the representation, analysis and use of geospatial information. The types of uncertainty that appear in geospatial information systems are not the just simple randomness of observation, as in weather data, but are manifested in many other forms including imprecision, incompleteness and ...

  1. A spatial model to predict the incidence of neural tube defects

    Directory of Open Access Journals (Sweden)

    Li Lianfa

    2012-11-01

    Full Text Available Abstract Background Environmental exposure may play an important role in the incidences of neural tube defects (NTD of birth defects. Their influence on NTD may likely be non-linear; few studies have considered spatial autocorrelation of residuals in the estimation of NTD risk. We aimed to develop a spatial model based on generalized additive model (GAM plus cokriging to examine and model the expected incidences of NTD and make the inference of the incidence risk. Methods We developed a spatial model to predict the expected incidences of NTD at village level in Heshun County, Shanxi Province, China, a region with high NTD cases. GAM was used to establish linear and non-linear relationships between local covariates and the expected NTD incidences. We examined the following village-level covariates in the model: projected coordinates, soil types, lithodological classes, distance to watershed, rivers, faults and major roads, annual average fertilizer uses, fruit and vegetable production, gross domestic product, and the number of doctors. The residuals from GAM were assumed to be spatially auto-correlative and cokriged with regional residuals to improve the prediction. Our approach was compared with three other models, universal kriging, generalized linear regression and GAM. Cross validation was conducted for validation. Results Our model predicted the expected incidences of NTD well, with a good CV R2 of 0.80. Important predictive factors included the fertilizer uses, locations of the centroid of each village, the shortest distance to rivers and faults and lithological classes with significant spatial autocorrelation of residuals. Our model out-performed the other three methods by 16% or more in term of R2. Conclusions The variance explained by our model was approximately 80%. This modeling approach is useful for NTD epidemiological studies and intervention planning.

  2. Quantifying spatial disparities in neonatal mortality using a structured additive regression model.

    Directory of Open Access Journals (Sweden)

    Lawrence N Kazembe

    Full Text Available BACKGROUND: Neonatal mortality contributes a large proportion towards early childhood mortality in developing countries, with considerable geographical variation at small areas within countries. METHODS: A geo-additive logistic regression model is proposed for quantifying small-scale geographical variation in neonatal mortality, and to estimate risk factors of neonatal mortality. Random effects are introduced to capture spatial correlation and heterogeneity. The spatial correlation can be modelled using the Markov random fields (MRF when data is aggregated, while the two dimensional P-splines apply when exact locations are available, whereas the unstructured spatial effects are assigned an independent Gaussian prior. Socio-economic and bio-demographic factors which may affect the risk of neonatal mortality are simultaneously estimated as fixed effects and as nonlinear effects for continuous covariates. The smooth effects of continuous covariates are modelled by second-order random walk priors. Modelling and inference use the empirical Bayesian approach via penalized likelihood technique. The methodology is applied to analyse the likelihood of neonatal deaths, using data from the 2000 Malawi demographic and health survey. The spatial effects are quantified through MRF and two dimensional P-splines priors. RESULTS: Findings indicate that both fixed and spatial effects are associated with neonatal mortality. CONCLUSIONS: Our study, therefore, suggests that the challenge to reduce neonatal mortality goes beyond addressing individual factors, but also require to understanding unmeasured covariates for potential effective interventions.

  3. Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases

    Directory of Open Access Journals (Sweden)

    Jean-Marie Aerts

    2012-11-01

    Full Text Available The gravity models are commonly used spatial interaction models. They have been widely applied in a large set of domains dealing with interactions amongst spatial entities. The spread of vector-borne diseases is also related to the intensity of interaction between spatial entities, namely, the physical habitat of pathogens’ vectors and/or hosts, and urban areas, thus humans. This study implements the concept behind gravity models in the spatial spread of two vector-borne diseases, nephropathia epidemica and Lyme borreliosis, based on current knowledge on the transmission mechanism of these diseases. Two sources of information on vegetated systems were tested: the CORINE land cover map and MODIS NDVI. The size of vegetated areas near urban centers and a local indicator of occupation-related exposure were found significant predictors of disease risk. Both the land cover map and the space-borne dataset were suited yet not equivalent input sources to locate and measure vegetated areas of importance for disease spread. The overall results point at the compatibility of the gravity model concept and the spatial spread of vector-borne diseases.

  4. A spatial multi-objective optimization model for sustainable urban wastewater system layout planning.

    Science.gov (United States)

    Dong, X; Zeng, S; Chen, J

    2012-01-01

    Design of a sustainable city has changed the traditional centralized urban wastewater system towards a decentralized or clustering one. Note that there is considerable spatial variability of the factors that affect urban drainage performance including urban catchment characteristics. The potential options are numerous for planning the layout of an urban wastewater system, which are associated with different costs and local environmental impacts. There is thus a need to develop an approach to find the optimal spatial layout for collecting, treating, reusing and discharging the municipal wastewater of a city. In this study, a spatial multi-objective optimization model, called Urban wastewateR system Layout model (URL), was developed. It is solved by a genetic algorithm embedding Monte Carlo sampling and a series of graph algorithms. This model was illustrated by a case study in a newly developing urban area in Beijing, China. Five optimized system layouts were recommended to the local municipality for further detailed design.

  5. Analysis of the impact of immigration on labour market using spatial models

    Science.gov (United States)

    Polonyankina, Tatiana

    2017-07-01

    This paper investigates the impact of immigration on employment and unemployment of a host country. The question to answer is: How does employment/unemployment in the host country change after an increase in number of immigrants? The analysis is taking into account only legal immigrants in recession period. The model is combining classical regression of cross-sectional data with spatial econometrics models where cross-section dependencies are captured by a spatial matrix. The intention is by using spatial models analyse the sensitivity of employment/unemployment rate on change in a share of immigration in a region. The used panel data are based on the Labour force survey and on available macro data in Eurostat for 3 European countries (Germany, Austria and Czech Republic) grouped into cells by NUTS regions in a recession period.

  6. Ontogenetic study of the supraorbital region in modern humans: a longitudinal test of the spatial model.

    Science.gov (United States)

    Fiscella, Gabriela N; Smith, Fred H

    2006-06-01

    The structural significance of the hominid supraorbital torus and its morphological variation have always been a controversial topic in physical anthropology. Understanding the function of browridge variation in living and fossil human populations is relevant to questions of human evolution. This study utilizes radiograph images to evaluate the spatial model in modern humans during ontogeny. This structural model attributes variation in the supraorbital region to the positional relationship between the neurocranium and the orbits. The relationship between measurements of the antero-posterior supraorbital length and the factors specified in the spatial model were assessed by correlation and partial correlation analyses. Growth rates were also examined to study ontogenetic trajectories and infer aspects of developmental relationships between critical variables. Results agree with previous research supporting the existence of spatial influences between the neural and orbital-upper facial regions on browridge length during ontogeny.

  7. Modeling Alveolar Epithelial Cell Behavior In Spatially Designed Hydrogel Microenvironments

    Science.gov (United States)

    Lewis, Katherine Jean Reeder

    . Aggregate formation in these microwells motivated us to develop a templating technique to create hollow cyst-like epithelial structures within PEG hydrogels. Photodegradable microspheres were used to form spherical epithelial layers, which were then encapsulated in a PEG hydrogel followed by template erosion with cytocompatible light. With these model alveoli, we investigated the interplay between the epithelium and mesenchyme by co-encapsulating healthy and diseased pulmonary fibroblasts with healthy and diseased epithelial cysts and measuring important cellular behaviors (i.e. proliferation, migration, and protein expression). This model of alveolar tissue represents a significant advance in culture platforms available to researchers interested in identifying the mechanisms involved in disease progression and for testing potential therapeutics in a controlled, tissue-appropriate setting.

  8. Scaling precipitation input to spatially distributed hydrological models by measured snow distribution

    Directory of Open Access Journals (Sweden)

    Christian Vögeli

    2016-12-01

    Full Text Available Accurate knowledge on snow distribution in alpine terrain is crucial for various applicationssuch as flood risk assessment, avalanche warning or managing water supply and hydro-power.To simulate the seasonal snow cover development in alpine terrain, the spatially distributed,physics-based model Alpine3D is suitable. The model is typically driven by spatial interpolationsof observations from automatic weather stations (AWS, leading to errors in the spatial distributionof atmospheric forcing. With recent advances in remote sensing techniques, maps of snowdepth can be acquired with high spatial resolution and accuracy. In this work, maps of the snowdepth distribution, calculated from summer and winter digital surface models based on AirborneDigital Sensors (ADS, are used to scale precipitation input data, with the aim to improve theaccuracy of simulation of the spatial distribution of snow with Alpine3D. A simple method toscale and redistribute precipitation is presented and the performance is analysed. The scalingmethod is only applied if it is snowing. For rainfall the precipitation is distributed by interpolation,with a simple air temperature threshold used for the determination of the precipitation phase.It was found that the accuracy of spatial snow distribution could be improved significantly forthe simulated domain. The standard deviation of absolute snow depth error is reduced up toa factor 3.4 to less than 20 cm. The mean absolute error in snow distribution was reducedwhen using representative input sources for the simulation domain. For inter-annual scaling, themodel performance could also be improved, even when using a remote sensing dataset from adifferent winter. In conclusion, using remote sensing data to process precipitation input, complexprocesses such as preferential snow deposition and snow relocation due to wind or avalanches,can be substituted and modelling performance of spatial snow distribution is improved.

  9. Fiscal Decentralization and Regional Financial Efficiency: An Empirical Analysis of Spatial Durbin Model

    Directory of Open Access Journals (Sweden)

    Jianmin Liu

    2016-01-01

    Full Text Available Based on panel data covering the period from 2003 to 2012 in China’s 281 prefecture-level cities, we use superefficiency SBM model to measure regional financial efficiency and empirically test the spatial effects of fiscal decentralization on regional financial efficiency with SDM. The estimated results indicate that there exist significant spatial spillover effects among regional financial efficiency with the features of time inertia and spatial dependence. The positive promoting effect of fiscal decentralization on financial efficiency in local region depends on the symmetry between fiscal expenditure decentralization and revenue decentralization. Additionally, there exists inconsistency in the spatial effects of fiscal expenditure decentralization and revenue decentralization on financial efficiency in neighboring regions. The negative effect of fiscal revenue decentralization on financial efficiency in neighboring regions is more significant than that of fiscal expenditure decentralization.

  10. A state-and-transition model of Iberian dehesas based on spatial patterns

    Directory of Open Access Journals (Sweden)

    Jesús Caballero

    2015-08-01

    Full Text Available Aim of study: In this study a process based on a Geographic Information System (GIS is proposed as a tool for analyzing the spatial structure of the Mediterranean dehesa ecosystem. Area of study: Western Peninsular Spain. Material and methods: The method allows a semi-quantitative “state and transitions” (S&T net that provides original information derived from spatial patterns that cannot be obtained by other means. Main results: This GIS analysis also supplies a spatial basis which complements the conceptual framework of vegetation series successional proposals. Moreover, the proposed method can be a useful tool for understanding landscape changes. Research highlights: Our work proposes not only a descriptive model but also a methodology based on spatial metrics and topological relationships.

  11. Modelling the distribution of fish accounting for spatial correlation and overdispersion

    DEFF Research Database (Denmark)

    Lewy, Peter; Kristensen, Kasper

    2009-01-01

    The spatial distribution of cod (Gadus morhua) in the North Sea and the Skagerrak was analysed over a 24-year period using the Log Gaussian Cox Process (LGCP). In contrast to other spatial models of the distribution of fish, LGCP avoids problems with zero observations and includes the spatial...... correlation between observations. It is therefore possible to predict and interpolate unobserved densities at any location in the area. This is important for obtaining unbiased estimates of stock concentration and other measures depending on the distribution in the entire area. Results show that the spatial...... correlation and dispersion of cod catches remained unchanged during winter throughout the period, in spite of a drastic decline in stock abundance and a movement of the centre of gravity of the distribution towards the northeast in the same period. For the age groups considered, the concentration of the stock...

  12. Applying Spatially Distributed Rainfall to a Hydrological Model in a Tropical Watershed, Manoa Watershed, in Hawaii

    Science.gov (United States)

    Huang, Y. F.; Tsang, Y. P.

    2017-12-01

    Rainfall in Hawaii is characterized with high spatial and temporal variability. In the south side of Oahu, the Manoa watershed, with an area of 11 km2, has the annual maximum rainfall of 3900mm and the minimum rainfall of 1000 mm. Despite this high spatial heterogeneity, the rain gage network seems insufficiently capture this pattern. When simulating stream flow and predicting floods with hydrological models in Hawaii, the model performance is often unsatisfactory because of inadequate representation of rainfall data. Longman et al. (in prep.) have developed the spatially distributed daily rainfall across the Hawaiian Islands by applying ordinary kriging, yet these data have not been applied to hydrological models. In this study, we used the Soil and Water Assessment Tool (SWAT) model to assess the streamflow simulation by applying spatially-distributed rainfall in the Manoa watershed. We first used point daily-rainfall at Lyon Arboretum from National Center of Environmental Information (NCEI) as the uniform rainfall input. Secondly, we summarized sub-watershed mean rainfall from the daily spatial-statistical rainfall. Both rainfall data are available from 1999 to 2014. The SWAT was set up for five-year warm-up, nine-year calibration, and two-year validation. The model parameters were calibrated and validated with four U.S. Geological Survey stream gages. We compared the calibrated watershed parameters, characteristics, and assess the streamflow hydrographs from these two rainfall inputs. The differences and improvement of using spatially distributed rainfall input in SWAT were discussed. In addition to improving the model by the representation of rainfall, this study helped us having a better understanding of the watershed hydrological response in Hawaii.

  13. Variability in results from negative binomial models for Lyme disease measured at different spatial scales.

    Science.gov (United States)

    Tran, Phoebe; Waller, Lance

    2015-01-01

    Lyme disease has been the subject of many studies due to increasing incidence rates year after year and the severe complications that can arise in later stages of the disease. Negative binomial models have been used to model Lyme disease in the past with some success. However, there has been little focus on the reliability and consistency of these models when they are used to study Lyme disease at multiple spatial scales. This study seeks to explore how sensitive/consistent negative binomial models are when they are used to study Lyme disease at different spatial scales (at the regional and sub-regional levels). The study area includes the thirteen states in the Northeastern United States with the highest Lyme disease incidence during the 2002-2006 period. Lyme disease incidence at county level for the period of 2002-2006 was linked with several previously identified key landscape and climatic variables in a negative binomial regression model for the Northeastern region and two smaller sub-regions (the New England sub-region and the Mid-Atlantic sub-region). This study found that negative binomial models, indeed, were sensitive/inconsistent when used at different spatial scales. We discuss various plausible explanations for such behavior of negative binomial models. Further investigation of the inconsistency and sensitivity of negative binomial models when used at different spatial scales is important for not only future Lyme disease studies and Lyme disease risk assessment/management but any study that requires use of this model type in a spatial context. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. Comparing spatial diversification and meta-population models in the Indo-Australian Archipelago.

    Science.gov (United States)

    Chalmandrier, Loïc; Albouy, Camille; Descombes, Patrice; Sandel, Brody; Faurby, Soren; Svenning, Jens-Christian; Zimmermann, Niklaus E; Pellissier, Loïc

    2018-03-01

    Reconstructing the processes that have shaped the emergence of biodiversity gradients is critical to understand the dynamics of diversification of life on Earth. Islands have traditionally been used as model systems to unravel the processes shaping biological diversity. MacArthur and Wilson's island biogeographic model predicts diversity to be based on dynamic interactions between colonization and extinction rates, while treating islands themselves as geologically static entities. The current spatial configuration of islands should influence meta-population dynamics, but long-term geological changes within archipelagos are also expected to have shaped island biodiversity, in part by driving diversification. Here, we compare two mechanistic models providing inferences on species richness at a biogeographic scale: a mechanistic spatial-temporal model of species diversification and a spatial meta-population model. While the meta-population model operates over a static landscape, the diversification model is driven by changes in the size and spatial configuration of islands through time. We compare the inferences of both models to floristic diversity patterns among land patches of the Indo-Australian Archipelago. Simulation results from the diversification model better matched observed diversity than a meta-population model constrained only by the contemporary landscape. The diversification model suggests that the dynamic re-positioning of islands promoting land disconnection and reconnection induced an accumulation of particularly high species diversity on Borneo, which is central within the island network. By contrast, the meta-population model predicts a higher diversity on the mainlands, which is less compatible with empirical data. Our analyses highlight that, by comparing models with contrasting assumptions, we can pinpoint the processes that are most compatible with extant biodiversity patterns.

  15. Lattice Three-Species Models of the Spatial Spread of Rabies among FOXES

    Science.gov (United States)

    Benyoussef, A.; Boccara, N.; Chakib, H.; Ez-Zahraouy, H.

    Lattice models describing the spatial spread of rabies among foxes are studied. In these models, the fox population is divided into three-species: susceptible (S), infected or incubating (I), and infectious or rabid (R). They are based on the fact that susceptible and incubating foxes are territorial while rabid foxes have lost their sense of direction and move erratically. Two different models are investigated: a one-dimensional coupled-map lattice model, and a two-dimensional automata network model. Both models take into account the short-range character of the infection process and the diffusive motion of rabid foxes. Numerical simulations show how the spatial distribution of rabies, and the speed of propagation of the epizootic front depend upon the carrying capacity of the environment and diffusion of rabid foxes out of their territory.

  16. Automating an integrated spatial data-mining model for landfill site selection

    Science.gov (United States)

    Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Aziz, Hamidi Abdul

    2017-10-01

    An integrated programming environment represents a robust approach to building a valid model for landfill site selection. One of the main challenges in the integrated model is the complicated processing and modelling due to the programming stages and several limitations. An automation process helps avoid the limitations and improve the interoperability between integrated programming environments. This work targets the automation of a spatial data-mining model for landfill site selection by integrating between spatial programming environment (Python-ArcGIS) and non-spatial environment (MATLAB). The model was constructed using neural networks and is divided into nine stages distributed between Matlab and Python-ArcGIS. A case study was taken from the north part of Peninsular Malaysia. 22 criteria were selected to utilise as input data and to build the training and testing datasets. The outcomes show a high-performance accuracy percentage of 98.2% in the testing dataset using 10-fold cross validation. The automated spatial data mining model provides a solid platform for decision makers to performing landfill site selection and planning operations on a regional scale.

  17. Spatial Durbin Model (SDM For Identified Influence Dengue Hemorrhagic Fever Factors in Kabupaten Malang

    Directory of Open Access Journals (Sweden)

    Indah Resti Ayuni Suri

    2012-05-01

    Full Text Available Dengue Hemorrhagic Fever or usually populer call DBD (Demam Berdarah Degue is the cronic desease that caused by virus infection who carry by Aedes Aegypti mousquito. The observation act by DBD descriptioning and some factors territorial view that influence them, also DBD’s modeling use Spatial Durbin Model (SDM. SDM is the particullary case from Spatial Autoregresive Model (SAR, it means modeling with spatial lag at dependen variable and independen variable. This observation use ratio DBD invectors amount with population amount of citizenry at Kabupaten Malang in 2009. Some variable was used, those are the precentation of existention free number embrio, ratio of civil amount between family, procentation of healthy clinic between invectors and procentase of the invectors who taking care by medical help with amount of invectors. The fourth variables are independen variable to ratio of DBD invector amount with population of citizenry amount, as dependen variable trough spatial SDM modelling. The result of SDM parameter modelling, the significant influence variable in session % is the procentation of free amount embrio existention from their own district, the procentation of healthy clinic amount with the DBD invector amount from their own district, the ratio of the population of citizenry with the family from their neighborhood district, and the procentation of healthy clinic amount with the DBD invector amount from their neighborhood district.

  18. Transport lattice models of heat transport in skin with spatially heterogeneous, temperature-dependent perfusion

    Directory of Open Access Journals (Sweden)

    Martin Gregory T

    2004-11-01

    Full Text Available Abstract Background Investigation of bioheat transfer problems requires the evaluation of temporal and spatial distributions of temperature. This class of problems has been traditionally addressed using the Pennes bioheat equation. Transport of heat by conduction, and by temperature-dependent, spatially heterogeneous blood perfusion is modeled here using a transport lattice approach. Methods We represent heat transport processes by using a lattice that represents the Pennes bioheat equation in perfused tissues, and diffusion in nonperfused regions. The three layer skin model has a nonperfused viable epidermis, and deeper regions of dermis and subcutaneous tissue with perfusion that is constant or temperature-dependent. Two cases are considered: (1 surface contact heating and (2 spatially distributed heating. The model is relevant to the prediction of the transient and steady state temperature rise for different methods of power deposition within the skin. Accumulated thermal damage is estimated by using an Arrhenius type rate equation at locations where viable tissue temperature exceeds 42°C. Prediction of spatial temperature distributions is also illustrated with a two-dimensional model of skin created from a histological image. Results The transport lattice approach was validated by comparison with an analytical solution for a slab with homogeneous thermal properties and spatially distributed uniform sink held at constant temperatures at the ends. For typical transcutaneous blood gas sensing conditions the estimated damage is small, even with prolonged skin contact to a 45°C surface. Spatial heterogeneity in skin thermal properties leads to a non-uniform temperature distribution during a 10 GHz electromagnetic field exposure. A realistic two-dimensional model of the skin shows that tissue heterogeneity does not lead to a significant local temperature increase when heated by a hot wire tip. Conclusions The heat transport system model of the

  19. 'spup' - an R package for uncertainty propagation analysis in spatial environmental modelling

    Science.gov (United States)

    Sawicka, Kasia; Heuvelink, Gerard

    2017-04-01

    Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Currently, advances in uncertainty propagation and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability and being able to deal with case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the 'spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques, as well as several uncertainty visualization functions. Uncertain environmental variables are represented in the package as objects whose attribute values may be uncertain and described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is also accommodated for. For uncertainty propagation the package has implemented the MC approach with efficient sampling algorithms, i.e. stratified random sampling and Latin hypercube sampling. The design includes facilitation of parallel computing to speed up MC computation. The MC realizations may be used as an input to the environmental models called from R, or externally. Selected visualization methods that are understandable by non-experts with limited background in

  20. Analysis and Research on Spatial Data Storage Model Based on Cloud Computing Platform

    Science.gov (United States)

    Hu, Yong

    2017-12-01

    In this paper, the data processing and storage characteristics of cloud computing are analyzed and studied. On this basis, a cloud computing data storage model based on BP neural network is proposed. In this data storage model, it can carry out the choice of server cluster according to the different attributes of the data, so as to complete the spatial data storage model with load balancing function, and have certain feasibility and application advantages.

  1. Modelling Spatial Interactions in the Arbuscular Mycorrhizal Symbiosis using the Calculus of Wrapped Compartments

    Directory of Open Access Journals (Sweden)

    Cristina Calcagno

    2011-09-01

    Full Text Available Arbuscular mycorrhiza (AM is the most wide-spread plant-fungus symbiosis on earth. Investigating this kind of symbiosis is considered one of the most promising ways to develop methods to nurture plants in more natural manners, avoiding the complex chemical productions used nowadays to produce artificial fertilizers. In previous work we used the Calculus of Wrapped Compartments (CWC to investigate different phases of the AM symbiosis. In this paper, we continue this line of research by modelling the colonisation of the plant root cells by the fungal hyphae spreading in the soil. This study requires the description of some spatial interaction. Although CWC has no explicit feature modelling a spatial geometry, the compartment labelling feature can be effectively exploited to define a discrete surface topology outlining the relevant sectors which determine the spatial properties of the system under consideration. Different situations and interesting spatial properties can be modelled and analysed in such a lightweight framework (which has not an explicit notion of geometry with coordinates and spatial metrics, thus exploiting the existing CWC simulation tool.

  2. Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation

    Directory of Open Access Journals (Sweden)

    Tatem Andrew J

    2012-05-01

    Full Text Available Abstract The use of Global Positioning Systems (GPS and Geographical Information Systems (GIS in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models. Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites. In this paper we discuss the deficiencies of existing

  3. Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation.

    Science.gov (United States)

    Tatem, Andrew J; Adamo, Susana; Bharti, Nita; Burgert, Clara R; Castro, Marcia; Dorelien, Audrey; Fink, Gunter; Linard, Catherine; John, Mendelsohn; Montana, Livia; Montgomery, Mark R; Nelson, Andrew; Noor, Abdisalan M; Pindolia, Deepa; Yetman, Greg; Balk, Deborah

    2012-05-16

    The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and

  4. Spatial Modeling of Flood Duration in Amazonian Floodplains Through Radar Remote Sensing and Generalized Linear Models

    Science.gov (United States)

    Ferreira-Ferreira, J.; Francisco, M. S.; Silva, T. S. F.

    2017-12-01

    Amazon floodplains play an important role in biodiversity maintenance and provide important ecosystem services. Flood duration is the prime factor modulating biogeochemical cycling in Amazonian floodplain systems, as well as influencing ecosystem structure and function. However, due to the absence of accurate terrain information, fine-scale hydrological modeling is still not possible for most of the Amazon floodplains, and little is known regarding the spatio-temporal behavior of flooding in these environments. Our study presents an new approach for spatial modeling of flood duration, using Synthetic Aperture Radar (SAR) and Generalized Linear Modeling. Our focal study site was Mamirauá Sustainable Development Reserve, in the Central Amazon. We acquired a series of L-band ALOS-1/PALSAR Fine-Beam mosaics, chosen to capture the widest possible range of river stage heights at regular intervals. We then mapped flooded area on each image, and used the resulting binary maps as the response variable (flooded/non-flooded) for multiple logistic regression. Explanatory variables were accumulated precipitation 15 days prior and the water stage height recorded in the Mamirauá lake gauging station observed for each image acquisition date, Euclidean distance from the nearest drainage, and slope, terrain curvature, profile curvature, planform curvature and Height Above the Nearest Drainage (HAND) derived from the 30-m SRTM DEM. Model results were validated with water levels recorded by ten pressure transducers installed within the floodplains, from 2014 to 2016. The most accurate model included water stage height and HAND as explanatory variables, yielding a RMSE of ±38.73 days of flooding per year when compared to the ground validation sites. The largest disagreements were 57 days and 83 days for two validation sites, while remaining locations achieved absolute errors lower than 38 days. In five out of nine validation sites, the model predicted flood durations with

  5. Role of spatial inhomogenity in GPCR dimerisation predicted by receptor association-diffusion models

    Science.gov (United States)

    Deshpande, Sneha A.; Pawar, Aiswarya B.; Dighe, Anish; Athale, Chaitanya A.; Sengupta, Durba

    2017-06-01

    G protein-coupled receptor (GPCR) association is an emerging paradigm with far reaching implications in the regulation of signalling pathways and therapeutic interventions. Recent super resolution microscopy studies have revealed that receptor dimer steady state exhibits sub-second dynamics. In particular the GPCRs, muscarinic acetylcholine receptor M1 (M1MR) and formyl peptide receptor (FPR), have been demonstrated to exhibit a fast association/dissociation kinetics, independent of ligand binding. In this work, we have developed a spatial kinetic Monte Carlo model to investigate receptor homo-dimerisation at a single receptor resolution. Experimentally measured association/dissociation kinetic parameters and diffusion coefficients were used as inputs to the model. To test the effect of membrane spatial heterogeneity on the simulated steady state, simulations were compared to experimental statistics of dimerisation. In the simplest case the receptors are assumed to be diffusing in a spatially homogeneous environment, while spatial heterogeneity is modelled to result from crowding, membrane micro-domains and cytoskeletal compartmentalisation or ‘corrals’. We show that a simple association-diffusion model is sufficient to reproduce M1MR association statistics, but fails to reproduce FPR statistics despite comparable kinetic constants. A parameter sensitivity analysis is required to reproduce the association statistics of FPR. The model reveals the complex interplay between cytoskeletal components and their influence on receptor association kinetics within the features of the membrane landscape. These results constitute an important step towards understanding the factors modulating GPCR organisation.

  6. The impact of design-based modeling instruction on seventh graders' spatial abilities and model-based argumentation

    Science.gov (United States)

    McConnell, William J.

    Due to the call of current science education reform for the integration of engineering practices within science classrooms, design-based instruction is receiving much attention in science education literature. Although some aspect of modeling is often included in well-known design-based instructional methods, it is not always a primary focus. The purpose of this study was to better understand how design-based instruction with an emphasis on scientific modeling might impact students' spatial abilities and their model-based argumentation abilities. In the following mixed-method multiple case study, seven seventh grade students attending a secular private school in the Mid-Atlantic region of the United States underwent an instructional intervention involving design-based instruction, modeling and argumentation. Through the course of a lesson involving students in exploring the interrelatedness of the environment and an animal's form and function, students created and used multiple forms of expressed models to assist them in model-based scientific argument. Pre/post data were collected through the use of The Purdue Spatial Visualization Test: Rotation, the Mental Rotation Test and interviews. Other data included a spatial activities survey, student artifacts in the form of models, notes, exit tickets, and video recordings of students throughout the intervention. Spatial abilities tests were analyzed using descriptive statistics while students' arguments were analyzed using the Instrument for the Analysis of Scientific Curricular Arguments and a behavior protocol. Models were analyzed using content analysis and interviews and all other data were coded and analyzed for emergent themes. Findings in the area of spatial abilities included increases in spatial reasoning for six out of seven participants, and an immense difference in the spatial challenges encountered by students when using CAD software instead of paper drawings to create models. Students perceived 3D printed

  7. A Spatial Allocation Procedure to Downscale Regional Crop Production Estimates from an Integrated Assessment Model

    Science.gov (United States)

    Moulds, S.; Djordjevic, S.; Savic, D.

    2017-12-01

    The Global Change Assessment Model (GCAM), an integrated assessment model, provides insight into the interactions and feedbacks between physical and human systems. The land system component of GCAM, which simulates land use activities and the production of major crops, produces output at the subregional level which must be spatially downscaled in order to use with gridded impact assessment models. However, existing downscaling routines typically consider cropland as a homogeneous class and do not provide information about land use intensity or specific management practices such as irrigation and multiple cropping. This paper presents a spatial allocation procedure to downscale crop production data from GCAM to a spatial grid, producing a time series of maps which show the spatial distribution of specific crops (e.g. rice, wheat, maize) at four input levels (subsistence, low input rainfed, high input rainfed and high input irrigated). The model algorithm is constrained by available cropland at each time point and therefore implicitly balances extensification and intensification processes in order to meet global food demand. It utilises a stochastic approach such that an increase in production of a particular crop is more likely to occur in grid cells with a high biophysical suitability and neighbourhood influence, while a fall in production will occur more often in cells with lower suitability. User-supplied rules define the order in which specific crops are downscaled as well as allowable transitions. A regional case study demonstrates the ability of the model to reproduce historical trends in India by comparing the model output with district-level agricultural inventory data. Lastly, the model is used to predict the spatial distribution of crops globally under various GCAM scenarios.

  8. On Spatially Explicit Models of Cholera Epidemics: Hydrologic controls, environmental drivers, human-mediated transmissions (Invited)

    Science.gov (United States)

    Rinaldo, A.; Bertuzzo, E.; Mari, L.; Righetto, L.; Gatto, M.; Casagrandi, R.; Rodriguez-Iturbe, I.

    2010-12-01

    A recently proposed model for cholera epidemics is examined. The model accounts for local communities of susceptibles and infectives in a spatially explicit arrangement of nodes linked by networks having different topologies. The vehicle of infection (Vibrio cholerae) is transported through the network links which are thought of as hydrological connections among susceptible communities. The mathematical tools used are borrowed from general schemes of reactive transport on river networks acting as the environmental matrix for the circulation and mixing of water-borne pathogens. The results of a large-scale application to the Kwa Zulu (Natal) epidemics of 2001-2002 will be discussed. Useful theoretical results derived in the spatially-explicit context will also be reviewed (like e.g. the exact derivation of the speed of propagation for traveling fronts of epidemics on regular lattices endowed with uniform population density). Network effects will be discussed. The analysis of the limit case of uniformly distributed population density proves instrumental in establishing the overall conditions for the relevance of spatially explicit models. To that extent, it is shown that the ratio between spreading and disease outbreak timescales proves the crucial parameter. The relevance of our results lies in the major differences potentially arising between the predictions of spatially explicit models and traditional compartmental models of the SIR-like type. Our results suggest that in many cases of real-life epidemiological interest timescales of disease dynamics may trigger outbreaks that significantly depart from the predictions of compartmental models. Finally, a view on further developments includes: hydrologically improved aquatic reservoir models for pathogens; human mobility patterns affecting disease propagation; double-peak emergence and seasonality in the spatially explicit epidemic context.

  9. Spatial and temporal agreement in climate model simulations of the Interdecadal Pacific Oscillation

    Science.gov (United States)

    Henley, Benjamin J.; Meehl, Gerald; Power, Scott B.; Folland, Chris K.; King, Andrew D.; Brown, Jaclyn N.; Karoly, David J.; Delage, Francois; E Gallant, Ailie J.; Freund, Mandy; Neukom, Raphael

    2017-04-01

    Accelerated warming and hiatus periods in the long-term rise of Global Mean Surface Temperature (GMST) have, in recent decades, been associated with the Interdecadal Pacific Oscillation (IPO). Critically, decadal climate prediction relies on the skill of state-of-the-art climate models to reliably represent these low-frequency climate variations. We undertake a systematic evaluation of the simulation of the IPO in the suite of Coupled Model Intercomparison Project 5 (CMIP5) models. We track the IPO in pre-industrial (control) and all-forcings (historical) experiments using the IPO tripole index (TPI). The TPI is explicitly aligned with the observed spatial pattern of the IPO, and circumvents assumptions about the nature of global warming. We find that many models underestimate the ratio of decadal-to-total variance in sea surface temperatures (SSTs). However, the basin-wide spatial pattern of positive and negative phases of the IPO are simulated reasonably well, with spatial pattern correlation coefficients between observations and models spanning the range 0.4-0.8. Deficiencies are mainly in the extratropical Pacific. Models that better capture the spatial pattern of the IPO also tend to more realistically simulate the ratio of decadal to total variance. Of the 13% of model centuries that have a fractional bias in the decadal-to-total TPI variance of 0.2 or less, 84% also have a spatial pattern correlation coefficient with the observed pattern exceeding 0.5. This result is highly consistent across both IPO positive and negative phases. This is evidence that the IPO is related to one or more inherent dynamical mechanisms of the climate system.

  10. Spectral-spatial classification combined with diffusion theory based inverse modeling of hyperspectral images

    Science.gov (United States)

    Paluchowski, Lukasz A.; Bjorgan, Asgeir; Nordgaard, Hâvard B.; Randeberg, Lise L.

    2016-02-01

    Hyperspectral imagery opens a new perspective for biomedical diagnostics and tissue characterization. High spectral resolution can give insight into optical properties of the skin tissue. However, at the same time the amount of collected data represents a challenge when it comes to decomposition into clusters and extraction of useful diagnostic information. In this study spectral-spatial classification and inverse diffusion modeling were employed to hyperspectral images obtained from a porcine burn model using a hyperspectral push-broom camera. The implemented method takes advantage of spatial and spectral information simultaneously, and provides information about the average optical properties within each cluster. The implemented algorithm allows mapping spectral and spatial heterogeneity of the burn injury as well as dynamic changes of spectral properties within the burn area. The combination of statistical and physics informed tools allowed for initial separation of different burn wounds and further detailed characterization of the injuries in short post-injury time.

  11. Exploring the inequality-mortality relationship in the US with Bayesian spatial modeling

    Science.gov (United States)

    Yang, Tse-Chuan; Jensen, Leif

    2014-01-01

    While there is evidence to suggest that socioeconomic inequality within places is associated with mortality rates among people living within them, the empirical connection between the two remains unsettled as potential confounders associated with racial and social structure are overlooked. This study seeks to test this relationship, to determine whether it is due to differential levels of deprivation and social capital, and does so with intrinsically conditional autoregressive Bayesian spatial modeling that effectively addresses the bias introduced by spatial dependence. We find that deprivation and social capital partly but not completely account for why inequality is positively associated with mortality and that spatial modeling generates more accurate predictions than does the traditional approach. We advance the literature by unveiling the intervening roles of social capital and deprivation in the inequality-mortality relationship and offering new evidence that inequality matters in US county mortality rates. PMID:26166920

  12. The Employment of spatial autoregressive models in predicting demand for natural gas

    International Nuclear Information System (INIS)

    Castro, Jorge Henrique de; Silva, Alexandre Pinto Alves da

    2010-01-01

    Develop the natural gas network is critical success factor for the distribution company. It is a decision that employs the demand given location 'x' and a future time 't' so that the net allows the best conditions for the return of the capital. In this segment, typical network industry, the spatial infra-structure vision associated to the market allows better evaluation of the business because to mitigate costs and risks. In fact, economic models little developed in order to assess the question of the location, due to its little employment by economists. The objective of this article is to analyze the application of spatial perspective in natural gas demand forecasting and to identify the models that can be employed observing issues of dependency and spatial heterogeneity; as well as the capacity of mapping of variables associated with the problem. (author)

  13. The influence of visual experience on the ability to form spatial mental models based on route and survey descriptions

    NARCIS (Netherlands)

    Noordzij, Matthijs Leendert; Zuidhoek, Sander; Postma, Albert

    2006-01-01

    The purpose of the present study is twofold: the first objective is to evaluate the importance of visual experience for the ability to form a spatial representation (spatial mental model) of fairly elaborate spatial descriptions. Secondly, we examine whether blind people exhibit the same preferences

  14. Advancements in Modelling of Land Surface Energy Fluxes with Remote Sensing at Different Spatial Scales

    DEFF Research Database (Denmark)

    Guzinski, Radoslaw

    uxes, such as sensible heat ux, ground heat ux and net radiation, are also necessary. While it is possible to measure those uxes with ground-based instruments at local scales, at region scales they usually need to be modelled or estimated with the help of satellite remote sensing data. Even though...... to increase the spatial resolution of the reliable DTD-modelled fluxes from 1 km to 30 m. Furthermore, synergies between remote sensing based models and distributed hydrological models were studied with the aim of improving spatial performance of the hydrological models through incorporation of remote sensing......Evaporation of water from soil and its transpiration by vegetation together form a ux between the land and the atmosphere called evapotranspiration (ET). ET is a key factor in many natural and anthropogenic processes. It forms the basis of the hydrological cycle and has a strong inuence on local...

  15. [Application of Land-use Regression Models in Spatial-temporal Differentiation of Air Pollution].

    Science.gov (United States)

    Wu, Jian-sheng; Xie, Wu-dan; Li, Jia-cheng

    2016-02-15

    With the rapid development of urbanization, industrialization and motorization, air pollution has become one of the most serious environmental problems in our country, which has negative impacts on public health and ecological environment. LUR model is one of the common methods simulating spatial-temporal differentiation of air pollution at city scale. It has broad application in Europe and North America, but not really in China. Based on many studies at home and abroad, this study started with the main steps to develop LUR model, including obtaining the monitoring data, generating variables, developing models, model validation and regression mapping. Then a conclusion was drawn on the progress of LUR models in spatial-temporal differentiation of air pollution. Furthermore, the research focus and orientation in the future were prospected, including highlighting spatial-temporal differentiation, increasing classes of model variables and improving the methods of model development. This paper was aimed to popularize the application of LUR model in China, and provide a methodological basis for human exposure, epidemiologic study and health risk assessment.

  16. Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

    KAUST Repository

    Sang, Huiyan

    2011-12-01

    This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.

  17. Spatial connections in regional climate model rainfall outputs at different temporal scales: Application of network theory

    Science.gov (United States)

    Naufan, Ihsan; Sivakumar, Bellie; Woldemeskel, Fitsum M.; Raghavan, Srivatsan V.; Vu, Minh Tue; Liong, Shie-Yui

    2018-01-01

    Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961-1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network's local density) and degree distribution (a measure of the network's spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are

  18. A watershed scale spatially-distributed model for streambank erosion rate driven by channel curvature

    Science.gov (United States)

    McMillan, Mitchell; Hu, Zhiyong

    2017-10-01

    Streambank erosion is a major source of fluvial sediment, but few large-scale, spatially distributed models exist to quantify streambank erosion rates. We introduce a spatially distributed model for streambank erosion applicable to sinuous, single-thread channels. We argue that such a model can adequately characterize streambank erosion rates, measured at the outsides of bends over a 2-year time period, throughout a large region. The model is based on the widely-used excess-velocity equation and comprised three components: a physics-based hydrodynamic model, a large-scale 1-dimensional model of average monthly discharge, and an empirical bank erodibility parameterization. The hydrodynamic submodel requires inputs of channel centerline, slope, width, depth, friction factor, and a scour factor A; the large-scale watershed submodel utilizes watershed-averaged monthly outputs of the Noah-2.8 land surface model; bank erodibility is based on tree cover and bank height as proxies for root density. The model was calibrated with erosion rates measured in sand-bed streams throughout the northern Gulf of Mexico coastal plain. The calibrated model outperforms a purely empirical model, as well as a model based only on excess velocity, illustrating the utility of combining a physics-based hydrodynamic model with an empirical bank erodibility relationship. The model could be improved by incorporating spatial variability in channel roughness and the hydrodynamic scour factor, which are here assumed constant. A reach-scale application of the model is illustrated on ∼1 km of a medium-sized, mixed forest-pasture stream, where the model identifies streambank erosion hotspots on forested and non-forested bends.

  19. Evaluating the Impacts of Spatial Uncertainties in Quantitative Precipitation Estimation (QPE) Products on Flood Modelling

    Science.gov (United States)

    Gao, Z.; Wu, H.; Li, J.; Hong, Y.; Huang, J.

    2017-12-01

    Precipitation is often the major uncertainty source of hydrologic modelling, e.g., for flood simulation. The quantitative precipitation estimation (QPE) products when used as input for hydrologic modelling can cause significant difference in model performance because of the large variations in their estimation of precipitation intensity, duration, and spatial distribution. Objectively evaluating QPE and deriving the best estimation of precipitation at river basin scale, represent a bottleneck which has been faced by the hydrometeorological community, despite they are desired by many researches including flood simulation, such as the Global Flood Monitoring System using the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model (Wu et al., 2014). Recently we developed a Multiple-product-driven hydrological Modeling Framework (MMF) for objective evaluation of QPE products using the DRIVE model (Wu et al., 2017). In this study based on the MMF, we (1) compare location, spatial characteristics, and geometric patterns of precipitation among QPE products at various temporal scales by adopting an object-oriented method; (2) demonstrate their effects on flood magnitude and timing simulation through the DRIVE model; and (3) further investigate and understand how different precipitation spatial patterns evolute and result in difference in streamflow and flood peak (magnitude and timing), through a linear routing scheme which is employed to decompose the contribution of flood peak during rain-flood events. This study shows that there can be significant difference in spatial patterns of accumulated precipitation at various temporal scales (from days to hourly) among QPE products, which cause significant difference in flood simulation particularly in peak timing prediction. Therefore, the evaluation of spatial pattern of precipitation should be considered as an important part of the framework for objective evaluation of QPE and the derivation of the best

  20. Spatial modeling

    Czech Academy of Sciences Publication Activity Database

    Klimešová, Dana

    2003-01-01

    Roč. 34, č. 6 (2003), s. 123-125 ISSN 1682-1750. [Geoinformation for Practice. Zagreb, 15.10.2003-18.10.2003] Institutional research plan: CEZ:AV0Z1075907 Keywords : object-oriented classification * contextual features * hierarchical representation Subject RIV: JD - Computer Applications, Robotics

  1. Discontinuous Galerkin Time-Domain Modeling of Graphene Nano-Ribbon Incorporating the Spatial Dispersion Effects

    KAUST Repository

    Li, Ping

    2018-04-13

    It is well known that graphene demonstrates spatial dispersion properties, i.e., its conductivity is nonlocal and a function of spectral wave number (momentum operator) q. In this paper, to account for effects of spatial dispersion on transmission of high speed signals along graphene nano-ribbon (GNR) interconnects, a discontinuous Galerkin time-domain (DGTD) algorithm is proposed. The atomically-thick GNR is modeled using a nonlocal transparent surface impedance boundary condition (SIBC) incorporated into the DGTD scheme. Since the conductivity is a complicated function of q (and one cannot find an analytical Fourier transform pair between q and spatial differential operators), an exact time domain SIBC model cannot be derived. To overcome this problem, the conductivity is approximated by its Taylor series in spectral domain under low-q assumption. This approach permits expressing the time domain SIBC in the form of a second-order partial differential equation (PDE) in current density and electric field intensity. To permit easy incorporation of this PDE with the DGTD algorithm, three auxiliary variables, which degenerate the second-order (temporal and spatial) differential operators to first-order ones, are introduced. Regarding to the temporal dispersion effects, the auxiliary differential equation (ADE) method is utilized to eliminates the expensive temporal convolutions. To demonstrate the applicability of the proposed scheme, numerical results, which involve characterization of spatial dispersion effects on the transfer impedance matrix of GNR interconnects, are presented.

  2. Integrated Spatial Modeling using Geoinformatics: A Prerequisite for Natural Resources Management

    Science.gov (United States)

    Katpatal, Y. B.

    2014-12-01

    Every natural system calls for complete visualization for its holistic and sustainable development. Many a times, especially in developing countries, the approaches deviate from this basic paradigm and results in ineffective management of the natural resources. This becomes more relevant in these countries which are witnessing heavy exodus of the rural population to urban areas increasing the pressures on the basic commodities. Spatial technologies which provide the opportunity to enhance the knowledge visualization of the policy makers and administrators which facilitates technical and scientific management of the resources. Increasing population has created negative impacts on the per capita availability of several resources, which has been well accepted in the statistical records of several developing countries. For instance, the per capita availability of water in India has decreased substantially in last decade and groundwater depletion is on the rise. There is hence a need of tool which helps in restoring the resource through visualization and evaluation temporally. Geological parameters play an important role in operation of several natural systems and earth sciences parameters may not be ignored. Spatial technologies enables application of 2D as well as 3D modeling taking into account variety of natural parameters related to diverse areas. The paper presents case studies where spatial technology has helped in not only understanding the natural systems but also providing solutions, especially in Indian context. The case studies relate to Groundwater Management, Watershed and Basin Management, Groundwater recharge, Environment sustainability using spatial technology. Key Words: Spatial model, Groundwater, Hydrogeology, Geoinformatics, Sustainable Development.

  3. Spatial demographic models to inform conservation planning of golden eagles in renewable energy landscapes

    Science.gov (United States)

    Wiens, J. David; Schumaker, Nathan H.; Inman, Richard D.; Esque, Todd C.; Longshore, Kathleen M.; Nussear, Kenneth E

    2017-01-01

    Spatial demographic models can help guide monitoring and management activities targeting at-risk species, even in cases where baseline data are lacking. Here, we provide an example of how site-specific changes in land use and anthropogenic stressors can be incorporated into a spatial demographic model to investigate effects on population dynamics of Golden Eagles (Aquila chrysaetos). Our study focused on a population of Golden Eagles exposed to risks associated with rapid increases in renewable energy development in southern California, U.S.A. We developed a spatially explicit, individual-based simulation model that integrated empirical data on demography of Golden Eagles with spatial data on the arrangement of nesting habitats, prey resources, and planned renewable energy development sites. Our model permitted simulated eagles of different stage-classes to disperse, establish home ranges, acquire prey resources, prospect for breeding sites, and reproduce. The distribution of nesting habitats, prey resources, and threats within each individual's home range influenced movement, reproduction, and survival. We used our model to explore potential effects of alternative disturbance scenarios, and proposed conservation strategies, on the future distribution and abundance of Golden Eagles in the study region. Results from our simulations suggest that probable increases in mortality associated with renewable energy infrastructure (e.g., collisions with wind turbines and vehicles, electrocution on power poles) could have negative consequences for population trajectories, but that site-specific conservation actions could reduce the magnitude of negative effects. Our study demonstrates the use of a flexible and expandable modeling framework to incorporate spatially dependent processes when determining relative effects of proposed management options to Golden Eagles and their habitats.

  4. A BAYESIAN HIERARCHICAL SPATIAL MODEL FOR DENTAL CARIES ASSESSMENT USING NON-GAUSSIAN MARKOV RANDOM FIELDS.

    Science.gov (United States)

    Jin, Ick Hoon; Yuan, Ying; Bandyopadhyay, Dipankar

    2016-01-01

    Research in dental caries generates data with two levels of hierarchy: that of a tooth overall and that of the different surfaces of the tooth. The outcomes often exhibit spatial referencing among neighboring teeth and surfaces, i.e., the disease status of a tooth or surface might be influenced by the status of a set of proximal teeth/surfaces. Assessments of dental caries (tooth decay) at the tooth level yield binary outcomes indicating the presence/absence of teeth, and trinary outcomes at the surface level indicating healthy, decayed, or filled surfaces. The presence of these mixed discrete responses complicates the data analysis under a unified framework. To mitigate complications, we develop a Bayesian two-level hierarchical model under suitable (spatial) Markov random field assumptions that accommodates the natural hierarchy within the mixed responses. At the first level, we utilize an autologistic model to accommodate the spatial dependence for the tooth-level binary outcomes. For the second level and conditioned on a tooth being non-missing, we utilize a Potts model to accommodate the spatial referencing for the surface-level trinary outcomes. The regression models at both levels were controlled for plausible covariates (risk factors) of caries, and remain connected through shared parameters. To tackle the computational challenges in our Bayesian estimation scheme caused due to the doubly-intractable normalizing constant, we employ a double Metropolis-Hastings sampler. We compare and contrast our model performances to the standard non-spatial (naive) model using a small simulation study, and illustrate via an application to a clinical dataset on dental caries.

  5. Modelling of spatially complex human-ecosystem, rural-urban and rich-poor interactions

    CSIR Research Space (South Africa)

    Naude, AH

    2008-06-01

    Full Text Available The paper outlines the challenges of modelling and assessing spatially complex human-ecosystem interactions, and the need to simultaneously consider rural-urban and rich-poor interactions. The context for exploring these challenges is South Africa...

  6. A mixed integer program to model spatial wildfire behavior and suppression placement decisions

    Science.gov (United States)

    Erin J. Belval; Yu Wei; Michael. Bevers

    2015-01-01

    Wildfire suppression combines multiple objectives and dynamic fire behavior to form a complex problem for decision makers. This paper presents a mixed integer program designed to explore integrating spatial fire behavior and suppression placement decisions into a mathematical programming framework. Fire behavior and suppression placement decisions are modeled using...

  7. A hierarchically adaptable spatial regression model to link aggregated health data and environmental data

    NARCIS (Netherlands)

    Truong Ngoc Phuong, Phuong; Stein, A.

    2017-01-01

    Health data and environmental data are commonly collected at different levels of aggregation. A persistent challenge of using a spatial regression model to link these data is that their associations can vary as a function of aggregation. This results into ecological fallacy if association at one

  8. Modelling the spatial shape of nondiffracting beams: Experimental generation of Frozen Waves via computer generated holograms

    OpenAIRE

    Vieira, Tárcio A.; Zamboni-Rached, Michel; Gesualdi, Marcos R. R.

    2013-01-01

    In this paper we implement experimentally the spatial shape modelling of nondiffracting optical beams via computer generated holograms. The results reported here are the experimental confirmation of the so called Frozen Wave method, developed few years ago. Optical beams of this type can possess potential applications in optical tweezers, medicine, atom guiding, remote sensing, etc..

  9. Combining habitat suitability models and spatial graphs for landscape conservation planning: a methodological framework

    OpenAIRE

    Duflot, R.; Avon, C.; Roche, P.; Bergès, L.

    2016-01-01

    In response to the negative effects of habitat fragmentation on biodiversity, ecological network conservation and restoration has become a central objective in biodiversity conservation planning. Evaluating landscape functional connectivity for species and mapping ecological networks are key steps towards effective implementation of relevant actions, but both remain challenging. Habitat suitability models and spatial graphs are thought to provide conservation practitioners with useful informa...

  10. Modelling spatial scales of water erosion in the West Usambara Mountains of Tanzania

    NARCIS (Netherlands)

    Vigiak, O.; van Loon, E.E.; Sterk, G.

    2006-01-01

    This study assessed the ability of several models to locate areas affected by severe erosion and identified the factors controlling the distribution of erosion in a catchment characterized by a dynamic Hortonian hydrologic regime. The spatial patterns of severely eroded areas predicted by five

  11. How Spatial Abilities and Dynamic Visualizations Interplay When Learning Functional Anatomy with 3D Anatomical Models

    Science.gov (United States)

    Berney, Sandra; Bétrancourt, Mireille; Molinari, Gaëlle; Hoyek, Nady

    2015-01-01

    The emergence of dynamic visualizations of three-dimensional (3D) models in anatomy curricula may be an adequate solution for spatial difficulties encountered with traditional static learning, as they provide direct visualization of change throughout the viewpoints. However, little research has explored the interplay between learning material…

  12. Spatial modelling of assumption of tourism development with geographic IT using

    Directory of Open Access Journals (Sweden)

    Jitka Machalová

    2010-01-01

    Full Text Available The aim of this article is to show the possibilities of spatial modelling and analysing of assumptions of tourism development in the Czech Republic with the objective to make decision-making processes in tourism easier and more efficient (for companies, clients as well as destination managements. The development and placement of tourism depend on the factors (conditions that influence its application in specific areas. These factors are usually divided into three groups: selective, localization and realization. Tourism is inseparably connected with space – countryside. The countryside can be modelled and consecutively analysed by the means of geographical information technologies. With the help of spatial modelling and following analyses the localization and realization conditions in the regions of the Czech Republic have been evaluated. The best localization conditions have been found in the Liberecký region. The capital city of Prague has negligible natural conditions; however, those social ones are on a high level. Next, the spatial analyses have shown that the best realization conditions are provided by the capital city of Prague. Then the Central-Bohemian, South-Moravian, Moravian-Silesian and Karlovarský regions follow. The development of tourism destination is depended not only on the localization and realization factors but it is basically affected by the level of local destination management. Spatial modelling can help destination managers in decision-making processes in order to optimal use of destination potential and efficient targeting their marketing activities.

  13. Modelling the spatial distribution of SO2 and NO(x) emissions in Ireland

    NARCIS (Netherlands)

    Kluizenaar, Y.de; Aherne, J.; Farrell, E.P.

    2001-01-01

    The spatial distributions of sulphur dioxide (SO2) and nitrogen oxides (NO(x)) emissions are essential inputs to models of atmospheric transport and deposition. Information of this type is required for international negotiations on emission reduction through the critical load approach.

  14. The Analysis of Elementary Mathematics Preservice Teachers' Spatial Orientation Skills with SOLO Model

    Science.gov (United States)

    Özdemir, Ahmet Sükrü; Göktepe Yildiz, Sevda

    2015-01-01

    Problem Statement: The SOLO model places responses provided by students on a certain level instead of placing students there themselves. SOLO taxonomy, including five sub-levels, is used for determining observed structures of learning outcomes in various disciplines and grade levels. On the other hand, the spatial orientation skill is the ability…

  15. A spatial model of tree α-diversity and tree density for the Amazon

    NARCIS (Netherlands)

    ter Steege, H.; Pitman, N.C.A.; Sabatier, D.; Castellanos, H.; van der Hout, P.; Daly, D.C.; Silveira, M.; Phillips, O.; Vasquez, R.; van Andel, T.; Duivenvoorden, J.; de Oliveira, A.A.; Ek, R.; Lilwah, R.; Thomas, R.; van Essen, J.; Baider, C.; Maas, P.; Mori, S.; Terborgh, J.; Nuñez-Vargas, P.; Mogollón, H.; Morawetz, W.

    2003-01-01

    Large-scale patterns of Amazonian biodiversity have until now been obscured by a sparse and scattered inventory record. Here we present the first comprehensive spatial model of tree α-diversity and tree density in Amazonian rainforests, based on the largest-yet compilation of forest inventories and

  16. A Comparative Analysis of Spatial Visualization Ability and Drafting Models for Industrial and Technology Education Students

    Science.gov (United States)

    Katsioloudis, Petros; Jovanovic, Vukica; Jones, Mildred

    2014-01-01

    The main purpose of this study was to determine significant positive effects among the use of three different types of drafting models, and to identify whether any differences exist towards promotion of spatial visualization ability for students in Industrial Technology and Technology Education courses. In particular, the study compared the use of…

  17. Spatial demographic models to inform conservation planning of golden eagles in renewable energy landscapes.

    Science.gov (United States)

    Spatial demographic models can help guide monitoring and management activities targeting at-risk species, even in cases where baseline data are lacking. Here, we provide an example of how site-specific changes in land-use and other anthropogenic stressors can be incorporated int...

  18. On Spatial Resolution in Habitat Models: Can Small-scale Forest Structure Explain Capercaillie Numbers?

    Directory of Open Access Journals (Sweden)

    Ilse Storch

    2002-06-01

    Full Text Available This paper explores the effects of spatial resolution on the performance and applicability of habitat models in wildlife management and conservation. A Habitat Suitability Index (HSI model for the Capercaillie (Tetrao urogallus in the Bavarian Alps, Germany, is presented. The model was exclusively built on non-spatial, small-scale variables of forest structure and without any consideration of landscape patterns. The main goal was to assess whether a HSI model developed from small-scale habitat preferences can explain differences in population abundance at larger scales. To validate the model, habitat variables and indirect sign of Capercaillie use (such as feathers or feces were mapped in six study areas based on a total of 2901 20 m radius (for habitat variables and 5 m radius sample plots (for Capercaillie sign. First, the model's representation of Capercaillie habitat preferences was assessed. Habitat selection, as expressed by Ivlev's electivity index, was closely related to HSI scores, increased from poor to excellent habitat suitability, and was consistent across all study areas. Then, habitat use was related to HSI scores at different spatial scales. Capercaillie use was best predicted from HSI scores at the small scale. Lowering the spatial resolution of the model stepwise to 36-ha, 100-ha, 400-ha, and 2000-ha areas and relating Capercaillie use to aggregated HSI scores resulted in a deterioration of fit at larger scales. Most importantly, there were pronounced differences in Capercaillie abundance at the scale of study areas, which could not be explained by the HSI model. The results illustrate that even if a habitat model correctly reflects a species' smaller scale habitat preferences, its potential to predict population abundance at larger scales may remain limited.

  19. Progressive refining of spatial and temporal resolutions in a hydrological model: how far should we go?

    Science.gov (United States)

    de Lavenne, Alban; Ficchi, Andrea; Goullet, Julien

    2017-04-01

    Choosing a modelling resolution for an hydrological model is an important preliminary question. However, it is quite often arbitrary determined by the modeller experience according to the objective, the model capacity or the available measurements. The hydrological literature provides numerous studies which focus on the effect of refining either spatial resolution or (sometimes) temporal resolution in order to better catch hydrological response. In this study, we investigate the impact of changing simultaneously both resolutions on hydrological model performance. The idea is that these resolutions are linked and should be considered together. Thus, we look for the combination of spatial and temporal resolutions fitting at best each catchment behaviour and type of rainfall events. A large data set of 240 catchments scattered all around France is used, and in particular, we benefit from a high-resolution precipitation database (ANTILOPE, Météo-France) that describes hourly precipitation at 1 km2 resolution. Data were aggregated at different time steps (1h, 3h, 6h, 12h and 24h). Streamflow simulations are performed at these different time steps using the GR5 model in its lumped and semi-distributed version (GRSD, de Lavenne et al. (2016)), with a mesh grid of 500, 250, 100 and 50 km2. Ten different indices are used to describe spatio-temporal characteristics of rainfall events, in order to analyse in which contexts refined resolutions are needed to improve the performance of the model. These indices characterise the spatial variability, localisation, movement, intensity and temporal variability of rainfall events. In addition to some indices already reported in the hydrological literature, we propose some new indices like an indice usually applied in economics. This analysis at different time steps, events and catchments demonstrates the limits for some of them and allows to propose some corrections (Goullet J., 2016). Model performances are shown to be

  20. Towards Linking 3D SAR and Lidar Models with a Spatially Explicit Individual Based Forest Model

    Science.gov (United States)

    Osmanoglu, B.; Ranson, J.; Sun, G.; Armstrong, A. H.; Fischer, R.; Huth, A.

    2017-12-01

    In this study, we present a parameterization of the FORMIND individual-based gap model (IBGM)for old growth Atlantic lowland rainforest in La Selva, Costa Rica for the purpose of informing multisensor remote sensing techniques for above ground biomass techniques. The model was successfully parameterized and calibrated for the study site; results show that the simulated forest reproduces the structural complexity of Costa Rican rainforest based on comparisons with CARBONO inventory plot data. Though the simulated stem numbers (378) slightly underestimated the plot data (418), particularly for canopy dominant intermediate shade tolerant trees and shade tolerant understory trees, overall there was a 9.7% difference. Aboveground biomass (kg/ha) showed a 0.1% difference between the simulated forest and inventory plot dataset. The Costa Rica FORMIND simulation was then used to parameterize a spatially explicit (3D) SAR and lidar backscatter models. The simulated forest stands were used to generate a Look Up Table as a tractable means to estimate aboveground forest biomass for these complex forests. Various combinations of lidar and radar variables were evaluated in the LUT inversion. To test the capability of future data for estimation of forest height and biomass, we considered data of 1) L- (or P-) band polarimetric data (backscattering coefficients of HH, HV and VV); 2) L-band dual-pol repeat-pass InSAR data (HH/HV backscattering coefficients and coherences, height of scattering phase center at HH and HV using DEM or surface height from lidar data as reference); 3) P-band polarimetric InSAR data (canopy height from inversion of PolInSAR data or use the coherences and height of scattering phase center at HH, HV and VV); 4) various height indices from waveform lidar data); and 5) surface and canopy top height from photon-counting lidar data. The methods for parameterizing the remote sensing models with the IBGM and developing Look Up Tables will be discussed. Results

  1. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

    Directory of Open Access Journals (Sweden)

    M. C. Demirel

    2018-02-01

    Full Text Available Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the

  2. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

    Science.gov (United States)

    Demirel, Mehmet C.; Mai, Juliane; Mendiguren, Gorka; Koch, Julian; Samaniego, Luis; Stisen, Simon

    2018-02-01

    Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex

  3. Numerical Simulation of a Grinding Process Model for the Spatial Work-pieces: Development of Modeling Techniques

    Directory of Open Access Journals (Sweden)

    S. A. Voronov

    2015-01-01

    Full Text Available The article presents a literature review in simulation of grinding processes. It takes into consideration the statistical, energy based, and imitation approaches to simulation of grinding forces. Main stages of interaction between abrasive grains and machined surface are shown. The article describes main approaches to the geometry modeling of forming new surfaces when grinding. The review of approaches to the chip and pile up effect numerical modeling is shown. Advantages and disadvantages of grain-to-surface interaction by means of finite element method and molecular dynamics method are considered. The article points out that it is necessary to take into consideration the system dynamics and its effect on the finished surface. Structure of the complex imitation model of grinding process dynamics for flexible work-pieces with spatial surface geometry is proposed from the literature review. The proposed model of spatial grinding includes the model of work-piece dynamics, model of grinding wheel dynamics, phenomenological model of grinding forces based on 3D geometry modeling algorithm. Model gives the following results for spatial grinding process: vibration of machining part and grinding wheel, machined surface geometry, static deflection of the surface and grinding forces under various cutting conditions.

  4. A spatial time series framework for modeling daily precipitationat regional scales

    Energy Technology Data Exchange (ETDEWEB)

    Kyriakidis, Phaedon C.; Miller, Norman L.; Kim, Jinwon

    2001-11-14

    In this paper, a framework for stochastic spatiotemporal modeling of daily precipitation in a hindcast mode is presented. Observed precipitation levels in space and time are modeled as a joint realization of a collection of space-indexed time series, one for each spatial location. Time series model parameters are spatially varying, thus capturing space-time interactions. Stochastic simulation, i.e., the procedure of generating alternative precipitation realizations (synthetic fields) over the space-time domain of interest (Deutsch and Journel, 1998), is employed for ensemble prediction. The simulated daily precipitation fields reproduce a data-based histogram and spatiotemporal covariance model, and identify the measured precipitation values at the rain gauges (conditional simulation). Such synthetic precipitation fields can be used in a Monte Carlo framework for risk analysis studies in hydrologic impact assessment investigations.

  5. Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localization

    Science.gov (United States)

    Khaki, M.; Schumacher, M.; Forootan, E.; Kuhn, M.; Awange, J. L.; van Dijk, A. I. J. M.

    2017-10-01

    Assimilation of terrestrial water storage (TWS) information from the Gravity Recovery And Climate Experiment (GRACE) satellite mission can provide significant improvements in hydrological modelling. However, the rather coarse spatial resolution of GRACE TWS and its spatially correlated errors pose considerable challenges for achieving realistic assimilation results. Consequently, successful data assimilation depends on rigorous modelling of the full error covariance matrix of the GRACE TWS estimates, as well as realistic error behavior for hydrological model simulations. In this study, we assess the application of local analysis (LA) to maximize the contribution of GRACE TWS in hydrological data assimilation. For this, we assimilate GRACE TWS into the World-Wide Water Resources Assessment system (W3RA) over the Australian continent while applying LA and accounting for existing spatial correlations using the full error covariance matrix. GRACE TWS data is applied with different spatial resolutions including 1° to 5° grids, as well as basin averages. The ensemble-based sequential filtering technique of the Square Root Analysis (SQRA) is applied to assimilate TWS data into W3RA. For each spatial scale, the performance of the data assimilation is assessed through comparison with independent in-situ ground water and soil moisture observations. Overall, the results demonstrate that LA is able to stabilize the inversion process (within the implementation of the SQRA filter) leading to less errors for all spatial scales considered with an average RMSE improvement of 54% (e.g., 52.23 mm down to 26.80 mm) for all the cases with respect to groundwater in-situ measurements. Validating the assimilated results with groundwater observations indicates that LA leads to 13% better (in terms of RMSE) assimilation results compared to the cases with Gaussian errors assumptions. This highlights the great potential of LA and the use of the full error covariance matrix of GRACE TWS

  6. Revealing the regime of shallow coral reefs at patch scale by continuous spatial modeling

    Directory of Open Access Journals (Sweden)

    Antoine eCollin

    2014-11-01

    Full Text Available Reliably translating real-world spatial patterns of ecosystems is critical for understanding processes susceptible to reinforce resilience. However the great majority of studies in spatial ecology use thematic maps to describe habitats and species in a binary scheme. By discretizing the transitional areas and neglecting the gradual replacement across a given space, the thematic approach may suffer from substantial limitations when interpreting patterns created by many continuous variables. Here, local and regional spectral proxies were used to design and spatially map at very fine scale a continuous index dedicated to one of the most complex seascapes, the coral reefscape. Through a groundbreaking merge of bottom-up and top-down approach, we demonstrate that three to seven-habitat continuous indices can be modeled by nine, six, four and three spectral proxies, respectively, at 0.5 m spatial resolution using hand- and spaceborne measurements. We map the seven-habitat continuous index, spanning major Indo-Pacific coral reef habitats through the far red-green normalized difference ratio over the entire lagoon of a low (Tetiaroa atoll and a high volcanic (Moorea island in French Polynesia with 84% and 82% accuracy, respectively. Further examinations of the two resulting spatial models using a customized histoscape (density function of model values distributed on a concentric strip across the reef crest-coastline distance show that Tetiaroa exhibits a greater variety of coral reef habitats than Moorea. By designing such easy-to-implement, transferrable spectral proxies of coral reef regime, this study initiates a framework for spatial ecologists tackling coral reef biodiversity, responses to stresses, perturbations and shifts. We discuss the limitations and contributions of our findings towards the study of worldwide coral reef resilience following stochastic environmental change.

  7. Modelling and predicting the spatial distribution of tree root density in heterogeneous forest ecosystems.

    Science.gov (United States)

    Mao, Zhun; Saint-André, Laurent; Bourrier, Franck; Stokes, Alexia; Cordonnier, Thomas

    2015-08-01

    In mountain ecosystems, predicting root density in three dimensions (3-D) is highly challenging due to the spatial heterogeneity of forest communities. This study presents a simple and semi-mechanistic model, named ChaMRoots, that predicts root interception density (RID, number of roots m(-2)). ChaMRoots hypothesizes that RID at a given point is affected by the presence of roots from surrounding trees forming a polygon shape. The model comprises three sub-models for predicting: (1) the spatial heterogeneity - RID of the finest roots in the top soil layer as a function of tree basal area at breast height, and the distance between the tree and a given point; (2) the diameter spectrum - the distribution of RID as a function of root diameter up to 50 mm thick; and (3) the vertical profile - the distribution of RID as a function of soil depth. The RID data used for fitting in the model were measured in two uneven-aged mountain forest ecosystems in the French Alps. These sites differ in tree density and species composition. In general, the validation of each sub-model indicated that all sub-models of ChaMRoots had good fits. The model achieved a highly satisfactory compromise between the number of aerial input parameters and the fit to the observed data. The semi-mechanistic ChaMRoots model focuses on the spatial distribution of root density at the tree cluster scale, in contrast to the majority of published root models, which function at the level of the individual. Based on easy-to-measure characteristics, simple forest inventory protocols and three sub-models, it achieves a good compromise between the complexity of the case study area and that of the global model structure. ChaMRoots can be easily coupled with spatially explicit individual-based forest dynamics models and thus provides a highly transferable approach for modelling 3-D root spatial distribution in complex forest ecosystems. © The Author 2015. Published by Oxford University Press on behalf of the

  8. Using a spatially-distributed hydrologic biogeochemistry model with nitrogen transport to study the spatial variation of carbon stocks and fluxes in a Critical Zone Observatory

    Science.gov (United States)

    Shi, Y.; Eissenstat, D. M.; He, Y.; Davis, K. J.

    2017-12-01

    Most current biogeochemical models are 1-D and represent one point in space. Therefore, they cannot resolve topographically driven land surface heterogeneity (e.g., lateral water flow, soil moisture, soil temperature, solar radiation) or the spatial pattern of nutrient availability. A spatially distributed forest biogeochemical model with nitrogen transport, Flux-PIHM-BGC, has been developed by coupling a 1-D mechanistic biogeochemical model Biome-BGC (BBGC) with a spatially distributed land surface hydrologic model, Flux-PIHM, and adding an advection dominated nitrogen transport module. Flux-PIHM is a coupled physically based model, which incorporates a land-surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model, and is augmented by adding a topographic solar radiation module. Flux-PIHM is able to represent the link between groundwater and the surface energy balance, as well as land surface heterogeneities caused by topography. In the coupled Flux-PIHM-BGC model, each Flux-PIHM model grid couples a 1-D BBGC model, while nitrogen is transported among model grids via surface and subsurface water flow. In each grid, Flux-PIHM provides BBGC with soil moisture, soil temperature, and solar radiation, while BBGC provides Flux-PIHM with spatially-distributed leaf area index. The coupled Flux-PIHM-BGC model has been implemented at the Susquehanna/Shale Hills Critical Zone Observatory. The model-predicted aboveground vegetation carbon and soil carbon distributions generally agree with the macro patterns observed within the watershed. The importance of abiotic variables (including soil moisture, soil temperature, solar radiation, and soil mineral nitrogen) in predicting aboveground carbon distribution is calculated using a random forest. The result suggests that the spatial pattern of aboveground carbon is controlled by the distribution of soil mineral nitrogen. A Flux-PIHM-BGC simulation

  9. Objective estimation of spatially variable parameters of epidemic type aftershock sequence model: Application to California

    Science.gov (United States)

    Nandan, Shyam; Ouillon, Guy; Wiemer, Stefan; Sornette, Didier

    2017-07-01

    The Epidemic Type Aftershock Sequence (ETAS) model is widely employed to model the spatiotemporal distribution of earthquakes, generally using spatially invariant parameters. We propose an efficient method for the estimation of spatially varying parameters, using the expectation maximization (EM) algorithm and spatial Voronoi tessellation ensembles. We use the Bayesian information criterion (BIC) to rank inverted models given their likelihood and complexity and select the best models to finally compute an ensemble model at any location. Using a synthetic catalog, we also check that the proposed method correctly inverts the known parameters. We apply the proposed method to earthquakes included in the Advanced National Seismic System catalog that occurred within the time period 1981-2015 in a spatial polygon around California. The results indicate significant spatial variation of the ETAS parameters. We find that the efficiency of earthquakes to trigger future ones (quantified by the branching ratio) positively correlates with surface heat flow. In contrast, the rate of earthquakes triggered by far-field tectonic loading or background seismicity rate shows no such correlation, suggesting the relevance of triggering possibly through fluid-induced activation. Furthermore, the branching ratio and background seismicity rate are found to be uncorrelated with hypocentral depths, indicating that the seismic coupling remains invariant of hypocentral depths in the study region. Additionally, triggering seems to be mostly dominated by small earthquakes. Consequently, the static stress change studies should not only focus on the Coulomb stress changes caused by specific moderate to large earthquakes but also account for the secondary static stress changes caused by smaller earthquakes.

  10. Spatial patterns and eco-epidemiological systems – part I: multi-scale spatial modelling of the occurrence of Chagas disease insect vectors

    Directory of Open Access Journals (Sweden)

    Emmanuel Roux

    2011-11-01

    Full Text Available Studies that explicitly and specifically take into account the spatial dimension within the study of eco-epidemiological systems remain rare. Our approach of modelling the spatial and/or temporal properties of the entomological and/or epidemiological data before their mapping with possible explanatory variables, objectively underline the significant patterns at different scales. The domiciliary and peri-domiciliary presence and abundance of juvenile and adult vectors of the Chagas disease (Triatoma sordida and Panstrongylus geniculatus in Bahia state in northeast Brazil, has been modelled by automatically identifying significant multi-scale spatial patterns of the entomological data by the application and adaptation of the spatial modelling methodology proposed by Dray et al. (2006 and based on principal coordinate analysis of neighbour matrices. We found that entomological data can be modelled by a set of eigenvectors that present a significant Moran’s I index of spatial autocorrelation. The models for juvenile and adult vectors are defined by 28 and 32 eigenvectors that explain 82.3% and 79.9%, respectively, of the total data variances. The results support insect presence as the outcome both of a local scale “near-to-near” dispersal and an infestation from the wild, surrounding environment that produces a higher insect density at the village periphery.

  11. Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

    Directory of Open Access Journals (Sweden)

    Jianguo Chen

    2017-05-01

    Full Text Available The relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB, Bayesian hierarchical model (BHM and the geographically weighted Poisson regression model (GWPR were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE of BHM and GWPR was decreased by 97.88% and 77.15%, and the R d 2 of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher R d 2 . The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density, and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries.

  12. Predicting detection performance with model observers: Fourier domain or spatial domain?

    Science.gov (United States)

    Chen, Baiyu; Yu, Lifeng; Leng, Shuai; Kofler, James; Favazza, Christopher; Vrieze, Thomas; McCollough, Cynthia

    2016-02-27

    The use of Fourier domain model observer is challenged by iterative reconstruction (IR), because IR algorithms are nonlinear and IR images have noise texture different from that of FBP. A modified Fourier domain model observer, which incorporates nonlinear noise and resolution properties, has been proposed for IR and needs to be validated with human detection performance. On the other hand, the spatial domain model observer is theoretically applicable to IR, but more computationally intensive than the Fourier domain method. The purpose of this study is to compare the modified Fourier domain model observer to the spatial domain model observer with both FBP and IR images, using human detection performance as the gold standard. A phantom with inserts of various low contrast levels and sizes was repeatedly scanned 100 times on a third-generation, dual-source CT scanner at 5 dose levels and reconstructed using FBP and IR algorithms. The human detection performance of the inserts was measured via a 2-alternative-forced-choice (2AFC) test. In addition, two model observer performances were calculated, including a Fourier domain non-prewhitening model observer and a spatial domain channelized Hotelling observer. The performance of these two mode observers was compared in terms of how well they correlated with human observer performance. Our results demonstrated that the spatial domain model observer correlated well with human observers across various dose levels, object contrast levels, and object sizes. The Fourier domain observer correlated well with human observers using FBP images, but overestimated the detection performance using IR images.

  13. A perturbation analysis of a mechanical model for stable spatial patterning in embryology

    Science.gov (United States)

    Bentil, D. E.; Murray, J. D.

    1992-12-01

    We investigate a mechanical cell-traction mechanism that generates stationary spatial patterns. A linear analysis highlights the model's potential for these heterogeneous solutions. We use multiple-scale perturbation techniques to study the evolution of these solutions and compare our solutions with numerical simulations of the model system. We discuss some potential biological applications among which are the formation of ridge patterns, dermatoglyphs, and wound healing.

  14. A Spatial Faithful Cooperative System Based on Mixed Presence Groupware Model

    Science.gov (United States)

    Wang, Wei; Wang, Xiangyu; Wang, Rui

    Traditional groupware platforms are found restrained and cumbersome for supporting geographically dispersed design collaboration. This paper starts with two groupware models, which are Single Display Groupware and Mixed Presence Groupware, and then discusses some of the limitations and argues how these limitations could possibly impair efficient communication among remote designers. Next, it suggests that the support for spatial faithfulness and Tangible User Interface (TUI) could help fill the gap between Face-to-Face (F2F) collaboration and computer-mediated remote collaboration. A spatial faithful groupware with TUI support is then developed to illustrate this concept.

  15. A description of spatial data infrastructure stakeholders in Ghana using the ICA model

    CSIR Research Space (South Africa)

    Owusu-Banahene, W

    2013-11-01

    Full Text Available , ICA model 1. Introduction A spatial data infrastructure (SDI) refers to the infrastructure, i.e. the basic physical and organizational structures required to facilitate and coordinate the efficient and effective discovery and use of spatial..., networks, people, policies, organizational aspects, geo-referenced data, and delivery mechanisms to end users. Ghana is a sub-saharan African country (shown in Figure 1) with a land surface area of 239,460km2, a population of 24.66 million (Ghana...

  16. A Matérn model of the spatial covariance structure of point rain rates

    KAUST Repository

    Sun, Ying

    2014-07-15

    It is challenging to model a precipitation field due to its intermittent and highly scale-dependent nature. Many models of point rain rates or areal rainfall observations have been proposed and studied for different time scales. Among them, the spectral model based on a stochastic dynamical equation for the instantaneous point rain rate field is attractive, since it naturally leads to a consistent space–time model. In this paper, we note that the spatial covariance structure of the spectral model is equivalent to the well-known Matérn covariance model. Using high-quality rain gauge data, we estimate the parameters of the Matérn model for different time scales and demonstrate that the Matérn model is superior to an exponential model, particularly at short time scales.

  17. Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms

    International Nuclear Information System (INIS)

    Liu Haibin; Davidson, Rachel A.; Apanasovich, Tatiyana V.

    2008-01-01

    This paper presents new statistical models that predict the number of hurricane- and ice storm-related electric power outages likely to occur in each 3 kmx3 km grid cell in a region. The models are based on a large database of recent outages experienced by three major East Coast power companies in six hurricanes and eight ice storms. A spatial generalized linear mixed modeling (GLMM) approach was used in which spatial correlation is incorporated through random effects. Models were fitted using a composite likelihood approach and the covariance matrix was estimated empirically. A simulation study was conducted to test the model estimation procedure, and model training, validation, and testing were done to select the best models and assess their predictive power. The final hurricane model includes number of protective devices, maximum gust wind speed, hurricane indicator, and company indicator covariates. The final ice storm model includes number of protective devices, ice thickness, and ice storm indicator covariates. The models should be useful for power companies as they plan for future storms. The statistical modeling approach offers a new way to assess the reliability of electric power and other infrastructure systems in extreme events

  18. Spatially-varying surface roughness and ground-level air quality in an operational dispersion model

    International Nuclear Information System (INIS)

    Barnes, M.J.; Brade, T.K.; MacKenzie, A.R.; Whyatt, J.D.; Carruthers, D.J.; Stocker, J.; Cai, X.; Hewitt, C.N.

    2014-01-01

    Urban form controls the overall aerodynamic roughness of a city, and hence plays a significant role in how air flow interacts with the urban landscape. This paper reports improved model performance resulting from the introduction of variable surface roughness in the operational air-quality model ADMS-Urban (v3.1). We then assess to what extent pollutant concentrations can be reduced solely through local reductions in roughness. The model results suggest that reducing surface roughness in a city centre can increase ground-level pollutant concentrations, both locally in the area of reduced roughness and downwind of that area. The unexpected simulation of increased ground-level pollutant concentrations implies that this type of modelling should be used with caution for urban planning and design studies looking at ventilation of pollution. We expect the results from this study to be relevant for all atmospheric dispersion models with urban-surface parameterisations based on roughness. -- Highlights: • Spatially variable roughness improved performance of an operational model. • Scenario modelling explored effect of reduced roughness on air pollution. • Reducing surface roughness can increase modelled ground-level pollution. • Damped vertical mixing outweighs increased horizontal advection in model study. • Result should hold for any model with a land-surface coupling based on roughness. -- Spatially varying roughness improves model simulations of urban air pollutant dispersion. Reducing roughness does not always decrease ground-level pollution concentrations

  19. A spatial simulation model for the dispersal of the bluetongue vector Culicoides brevitarsis in Australia.

    Directory of Open Access Journals (Sweden)

    Joel K Kelso

    Full Text Available The spread of Bluetongue virus (BTV among ruminants is caused by movement of infected host animals or by movement of infected Culicoides midges, the vector of BTV. Biologically plausible models of Culicoides dispersal are necessary for predicting the spread of BTV and are important for planning control and eradication strategies.A spatially-explicit simulation model which captures the two underlying population mechanisms, population dynamics and movement, was developed using extensive data from a trapping program for C. brevitarsis on the east coast of Australia. A realistic midge flight sub-model was developed and the annual incursion and population establishment of C. brevitarsis was simulated. Data from the literature was used to parameterise the model.The model was shown to reproduce the spread of C. brevitarsis southwards along the east Australian coastline in spring, from an endemic population to the north. Such incursions were shown to be reliant on wind-dispersal; Culicoides midge active flight on its own was not capable of achieving known rates of southern spread, nor was re-emergence of southern populations due to overwintering larvae. Data from midge trapping programmes were used to qualitatively validate the resulting simulation model.The model described in this paper is intended to form the vector component of an extended model that will also include BTV transmission. A model of midge movement and population dynamics has been developed in sufficient detail such that the extended model may be used to evaluate the timing and extent of BTV outbreaks. This extended model could then be used as a platform for addressing the effectiveness of spatially targeted vaccination strategies or animal movement bans as BTV spread mitigation measures, or the impact of climate change on the risk and extent of outbreaks. These questions involving incursive Culicoides spread cannot be simply addressed with non-spatial models.

  20. Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore

    Directory of Open Access Journals (Sweden)

    Jan Huwald

    2013-07-01

    Full Text Available A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models.

  1. The use of spatial empirical models to estimate soil erosion in arid ecosystems.

    Science.gov (United States)

    Abdullah, Meshal; Feagin, Rusty; Musawi, Layla

    2017-02-01

    The central objective of this project was to utilize geographical information systems and remote sensing to compare soil erosion models, including Modified Pacific South-west Inter Agency Committee (MPSIAC), Erosion Potential Method (EPM), and Revised Universal Soil Loss Equation (RUSLE), and to determine their applicability for arid regions such as Kuwait. The northern portion of Umm Nigga, containing both coastal and desert ecosystems, falls within the boundaries of the de-militarized zone (DMZ) adjacent to Iraq and has been fenced off to restrict public access since 1994. Results showed that the MPSIAC and EPM models were similar in spatial distribution of erosion, though the MPSIAC had a more realistic spatial distribution of erosion and presented finer level details. The RUSLE presented unrealistic results. We then predicted the amount of soil loss between coastal and desert areas and fenced and unfenced sites for each model. In the MPSIAC and EPM models, soil loss was different between fenced and unfenced sites at the desert areas, which was higher at the unfenced due to the low vegetation cover. The overall results implied that vegetation cover played an important role in reducing soil erosion and that fencing is much more important in the desert ecosystems to protect against human activities such as overgrazing. We conclude that the MPSIAC model is best for predicting soil erosion for arid regions such as Kuwait. We also recommend the integration of field-based experiments with lab-based spatial analysis and modeling in future research.

  2. Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies

    KAUST Repository

    Philbin, R.

    2015-05-22

    This study validates the near-surface temperature and precipitation output from decadal runs of eight atmospheric ocean general circulation models (AOGCMs) against observational proxy data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis temperatures and Global Precipitation Climatology Project (GPCP) precipitation data. We model the joint distribution of these two fields with a parsimonious bivariate Matérn spatial covariance model, accounting for the two fields\\' spatial cross-correlation as well as their own smoothnesses. We fit output from each AOGCM (30-year seasonal averages from 1981 to 2010) to a statistical model on each of 21 land regions. Both variance and smoothness values agree for both fields over all latitude bands except southern mid-latitudes. Our results imply that temperature fields have smaller smoothness coefficients than precipitation fields, while both have decreasing smoothness coefficients with increasing latitude. Models predict fields with smaller smoothness coefficients than observational proxy data for the tropics. The estimated spatial cross-correlations of these two fields, however, are quite different for most GCMs in mid-latitudes. Model correlation estimates agree well with those for observational proxy data for Australia, at high northern latitudes across North America, Europe and Asia, as well as across the Sahara, India, and Southeast Asia, but elsewhere, little consistent agreement exists.

  3. Spatial extrapolation of light use efficiency model parameters to predict gross primary production

    Directory of Open Access Journals (Sweden)

    Karsten Schulz

    2011-12-01

    Full Text Available To capture the spatial and temporal variability of the gross primary production as a key component of the global carbon cycle, the light use efficiency modeling approach in combination with remote sensing data has shown to be well suited. Typically, the model parameters, such as the maximum light use efficiency, are either set to a universal constant or to land class dependent values stored in look-up tables. In this study, we employ the machine learning technique support vector regression to explicitly relate the model parameters of a light use efficiency model calibrated at several FLUXNET sites to site-specific characteristics obtained by meteorological measurements, ecological estimations and remote sensing data. A feature selection algorithm extracts the relevant site characteristics in a cross-validation, and leads to an individual set of characteristic attributes for each parameter. With this set of attributes, the model parameters can be estimated at sites where a parameter calibration is not possible due to the absence of eddy covariance flux measurement data. This will finally allow a spatially continuous model application. The performance of the spatial extrapolation scheme is evaluated with a cross-validation approach, which shows the methodology to be well suited to recapture the variability of gross primary production across the study sites.

  4. Modeling of the spatial distribution of ten endangered bird species in jurisdiction of Corantioquia

    International Nuclear Information System (INIS)

    Gomez M, Ana Maria; Alvarez, Esteban

    2006-01-01

    Recently, thanks to advances made in Geographic Information Systems (GIS), techniques have been developed for the construction of models that predict the spatial distribution of species and other attributes of biodiversity. These methods have allowed for the development of objective criteria that are fundamental for making decisions regarding the creation of protected areas systems throughout the world. In this research, the spatial distribution of ten endangered species of birds found within the jurisdiction of CORANTIOQUIA (JDC from here on) was modelled, using GIS techniques. The JDC was divided into 177 squares of 15 x 10 Km and the following variables were quantified within each one: presence or absence of endangered species of birds, rainfall, temperature, sun brightness, relative humidity, day duration, altitude, vegetal cover, slope and primary net productivity. With the help of logistic regression were made predictive models. Based on logistic regressions techniques predictive models were made. These models allow to explain a percentage between 24% and 80% of spatial distribution variability of these species. Those results can help in the identification of valuable zones for the biodiversity conservation. In places where there are neither the time or the economic resources to carry out exhaustive analyses of biodiversity, the models can predict the probable presence of this endangered species

  5. Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

    Science.gov (United States)

    Gruber, A.; Crow, W. T.; Dorigo, W. A.

    2018-02-01

    Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

  6. Spatial Impairment and Memory in Genetic Disorders: Insights from Mouse Models

    Directory of Open Access Journals (Sweden)

    Sang Ah Lee

    2017-02-01

    Full Text Available Research across the cognitive and brain sciences has begun to elucidate some of the processes that guide navigation and spatial memory. Boundary geometry and featural landmarks are two distinct classes of environmental cues that have dissociable neural correlates in spatial representation and follow different patterns of learning. Consequently, spatial navigation depends both on the type of cue available and on the type of learning provided. We investigated this interaction between spatial representation and memory by administering two different tasks (working memory, reference memory using two different environmental cues (rectangular geometry, striped landmark in mouse models of human genetic disorders: Prader-Willi syndrome (PWScrm+/p− mice, n = 12 and Beta-catenin mutation (Thr653Lys-substituted mice, n = 12. This exploratory study provides suggestive evidence that these models exhibit different abilities and impairments in navigating by boundary geometry and featural landmarks, depending on the type of memory task administered. We discuss these data in light of the specific deficits in cognitive and brain function in these human syndromes and their animal model counterparts.

  7. Modeling the Impact of Spatial Structure on Growth Dynamics of Invasive Plant Species

    Science.gov (United States)

    Murphy, James T.; Johnson, Mark P.; Walshe, Ray

    2013-07-01

    Invasive nonindigenous plant species can have potentially serious detrimental effects on local ecosystems and, as a result, costly control efforts often have to be put in place to protect habitats. An example of an invasive problem on a global scale involves the salt marsh grass species from the genus Spartina. The spread of Spartina anglica in Europe and Asia has drawn much concern due to its ability to convert coastal habitats into cord-grass monocultures and to alter the native food webs. However, the patterns of invasion of Spartina species are amenable to spatially-explicit modeling strategies that take into account both temporal and spatio-temporal processes. In this study, an agent-based model of Spartina growth on a simulated mud flat environment was developed in order to study the effects of spatial pattern and initial seedling placement on the invasion dynamics of the population. The spatial pattern of an invasion plays a key role in the rate of spread of the species and understanding this can lead to significant cost savings when designing efficient control strategies. We present here a model framework that can be used to explicitly represent complex spatial and temporal patterns of invasion in order to be able to predict quantitatively the impact of these factors on invasion dynamics. This would be a useful tool for assessing eradication strategies and choosing optimal control solutions in order to be able to minimize future control costs.

  8. Bayesian model and spatial analysis of oral and oropharynx cancer mortality in Minas Gerais, Brazil.

    Science.gov (United States)

    Fonseca, Emílio Prado da; Oliveira, Cláudia Di Lorenzo; Chiaravalloti, Francisco; Pereira, Antonio Carlos; Vedovello, Silvia Amélia Scudeler; Meneghim, Marcelo de Castro

    2018-01-01

    The objective of this study was to determine of oral and oropharynx cancer mortality rate and the results were analyzed by applying the Spatial Analysis of Empirical Bayesian Model. To this end, we used the information contained in the International Classification of Diseases (ICD-10), Chapter II, Category C00 to C14 and Brazilian Mortality Information System (SIM) of Minas Gerais State. Descriptive statistics were observed and the gross rate of mortality was calculated for each municipality. Then Empirical Bayesian estimators were applied. The results showed that, in 2012, in the state of Minas Gerais, were registered 769 deaths of patients with cancer of oral and oropharynx, with 607 (78.96%) men and 162 (21.04%) women. There was a wide variation in spatial distribution of crude mortality rate and were identified agglomeration in the South, Central and North more accurately by Bayesian Estimator Global and Local Model. Through Bayesian models was possible to map the spatial clustering of deaths from oral cancer more accurately, and with the application of the method of spatial epidemiology, it was possible to obtain more accurate results and provide subsidies to reduce the number of deaths from this type of cancer.

  9. Epidemiological study and spatial modeling of peste des petits ruminants (PPR in central area of Iran

    Directory of Open Access Journals (Sweden)

    Azam Mokhtari

    2017-05-01

    Full Text Available Objective. Estimate the prevalence and spatial modeling of PPR in the small ruminant population of Chaharmahal and Bakhtiari, Iran, during 2009–2014. Materials and methods. Data were collected from veterinary organization and Offices in Chaharmahal and Bakhtiari province and data analysis was carried out using and IBM SPSS version 22 and Office 2010. For spatial modeling geographic information system (QGIS and PCI-Geomatic was used. Results. This study showed that the overall prevalence of PPR during the years 2009 to 2014 was 1.37%. Koohrang, Ardal, Lordegan, Ben, Borougen, Shahrekord, Farsan and Kiar cities had the highest prevalence of PPR, respectively. The highest PPR infection rate was observed in the March and goat more affected rather than other ruminants. Conclusions. Our findings provide evidence of a rather common prevalence of PPR and its spatial distribution in Chaharmahal and Bakhtiari province. Using statistical tests for data analysis of PPR and its spatial modeling researchers can predict the incidence of disease in the future and could select appropriate measures of disease control.

  10. Investigation of Spatial Variation of Sea States Offshore of Humboldt Bay CA Using a Hindcast Model.

    Energy Technology Data Exchange (ETDEWEB)

    Dallman, Ann Renee; Neary, Vincent Sinclair

    2014-10-01

    Spatial variability of sea states is an important consideration when performing wave resource assessments and wave resource characterization studies for wave energy converter (WEC) test sites and commercial WEC deployments. This report examines the spatial variation of sea states offshore of Humboldt Bay, CA, using the wave model SWAN . The effect of depth and shoaling on bulk wave parameters is well resolved using the model SWAN with a 200 m grid. At this site, the degree of spatial variation of these bulk wave parameters, with shoaling generally perpendicular to the depth contours, is found to depend on the season. The variation in wave height , for example, was higher in the summer due to the wind and wave sheltering from the protruding land on the coastline north of the model domain. Ho wever, the spatial variation within an area of a potential Tier 1 WEC test site at 45 m depth and 1 square nautical mile is almost negligible; at most about 0.1 m in both winter and summer. The six wave characterization parameters recommended by the IEC 6 2600 - 101 TS were compared at several points along a line perpendicular to shore from the WEC test site . As expected, these parameters varied based on depth , but showed very similar seasonal trends.

  11. Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

    International Nuclear Information System (INIS)

    Rasam, A R A; Ghazali, R; Noor, A M M; Mohd, W M N W; Hamid, J R A; Bazlan, M J; Ahmad, N

    2014-01-01

    Cholera spatial epidemiology is the study of the spread and control of the disease spatial pattern and epidemics. Previous studies have shown that multi-factorial causation such as human behaviour, ecology and other infectious risk factors influence the disease outbreaks. Thus, understanding spatial pattern and possible interrelationship factors of the outbreaks are crucial to be explored an in-depth study. This study focuses on the integration of geographical information system (GIS) and epidemiological techniques in exploratory analyzing the cholera spatial pattern and distribution in the selected district of Sabah. Spatial Statistic and Pattern tools in ArcGIS and Microsoft Excel software were utilized to map and analyze the reported cholera cases and other data used. Meanwhile, cohort study in epidemiological technique was applied to investigate multiple outcomes of the disease exposure. The general spatial pattern of cholera was highly clustered showed the disease spread easily at a place or person to others especially 1500 meters from the infected person and locations. Although the cholera outbreaks in the districts are not critical, it could be endemic at the crowded areas, unhygienic environment, and close to contaminated water. It was also strongly believed that the coastal water of the study areas has possible relationship with the cholera transmission and phytoplankton bloom since the areas recorded higher cases. GIS demonstrates a vital spatial epidemiological technique in determining the distribution pattern and elucidating the hypotheses generating of the disease. The next research would be applying some advanced geo-analysis methods and other disease risk factors for producing a significant a local scale predictive risk model of the disease in Malaysia

  12. Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

    Science.gov (United States)

    Rasam, A. R. A.; Ghazali, R.; Noor, A. M. M.; Mohd, W. M. N. W.; Hamid, J. R. A.; Bazlan, M. J.; Ahmad, N.

    2014-02-01

    Cholera spatial epidemiology is the study of the spread and control of the disease spatial pattern and epidemics. Previous studies have shown that multi-factorial causation such as human behaviour, ecology and other infectious risk factors influence the disease outbreaks. Thus, understanding spatial pattern and possible interrelationship factors of the outbreaks are crucial to be explored an in-depth study. This study focuses on the integration of geographical information system (GIS) and epidemiological techniques in exploratory analyzing the cholera spatial pattern and distribution in the selected district of Sabah. Spatial Statistic and Pattern tools in ArcGIS and Microsoft Excel software were utilized to map and analyze the reported cholera cases and other data used. Meanwhile, cohort study in epidemiological technique was applied to investigate multiple outcomes of the disease exposure. The general spatial pattern of cholera was highly clustered showed the disease spread easily at a place or person to others especially 1500 meters from the infected person and locations. Although the cholera outbreaks in the districts are not critical, it could be endemic at the crowded areas, unhygienic environment, and close to contaminated water. It was also strongly believed that the coastal water of the study areas has possible relationship with the cholera transmission and phytoplankton bloom since the areas recorded higher cases. GIS demonstrates a vital spatial epidemiological technique in determining the distribution pattern and elucidating the hypotheses generating of the disease. The next research would be applying some advanced geo-analysis methods and other disease risk factors for producing a significant a local scale predictive risk model of the disease in Malaysia.

  13. Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning.

    Directory of Open Access Journals (Sweden)

    Simon Dedman

    Full Text Available Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage.We have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs, based on stakeholder priorities, such as the minimisation of fishing effort displacement.By bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these

  14. WHERE THERE’S SMOKE: CIGARETTE USE, SOCIAL ACCEPTABILITY, AND SPATIAL APPROACHES TO MULTILEVEL MODELING

    Science.gov (United States)

    O’Connell, Heather A.

    2015-01-01

    I contribute to understandings of how context is related to individual outcomes by assessing the added value of combining multilevel and spatial modeling techniques. This methodological approach leads to substantive contributions to the smoking literature, including improved clarity on the central contextual factors and the examination of one manifestation of the social acceptability hypothesis. For this analysis I use restricted-use natality data from the Vital Statistics, and county-level data from the 2005–9 ACS. Critically, the results suggest that spatial considerations are still relevant in a multilevel framework. In addition, I argue that spatial processes help explain the relationships linking racial/ethnic minority concentration to lower overall odds of smoking. PMID:26188587

  15. Where there's smoke: Cigarette use, social acceptability, and spatial approaches to multilevel modeling.

    Science.gov (United States)

    O'Connell, Heather A

    2015-09-01

    I contribute to understandings of how context is related to individual outcomes by assessing the added value of combining multilevel and spatial modeling techniques. This methodological approach leads to substantive contributions to the smoking literature, including improved clarity on the central contextual factors and the examination of one manifestation of the social acceptability hypothesis. For this analysis I use restricted-use natality data from the Vital Statistics, and county-level data from the 2005-9 ACS. Critically, the results suggest that spatial considerations are still relevant in a multilevel framework. In addition, I argue that spatial processes help explain the relationships linking racial/ethnic minority concentration to lower overall odds of smoking. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Spatial and Feature-Based Attention in a Layered Cortical Microcircuit Model

    Science.gov (United States)

    Wagatsuma, Nobuhiko; Potjans, Tobias C.; Diesmann, Markus; Sakai, Ko; Fukai, Tomoki

    2013-01-01

    Directing attention to the spatial location or the distinguishing feature of a visual object modulates neuronal responses in the visual cortex and the stimulus discriminability of subjects. However, the spatial and feature-based modes of attention differently influence visual processing by changing the tuning properties of neurons. Intriguingly, neurons' tuning curves are modulated similarly across different visual areas under both these modes of attention. Here, we explored the mechanism underlying the effects of these two modes of visual attention on the orientation selectivity of visual cortical neurons. To do this, we developed a layered microcircuit model. This model describes multiple orientation-specific microcircuits sharing their receptive fields and consisting of layers 2/3, 4, 5, and 6. These microcircuits represent a functional grouping of cortical neurons and mutually interact via lateral inhibition and excitatory connections between groups with similar selectivity. The individual microcircuits receive bottom-up visual stimuli and top-down attention in different layers. A crucial assumption of the model is that feature-based attention activates orientation-specific microcircuits for the relevant feature selectively, whereas spatial attention activates all microcircuits homogeneously, irrespective of their orientation selectivity. Consequently, our model simultaneously accounts for the multiplicative scaling of neuronal responses in spatial attention and the additive modulations of orientation tuning curves in feature-based attention, which have been observed widely in various visual cortical areas. Simulations of the model predict contrasting differences between excitatory and inhibitory neurons in the two modes of attentional modulations. Furthermore, the model replicates the modulation of the psychophysical discriminability of visual stimuli in the presence of external noise. Our layered model with a biologically suggested laminar structure describes

  17. Hydroclimatology of Dual-Peak Annual Cholera Incidence: Insights from a Spatially Explicit Model

    Science.gov (United States)

    Bertuzzo, E.; Mari, L.; Righetto, L.; Gatto, M.; Casagrandi, R.; Rodriguez-Iturbe, I.; Rinaldo, A.

    2012-12-01

    Cholera incidence in some regions of the Indian subcontinent may exhibit two annual peaks although the main environmental drivers that have been linked to the disease (e.g. sea surface temperature, zooplankton abundance, river discharge) peak once per year during the summer. An empirical hydroclimatological explanation relating cholera transmission to river flows and to the disease spatial spreading has been recently proposed. We specifically support and substantiate mechanistically such hypothesis by means of a spatially explicit model of cholera transmission. Our framework directly accounts for the role of the river network in transporting and redistributing cholera bacteria among human communities as well as for spatial and temporal annual fluctuations of precipitation and river flows. To single out the single out the hydroclimatologic controls on the prevalence patterns in a non-specific geographical context, we first apply the model to Optimal Channel Networks as a general model of hydrological networks. Moreover, we impose a uniform distribution of population. The model is forced by seasonal environmental drivers, namely precipitation, temperature and chlorophyll concentration in the coastal environment, a proxy for Vibrio cholerae concentration. Our results show that these drivers may suffice to generate dual-peak cholera prevalence patterns for proper combinations of timescales involved in pathogen transport, hydrologic variability and disease unfolding. The model explains the possible occurrence of spatial patterns of cholera incidence characterized by a spring peak confined to coastal areas and a fall peak involving inland regions. We then proceed applying the model to the specific settings of Bay of Bengal accounting for the actual river networks (derived from digital terrain map manipulations), the proper distribution of population (estimated from downscaling of census data based on remotely sensed features) and precipitation patterns. Overall our

  18. Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models.

    Science.gov (United States)

    Saha, Dibakar; Alluri, Priyanka; Gan, Albert; Wu, Wanyang

    2018-02-21

    The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies. Published by Elsevier Ltd.

  19. Advances in nonmarket valuation econometrics: Spatial heterogeneity in hedonic pricing models and preference heterogeneity in stated preference models

    Science.gov (United States)

    Yoo, Jin Woo

    Counties. The spatial-lag (SLM), the spatial error (SEM) and the spatial error component (SEC) models were compared. A geographically weighted regression (GWR) model is estimated to study the spatial heterogeneity of the marginal implicit prices of ACE impact within each county. New hybrid spatial hedonic models, the GWR-SEC and a modified GWR-SEM, are estimated such that both spatial autocorrelation and heterogeneity are accounted. The results show that the coefficient of land under easement contract varies spatially within one county, but not within the other county studied. Also, ACE's are found to have both positive and negative impacts on the values of nearby residential properties. Among global spatial models, the SEM fit better than the SLM and the SEC. Statistical goodness of fit measures showed that the GWR-SEC model fit better than the GWR or the GWR-SEC model. Finally, the GWR-SEC showed spatial autocorrelation is stronger in one county than in the other county.

  20. Modeling the effect of urban infrastructure on hydrologic processes within i-Tree Hydro, a statistically and spatially distributed model

    Science.gov (United States)

    Taggart, T. P.; Endreny, T. A.; Nowak, D.

    2014-12-01

    Gray and green infrastructure in urban environments alters many natural hydrologic processes, creating an urban water balance unique to the developed environment. A common way to assess the consequences of impervious cover and grey infrastructure is by measuring runoff hydrographs. This focus on the watershed outlet masks the spatial variation of hydrologic process alterations across the urban environment in response to localized landscape characteristics. We attempt to represent this spatial variation in the urban environment using the statistically and spatially distributed i-Tree Hydro model, a scoping level urban forest effects water balance model. i-Tree Hydro has undergone expansion and modification to include the effect of green infrastructure processes, road network attributes, and urban pipe system leakages. These additions to the model are intended to increase the understanding of the altered urban hydrologic cycle by examining the effects of the location of these structures on the water balance. Specifically, the effect of these additional structures and functions on the spatially varying properties of interception, soil moisture and runoff generation. Differences in predicted properties and optimized parameter sets between the two models are examined and related to the recent landscape modifications. Datasets used in this study consist of watersheds and sewersheds within the Syracuse, NY metropolitan area, an urban area that has integrated green and gray infrastructure practices to alleviate stormwater problems.

  1. STARS: An ArcGIS Toolset Used to Calculate the Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data

    Directory of Open Access Journals (Sweden)

    Erin Peterson

    2014-01-01

    Full Text Available This paper describes the STARS ArcGIS geoprocessing toolset, which is used to calcu- late the spatial information needed to fit spatial statistical models to stream network data using the SSN package. The STARS toolset is designed for use with a landscape network (LSN, which is a topological data model produced by the FLoWS ArcGIS geoprocessing toolset. An overview of the FLoWS LSN structure and a few particularly useful tools is also provided so that users will have a clear understanding of the underlying data struc- ture that the STARS toolset depends on. This document may be used as an introduction to new users. The methods used to calculate the spatial information and format the final .ssn object are also explicitly described so that users may create their own .ssn object using other data models and software.

  2. A Spatially Continuous Model of Carbohydrate Digestion and Transport Processes in the Colon.

    Directory of Open Access Journals (Sweden)

    Arun S Moorthy

    Full Text Available A spatially continuous mathematical model of transport processes, anaerobic digestion and microbial complexity as would be expected in the human colon is presented. The model is a system of first-order partial differential equations with context determined number of dependent variables, and stiff, non-linear source terms. Numerical simulation of the model is used to elucidate information about the colon-microbiota complex. It is found that the composition of materials on outflow of the model does not well-describe the composition of material in other model locations, and inferences using outflow data varies according to model reactor representation. Additionally, increased microbial complexity allows the total microbial community to withstand major system perturbations in diet and community structure. However, distribution of strains and functional groups within the microbial community can be modified depending on perturbation length and microbial kinetic parameters. Preliminary model extensions and potential investigative opportunities using the computational model are discussed.

  3. A Spatial Disaster Assessment Model of Social Resilience Based on Geographically Weighted Regression

    Directory of Open Access Journals (Sweden)

    Hwikyung Chun

    2017-12-01

    Full Text Available Since avoiding the occurrence of natural disasters is difficult, building ‘resilient cities’ is gaining more attention as a common objective within urban communities. By enhancing community resilience, it is possible to minimize the direct and indirect losses from disasters. However, current studies have focused more on physical aspects, despite the fact that social aspects may have a closer relation to the inhabitants. The objective of this paper is to develop an assessment model for social resilience by measuring the heterogeneity of local indicators that are related to disaster risk. Firstly, variables were selected by investigating previous assessment models with statistical verification. Secondly, spatial heterogeneity was analyzed using the Geographically Weighted Regression (GWR method. A case study was then undertaken on a flood-prone area in the metropolitan city, Seoul, South Korea. Based on the findings, the paper proposes a new spatial disaster assessment model that can be used for disaster management at the local levels.

  4. Maximum likelihood pixel labeling using a spatially variant finite mixture model

    International Nuclear Information System (INIS)

    Gopal, S.S.; Hebert, T.J.

    1996-01-01

    We propose a spatially-variant mixture model for pixel labeling. Based on this spatially-variant mixture model we derive an expectation maximization algorithm for maximum likelihood estimation of the pixel labels. While most algorithms using mixture models entail the subsequent use of a Bayes classifier for pixel labeling, the proposed algorithm yields maximum likelihood estimates of the labels themselves and results in unambiguous pixel labels. The proposed algorithm is fast, robust, easy to implement, flexible in that it can be applied to any arbitrary image data where the number of classes is known and, most importantly, obviates the need for an explicit labeling rule. The algorithm is evaluated both quantitatively and qualitatively on simulated data and on clinical magnetic resonance images of the human brain

  5. Spatially explicit control of invasive species using a reaction-diffusion model

    Science.gov (United States)

    Bonneau, Mathieu; Johnson, Fred A.; Romagosa, Christina M.

    2016-01-01

    Invasive species, which can be responsible for severe economic and environmental damages, must often be managed over a wide area with limited resources, and the optimal allocation of effort in space and time can be challenging. If the spatial range of the invasive species is large, control actions might be applied only on some parcels of land, for example because of property type, accessibility, or limited human resources. Selecting the locations for control is critical and can significantly impact management efficiency. To help make decisions concerning the spatial allocation of control actions, we propose a simulation based approach, where the spatial distribution of the invader is approximated by a reaction–diffusion model. We extend the classic Fisher equation to incorporate the effect of control both in the diffusion and local growth of the invader. The modified reaction–diffusion model that we propose accounts for the effect of control, not only on the controlled locations, but on neighboring locations, which are based on the theoretical speed of the invasion front. Based on simulated examples, we show the superiority of our model compared to the state-of-the-art approach. We illustrate the use of this model for the management of Burmese pythons in the Everglades (Florida, USA). Thanks to the generality of the modified reaction–diffusion model, this framework is potentially suitable for a wide class of management problems and provides a tool for managers to predict the effects of different management strategies.

  6. Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes

    Science.gov (United States)

    Kang, Peter K.; Dentz, Marco; Le Borgne, Tanguy; Lee, Seunghak; Juanes, Ruben

    2017-08-01

    We investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that-even if the Eulerian fluid velocity is steady-the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationary velocity distribution. We quantify this evolution by a Markov model for particle velocities that are equidistantly sampled along trajectories. This stochastic approach allows for the systematic incorporation of the initial velocity distribution and quantifies the interplay between velocity distribution and spatial and temporal correlation. The proposed spatial Markov model is characterized by the initial velocity distribution, which is determined by the particle injection mode, the stationary Lagrangian velocity distribution, which is derived from the Eulerian velocity distribution, and the spatial velocity correlation length, which is related to the characteristic fracture length. This effective model leads to a time-domain random walk for the evolution of particle positions and velocities, whose joint distribution follows a Boltzmann equation. Finally, we demonstrate that the proposed model can successfully predict anomalous transport through discrete fracture networks with different levels of heterogeneity and arbitrary tracer injection modes.

  7. Density estimation in a wolverine population using spatial capture-recapture models

    Science.gov (United States)

    Royle, J. Andrew; Magoun, Audrey J.; Gardner, Beth; Valkenbury, Patrick; Lowell, Richard E.; McKelvey, Kevin

    2011-01-01

    Classical closed-population capture-recapture models do not accommodate the spatial information inherent in encounter history data obtained from camera-trapping studies. As a result, individual heterogeneity in encounter probability is induced, and it is not possible to estimate density objectively because trap arrays do not have a well-defined sample area. We applied newly-developed, capture-recapture models that accommodate the spatial attribute inherent in capture-recapture data to a population of wolverines (Gulo gulo) in Southeast Alaska in 2008. We used camera-trapping data collected from 37 cameras in a 2,140-km2 area of forested and open habitats largely enclosed by ocean and glacial icefields. We detected 21 unique individuals 115 times. Wolverines exhibited a strong positive trap response, with an increased tendency to revisit previously visited traps. Under the trap-response model, we estimated wolverine density at 9.7 individuals/1,000-km2(95% Bayesian CI: 5.9-15.0). Our model provides a formal statistical framework for estimating density from wolverine camera-trapping studies that accounts for a behavioral response due to baited traps. Further, our model-based estimator does not have strict requirements about the spatial configuration of traps or length of trapping sessions, providing considerable operational flexibility in the development of field studies.

  8. Objective Tuning of Model Parameters in CAM5 Across Different Spatial Resolutions

    Science.gov (United States)

    Bulaevskaya, V.; Lucas, D. D.

    2014-12-01

    Parameterizations of physical processes in climate models are highly dependent on the spatial and temporal resolution and must be tuned for each resolution under consideration. At high spatial resolutions, objective methods for parameter tuning are computationally prohibitive. Our work has focused on calibrating parameters in the Community Atmosphere Model 5 (CAM5) for three spatial resolutions: 1, 2, and 4 degrees. Using perturbed-parameter ensembles and uncertainty quantification methodology, we have identified input parameters that minimize discrepancies of energy fluxes simulated by CAM5 across the three resolutions and with respect to satellite observations. We are also beginning to exploit the parameter-resolution relationships to objectively tune parameters in a high-resolution version of CAM5 by leveraging cheaper, low-resolution simulations and statistical models. We will present our approach to multi-resolution climate model parameter tuning, as well as the key findings. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 and was supported from the DOE Office of Science through the Scientific Discovery Through Advanced Computing (SciDAC) project on Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System.

  9. Relating mesocarnivore relative abundance to anthropogenic land-use with a hierarchical spatial count model

    Science.gov (United States)

    Crimmins, Shawn M.; Walleser, Liza R.; Hertel, Dan R.; McKann, Patrick C.; Rohweder, Jason J.; Thogmartin, Wayne E.

    2016-01-01

    There is growing need to develop models of spatial patterns in animal abundance, yet comparatively few examples of such models exist. This is especially true in situations where the abundance of one species may inhibit that of another, such as the intensively-farmed landscape of the Prairie Pothole Region (PPR) of the central United States, where waterfowl production is largely constrained by mesocarnivore nest predation. We used a hierarchical Bayesian approach to relate the distribution of various land-cover types to the relative abundances of four mesocarnivores in the PPR: coyote Canis latrans, raccoon Procyon lotor, red fox Vulpes vulpes, and striped skunk Mephitis mephitis. We developed models for each species at multiple spatial resolutions (41.4 km2, 10.4 km2, and 2.6 km2) to address different ecological and management-related questions. Model results for each species were similar irrespective of resolution. We found that the amount of row-crop agriculture was nearly ubiquitous in our best models, exhibiting a positive relationship with relative abundance for each species. The amount of native grassland land-cover was positively associated with coyote and raccoon relative abundance, but generally absent from models for red fox and skunk. Red fox and skunk were positively associated with each other, suggesting potential niche overlap. We found no evidence that coyote abundance limited that of other mesocarnivore species, as might be expected under a hypothesis of mesopredator release. The relationships between relative abundance and land-cover types were similar across spatial resolutions. Our results indicated that mesocarnivores in the PPR are most likely to occur in portions of the landscape with large amounts of agricultural land-cover. Further, our results indicated that track-survey data can be used in a hierarchical framework to gain inferences regarding spatial patterns in animal relative abundance.

  10. Penultimate modeling of spatial extremes: statistical inference for max-infinitely divisible processes

    KAUST Repository

    Huser, Raphaël

    2018-01-09

    Extreme-value theory for stochastic processes has motivated the statistical use of max-stable models for spatial extremes. However, fitting such asymptotic models to maxima observed over finite blocks is problematic when the asymptotic stability of the dependence does not prevail in finite samples. This issue is particularly serious when data are asymptotically independent, such that the dependence strength weakens and eventually vanishes as events become more extreme. We here aim to provide flexible sub-asymptotic models for spatially indexed block maxima, which more realistically account for discrepancies between data and asymptotic theory. We develop models pertaining to the wider class of max-infinitely divisible processes, extending the class of max-stable processes while retaining dependence properties that are natural for maxima: max-id models are positively associated, and they yield a self-consistent family of models for block maxima defined over any time unit. We propose two parametric construction principles for max-id models, emphasizing a point process-based generalized spectral representation, that allows for asymptotic independence while keeping the max-stable extremal-$t$ model as a special case. Parameter estimation is efficiently performed by pairwise likelihood, and we illustrate our new modeling framework with an application to Dutch wind gust maxima calculated over different time units.

  11. Generalized accelerated failure time spatial frailty model for arbitrarily censored data.

    Science.gov (United States)

    Zhou, Haiming; Hanson, Timothy; Zhang, Jiajia

    2017-07-01

    Flexible incorporation of both geographical patterning and risk effects in cancer survival models is becoming increasingly important, due in part to the recent availability of large cancer registries. Most spatial survival models stochastically order survival curves from different subpopulations. However, it is common for survival curves from two subpopulations to cross in epidemiological cancer studies and thus interpretable standard survival models can not be used without some modification. Common fixes are the inclusion of time-varying regression effects in the proportional hazards model or fully nonparametric modeling, either of which destroys any easy interpretability from the fitted model. To address this issue, we develop a generalized accelerated failure time model which allows stratification on continuous or categorical covariates, as well as providing per-variable tests for whether stratification is necessary via novel approximate Bayes factors. The model is interpretable in terms of how median survival changes and is able to capture crossing survival curves in the presence of spatial correlation. A detailed Markov chain Monte Carlo algorithm is presented for posterior inference and a freely available function frailtyGAFT is provided to fit the model in the R package spBayesSurv. We apply our approach to a subset of the prostate cancer data gathered for Louisiana by the surveillance, epidemiology, and end results program of the National Cancer Institute.

  12. Spatial analysis methods and land-use planning models for rural areas

    Directory of Open Access Journals (Sweden)

    Zuzanna Ludwiczak

    2009-10-01

    Full Text Available The work presents a brief report of the main results of a study carried out by the Spatial Engineering Division of the Department of Agricultural Economics and Engineering of the University of Bologna, within a broader PRIN 2005 research project concerning landscape and economic analysis, planning and programming. In particular, the study focuses on the design of spatial analysis methods aimed at building knowledge frameworks of the various natural and anthropic resources of rural areas. The goal is to increase the level of spatial and information detail of common databases, thus allowing higher accuracy and effectiveness of the analyses needed to achieve the goals of new generation spatial and agriculture planning. Specific in-depth analyses allowed to define techniques useful in order to reduce the increase in survey costs. Moreover, the work reports the main results regarding a multicriteria model for the analysis of the countryside defined by the research. Such model is aimed to assess the various agricultural, environmental and landscape features, vocations, expressions and attitudes, and support the definition and implementation of specific and targeted planning and programming policies.

  13. Integrated land-use models for spatial planning support: country-specific solutions

    Directory of Open Access Journals (Sweden)

    Vilja Vaszóczik

    2017-12-01

    Full Text Available The key issue of contemporary planning is that it has to work within and respond to the scarcity of resources. The competition for land is particularly intense in growth areas, especially around cities, where suburbanisation is accelerating and causes fundamental change of the evolved settlement pattern and the structure of land use. Planning in this situation must perform the difficult role of mediator in the competition for land, guiding the growth process and at the same time saving and protecting the open spaces which are indispensable for healthy living and are significant natural heritage. The integrated decision support systems (DSS are more and more popular among spatial planners and decision makers since 2000. These systems facilitate to incorporate scientific knowledge in the decision making process. During the last years in the unit for spatial planning of the Lechner Knowledge Centre we developed for a country specific spatial decision support, the Hungary Spatial Decision Support model. This study introduces this model and how it can assist territorial planning and decision making.

  14. Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa.

    Science.gov (United States)

    Redding, David W; Tiedt, Sonia; Lo Iacono, Gianni; Bett, Bernard; Jones, Kate E

    2017-07-19

    Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'. © 2017 The Authors.

  15. The effect of spatially heterogeneous damage in simple models of earthquake fault networks

    Science.gov (United States)

    Tiampo, K. F.; Dominguez, R.; Klein, W.; Serino, C.; Kazemian, J.

    2011-12-01

    Natural earthquake fault systems are highly heterogeneous in space; inhomogeneities occur because the earth is made of a variety of materials of different strengths and dissipate stress differently. Because the spatial arrangement of these materials is dependent on the geologic history, the spatial distribution of these various materials can be quite complex and occur over a variety of length scales. One way that the inhomogeneous nature of fault systems manifests itself is in the spatial patterns which emerge in seismicity (Tiampo et al., 2002, 2007). Despite their inhomogeneous nature, real faults are often modeled as spatially homogeneous systems. One argument for this approach is that earthquake faults experience long range stress transfer, and if this range is longer than the length scales associated with the inhomogeneities of the system, the dynamics of the system may be unaffected by the inhomogeneities. However, it is not clear that this is always the case. In this work we study the scaling of earthquake models that are variations of Olami-Feder-Christensen (OFC) and Burridge-Knopoff (BK) models, in order to explore the effect of spatial inhomogeneities on earthquake-like systems when interaction ranges are long, but not necessarily longer than the distances associated with the inhomogeneities of the system (Burridge and L. Knopoff, 1967; Rundle and Jackson, 1977; Olami et al., 1988). For long ranges and without inhomogeneities, such models have been found to produce scaling similar to GR scaling found in real earthquake systems (Rundle and Klein, 1993). In the earthquake models discussed here, damage is distributed inhomogeneously throughout and the interaction ranges, while long, are not longer than all of the damage length scales. In addition, we attempt to model the effect of a fixed distribution of asperities, and find that this has an effect on the magnitude-frequency relation, producing larger events at regular intervals, We find that the scaling

  16. A multi-scale spatial model of hepatitis-B viral dynamics.

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    Quentin Cangelosi

    Full Text Available Chronic hepatitis B viral infection (HBV afflicts around 250 million individuals globally and few options for treatment exist. Once infected, the virus entrenches itself in the liver with a notoriously resilient colonisation of viral DNA (covalently-closed circular DNA, cccDNA. The majority of infections are cleared, yet we do not understand why 5% of adult immune responses fail leading to the chronic state with its collateral morbid effects such as cirrhosis and eventual hepatic carcinoma. The liver environment exhibits particularly complex spatial structures for metabolic processing and corresponding distributions of nutrients and transporters that may influence successful HBV entrenchment. We assembled a multi-scaled mathematical model of the fundamental hepatic processing unit, the sinusoid, into a whole-liver representation to investigate the impact of this intrinsic spatial heterogeneity on the HBV dynamic. Our results suggest HBV may be exploiting spatial aspects of the liver environment. We distributed increased HBV replication rates coincident with elevated levels of nutrients in the sinusoid entry point (the periportal region in tandem with similar distributions of hepatocyte transporters key to HBV invasion (e.g., the sodium-taurocholate cotransporting polypeptide or NTCP, or immune system activity. According to our results, such co-alignment of spatial distributions may contribute to persistence of HBV infections, depending on spatial distributions and intensity of immune response as well. Moreover, inspired by previous HBV models and experimentalist suggestions of extra-hepatic HBV replication, we tested in our model influence of HBV blood replication and observe an overall nominal effect on persistent liver infection. Regardless, we confirm prior results showing a solo cccDNA is sufficient to re-infect an entire liver, with corresponding concerns for transplantation and treatment.

  17. A Spatial Model for the Instantaneous Estimation of Wind Power at a Large Number of Unobserved Sites

    DEFF Research Database (Denmark)

    Lenzi, Amanda; Guillot, Gilles; Pinson, Pierre

    2015-01-01

    We propose a hierarchical Bayesian spatial model to obtain predictive densities of wind power at a set of un-monitored locations. The model consists of a mixture of Gamma density for the non-zero values and degenerated distributions at zero. The spatial dependence is described through a common Ga...

  18. Effect of head model on Monte Carlo modeling of spatial sensitivity distribution for functional near-infrared spectroscopy

    Directory of Open Access Journals (Sweden)

    Ting Li

    2015-09-01

    Full Text Available Modeling Light propagation within human head to deduce spatial sensitivity distribution (SSD is important for Near-infrared spectroscopy (NIRS/imaging (NIRI and diffuse correlation tomography. Lots of head models have been used on this issue, including layered head model, artificial simplified head model, MRI slices described head model, and visible human head model. Hereinto, visible Chinese human (VCH head model is considered to be a most faithful presentation of anatomical structure, and has been highlighted to be employed in modeling light propagation. However, it is not practical for all researchers to use VCH head models and actually increasing number of people are using magnet resonance imaging (MRI head models. Here, all the above head models were simulated and compared, and we focused on the effect of using different head models on predictions of SSD. Our results were in line with the previous reports on the effect of cerebral cortex folding geometry. Moreover, the influence on SSD increases with the fidelity of head models. And surprisingly, the SSD percentages in scalp and gray matter (region of interest in MRI head model were found to be 80% and 125% higher than in VCH head model. MRI head models induced nonignorable discrepancy in SSD estimation when compared with VCH head model. This study, as we believe, is the first to focus on comparison among full serials of head model on estimating SSD, and provided quantitative evidence for MRI head model users to calibrate their SSD estimation.

  19. Selective 4D modelling framework for spatial-temporal land information management system

    Science.gov (United States)

    Doulamis, Anastasios; Soile, Sofia; Doulamis, Nikolaos; Chrisouli, Christina; Grammalidis, Nikos; Dimitropoulos, Kosmas; Manesis, Charalambos; Potsiou, Chryssy; Ioannidis, Charalabos

    2015-06-01

    This paper introduces a predictive (selective) 4D modelling framework where only the spatial 3D differences are modelled at the forthcoming time instances, while regions of no significant spatial-temporal alterations remain intact. To accomplish this, initially spatial-temporal analysis is applied between 3D digital models captured at different time instances. So, the creation of dynamic change history maps is made. Change history maps indicate spatial probabilities of regions needed further 3D modelling at forthcoming instances. Thus, change history maps are good examples for a predictive assessment, that is, to localize surfaces within the objects where a high accuracy reconstruction process needs to be activated at the forthcoming time instances. The proposed 4D Land Information Management System (LIMS) is implemented using open interoperable standards based on the CityGML framework. CityGML allows the description of the semantic metadata information and the rights of the land resources. Visualization aspects are also supported to allow easy manipulation, interaction and representation of the 4D LIMS digital parcels and the respective semantic information. The open source 3DCityDB incorporating a PostgreSQL geo-database is used to manage and manipulate 3D data and their semantics. An application is made to detect the change through time of a 3D block of plots in an urban area of Athens, Greece. Starting with an accurate 3D model of the buildings in 1983, a change history map is created using automated dense image matching on aerial photos of 2010. For both time instances meshes are created and through their comparison the changes are detected.

  20. STATE OF THE ART OF THE LANDSCAPE ARCHITECTURE SPATIAL DATA MODEL FROM A GEOSPATIAL PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    A. Kastuari

    2016-10-01

    Full Text Available Spatial data and information had been used for some time in planning or landscape design. For a long time, architects were using spatial data in the form of topographic map for their designs. This method is not efficient, and it is also not more accurate than using spatial analysis by utilizing GIS. Architects are sometimes also only accentuating the aesthetical aspect for their design, but not taking landscape process into account which could cause the design could be not suitable for its use and its purpose. Nowadays, GIS role in landscape architecture has been formalized by the emergence of Geodesign terminology that starts in Representation Model and ends in Decision Model. The development of GIS could be seen in several fields of science that now have the urgency to use 3 dimensional GIS, such as in: 3D urban planning, flood modeling, or landscape planning. In this fields, 3 dimensional GIS is able to support the steps in modeling, analysis, management, and integration from related data, that describe the human activities and geophysics phenomena in more realistic way. Also, by applying 3D GIS and geodesign in landscape design, geomorphology information can be better presented and assessed. In some research, it is mentioned that the development of 3D GIS is not established yet, either in its 3D data structure, or in its spatial analysis function. This study literature will able to accommodate those problems by providing information on existing development of 3D GIS for landscape architecture, data modeling, the data accuracy, representation of data that is needed by landscape architecture purpose, specifically in the river area.

  1. Kinematic modeling of a 7-degree of freedom spatial hybrid manipulator for medical surgery.

    Science.gov (United States)

    Singh, Amanpreet; Singla, Ekta; Soni, Sanjeev; Singla, Ashish

    2018-01-01

    The prime objective of this work is to deal with the kinematics of spatial hybrid manipulators. In this direction, in 1955, Denavit and Hartenberg proposed a consistent and concise method, known as D-H parameters method, to deal with kinematics of open serial chains. From literature review, it is found that D-H parameter method is widely used to model manipulators consisting of lower pairs. However, the method leads to ambiguities when applied to closed-loop, tree-like and hybrid manipulators. Furthermore, in the dearth of any direct method to model closed-loop, tree-like and hybrid manipulators, revisions of this method have been proposed from time-to-time by different researchers. One such kind of revision using the concept of dummy frames has successfully been proposed and implemented by the authors on spatial hybrid manipulators. In that work, authors have addressed the orientational inconsistency of the D-H parameter method, restricted to body-attached frames only. In the current work, the condition of body-attached frames is relaxed and spatial frame attachment is considered to derive the kinematic model of a 7-degree of freedom spatial hybrid robotic arm, along with the development of closed-loop constraints. The validation of the new kinematic model has been performed with the help of a prototype of this 7-degree of freedom arm, which is being developed at Council of Scientific & Industrial Research-Central Scientific Instruments Organisation Chandigarh to aid the surgeon during a medical surgical task. Furthermore, the developed kinematic model is used to develop the first column of the Jacobian matrix, which helps in providing the estimate of the tip velocity of the 7-degree of freedom manipulator when the first joint velocity is known.

  2. CONCEPTS, MODELS AND IMPLEMENTATION OF THE MARINE SPATIAL DATA INFRASTRUCTURE IN GERMANY (MDI-DE

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    C. Rüh

    2012-07-01

    Full Text Available In Germany currently the development of a marine data infrastructure takes place with the aim of merging information concerning the fields coastal engineering, hydrography and surveying, protection of the marine environment, maritime conservation, regional planning and coastal research. This undertaking is embedded in a series of regulations and developments on many administrative levels from which specifications and courses of action derive. To set up a conceptual framework for the marine data infrastructure (MDI-DE scientists at the Professorship for Geodesy and Geoinformatics at Rostock University are building a reference model, evaluating meta-information systems and developing models to support common workflows in marine applications. The reference model for the marine spatial data infrastructure of Germany (MDI-DE is the guideline for all developments inside this infrastructure. Because the undertaking is embedded in a series of regulations and developments this paper illustrates an approach on modelling a scenario for the Marine Strategy Framework Directive (MSFD using the Unified Modelling Language (UML. Evaluating how other countries built their marine spatial infrastructures is of main importance, to learn where obstacles are and errors are likely to occur. To be able to look at other initiatives from a neutral point of view it is necessary to construct a framework for evaluation of marine spatial data infrastructures. Spatial data infrastructure assessment approaches were used as bases and were expanded to meet the requirements of the marine domain. As an international case-study this paper will look at Canada's Marine Geospatial Data Infrastructure (MGDI, COINAtlantic and GeoPortal.

  3. Concepts, Models and Implementation of the Marine Spatial Data Infrastructure in Germany Mdi-De

    Science.gov (United States)

    Rüh, C.; Bill, R.

    2012-07-01

    In Germany currently the development of a marine data infrastructure takes place with the aim of merging information concerning the fields coastal engineering, hydrography and surveying, protection of the marine environment, maritime conservation, regional planning and coastal research. This undertaking is embedded in a series of regulations and developments on many administrative levels from which specifications and courses of action derive. To set up a conceptual framework for the marine data infrastructure (MDI-DE) scientists at the Professorship for Geodesy and Geoinformatics at Rostock University are building a reference model, evaluating meta-information systems and developing models to support common workflows in marine applications. The reference model for the marine spatial data infrastructure of Germany (MDI-DE) is the guideline for all developments inside this infrastructure. Because the undertaking is embedded in a series of regulations and developments this paper illustrates an approach on modelling a scenario for the Marine Strategy Framework Directive (MSFD) using the Unified Modelling Language (UML). Evaluating how other countries built their marine spatial infrastructures is of main importance, to learn where obstacles are and errors are likely to occur. To be able to look at other initiatives from a neutral point of view it is necessary to construct a framework for evaluation of marine spatial data infrastructures. Spatial data infrastructure assessment approaches were used as bases and were expanded to meet the requirements of the marine domain. As an international case-study this paper will look at Canada's Marine Geospatial Data Infrastructure (MGDI), COINAtlantic and GeoPortal.

  4. Bayesian spatial semi-parametric modeling of HIV variation in Kenya.

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    Oscar Ngesa

    Full Text Available Spatial statistics has seen rapid application in many fields, especially epidemiology and public health. Many studies, nonetheless, make limited use of the geographical location information and also usually assume that the covariates, which are related to the response variable, have linear effects. We develop a Bayesian semi-parametric regression model for HIV prevalence data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (McMC. The model is applied to HIV prevalence data among men in Kenya, derived from the Kenya AIDS indicator survey, with n = 3,662. Past studies have concluded that HIV infection has a nonlinear association with age. In this study a smooth function based on penalized regression splines is used to estimate this nonlinear effect. Other covariates were assumed to have a linear effect. Spatial references to the counties were modeled as both structured and unstructured spatial effects. We observe that circumcision reduces the risk of HIV infection. The results also indicate that men in the urban areas were more likely to be infected by HIV as compared to their rural counterpart. Men with higher education had the lowest risk of HIV infection. A nonlinear relationship between HIV infection and age was established. Risk of HIV infection increases with age up to the age of 40 then declines with increase in age. Men who had STI in the last 12 months were more likely to be infected with HIV. Also men who had ever used a condom were found to have higher likelihood to be infected by HIV. A significant spatial variation of HIV infection in Kenya was also established. The study shows the practicality and flexibility of Bayesian semi-parametric regression model in analyzing epidemiological data.

  5. Field Scale Spatial Modelling of Surface Soil Quality Attributes in Controlled Traffic Farming

    Science.gov (United States)

    Guenette, Kris; Hernandez-Ramirez, Guillermo

    2017-04-01

    The employment of controlled traffic farming (CTF) can yield improvements to soil quality attributes through the confinement of equipment traffic to tramlines with the field. There is a need to quantify and explain the spatial heterogeneity of soil quality attributes affected by CTF to further improve our understanding and modelling ability of field scale soil dynamics. Soil properties such as available nitrogen (AN), pH, soil total nitrogen (STN), soil organic carbon (SOC), bulk density, macroporosity, soil quality S-Index, plant available water capacity (PAWC) and unsaturated hydraulic conductivity (Km) were analysed and compared among trafficked and un-trafficked areas. We contrasted standard geostatistical methods such as ordinary kriging (OK) and covariate kriging (COK) as well as the hybrid method of regression kriging (ROK) to predict the spatial distribution of soil properties across two annual cropland sites actively employing CTF in Alberta, Canada. Field scale variability was quantified more accurately through the inclusion of covariates; however, the use of ROK was shown to improve model accuracy despite the regression model composition limiting the robustness of the ROK method. The exclusion of traffic from the un-trafficked areas displayed significant improvements to bulk density, macroporosity and Km while subsequently enhancing AN, STN and SOC. The ability of the regression models and the ROK method to account for spatial trends led to the highest goodness-of-fit and lowest error achieved for the soil physical properties, as the rigid traffic regime of CTF altered their spatial distribution at the field scale. Conversely, the COK method produced the most optimal predictions for the soil nutrient properties and Km. The use of terrain covariates derived from light ranging and detection (LiDAR), such as of elevation and topographic position index (TPI), yielded the best models in the COK method at the field scale.

  6. Continuous spatial modelling to analyse planning and economic consequences of offshore wind energy

    International Nuclear Information System (INIS)

    Moeller, Bernd

    2011-01-01

    Offshore wind resources appear abundant, but technological, economic and planning issues significantly reduce the theoretical potential. While massive investments are anticipated and planners and developers are scouting for viable locations and consider risk and impact, few studies simultaneously address potentials and costs together with the consequences of proposed planning in an analytical and continuous manner and for larger areas at once. Consequences may be investments short of efficiency and equity, and failed planning routines. A spatial resource economic model for the Danish offshore waters is presented, used to analyse area constraints, technological risks, priorities for development and opportunity costs of maintaining competing area uses. The SCREAM-offshore wind model (Spatially Continuous Resource Economic Analysis Model) uses raster-based geographical information systems (GIS) and considers numerous geographical factors, technology and cost data as well as planning information. Novel elements are weighted visibility analysis and geographically recorded shipping movements as variable constraints. A number of scenarios have been described, which include restrictions of using offshore areas, as well as alternative uses such as conservation and tourism. The results comprise maps, tables and cost-supply curves for further resource economic assessment and policy analysis. A discussion of parameter variations exposes uncertainties of technology development, environmental protection as well as competing area uses and illustrates how such models might assist in ameliorating public planning, while procuring decision bases for the political process. The method can be adapted to different research questions, and is largely applicable in other parts of the world. - Research Highlights: → A model for the spatially continuous evaluation of offshore wind resources. → Assessment of spatial constraints, costs and resources for each location. → Planning tool for

  7. State of the Art of the Landscape Architecture Spatial Data Model from a Geospatial Perspective

    Science.gov (United States)

    Kastuari, A.; Suwardhi, D.; Hanan, H.; Wikantika, K.

    2016-10-01

    Spatial data and information had been used for some time in planning or landscape design. For a long time, architects were using spatial data in the form of topographic map for their designs. This method is not efficient, and it is also not more accurate than using spatial analysis by utilizing GIS. Architects are sometimes also only accentuating the aesthetical aspect for their design, but not taking landscape process into account which could cause the design could be not suitable for its use and its purpose. Nowadays, GIS role in landscape architecture has been formalized by the emergence of Geodesign terminology that starts in Representation Model and ends in Decision Model. The development of GIS could be seen in several fields of science that now have the urgency to use 3 dimensional GIS, such as in: 3D urban planning, flood modeling, or landscape planning. In this fields, 3 dimensional GIS is able to support the steps in modeling, analysis, management, and integration from related data, that describe the human activities and geophysics phenomena in more realistic way. Also, by applying 3D GIS and geodesign in landscape design, geomorphology information can be better presented and assessed. In some research, it is mentioned that the development of 3D GIS is not established yet, either in its 3D data structure, or in its spatial analysis function. This study literature will able to accommodate those problems by providing information on existing development of 3D GIS for landscape architecture, data modeling, the data accuracy, representation of data that is needed by landscape architecture purpose, specifically in the river area.

  8. A model for spatial variations in life expectancy; mortality in Chinese regions in 2000

    Directory of Open Access Journals (Sweden)

    Congdon Peter

    2007-05-01

    Full Text Available Abstract Background Life expectancy in China has been improving markedly but health gains have been uneven and there is inequality in survival chances between regions and in rural as against urban areas. This paper applies a statistical modelling approach to mortality data collected in conjunction with the 2000 Census to formally assess spatial mortality contrasts in China. The modelling approach provides interpretable summary parameters (e.g. the relative mortality risk in rural as against urban areas and is more parsimonious in terms of parameters than the conventional life table model. Results Predictive fit is assessed both globally and at the level of individual five year age groups. A proportional model (age and area effects independent has a worse fit than one allowing age-area interactions following a bilinear form. The best fit is obtained by allowing for child and oldest age mortality rates to vary spatially. Conclusion There is evidence that age (21 age groups and area (31 Chinese administrative divisions are not proportional (i.e. independent mortality risk factors. In fact, spatial contrasts are greatest at young ages. There is a pronounced rural survival disadvantage, and large differences in life expectancy between provinces.<