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 ...
Thermodynamic Model of Spatial Memory
Kaufman, Miron; Allen, P.
1998-03-01
We develop and test a thermodynamic model of spatial memory. Our model is an application of statistical thermodynamics to cognitive science. It is related to applications of the statistical mechanics framework in parallel distributed processes research. Our macroscopic model allows us to evaluate an entropy associated with spatial memory tasks. We find that older adults exhibit higher levels of entropy than younger adults. Thurstone's Law of Categorical Judgment, according to which the discriminal processes along the psychological continuum produced by presentations of a single stimulus are normally distributed, is explained by using a Hooke spring model of spatial memory. We have also analyzed a nonlinear modification of the ideal spring model of spatial memory. This work is supported by NIH/NIA grant AG09282-06.
Competition in spatial location models
Webers, H.M.
1996-01-01
Models of spatial competition are designed and analyzed to describe the fact that space, by its very nature, is a source of market power. This field of research, lying at the interface of game theory and economics, has attracted much interest because location problems are related to many aspects of
Continuous Spatial Process Models for Spatial Extreme Values
Sang, Huiyan; Gelfand, Alan E.
2010-01-01
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
Continuous Spatial Process Models for Spatial Extreme Values
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.
Spatial data quality and coastal spill modelling
International Nuclear Information System (INIS)
Li, Y.; Brimicombe, A.J.; Ralphs, M.P.
1998-01-01
Issues of spatial data quality are central to the whole oil spill modelling process. Both model and data quality performance issues should be considered as indispensable parts of a complete oil spill model specification and testing procedure. This paper presents initial results of research that will emphasise to modeler and manager alike the practical issues of spatial data quality for coastal oil spill modelling. It is centred around a case study of Jiao Zhou Bay in the People's Republic of China. The implications for coastal oil spill modelling are discussed and some strategies for managing the effects of spatial data quality in the outputs of oil spill modelling are explored. (author)
Bayesian Spatial Modelling with R-INLA
Directory of Open Access Journals (Sweden)
Finn Lindgren
2015-02-01
Full Text Available The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA approach proposed by Rue, Martino, and Chopin (2009 is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized linear mixed to spatial and spatio-temporal models. Combined with the stochastic partial differential equation approach (SPDE, Lindgren, Rue, and Lindstrm 2011, one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial mod- els, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.
Intelligent spatial ecosystem modeling using parallel processors
International Nuclear Information System (INIS)
Maxwell, T.; Costanza, R.
1993-01-01
Spatial modeling of ecosystems is essential if one's modeling goals include developing a relatively realistic description of past behavior and predictions of the impacts of alternative management policies on future ecosystem behavior. Development of these models has been limited in the past by the large amount of input data required and the difficulty of even large mainframe serial computers in dealing with large spatial arrays. These two limitations have begun to erode with the increasing availability of remote sensing data and GIS systems to manipulate it, and the development of parallel computer systems which allow computation of large, complex, spatial arrays. Although many forms of dynamic spatial modeling are highly amenable to parallel processing, the primary focus in this project is on process-based landscape models. These models simulate spatial structure by first compartmentalizing the landscape into some geometric design and then describing flows within compartments and spatial processes between compartments according to location-specific algorithms. The authors are currently building and running parallel spatial models at the regional scale for the Patuxent River region in Maryland, the Everglades in Florida, and Barataria Basin in Louisiana. The authors are also planning a project to construct a series of spatially explicit linked ecological and economic simulation models aimed at assessing the long-term potential impacts of global climate change
Dynamic spatial panels : models, methods, and inferences
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
Spherical Process Models for Global Spatial Statistics
Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G.
2017-01-01
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
Hierarchical modeling and analysis for spatial data
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
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.
Spatial occupancy models for large data sets
Johnson, Devin S.; Conn, Paul B.; Hooten, Mevin B.; Ray, Justina C.; Pond, Bruce A.
2013-01-01
Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km2) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.
Evaluating spatial patterns in hydrological modelling
DEFF Research Database (Denmark)
Koch, Julian
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...... 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...
Crime Modeling using Spatial Regression Approach
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.
A simplified spatial model for BWR stability
International Nuclear Information System (INIS)
Berman, Y.; Lederer, Y.; Meron, E.
2012-01-01
A spatial reduced order model for the study of BWR stability, based on the phenomenological model of March-Leuba et al., is presented. As one dimensional spatial dependence of the neutron flux, fuel temperature and void fraction is introduced, it is possible to describe both global and regional oscillations of the reactor power. Both linear stability analysis and numerical analysis were applied in order to describe the parameters which govern the model stability. The results were found qualitatively similar to past results. Doppler reactivity feedback was found essential for the explanation of the different regions of the flow-power stability map. (authors)
Spatial scale separation in regional climate modelling
Energy Technology Data Exchange (ETDEWEB)
Feser, F.
2005-07-01
In this thesis the concept of scale separation is introduced as a tool for first improving regional climate model simulations and, secondly, to explicitly detect and describe the added value obtained by regional modelling. The basic idea behind this is that global and regional climate models have their best performance at different spatial scales. Therefore the regional model should not alter the global model's results at large scales. The for this purpose designed concept of nudging of large scales controls the large scales within the regional model domain and keeps them close to the global forcing model whereby the regional scales are left unchanged. For ensemble simulations nudging of large scales strongly reduces the divergence of the different simulations compared to the standard approach ensemble that occasionally shows large differences for the individual realisations. For climate hindcasts this method leads to results which are on average closer to observed states than the standard approach. Also the analysis of the regional climate model simulation can be improved by separating the results into different spatial domains. This was done by developing and applying digital filters that perform the scale separation effectively without great computational effort. The separation of the results into different spatial scales simplifies model validation and process studies. The search for 'added value' can be conducted on the spatial scales the regional climate model was designed for giving clearer results than by analysing unfiltered meteorological fields. To examine the skill of the different simulations pattern correlation coefficients were calculated between the global reanalyses, the regional climate model simulation and, as a reference, of an operational regional weather analysis. The regional climate model simulation driven with large-scale constraints achieved a high increase in similarity to the operational analyses for medium-scale 2 meter
Nonparametric Bayesian models for a spatial covariance.
Reich, Brian J; Fuentes, Montserrat
2012-01-01
A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.
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.
Spatial Modeling for Resources Framework (SMRF)
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...
The 3-D global spatial data model foundation of the spatial data infrastructure
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...
Spatially explicit modeling in ecology: A review
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.
Control of spatial discretisation in coastal oil spill modelling
Li, Yang
2007-01-01
Spatial discretisation plays an important role in many numerical environmental models. This paper studies the control of spatial discretisation in coastal oil spill modelling with a view to assure the quality of modelling outputs for given spatial data inputs. Spatial data analysis techniques are effective for investigating and improving the spatial discretisation in different phases of the modelling. Proposed methods are implemented and tested with experimental models. A new “Automatic Searc...
Spatial Models and Networks of Living Systems
DEFF Research Database (Denmark)
Juul, Jeppe Søgaard
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...... variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network...... 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...
A nonlocal spatial model for Lyme disease
Yu, Xiao; Zhao, Xiao-Qiang
2016-07-01
This paper is devoted to the study of a nonlocal and time-delayed reaction-diffusion model for Lyme disease with a spatially heterogeneous structure. In the case of a bounded domain, we first prove the existence of the positive steady state and a threshold type result for the disease-free system, and then establish the global dynamics for the model system in terms of the basic reproduction number. In the case of an unbound domain, we obtain the existence of the disease spreading speed and its coincidence with the minimal wave speed. At last, we use numerical simulations to verify our analytic results and investigate the influence of model parameters and spatial heterogeneity on the disease infection risk.
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...
Developing a modelling for the spatial data infrastructure
CSIR Research Space (South Africa)
Hjelmager, J
2005-07-01
Full Text Available The Commission on Spatial Data Standards of the International Cartographic Association (ICA) is working on defining spatial models and technical characteristics of a Spatial Data Infrastructure (SDI). To date, this work has been restricted...
Factor Copula Models for Replicated Spatial Data
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.
Factor Copula Models for Replicated Spatial Data
Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.
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.
Modeling Spatially Unrestricted Pedestrian Traffic on Footbridges
DEFF Research Database (Denmark)
Zivanovic, Stana; Pavic, Aleksandar; Ingólfsson, Einar Thór
2010-01-01
restricted movement of pedestrians, has kept attracting attention of researchers. However, it is the normal spatially unrestricted pedestrian traffic, and its vertical dynamic loading component, that are most relevant for vibration serviceability checks for most footbridges. Despite the existence of numerous...... design procedures concerned with this loading, the current confidence in its modelling is low due to lack of verification of the models on as-built structures. This is the motivation behind reviewing the existing design procedures for modelling normal pedestrian traffic in this paper and evaluating...
Human Plague Risk: Spatial-Temporal Models
Pinzon, Jorge E.
2010-01-01
This chpater reviews the use of spatial-temporal models in identifying potential risks of plague outbreaks into the human population. Using earth observations by satellites remote sensing there has been a systematic analysis and mapping of the close coupling between the vectors of the disease and climate variability. The overall result is that incidence of plague is correlated to positive El Nino/Southem Oscillation (ENSO).
The quantitative modelling of human spatial habitability
Wise, James A.
1988-01-01
A theoretical model for evaluating human spatial habitability (HuSH) in the proposed U.S. Space Station is developed. Optimizing the fitness of the space station environment for human occupancy will help reduce environmental stress due to long-term isolation and confinement in its small habitable volume. The development of tools that operationalize the behavioral bases of spatial volume for visual kinesthetic, and social logic considerations is suggested. This report further calls for systematic scientific investigations of how much real and how much perceived volume people need in order to function normally and with minimal stress in space-based settings. The theoretical model presented in this report can be applied to any size or shape interior, at any scale of consideration, for the Space Station as a whole to an individual enclosure or work station. Using as a point of departure the Isovist model developed by Dr. Michael Benedikt of the U. of Texas, the report suggests that spatial habitability can become as amenable to careful assessment as engineering and life support concerns.
The quantitative modelling of human spatial habitability
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).
Modeling mental spatial reasoning about cardinal directions.
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.
Helbich, M; Griffith, D
2016-01-01
Real estate policies in urban areas require the recognition of spatial heterogeneity in housing prices to account for local settings. In response to the growing number of spatially varying coefficient models in housing applications, this study evaluated four models in terms of their spatial patterns
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.
A Computational Model of Spatial Development
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.
Spherical Process Models for Global Spatial Statistics
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.
Latent spatial models and sampling design for landscape genetics
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.
Spatially varying dispersion to model breakthrough curves.
Li, Guangquan
2011-01-01
Often the water flowing in a karst conduit is a combination of contaminated water entering at a sinkhole and cleaner water released from the limestone matrix. Transport processes in the conduit are controlled by advection, mixing (dilution and dispersion), and retention-release. In this article, a karst transport model considering advection, spatially varying dispersion, and dilution (from matrix seepage) is developed. Two approximate Green's functions are obtained using transformation of variables, respectively, for the initial-value problem and for the boundary-value problem. A numerical example illustrates that mixing associated with strong spatially varying conduit dispersion can cause strong skewness and long tailing in spring breakthrough curves. Comparison of the predicted breakthrough curve against that measured from a dye-tracing experiment between Ames Sink and Indian Spring, Northwest Florida, shows that the conduit dispersivity can be as large as 400 m. Such a large number is believed to imply strong solute interaction between the conduit and the matrix and/or multiple flow paths in a conduit network. It is concluded that Taylor dispersion is not dominant in transport in a karst conduit, and the complicated retention-release process between mobile- and immobile waters may be described by strong spatially varying conduit dispersion. Copyright © 2010 The Author(s). Journal compilation © 2010 National Ground Water Association.
An Evolutionary Model of Spatial Competition
DEFF Research Database (Denmark)
Knudsen, Thorbjørn; Winter, Sidney G.
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...... as well in the new environment as they did in the old; the firm may respond with effort to locate appropriate environments or by modification of its routines. Tradeoffs are presented between the complexity of a business model and its replication costs, as well as issues involving response....... Randomly generated firm policies are tested first by a local market environment, and then, if success leads the firm to grow spatially, in a gradually expanding environment. In the initial experiments reported here, we show that the model generates configurations that reflect features of the exogenous...
Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable
Elhorst, J. Paul
2001-01-01
This paper surveys panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable. In particular, it focuses on the specification and estimation of four panel data models commonly used in applied research: the fixed effects model, the random effects model, the
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.
Theoretical aspects of spatial-temporal modeling
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...
Models and Inference for Multivariate Spatial Extremes
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
Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models
Haslauer, C. P.; Bárdossy, A.
2017-12-01
A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.
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.
Spatially explicit modelling of cholera epidemics
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.
Modeling strategic investment decisions in spatial markets
Energy Technology Data Exchange (ETDEWEB)
Lorenczik, Stefan; Malischek, Raimund [Koeln Univ. (Germany). Energiewirtschaftliches Inst.; Trueby, Johannes [International Energy Agency, 75 - Paris (France)
2014-04-15
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.
A physically based analytical spatial air temperature and humidity model
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...
Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.
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
Spatial Data Web Services Pricing Model Infrastructure
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.
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
Reducing Spatial Data Complexity for Classification Models
Ruta, Dymitr; Gabrys, Bogdan
2007-11-01
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
Panchromatic SED modelling of spatially resolved galaxies
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.
Consequences of spatial autocorrelation for niche-based models
DEFF Research Database (Denmark)
Segurado, P.; Araújo, Miguel B.; Kunin, W. E.
2006-01-01
1. Spatial autocorrelation is an important source of bias in most spatial analyses. We explored the bias introduced by spatial autocorrelation on the explanatory and predictive power of species' distribution models, and make recommendations for dealing with the problem. 2. Analyses were based o...
Spatial Econometric data analysis: moving beyond traditional models
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
The spatial limitations of current neutral models of biodiversity.
Directory of Open Access Journals (Sweden)
Rampal S Etienne
Full Text Available The unified neutral theory of biodiversity and biogeography is increasingly accepted as an informative null model of community composition and dynamics. It has successfully produced macro-ecological patterns such as species-area relationships and species abundance distributions. However, the models employed make many unrealistic auxiliary assumptions. For example, the popular spatially implicit version assumes a local plot exchanging migrants with a large panmictic regional source pool. This simple structure allows rigorous testing of its fit to data. In contrast, spatially explicit models assume that offspring disperse only limited distances from their parents, but one cannot as yet test the significance of their fit to data. Here we compare the spatially explicit and the spatially implicit model, fitting the most-used implicit model (with two levels, local and regional to data simulated by the most-used spatially explicit model (where offspring are distributed about their parent on a grid according to either a radially symmetric Gaussian or a 'fat-tailed' distribution. Based on these fits, we express spatially implicit parameters in terms of spatially explicit parameters. This suggests how we may obtain estimates of spatially explicit parameters from spatially implicit ones. The relationship between these parameters, however, makes no intuitive sense. Furthermore, the spatially implicit model usually fits observed species-abundance distributions better than those calculated from the spatially explicit model's simulated data. Current spatially explicit neutral models therefore have limited descriptive power. However, our results suggest that a fatter tail of the dispersal kernel seems to improve the fit, suggesting that dispersal kernels with even fatter tails should be studied in future. We conclude that more advanced spatially explicit models and tools to analyze them need to be developed.
Model for Atmospheric Propagation of Spatially Combined Laser Beams
2016-09-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS by Kum Leong Lee September...MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS 5. FUNDING NUMBERS 6. AUTHOR(S) Kum Leong Lee 7. PERFORMING ORGANIZATION NAME(S) AND...BLANK ii Approved for public release. Distribution is unlimited. MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS Kum Leong Lee
A Spatial Model of the Mere Exposure Effect.
Fink, Edward L.; And Others
1989-01-01
Uses a spatial model to examine the relationship between stimulus exposure, cognition, and affect. Notes that this model accounts for cognitive changes that a stimulus may acquire as a result of exposure. Concludes that the spatial model is useful for evaluating the mere exposure effect and that affective change does not require cognitive change.…
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
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.
Modeling spatial processes with unknown extremal dependence class
Huser, Raphaë l G.; Wadsworth, Jennifer L.
2017-01-01
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
Modeling the spatial reach of the LFP
DEFF Research Database (Denmark)
Lindén, Henrik; Tetzlaff, Tom; Potjans, Tobias C
2011-01-01
The local field potential (LFP) reflects activity of many neurons in the vicinity of the recording electrode and is therefore useful for studying local network dynamics. Much of the nature of the LFP is, however, still unknown. There are, for instance, contradicting reports on the spatial extent ...
Spatial modeling of potential woody biomass flow
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...
Modeling fixation locations using spatial point processes.
Barthelmé, Simon; Trukenbrod, Hans; Engbert, Ralf; Wichmann, Felix
2013-10-01
Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.
Chonggang Xu; Hong S. He; Yuanman Hu; Yu Chang; Xiuzhen Li; Rencang Bu
2005-01-01
Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great...
. Redundancy and blocking in the spatial domain: A connectionist model
Directory of Open Access Journals (Sweden)
I. P. L. Mc Laren
2002-01-01
Full Text Available How can the observations of spatial blocking (Rodrigo, Chamizo, McLaren & Mackintosh, 1997 and cue redundancy (OKeefe and Conway, 1978 be reconciled within the framework provided by an error-correcting, connectionist account of spatial navigation? I show that an implementation of McLarens (1995 better beta model can serve this purpose, and examine some of the implications for spatial learning and memory.
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.
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.
Spatial uncertainty model for visual features using a Kinect™ sensor.
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.
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.
Extinction threshold of a population in spatial and stochastic model
Soroka, Yevheniia; Rublyov, Bogdan
2016-01-01
In this study, spatial stochastic and logistic model (SSLM) describing dynamics of a population of a certain species was analysed. The behaviour of the extinction threshold as a function of model parameters was studied. More specifically, we studied how the critical values for the model parameters that separate the cases of extinction and persistence depend on the spatial scales of the competition and dispersal kernels. We compared the simulations and analytical results to examine if and how ...
A spatial Mankiw-Romer-Weil model: Theory and evidence
Fischer, Manfred M.
2009-01-01
This paper presents a theoretical growth model that extends the Mankiw-Romer-Weil [MRW] model by accounting for technological interdependence among regional economies. Interdependence is assumed to work through spatial externalities caused by disembodied knowledge diffusion. The transition from theory to econometrics leads to a reduced-form empirical spatial Durbin model specification that explains the variation in regional levels of per worker output at steady state. A system ...
Spatial Modeling of Deforestation in FMU of Poigar, North Sulawesi
Ahmad, Afandi; Saleh, Muhammad Buce; Rusolono, Teddy
2016-01-01
Forest is a part of the ecosystem that provides environmental services. Deforestation may decrease forest function in an ecosystem. This study aims to build a spatial model of deforestation in a forest management unit (FMU) of Poigar. Deforestation analysis carried out by analyze the change of forest cover into non-forest cover with post classification comparison technique. Driving forces of deforestation carried out by spatial modeling using binary logistic regression models (LRM). Result of...
A model relating Eulerian spatial and temporal velocity correlations
Cholemari, Murali R.; Arakeri, Jaywant H.
2006-03-01
In this paper we propose a model to relate Eulerian spatial and temporal velocity autocorrelations in homogeneous, isotropic and stationary turbulence. We model the decorrelation as the eddies of various scales becoming decorrelated. This enables us to connect the spatial and temporal separations required for a certain decorrelation through the ‘eddy scale’. Given either the spatial or the temporal velocity correlation, we obtain the ‘eddy scale’ and the rate at which the decorrelation proceeds. This leads to a spatial separation from the temporal correlation and a temporal separation from the spatial correlation, at any given value of the correlation relating the two correlations. We test the model using experimental data from a stationary axisymmetric turbulent flow with homogeneity along the axis.
Stochastic Spatial Models in Ecology: A Statistical Physics Approach
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.
Updates to the Demographic and Spatial Allocation Models to ...
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.
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....
Spatial modeling of households' knowledge about arsenic pollution in Bangladesh.
Sarker, M Mizanur Rahman
2012-04-01
Arsenic in drinking water is an important public health issue in Bangladesh, which is affected by households' knowledge about arsenic threats from their drinking water. In this study, spatial statistical models were used to investigate the determinants and spatial dependence of households' knowledge about arsenic risk. The binary join matrix/binary contiguity matrix and inverse distance spatial weight matrix techniques are used to capture spatial dependence in the data. This analysis extends the spatial model by allowing spatial dependence to vary across divisions and regions. A positive spatial correlation was found in households' knowledge across neighboring districts at district, divisional and regional levels, but the strength of this spatial correlation varies considerably by spatial weight. Literacy rate, daily wage rate of agricultural labor, arsenic status, and percentage of red mark tube well usage in districts were found to contribute positively and significantly to households' knowledge. These findings have policy implications both at regional and national levels in mitigating the present arsenic crisis and to ensure arsenic-free water in Bangladesh. Copyright © 2012 Elsevier Ltd. All rights reserved.
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...
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...
Testing spatial heterogeneity with stock assessment models
DEFF Research Database (Denmark)
Jardim, Ernesto; Eero, Margit; Silva, Alexandra
2018-01-01
sub-populations and applied to two case studies, North Sea cod (Gadus morua) and Northeast Atlantic sardine (Sardina pilchardus). Considering that the biological components of a population can be partitioned into discrete spatial units, we extended this idea into a property of additivity of sub......, the better the diffusion process will be detected. On the other hand it showed that weak to moderate diffusion processes are not easy to identify and large differences between sub-populations productivities may be confounded with weak diffusion processes. The application to North Sea cod and Atlantic sardine...... exemplified how much insight can be gained. In both cases the results obtained were sufficiently robust to support the regional analysis....
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.
On Angular Sampling Methods for 3-D Spatial Channel Models
DEFF Research Database (Denmark)
Fan, Wei; Jämsä, Tommi; Nielsen, Jesper Ødum
2015-01-01
This paper discusses generating three dimensional (3D) spatial channel models with emphasis on the angular sampling methods. Three angular sampling methods, i.e. modified uniform power sampling, modified uniform angular sampling, and random pairing methods are proposed and investigated in detail....... The random pairing method, which uses only twenty sinusoids in the ray-based model for generating the channels, presents good results if the spatial channel cluster is with a small elevation angle spread. For spatial clusters with large elevation angle spreads, however, the random pairing method would fail...... and the other two methods should be considered....
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.
Spatial Epidemic Modelling in Social Networks
Simoes, Joana Margarida
2005-06-01
The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.
Spatial modelling with R-INLA: A review
Bakka, Haakon; Rue, Haavard; Fuglstad, Geir-Arne; Riebler, Andrea; Bolin, David; Krainski, Elias; Simpson, Daniel; Lindgren, Finn
2018-01-01
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.
Spatial modelling with R-INLA: A review
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.
Modeling structural change in spatial system dynamics: A Daisyworld example.
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.
A Structural Equation Approach to Models with Spatial Dependence
Oud, Johan H. L.; Folmer, Henk
We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it
A structural equation approach to models with spatial dependence
Oud, J.H.L.; Folmer, H.
2008-01-01
We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it
A Structural Equation Approach to Models with Spatial Dependence
Oud, J.H.L.; Folmer, H.
2008-01-01
We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it
Can spatial statistical river temperature models be transferred between catchments?
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
Spatial Modeling Tools for Cell Biology
National Research Council Canada - National Science Library
Przekwas, Andrzej; Friend, Tom; Teixeira, Rodrigo; Chen, Z. J; Wilkerson, Patrick
2006-01-01
.... Scientific potentials and military relevance of computational biology and bioinformatics have inspired DARPA/IPTO's visionary BioSPICE project to develop computational framework and modeling tools for cell biology...
Bayesian spatial transformation models with applications in neuroimaging data.
Miranda, Michelle F; Zhu, Hongtu; Ibrahim, Joseph G
2013-12-01
The aim of this article is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. The proposed STM include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov random field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. © 2013, The International Biometric Society.
Crash rates analysis in China using a spatial panel model
Directory of Open Access Journals (Sweden)
Wonmongo Lacina Soro
2017-10-01
Full Text Available The consideration of spatial externalities in traffic safety analysis is of paramount importance for the success of road safety policies. Yet, the quasi-totality of spatial dependence studies on crash rates is performed within the framework of single-equation spatial cross-sectional studies. The present study extends the spatial cross-sectional scheme to a spatial fixed-effects panel model estimated using the maximum likelihood method. The spatial units are the 31 administrative regions of mainland China over the period 2004–2013. The presence of neighborhood effects is evidenced through the Moran's I statistic. Consistent with previous studies, the analysis reveals that omitting the spatial effects in traffic safety analysis is likely to bias the estimation results. The spatial and error lags are all positive and statistically significant suggesting similarities of crash rates pattern in neighboring regions. Some other explanatory variables, such as freight traffic, the length of paved roads and the populations of age 65 and above are related to higher rates while the opposite trend is observed for the Gross Regional Product, the urban unemployment rate and passenger traffic.
Linear multivariate evaluation models for spatial perception of soundscape.
Deng, Zhiyong; Kang, Jian; Wang, Daiwei; Liu, Aili; Kang, Joe Zhengyu
2015-11-01
Soundscape is a sound environment that emphasizes the awareness of auditory perception and social or cultural understandings. The case of spatial perception is significant to soundscape. However, previous studies on the auditory spatial perception of the soundscape environment have been limited. Based on 21 native binaural-recorded soundscape samples and a set of auditory experiments for subjective spatial perception (SSP), a study of the analysis among semantic parameters, the inter-aural-cross-correlation coefficient (IACC), A-weighted-equal sound-pressure-level (L(eq)), dynamic (D), and SSP is introduced to verify the independent effect of each parameter and to re-determine some of their possible relationships. The results show that the more noisiness the audience perceived, the worse spatial awareness they received, while the closer and more directional the sound source image variations, dynamics, and numbers of sound sources in the soundscape are, the better the spatial awareness would be. Thus, the sensations of roughness, sound intensity, transient dynamic, and the values of Leq and IACC have a suitable range for better spatial perception. A better spatial awareness seems to promote the preference slightly for the audience. Finally, setting SSPs as functions of the semantic parameters and Leq-D-IACC, two linear multivariate evaluation models of subjective spatial perception are proposed.
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.
Empirical spatial econometric modelling of small scale neighbourhood
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.
Spatial memory tasks in rodents: what do they model?
Morellini, Fabio
2013-10-01
The analysis of spatial learning and memory in rodents is commonly used to investigate the mechanisms underlying certain forms of human cognition and to model their dysfunction in neuropsychiatric and neurodegenerative diseases. Proper interpretation of rodent behavior in terms of spatial memory and as a model of human cognitive functions is only possible if various navigation strategies and factors controlling the performance of the animal in a spatial task are taken into consideration. The aim of this review is to describe the experimental approaches that are being used for the study of spatial memory in rats and mice and the way that they can be interpreted in terms of general memory functions. After an introduction to the classification of memory into various categories and respective underlying neuroanatomical substrates, I explain the concept of spatial memory and its measurement in rats and mice by analysis of their navigation strategies. Subsequently, I describe the most common paradigms for spatial memory assessment with specific focus on methodological issues relevant for the correct interpretation of the results in terms of cognitive function. Finally, I present recent advances in the use of spatial memory tasks to investigate episodic-like memory in mice.
Unleashing spatially distributed ecohydrology modeling using Big Data tools
Miles, B.; Idaszak, R.
2015-12-01
Physically based spatially distributed ecohydrology models are useful for answering science and management questions related to the hydrology and biogeochemistry of prairie, savanna, forested, as well as urbanized ecosystems. However, these models can produce hundreds of gigabytes of spatial output for a single model run over decadal time scales when run at regional spatial scales and moderate spatial resolutions (~100-km2+ at 30-m spatial resolution) or when run for small watersheds at high spatial resolutions (~1-km2 at 3-m spatial resolution). Numerical data formats such as HDF5 can store arbitrarily large datasets. However even in HPC environments, there are practical limits on the size of single files that can be stored and reliably backed up. Even when such large datasets can be stored, querying and analyzing these data can suffer from poor performance due to memory limitations and I/O bottlenecks, for example on single workstations where memory and bandwidth are limited, or in HPC environments where data are stored separately from computational nodes. The difficulty of storing and analyzing spatial data from ecohydrology models limits our ability to harness these powerful tools. Big Data tools such as distributed databases have the potential to surmount the data storage and analysis challenges inherent to large spatial datasets. Distributed databases solve these problems by storing data close to computational nodes while enabling horizontal scalability and fault tolerance. Here we present the architecture of and preliminary results from PatchDB, a distributed datastore for managing spatial output from the Regional Hydro-Ecological Simulation System (RHESSys). The initial version of PatchDB uses message queueing to asynchronously write RHESSys model output to an Apache Cassandra cluster. Once stored in the cluster, these data can be efficiently queried to quickly produce both spatial visualizations for a particular variable (e.g. maps and animations), as well
Modelling the effects of spatial variability on radionuclide migration
International Nuclear Information System (INIS)
1998-01-01
The NEA workshop reflect the present status in national waste management program, specifically in spatial variability and performance assessment of geologic disposal sites for deed repository system the four sessions were: Spatial Variability: Its Definition and Significance to Performance Assessment and Site Characterisation; Experience with the Modelling of Radionuclide Migration in the Presence of Spatial Variability in Various Geological Environments; New Areas for Investigation: Two Personal Views; What is Wanted and What is Feasible: Views and Future Plans in Selected Waste Management Organisations. The 26 papers presented on the four oral sessions and on the poster session have been abstracted and indexed individually for the INIS database. (R.P.)
Spatial distance in a technology gap model
Verspagen, B.; Caniëls, M.C.J.
1999-01-01
This paper analyses the effect of locally bounded knowledge spillovers on regional differences in growth. A model will be developed that allows spillovers to take place across regions. Certain conditions determine the amount of spillovers a region receives. By use of simulations (with randomised
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....
A random spatial network model based on elementary postulates
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.
Spatial-temporal modeling of malware propagation in networks.
Chen, Zesheng; Ji, Chuanyi
2005-09-01
Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this work is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. Our model is motivated by probabilistic graphs, which have been widely investigated in machine learning. We first use a graphical representation to abstract the propagation of malwares that employ different scanning methods. We then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies. As the spatial dependence is particularly difficult to characterize, the problem becomes how to use simple (i.e., biased) models to approximate the spatially dependent process. In particular, we propose the independent model and the Markov model as simple approximations. We conduct both theoretical analysis and extensive simulations on large networks using both real measurements and synthesized topologies to test the performance of the proposed models. Our results show that the independent model can capture temporal dependence and detailed topology information and, thus, outperforms the previous models, whereas the Markov model incorporates a certain spatial dependence and, thus, achieves a greater accuracy in characterizing both transient and equilibrium behaviors of malware propagation.
Spatial analysis and modelling based on activities
CSIR Research Space (South Africa)
Conradie, Dirk CU
2010-01-01
Full Text Available (deliberative attitudes) (Pokahr, 2005). The BDI model does not cover emotional and other ‘higher’ human attitudes. KRONOS is a generic Computational Building Simulation (CBS) tool that was developed over the past three years to work on advanced... featured, stable, mature and platform independent with an easy to use C/C++ Application Program Interface (API). It has advanced joint types and integrated collision detection with friction. ODE is particularly useful for simulating vehicles, objects...
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.
Uncertainty in a spatial evacuation model
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.
Stability of a spatial model of social interactions
International Nuclear Information System (INIS)
Bragard, Jean; Mossay, Pascal
2016-01-01
We study a spatial model of social interactions. Though the properties of the spatial equilibrium have been largely discussed in the existing literature, the stability of equilibrium remains an unaddressed issue. Our aim is to fill up this gap by introducing dynamics in the model and by determining the stability of equilibrium. First we derive a variational equation useful for the stability analysis. This allows to study the corresponding eigenvalue problem. While odd modes are shown to be always stable, there is a single even mode of which stability depends on the model parameters. Finally various numerical simulations illustrate our theoretical results.
Modeling individuals’ cognitive and affective responses in spatial learning behavior
Han, Q.; Arentze, T.A.; Timmermans, H.J.P.; Janssens, D.; Wets, G.; Lo, H.P.; Leung, Stephen C.H.; Tan, Susanna M.L.
2008-01-01
Activity-based analysis has slowly shifted gear from analysis of daily activity patterns to analysis and modeling of dynamic activity-travel patterns. In this paper, we describe a dynamic model that is concerned with simulating cognitive and affective responses in spatial learning behavior for a
Modelling firm heterogeneity with spatial 'trends'
Energy Technology Data Exchange (ETDEWEB)
Sarmiento, C. [North Dakota State University, Fargo, ND (United States). Dept. of Agricultural Business & Applied Economics
2004-04-15
The hypothesis underlying this article is that firm heterogeneity can be captured by spatial characteristics of the firm (similar to the inclusion of a time trend in time series models). The hypothesis is examined in the context of modelling electric generation by coal powered plants in the presence of firm heterogeneity.
Appropriatie spatial scales to achieve model output uncertainty goals
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
Spatially adaptive mixture modeling for analysis of FMRI time series.
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
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.
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.
A study of spatial resolution in pollution exposure modelling
Directory of Open Access Journals (Sweden)
Gustafsson Susanna
2007-06-01
Full Text Available Abstract Background This study is part of several ongoing projects concerning epidemiological research into the effects on health of exposure to air pollutants in the region of Scania, southern Sweden. The aim is to investigate the optimal spatial resolution, with respect to temporal resolution, for a pollutant database of NOx-values which will be used mainly for epidemiological studies with durations of days, weeks or longer periods. The fact that a pollutant database has a fixed spatial resolution makes the choice critical for the future use of the database. Results The results from the study showed that the accuracy between the modelled concentrations of the reference grid with high spatial resolution (100 m, denoted the fine grid, and the coarser grids (200, 400, 800 and 1600 meters improved with increasing spatial resolution. When the pollutant values were aggregated in time (from hours to days and weeks the disagreement between the fine grid and the coarser grids were significantly reduced. The results also illustrate a considerable difference in optimal spatial resolution depending on the characteristic of the study area (rural or urban areas. To estimate the accuracy of the modelled values comparison were made with measured NOx values. The mean difference between the modelled and the measured value were 0.6 μg/m3 and the standard deviation 5.9 μg/m3 for the daily difference. Conclusion The choice of spatial resolution should not considerably deteriorate the accuracy of the modelled NOx values. Considering the comparison between modelled and measured values we estimate that an error due to coarse resolution greater than 1 μg/m3 is inadvisable if a time resolution of one day is used. Based on the study of different spatial resolutions we conclude that for urban areas a spatial resolution of 200–400 m is suitable; and for rural areas the spatial resolution could be coarser (about 1600 m. This implies that we should develop a pollutant
Global sensitivity analysis for models with spatially dependent outputs
International Nuclear Information System (INIS)
Iooss, B.; Marrel, A.; Jullien, M.; Laurent, B.
2011-01-01
The global sensitivity analysis of a complex numerical model often calls for the estimation of variance-based importance measures, named Sobol' indices. Meta-model-based techniques have been developed in order to replace the CPU time-expensive computer code with an inexpensive mathematical function, which predicts the computer code output. The common meta-model-based sensitivity analysis methods are well suited for computer codes with scalar outputs. However, in the environmental domain, as in many areas of application, the numerical model outputs are often spatial maps, which may also vary with time. In this paper, we introduce an innovative method to obtain a spatial map of Sobol' indices with a minimal number of numerical model computations. It is based upon the functional decomposition of the spatial output onto a wavelet basis and the meta-modeling of the wavelet coefficients by the Gaussian process. An analytical example is presented to clarify the various steps of our methodology. This technique is then applied to a real hydrogeological case: for each model input variable, a spatial map of Sobol' indices is thus obtained. (authors)
A physically based analytical spatial air temperature and humidity model
Yang, Yang; Endreny, Theodore A.; Nowak, David J.
2013-09-01
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 storage based on semiempirical functions and generates spatially distributed estimates based on inputs of topography, land cover, and the weather data measured at a reference site. The model assumes that for all grids under the same mesoscale climate, grid air temperature and humidity are modified by local variation in absorbed solar radiation and the partitioning of sensible and latent heat. The model uses a reference grid site for time series meteorological data and the air temperature and humidity of any other grid can be obtained by solving the heat flux network equations. PASATH was coupled with the USDA iTree-Hydro water balance model to obtain evapotranspiration terms and run from 20 to 29 August 2010 at a 360 m by 360 m grid scale and hourly time step across a 285 km2 watershed including the urban area of Syracuse, NY. PASATH predictions were tested at nine urban weather stations representing variability in urban topography and land cover. The PASATH model predictive efficiency R2 ranged from 0.81 to 0.99 for air temperature and 0.77 to 0.97 for dew point temperature. PASATH is expected to have broad applications on environmental and ecological models.
Toward micro-scale spatial modeling of gentrification
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.
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.
Hydrological model uncertainty due to spatial evapotranspiration estimation methods
Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub
2016-05-01
Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.
Spatial capture-recapture models for search-encounter data
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.
Analysing earthquake slip models with the spatial prediction comparison test
Zhang, L.; Mai, Paul Martin; Thingbaijam, Kiran Kumar; Razafindrakoto, H. N. T.; Genton, Marc G.
2014-01-01
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.
Analysing earthquake slip models with the spatial prediction comparison test
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.
Gaussian Process Regression Model in Spatial Logistic Regression
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.
Spatial modeling of agricultural land use change at global scale
Meiyappan, P.; Dalton, M.; O'Neill, B. C.; Jain, A. K.
2014-11-01
Long-term modeling of agricultural land use is central in global scale assessments of climate change, food security, biodiversity, and climate adaptation and mitigation policies. We present a global-scale dynamic land use allocation model and show that it can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. The modeling approach integrates economic theory, observed land use history, and data on both socioeconomic and biophysical determinants of land use change, and estimates relationships using long-term historical data, thereby making it suitable for long-term projections. The underlying economic motivation is maximization of expected profits by hypothesized landowners within each grid cell. The model predicts fractional land use for cropland and pastureland within each grid cell based on socioeconomic and biophysical driving factors that change with time. The model explicitly incorporates the following key features: (1) land use competition, (2) spatial heterogeneity in the nature of driving factors across geographic regions, (3) spatial heterogeneity in the relative importance of driving factors and previous land use patterns in determining land use allocation, and (4) spatial and temporal autocorrelation in land use patterns. We show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factors), or those accounting for both land use history and driving factors by mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, we show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. The modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling
A spatial emergy model for Alachua County, Florida
Lambert, James David
A spatial model of the distribution of energy flows and storages in Alachua County, Florida, was created and used to analyze spatial patterns of energy transformation hierarchy in relation to spatial patterns of human settlement. Emergy, the available energy of one kind previously required directly or indirectly to make a product or service, was used as a measure of the quality of the different forms of energy flows and storages. Emergy provides a common unit of measure for comparing the productive contributions of natural processes with those of economic and social processes---it is an alternative to using money for measuring value. A geographic information system was used to create a spatial model and make maps that show the distribution and magnitude of different types of energy and emergy flows and storages occurring in one-hectare land units. Energy transformities were used to convert individual energy flows and storages into emergy units. Maps of transformities were created that reveal a clear spatial pattern of energy transformation hierarchy. The maps display patterns of widely-dispersed areas with lower transformity energy flows and storages, and smaller, centrally-located areas with higher transformities. Energy signature graphs and spatial unit transformities were used to characterize and compare the types and amounts of energy being consumed and stored according to land use classification, planning unit, and neighborhood categories. Emergy ratio maps and spatial unit ratios were created by dividing the values for specific emergy flows or storages by the values for other emergy flows or storages. Spatial context analysis was used to analyze the spatial distribution patterns of mean and maximum values for emergy flows and storages. The modeling method developed for this study is general and applicable to all types of landscapes and could be applied at any scale. An advantage of this general approach is that the results of other studies using this method
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
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.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
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.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.
2012-01-01
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.
Practical likelihood analysis for spatial generalized linear mixed models
DEFF Research Database (Denmark)
Bonat, W. H.; Ribeiro, Paulo Justiniano
2016-01-01
We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are......, 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...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...
International Nuclear Information System (INIS)
Drechsler, Martin; Ohl, Cornelia; Meyerhoff, Juergen; Eichhorn, Marcus; Monsees, Jan
2011-01-01
Although wind power is currently the most efficient source of renewable energy, the installation of wind turbines (WT) in landscapes often leads to conflicts in the affected communities. We propose that such conflicts can be mitigated by a welfare-optimal spatial allocation of WT in the landscape so that a given energy target is reached at minimum social costs. The energy target is motivated by the fact that wind power production is associated with relatively low CO 2 emissions. Social costs comprise energy production costs as well as external costs caused by harmful impacts on humans and biodiversity. We present a modeling approach that combines spatially explicit ecological-economic modeling and choice experiments to determine the welfare-optimal spatial allocation of WT in West Saxony, Germany. The welfare-optimal sites balance production and external costs. Results indicate that in the welfare-optimal allocation the external costs represent about 14% of the total costs (production costs plus external costs). Optimizing wind power production without consideration of the external costs would lead to a very different allocation of WT that would marginally reduce the production costs but strongly increase the external costs and thus lead to substantial welfare losses. - Highlights: → We combine modeling and economic valuation to optimally allocate wind turbines. → Welfare-optimal allocation balances energy production costs and external costs. → External costs (impacts on the environment) can be substantial. → Ignoring external costs leads to suboptimal allocations and welfare losses.
Towards a 3d Spatial Urban Energy Modelling Approach
Bahu, J.-M.; Koch, A.; Kremers, E.; Murshed, S. M.
2013-09-01
Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies
Modelling the Spatial Isotope Variability of Precipitation in Syria
Energy Technology Data Exchange (ETDEWEB)
Kattan, Z.; Kattaa, B. [Department of Geology, Atomic Energy Commission of Syria (AECS), Damascus (Syrian Arab Republic)
2013-07-15
Attempts were made to model the spatial variability of environmental isotope ({sup 18}O, {sup 2}H and {sup 3}H) compositions of precipitation in syria. Rainfall samples periodically collected on a monthly basis from 16 different stations were used for processing and demonstrating the spatial distributions of these isotopes, together with those of deuterium excess (d) values. Mathematically, the modelling process was based on applying simple polynomial models that take into consideration the effects of major geographic factors (Lon.E., Lat.N., and altitude). The modelling results of spatial distribution of stable isotopes ({sup 18}O and {sup 2}H) were generally good, as shown from the high correlation coefficients (R{sup 2} = 0.7-0.8), calculated between the observed and predicted values. In the case of deuterium excess and tritium distributions, the results were most likely approximates (R{sup 2} = 0.5-0.6). Improving the simulation of spatial isotope variability probably requires the incorporation of other local meteorological factors, such as relative air humidity, precipitation amount and vapour pressure, which are supposed to play an important role in such an arid country. (author)
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.
Classifying and comparing spatial models of fire dynamics
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...
Thinking Egyptian: Active Models for Understanding Spatial Representation.
Schiferl, Ellen
This paper highlights how introductory textbooks on Egyptian art inhibit understanding by reinforcing student preconceptions, and demonstrates another approach to discussing space with a classroom exercise and software. The alternative approach, an active model for spatial representation, introduced here was developed by adapting classroom…
Spatially Informed Plant PRA Models for Security Assessment
International Nuclear Information System (INIS)
Wheeler, Timothy A.; Thomas, Willard; Thornsbury, Eric
2006-01-01
Traditional risk models can be adapted to evaluate plant response for situations where plant systems and structures are intentionally damaged, such as from sabotage or terrorism. This paper describes a process by which traditional risk models can be spatially informed to analyze the effects of compound and widespread harsh environments through the use of 'damage footprints'. A 'damage footprint' is a spatial map of regions of the plant (zones) where equipment could be physically destroyed or disabled as a direct consequence of an intentional act. The use of 'damage footprints' requires that the basic events from the traditional probabilistic risk assessment (PRA) be spatially transformed so that the failure of individual components can be linked to the destruction of or damage to specific spatial zones within the plant. Given the nature of intentional acts, extensive modifications must be made to the risk models to account for the special nature of the 'initiating events' associated with deliberate adversary actions. Intentional acts might produce harsh environments that in turn could subject components and structures to one or more insults, such as structural, fire, flood, and/or vibration and shock damage. Furthermore, the potential for widespread damage from some of these insults requires an approach that addresses the impacts of these potentially severe insults even when they occur in locations distant from the actual physical location of a component or structure modeled in the traditional PRA. (authors)
Rockfall hazard analysis using LiDAR and spatial modeling
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.
Individual based model of slug population and spatial dynamics
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
Differences in spatial understanding between physical and virtual models
Directory of Open Access Journals (Sweden)
Lei Sun
2014-03-01
Full Text Available In the digital age, physical models are still used as major tools in architectural and urban design processes. The reason why designers still use physical models remains unclear. In addition, physical and 3D virtual models have yet to be differentiated. The answers to these questions are too complex to account for in all aspects. Thus, this study only focuses on the differences in spatial understanding between physical and virtual models. In particular, it emphasizes on the perception of scale. For our experiment, respondents were shown a physical model and a virtual model consecutively. A questionnaire was then used to ask the respondents to evaluate these models objectively and to establish which model was more accurate in conveying object size. Compared with the virtual model, the physical model tended to enable quicker and more accurate comparisons of building heights.
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)
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.
A spatial haplotype copying model with applications to genotype imputation.
Yang, Wen-Yun; Hormozdiari, Farhad; Eskin, Eleazar; Pasaniuc, Bogdan
2015-05-01
Ever since its introduction, the haplotype copy model has proven to be one of the most successful approaches for modeling genetic variation in human populations, with applications ranging from ancestry inference to genotype phasing and imputation. Motivated by coalescent theory, this approach assumes that any chromosome (haplotype) can be modeled as a mosaic of segments copied from a set of chromosomes sampled from the same population. At the core of the model is the assumption that any chromosome from the sample is equally likely to contribute a priori to the copying process. Motivated by recent works that model genetic variation in a geographic continuum, we propose a new spatial-aware haplotype copy model that jointly models geography and the haplotype copying process. We extend hidden Markov models of haplotype diversity such that at any given location, haplotypes that are closest in the genetic-geographic continuum map are a priori more likely to contribute to the copying process than distant ones. Through simulations starting from the 1000 Genomes data, we show that our model achieves superior accuracy in genotype imputation over the standard spatial-unaware haplotype copy model. In addition, we show the utility of our model in selecting a small personalized reference panel for imputation that leads to both improved accuracy as well as to a lower computational runtime than the standard approach. Finally, we show our proposed model can be used to localize individuals on the genetic-geographical map on the basis of their genotype data.
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
Modern methodology and applications in spatial-temporal modeling
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...
Stochastic geometry, spatial statistics and random fields models and algorithms
2015-01-01
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R, which are widely used in the mathematical community. It can be regarded as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered, with a focus on asymptotic methods.
Analog model for analysis of spatial instability of neutron flux
International Nuclear Information System (INIS)
Radanovic, Lj.
1964-12-01
The objective of this task was to develop a model for analysing spatial instability of the neutron flux and defining the optimum number and position of regulating rods. The developed model enables calculation of higher harmonics to be taken into account for each type of reactor, to define zones for regulation rods, position and number of points for detecting reactor state, and number and position of the regulating rods
Integrating remote sensing and spatially explicit epidemiological modeling
Finger, Flavio; Knox, Allyn; Bertuzzo, Enrico; Mari, Lorenzo; Bompangue, Didier; Gatto, Marino; Rinaldo, Andrea
2015-04-01
Spatially explicit epidemiological models are a crucial tool for the prediction of epidemiological patterns in time and space as well as for the allocation of health care resources. In addition they can provide valuable information about epidemiological processes and allow for the identification of environmental drivers of the disease spread. Most epidemiological models rely on environmental data as inputs. They can either be measured in the field by the means of conventional instruments or using remote sensing techniques to measure suitable proxies of the variables of interest. The later benefit from several advantages over conventional methods, including data availability, which can be an issue especially in developing, and spatial as well as temporal resolution of the data, which is particularly crucial for spatially explicit models. Here we present the case study of a spatially explicit, semi-mechanistic model applied to recurring cholera outbreaks in the Lake Kivu area (Democratic Republic of the Congo). The model describes the cholera incidence in eight health zones on the shore of the lake. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers. Human mobility and its effect on the disease spread is also taken into account. Several model configurations are tested on a data set of reported cases. The best models, accounting for different environmental drivers, and selected using the Akaike information criterion, are formally compared via cross validation. The best performing model accounts for seasonality, El Niño Southern Oscillation, precipitation and human mobility.
Spatial Temporal Modelling of Particulate Matter for Health Effects Studies
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.
Spatial modelling and ecology of Echinococcus multilocularis transmission in China.
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.
Indoor 3D Route Modeling Based On Estate Spatial Data
Zhang, H.; Wen, Y.; Jiang, J.; Huang, W.
2014-04-01
Indoor three-dimensional route model is essential for space intelligence navigation and emergency evacuation. This paper is motivated by the need of constructing indoor route model automatically and as far as possible. By comparing existing building data sources, this paper firstly explained the reason why the estate spatial management data is chosen as the data source. Then, an applicable method of construction three-dimensional route model in a building is introduced by establishing the mapping relationship between geographic entities and their topological expression. This data model is a weighted graph consist of "node" and "path" to express the spatial relationship and topological structure of a building components. The whole process of modelling internal space of a building is addressed by two key steps: (1) each single floor route model is constructed, including path extraction of corridor using Delaunay triangulation algorithm with constrained edge, fusion of room nodes into the path; (2) the single floor route model is connected with stairs and elevators and the multi-floor route model is eventually generated. In order to validate the method in this paper, a shopping mall called "Longjiang New City Plaza" in Nanjing is chosen as a case of study. And the whole building space is constructed according to the modelling method above. By integrating of existing path finding algorithm, the usability of this modelling method is verified, which shows the indoor three-dimensional route modelling method based on estate spatial data in this paper can support indoor route planning and evacuation route design very well.
Single Canonical Model of Reflexive Memory and Spatial Attention
Patel, Saumil S.; Red, Stuart; Lin, Eric; Sereno, Anne B.
2015-01-01
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. PMID:26493949
Chaotic and stable perturbed maps: 2-cycles and spatial models
Braverman, E.; Haroutunian, J.
2010-06-01
As the growth rate parameter increases in the Ricker, logistic and some other maps, the models exhibit an irreversible period doubling route to chaos. If a constant positive perturbation is introduced, then the Ricker model (but not the classical logistic map) experiences period doubling reversals; the break of chaos finally gives birth to a stable two-cycle. We outline the maps which demonstrate a similar behavior and also study relevant discrete spatial models where the value in each cell at the next step is defined only by the values at the cell and its nearest neighbors. The stable 2-cycle in a scalar map does not necessarily imply 2-cyclic-type behavior in each cell for the spatial generalization of the map.
Single Canonical Model of Reflexive Memory and Spatial Attention.
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.
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
Modeling spin magnetization transport in a spatially varying magnetic field
International Nuclear Information System (INIS)
Picone, Rico A.R.; Garbini, Joseph L.; Sidles, John A.
2015-01-01
We present a framework for modeling the transport of any number of globally conserved quantities in any spatial configuration and apply it to obtain a model of magnetization transport for spin-systems that is valid in new regimes (including high-polarization). The framework allows an entropy function to define a model that explicitly respects the laws of thermodynamics. Three facets of the model are explored. First, it is expressed as nonlinear partial differential equations that are valid for the new regime of high dipole-energy and polarization. Second, the nonlinear model is explored in the limit of low dipole-energy (semi-linear), from which is derived a physical parameter characterizing separative magnetization transport (SMT). It is shown that the necessary and sufficient condition for SMT to occur is that the parameter is spatially inhomogeneous. Third, the high spin-temperature (linear) limit is shown to be equivalent to the model of nuclear spin transport of Genack and Redfield (1975) [1]. Differences among the three forms of the model are illustrated by numerical solution with parameters corresponding to a magnetic resonance force microscopy (MRFM) experiment (Degen et al., 2009 [2]; Kuehn et al., 2008 [3]; Sidles et al., 2003 [4]; Dougherty et al., 2000 [5]). A family of analytic, steady-state solutions to the nonlinear equation is derived and shown to be the spin-temperature analog of the Langevin paramagnetic equation and Curie's law. Finally, we analyze the separative quality of magnetization transport, and a steady-state solution for the magnetization is shown to be compatible with Fenske's separative mass transport equation (Fenske, 1932 [6]). - Highlights: • A framework for modeling the transport of conserved magnetic and thermodynamic quantities in any spatial configuration. • A thermodynamically grounded model of spin magnetization transport valid in new regimes, including high-polarization. • Analysis of the separative quality of
Modeling spin magnetization transport in a spatially varying magnetic field
Energy Technology Data Exchange (ETDEWEB)
Picone, Rico A.R., E-mail: rpicone@stmartin.edu [Department of Mechanical Engineering, University of Washington, Seattle (United States); Garbini, Joseph L. [Department of Mechanical Engineering, University of Washington, Seattle (United States); Sidles, John A. [Department of Orthopædics, University of Washington, Seattle (United States)
2015-01-15
We present a framework for modeling the transport of any number of globally conserved quantities in any spatial configuration and apply it to obtain a model of magnetization transport for spin-systems that is valid in new regimes (including high-polarization). The framework allows an entropy function to define a model that explicitly respects the laws of thermodynamics. Three facets of the model are explored. First, it is expressed as nonlinear partial differential equations that are valid for the new regime of high dipole-energy and polarization. Second, the nonlinear model is explored in the limit of low dipole-energy (semi-linear), from which is derived a physical parameter characterizing separative magnetization transport (SMT). It is shown that the necessary and sufficient condition for SMT to occur is that the parameter is spatially inhomogeneous. Third, the high spin-temperature (linear) limit is shown to be equivalent to the model of nuclear spin transport of Genack and Redfield (1975) [1]. Differences among the three forms of the model are illustrated by numerical solution with parameters corresponding to a magnetic resonance force microscopy (MRFM) experiment (Degen et al., 2009 [2]; Kuehn et al., 2008 [3]; Sidles et al., 2003 [4]; Dougherty et al., 2000 [5]). A family of analytic, steady-state solutions to the nonlinear equation is derived and shown to be the spin-temperature analog of the Langevin paramagnetic equation and Curie's law. Finally, we analyze the separative quality of magnetization transport, and a steady-state solution for the magnetization is shown to be compatible with Fenske's separative mass transport equation (Fenske, 1932 [6]). - Highlights: • A framework for modeling the transport of conserved magnetic and thermodynamic quantities in any spatial configuration. • A thermodynamically grounded model of spin magnetization transport valid in new regimes, including high-polarization. • Analysis of the separative quality of
Koch, Julian; Cüneyd Demirel, Mehmet; Stisen, Simon
2018-05-01
The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond 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 makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.
Modeling molecular mixing in a spatially inhomogeneous turbulent flow
Meyer, Daniel W.; Deb, Rajdeep
2012-02-01
Simulations of spatially inhomogeneous turbulent mixing in decaying grid turbulence with a joint velocity-concentration probability density function (PDF) method were conducted. The inert mixing scenario involves three streams with different compositions. The mixing model of Meyer ["A new particle interaction mixing model for turbulent dispersion and turbulent reactive flows," Phys. Fluids 22(3), 035103 (2010)], the interaction by exchange with the mean (IEM) model and its velocity-conditional variant, i.e., the IECM model, were applied. For reference, the direct numerical simulation data provided by Sawford and de Bruyn Kops ["Direct numerical simulation and lagrangian modeling of joint scalar statistics in ternary mixing," Phys. Fluids 20(9), 095106 (2008)] was used. It was found that velocity conditioning is essential to obtain accurate concentration PDF predictions. Moreover, the model of Meyer provides significantly better results compared to the IECM model at comparable computational expense.
A general modeling framework for describing spatially structured population dynamics
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
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)
Sparse modeling of spatial environmental variables associated with asthma.
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.
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)
Tapered composite likelihood for spatial max-stable models
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.
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.
Tapered composite likelihood for spatial max-stable models
Sang, Huiyan; Genton, Marc G.
2014-01-01
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.
Spatial Modelling of Sediment Transport over the Upper Citarum Catchment
Directory of Open Access Journals (Sweden)
Poerbandono
2006-05-01
Full Text Available This paper discusses set up of a spatial model applied in Geographic Information System (GIS environment for predicting annual erosion rate and sediment yield of a watershed. The study area is situated in the Upper Citarum Catchment of West Java. Annual sediment yield is considered as product of erosion rate and sediment delivery ratio to be modelled under similar modeling tool. Sediment delivery ratio is estimated on the basis of sediment resident time. The modeling concept is based on the calculation of water flow velocity through sub-catchment surface, which is controlled by topography, rainfall, soil characteristics and various types of land use. Relating velocity to known distance across digital elevation model, sediment resident time can be estimated. Data from relevance authorities are used. Bearing in mind limited knowledge of some governing factors due to lack of observation, the result has shown the potential of GIS for spatially modeling regional sediment transport. Validation of model result is carried out by evaluating measured and computed total sediment yield at the main outlet. Computed total sediment yields for 1994 and 2001 are found to be 1.96×106 and 2.10×106tons/year. They deviate roughly 54 and 8% with respect to those measured in the field. Model response due to land use change observed in 2001 and 1994 is also recognised. Under presumably constant rainfall depth, an increase of overall average annual erosion rate of 11% resulted in an increase of overall average sediment yield of 7%.
Supplementary Material for: Factor Copula Models for Replicated Spatial Data
Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.
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.
A spatial structural derivative model for ultraslow diffusion
Directory of Open Access Journals (Sweden)
Xu Wei
2017-01-01
Full Text Available This study investigates the ultraslow diffusion by a spatial structural derivative, in which the exponential function ex is selected as the structural function to construct the local structural derivative diffusion equation model. The analytical solution of the diffusion equation is a form of Biexponential distribution. Its corresponding mean squared displacement is numerically calculated, and increases more slowly than the logarithmic function of time. The local structural derivative diffusion equation with the structural function ex in space is an alternative physical and mathematical modeling model to characterize a kind of ultraslow diffusion.
Spatial modeling of HIV and HSV-2 among women in Kenya with spatially varying coefficients
Directory of Open Access Journals (Sweden)
Elphas Okango
2016-04-01
Full Text Available Abstract Background Disease mapping has become popular in the field of statistics as a method to explain the spatial distribution of disease outcomes and as a tool to help design targeted intervention strategies. Most of these models however have been implemented with assumptions that may be limiting or altogether lead to less meaningful results and hence interpretations. Some of these assumptions include the linearity, stationarity and normality assumptions. Studies have shown that the linearity assumption is not necessarily true for all covariates. Age for example has been found to have a non-linear relationship with HIV and HSV-2 prevalence. Other studies have made stationarity assumption in that one stimulus e.g. education, provokes the same response in all the regions under study and this is also quite restrictive. Responses to stimuli may vary from region to region due to aspects like culture, preferences and attitudes. Methods We perform a spatial modeling of HIV and HSV-2 among women in Kenya, while relaxing these assumptions i.e. the linearity assumption by allowing the covariate age to have a non-linear effect on HIV and HSV-2 prevalence using the random walk model of order 2 and the stationarity assumption by allowing the rest of the covariates to vary spatially using the conditional autoregressive model. The women data used in this study were derived from the 2007 Kenya AIDS indicator survey where women aged 15–49 years were surveyed. A full Bayesian approach was used and the models were implemented in R-INLA software. Results Age was found to have a non-linear relationship with both HIV and HSV-2 prevalence, and the spatially varying coefficient model provided a significantly better fit for HSV-2. Age-at first sex also had a greater effect on HSV-2 prevalence in the Coastal and some parts of North Eastern regions suggesting either early marriages or child prostitution. The effect of education on HIV prevalence among women was more
Models of chromatin spatial organisation in the cell nucleus
Nicodemi, Mario
2014-03-01
In the cell nucleus chromosomes have a complex architecture serving vital functional purposes. Recent experiments have started unveiling the interaction map of DNA sites genome-wide, revealing different levels of organisation at different scales. The principles, though, which orchestrate such a complex 3D structure remain still mysterious. I will overview the scenario emerging from some classical polymer physics models of the general aspect of chromatin spatial organisation. The available experimental data, which can be rationalised in a single framework, support a picture where chromatin is a complex mixture of differently folded regions, self-organised across spatial scales according to basic physical mechanisms. I will also discuss applications to specific DNA loci, e.g. the HoxB locus, where models informed with biological details, and tested against targeted experiments, can help identifying the determinants of folding.
Modeling Spatial Dependence of Rainfall Extremes Across Multiple Durations
Le, Phuong Dong; Leonard, Michael; Westra, Seth
2018-03-01
Determining the probability of a flood event in a catchment given that another flood has occurred in a nearby catchment is useful in the design of infrastructure such as road networks that have multiple river crossings. These conditional flood probabilities can be estimated by calculating conditional probabilities of extreme rainfall and then transforming rainfall to runoff through a hydrologic model. Each catchment's hydrological response times are unlikely to be the same, so in order to estimate these conditional probabilities one must consider the dependence of extreme rainfall both across space and across critical storm durations. To represent these types of dependence, this study proposes a new approach for combining extreme rainfall across different durations within a spatial extreme value model using max-stable process theory. This is achieved in a stepwise manner. The first step defines a set of common parameters for the marginal distributions across multiple durations. The parameters are then spatially interpolated to develop a spatial field. Storm-level dependence is represented through the max-stable process for rainfall extremes across different durations. The dependence model shows a reasonable fit between the observed pairwise extremal coefficients and the theoretical pairwise extremal coefficient function across all durations. The study demonstrates how the approach can be applied to develop conditional maps of the return period and return level across different durations.
Spatial Models of Prebiotic Evolution: Soup Before Pizza?
Scheuring, István; Czárán, Tamás; Szabó, Péter; Károlyi, György; Toroczkai, Zoltán
2003-10-01
The problem of information integration and resistance to the invasion of parasitic mutants in prebiotic replicator systems is a notorious issue of research on the origin of life. Almost all theoretical studies published so far have demonstrated that some kind of spatial structure is indispensable for the persistence and/or the parasite resistance of any feasible replicator system. Based on a detailed critical survey of spatial models on prebiotic information integration, we suggest a possible scenario for replicator system evolution leading to the emergence of the first protocells capable of independent life. We show that even the spatial versions of the hypercycle model are vulnerable to selfish parasites in heterogeneous habitats. Contrary, the metabolic system remains persistent and coexistent with its parasites both on heterogeneous surfaces and in chaotically mixing flowing media. Persistent metabolic parasites can be converted to metabolic cooperators, or they can gradually obtain replicase activity. Our simulations show that, once replicase activity emerged, a gradual and simultaneous evolutionary improvement of replicase functionality (speed and fidelity) and template efficiency is possible only on a surface that constrains the mobility of macromolecule replicators. Based on the results of the models reviewed, we suggest that open chaotic flows (`soup') and surface dynamics (`pizza') both played key roles in the sequence of evolutionary events ultimately concluding in the appearance of the first living cell on Earth.
Modeling spatial invasion of Ebola in West Africa.
D'Silva, Jeremy P; Eisenberg, Marisa C
2017-09-07
The 2014-2016 Ebola Virus Disease (EVD) epidemic in West Africa was the largest ever recorded, representing a fundamental shift in Ebola epidemiology with unprecedented spatiotemporal complexity. To understand the spatiotemporal dynamics of EVD in West Africa, we developed spatial transmission models using a gravity-model framework at both the national and district-level scales, which we used to compare effectiveness of local interventions (e.g. local quarantine) and long-range interventions (e.g. border-closures). The country-level gravity model captures the epidemic data, including multiple waves of initial epidemic growth observed in Guinea. We found that local-transmission reductions were most effective in Liberia, while long-range transmission was dominant in Sierra Leone. Both models illustrated that interventions in one region result in an amplified protective effect on other regions by preventing spatial transmission. In the district-level model, interventions in the strongest of these amplifying regions reduced total cases in all three countries by over 20%, in spite of the region itself generating only ∼0.1% of total cases. This model structure and associated intervention analysis provide information that can be used by public health policymakers to assist planning and response efforts for future epidemics. Copyright © 2017 Elsevier Ltd. All rights reserved.
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.
Spatial interpolation schemes of daily precipitation for hydrologic modeling
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.
Propagation dynamics for a spatially periodic integrodifference competition model
Wu, Ruiwen; Zhao, Xiao-Qiang
2018-05-01
In this paper, we study the propagation dynamics for a class of integrodifference competition models in a periodic habitat. An interesting feature of such a system is that multiple spreading speeds can be observed, which biologically means different species may have different spreading speeds. We show that the model system admits a single spreading speed, and it coincides with the minimal wave speed of the spatially periodic traveling waves. A set of sufficient conditions for linear determinacy of the spreading speed is also given.
How to get rid of W: a latent variables approach to modelling spatially lagged variables
Folmer, H.; Oud, J.
2008-01-01
In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are
How to get rid of W : a latent variables approach to modelling spatially lagged variables
Folmer, Henk; Oud, Johan
2008-01-01
In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are
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.
Spatial stochastic regression modelling of urban land use
International Nuclear Information System (INIS)
Arshad, S H M; Jaafar, J; Abiden, M Z Z; Latif, Z A; Rasam, A R A
2014-01-01
Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. This results in innumerable benefits of the quantity and quality of the urban environment and lifestyle but on the other hand contributes to unbounded development, urban sprawl, overcrowding and decreasing standard of living. Regulation and observation of urban development activities is crucial. The understanding of urban systems that promotes urban growth are also essential for the purpose of policy making, formulating development strategies as well as development plan preparation. This study aims to compare two different stochastic regression modeling techniques for spatial structure models of urban growth in the same specific study area. Both techniques will utilize the same datasets and their results will be analyzed. The work starts by producing an urban growth model by using stochastic regression modeling techniques namely the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The two techniques are compared to and it is found that, GWR seems to be a more significant stochastic regression model compared to OLS, it gives a smaller AICc (Akaike's Information Corrected Criterion) value and its output is more spatially explainable
Spatial modeling for groundwater arsenic levels in North Carolina.
Kim, Dohyeong; Miranda, Marie Lynn; Tootoo, Joshua; Bradley, Phil; Gelfand, Alan E
2011-06-01
To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area.
Modeling spatial processes with unknown extremal dependence class
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.
Spatial Modeling for Groundwater Arsenic Levels in North Carolina
Kim, Dohyeong; Miranda, Marie Lynn; Tootoo, Joshua; Bradley, Phil; Gelfand, Alan E.
2013-01-01
To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area. PMID:21528844
Spatially balanced topological interaction grants optimal cohesion in flocking models.
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.
Spatial modeling for groundwater arsenic levels in North Carolina
Kim, D.; Miranda, M.L.; Tootoo, J.; Bradley, P.; Gelfand, A.E.
2011-01-01
To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area. ?? 2011 American Chemical Society.
Spatial Durbin model analysis macroeconomic loss due to natural disasters
Kusrini, D. E.; Mukhtasor
2015-03-01
Magnitude of the damage and losses caused by natural disasters is huge for Indonesia, therefore this study aimed to analyze the effects of natural disasters for macroeconomic losses that occurred in 115 cities/districts across Java during 2012. Based on the results of previous studies it is suspected that it contains effects of spatial dependencies in this case, so that the completion of this case is performed using a regression approach to the area, namely Analysis of Spatial Durbin Model (SDM). The obtained significant predictor variable is population, and predictor variable with a significant weighting is the number of occurrences of disasters, i.e., disasters in the region which have an impact on other neighboring regions. Moran's I index value using the weighted Queen Contiguity also showed significant results, meaning that the incidence of disasters in the region will decrease the value of GDP in other.
Spatial and spatio-temporal bayesian models with R - INLA
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
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
Directory of Open Access Journals (Sweden)
Jesse Whittington
Full Text Available 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
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.
Assessing fit in Bayesian models for spatial processes
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.
Assessing fit in Bayesian models for spatial processes
Jun, M.; Katzfuss, M.; Hu, J.; Johnson, V. E.
2014-01-01
© 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.
Modeling spin magnetization transport in a spatially varying magnetic field
Picone, Rico A. R.; Garbini, Joseph L.; Sidles, John A.
2015-01-01
We present a framework for modeling the transport of any number of globally conserved quantities in any spatial configuration and apply it to obtain a model of magnetization transport for spin-systems that is valid in new regimes (including high-polarization). The framework allows an entropy function to define a model that explicitly respects the laws of thermodynamics. Three facets of the model are explored. First, it is expressed as nonlinear partial differential equations that are valid for the new regime of high dipole-energy and polarization. Second, the nonlinear model is explored in the limit of low dipole-energy (semi-linear), from which is derived a physical parameter characterizing separative magnetization transport (SMT). It is shown that the necessary and sufficient condition for SMT to occur is that the parameter is spatially inhomogeneous. Third, the high spin-temperature (linear) limit is shown to be equivalent to the model of nuclear spin transport of Genack and Redfield (1975) [1]. Differences among the three forms of the model are illustrated by numerical solution with parameters corresponding to a magnetic resonance force microscopy (MRFM) experiment (Degen et al., 2009 [2]; Kuehn et al., 2008 [3]; Sidles et al., 2003 [4]; Dougherty et al., 2000 [5]). A family of analytic, steady-state solutions to the nonlinear equation is derived and shown to be the spin-temperature analog of the Langevin paramagnetic equation and Curie's law. Finally, we analyze the separative quality of magnetization transport, and a steady-state solution for the magnetization is shown to be compatible with Fenske's separative mass transport equation (Fenske, 1932 [6]).
Toward Accessing Spatial Structure from Building Information Models
Schultz, C.; Bhatt, M.
2011-08-01
Data about building designs and layouts is becoming increasingly more readily available. In the near future, service personal (such as maintenance staff or emergency rescue workers) arriving at a building site will have immediate real-time access to enormous amounts of data relating to structural properties, utilities, materials, temperature, and so on. The critical problem for users is the taxing and error prone task of interpreting such a large body of facts in order to extract salient information. This is necessary for comprehending a situation and deciding on a plan of action, and is a particularly serious issue in time-critical and safety-critical activities such as firefighting. Current unifying building models such as the Industry Foundation Classes (IFC), while being comprehensive, do not directly provide data structures that focus on spatial reasoning and spatial modalities that are required for high-level analytical tasks. The aim of the research presented in this paper is to provide computational tools for higher level querying and reasoning that shift the cognitive burden of dealing with enormous amounts of data away from the user. The user can then spend more energy and time in planning and decision making in order to accomplish the tasks at hand. We present an overview of our framework that provides users with an enhanced model of "built-up space". In order to test our approach using realistic design data (in terms of both scale and the nature of the building models) we describe how our system interfaces with IFC, and we conduct timing experiments to determine the practicality of our approach. We discuss general computational approaches for deriving higher-level spatial modalities by focusing on the example of route graphs. Finally, we present a firefighting scenario with alternative route graphs to motivate the application of our framework.
A new spatial multiple discrete-continuous modeling approach to land use change analysis.
2013-09-01
This report formulates a multiple discrete-continuous probit (MDCP) land-use model within a : spatially explicit economic structural framework for land-use change decisions. The spatial : MDCP model is capable of predicting both the type and intensit...
Stochastic population oscillations in spatial predator-prey models
International Nuclear Information System (INIS)
Taeuber, Uwe C
2011-01-01
It is well-established that including spatial structure and stochastic noise in models for predator-prey interactions invalidates the classical deterministic Lotka-Volterra picture of neutral population cycles. In contrast, stochastic models yield long-lived, but ultimately decaying erratic population oscillations, which can be understood through a resonant amplification mechanism for density fluctuations. In Monte Carlo simulations of spatial stochastic predator-prey systems, one observes striking complex spatio-temporal structures. These spreading activity fronts induce persistent correlations between predators and prey. In the presence of local particle density restrictions (finite prey carrying capacity), there exists an extinction threshold for the predator population. The accompanying continuous non-equilibrium phase transition is governed by the directed-percolation universality class. We employ field-theoretic methods based on the Doi-Peliti representation of the master equation for stochastic particle interaction models to (i) map the ensuing action in the vicinity of the absorbing state phase transition to Reggeon field theory, and (ii) to quantitatively address fluctuation-induced renormalizations of the population oscillation frequency, damping, and diffusion coefficients in the species coexistence phase.
Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study
Energy Technology Data Exchange (ETDEWEB)
Deutsch, Jared L., E-mail: jdeutsch@ualberta.ca [University of Alberta, School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering (Canada); Palmer, Kevin [Teck Resources Limited (Canada); Deutsch, Clayton V.; Szymanski, Jozef [University of Alberta, School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering (Canada); Etsell, Thomas H. [University of Alberta, Department of Chemical and Materials Engineering (Canada)
2016-06-15
High-resolution spatial numerical models of metallurgical properties constrained by geological controls and more extensively by measured grade and geomechanical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are adapted and developed to construct these high-resolution models accounting for all available data. Important issues that must be addressed include unequal sampling of the metallurgical properties versus grade assays, measurements at different scale, and complex nonlinear averaging of many metallurgical parameters. This paper establishes techniques to address each of these issues with the required implementation details and also demonstrates geometallurgical mineral deposit characterization for a copper–molybdenum deposit in South America. High-resolution models of grades and comminution indices are constructed, checked, and are rigorously validated. The workflow demonstrated in this case study is applicable to many other deposit types.
Spatial generalised linear mixed models based on distances.
Melo, Oscar O; Mateu, Jorge; Melo, Carlos E
2016-10-01
Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.
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...
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
Spatial and functional modeling of carnivore and insectivore molariform teeth.
Evans, Alistair R; Sanson, Gordon D
2006-06-01
The interaction between the two main competing geometric determinants of teeth (the geometry of function and the geometry of occlusion) were investigated through the construction of three-dimensional spatial models of several mammalian tooth forms (carnassial, insectivore premolar, zalambdodont, dilambdodont, and tribosphenic). These models aim to emulate the shape and function of mammalian teeth. The geometric principles of occlusion relating to single- and double-crested teeth are reviewed. Function was considered using engineering principles that relate tooth shape to function. Substantial similarity between the models and mammalian teeth were achieved. Differences between the two indicate the influence of tooth strength, geometric relations between upper and lower teeth (including the presence of the protocone), and wear on tooth morphology. The concept of "autocclusion" is expanded to include any morphological features that ensure proper alignment of cusps on the same tooth and other teeth in the tooth row. It is concluded that the tooth forms examined are auto-aligning, and do not require additional morphological guides for correct alignment. The model of therian molars constructed by Crompton and Sita-Lumsden ([1970] Nature 227:197-199) is reconstructed in 3D space to show that their hypothesis of crest geometry is erroneous, and that their model is a special case of a more general class of models. (c) 2004 Wiley-Liss, Inc.
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
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......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...... parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we...
Estimation of spatial uncertainties of tomographic velocity models
Energy Technology Data Exchange (ETDEWEB)
Jordan, M.; Du, Z.; Querendez, E. [SINTEF Petroleum Research, Trondheim (Norway)
2012-12-15
This research project aims to evaluate the possibility of assessing the spatial uncertainties in tomographic velocity model building in a quantitative way. The project is intended to serve as a test of whether accurate and specific uncertainty estimates (e.g., in meters) can be obtained. The project is based on Monte Carlo-type perturbations of the velocity model as obtained from the tomographic inversion guided by diagonal and off-diagonal elements of the resolution and the covariance matrices. The implementation and testing of this method was based on the SINTEF in-house stereotomography code, using small synthetic 2D data sets. To test the method the calculation and output of the covariance and resolution matrices was implemented, and software to perform the error estimation was created. The work included the creation of 2D synthetic data sets, the implementation and testing of the software to conduct the tests (output of the covariance and resolution matrices which are not implicitly provided by stereotomography), application to synthetic data sets, analysis of the test results, and creating the final report. The results show that this method can be used to estimate the spatial errors in tomographic images quantitatively. The results agree with' the known errors for our synthetic models. However, the method can only be applied to structures in the model, where the change of seismic velocity is larger than the predicted error of the velocity parameter amplitudes. In addition, the analysis is dependent on the tomographic method, e.g., regularization and parameterization. The conducted tests were very successful and we believe that this method could be developed further to be applied to third party tomographic images.
Parameterizing the Spatial Markov Model From Breakthrough Curve Data Alone
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.
Infection dynamics on spatial small-world network models
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.
Spatial Distribution of Hydrologic Ecosystem Service Estimates: Comparing Two Models
Dennedy-Frank, P. J.; Ghile, Y.; Gorelick, S.; Logsdon, R. A.; Chaubey, I.; Ziv, G.
2014-12-01
We compare estimates of the spatial distribution of water quantity provided (annual water yield) from two ecohydrologic models: the widely-used Soil and Water Assessment Tool (SWAT) and the much simpler water models from the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) toolbox. These two models differ significantly in terms of complexity, timescale of operation, effort, and data required for calibration, and so are often used in different management contexts. We compare two study sites in the US: the Wildcat Creek Watershed (2083 km2) in Indiana, a largely agricultural watershed in a cold aseasonal climate, and the Upper Upatoi Creek Watershed (876 km2) in Georgia, a mostly forested watershed in a temperate aseasonal climate. We evaluate (1) quantitative estimates of water yield to explore how well each model represents this process, and (2) ranked estimates of water yield to indicate how useful the models are for management purposes where other social and financial factors may play significant roles. The SWAT and InVEST models provide very similar estimates of the water yield of individual subbasins in the Wildcat Creek Watershed (Pearson r = 0.92, slope = 0.89), and a similar ranking of the relative water yield of those subbasins (Spearman r = 0.86). However, the two models provide relatively different estimates of the water yield of individual subbasins in the Upper Upatoi Watershed (Pearson r = 0.25, slope = 0.14), and very different ranking of the relative water yield of those subbasins (Spearman r = -0.10). The Upper Upatoi watershed has a significant baseflow contribution due to its sandy, well-drained soils. InVEST's simple seasonality terms, which assume no change in storage over the time of the model run, may not accurately estimate water yield processes when baseflow provides such a strong contribution. Our results suggest that InVEST users take care in situations where storage changes are significant.
Is a matrix exponential specification suitable for the modeling of spatial correlation structures?
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.
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
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...
Using the gravity model to estimate the spatial spread of vector-borne diseases
Barrios, J.M.; Verstraeten, W.W.; Maes, P.; Aerts, J.; Farifteh, J.; Coppin, P.
2012-01-01
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
Lessons learned for spatial modelling of ecosystem services in support of ecosystem accounting
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
Unemployment estimation: Spatial point referenced methods and models
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
Spatial Fleming-Viot models with selection and mutation
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.
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.
Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent
2016-04-01
Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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
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 is...... varying characteristics markedly. This suggests that omitted variable bias may remain an important problem. We advocate for an increased use of sensitivity analysis to determine robustness of estimates to different models of the (omitted) spatial processes....
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.
The backbone of a City Information Model (CIM) : Implementing a spatial data model for urban design
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
Pattern formation through spatial interactions in a modified Daisyworld model
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
Extending Primitive Spatial Data Models to Include Semantics
Reitsma, F.; Batcheller, J.
2009-04-01
Our traditional geospatial data model involves associating some measurable quality, such as temperature, or observable feature, such as a tree, with a point or region in space and time. When capturing data we implicitly subscribe to some kind of conceptualisation. If we can make this explicit in an ontology and associate it with the captured data, we can leverage formal semantics to reason with the concepts represented in our spatial data sets. To do so, we extend our fundamental representation of geospatial data in a data model by including a URI in our basic data model that links it to our ontology defining our conceptualisation, We thus extend Goodchild et al's geo-atom [1] with the addition of a URI: (x, Z, z(x), URI) . This provides us with pixel or feature level knowledge and the ability to create layers of data from a set of pixels or features that might be drawn from a database based on their semantics. Using open source tools, we present a prototype that involves simple reasoning as a proof of concept. References [1] M.F. Goodchild, M. Yuan, and T.J. Cova. Towards a general theory of geographic representation in gis. International Journal of Geographical Information Science, 21(3):239-260, 2007.
Spatial and Temporal Self-Calibration of a Hydroeconomic Model
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'>
Sang, Huiyan; Jun, Mikyoung; Huang, Jianhua Z.
2011-01-01
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
Broms, Kristin M; Johnson, Devin S; Altwegg, Res; Conquest, Loveday L
2014-03-01
Determining the range of a species and exploring species--habitat associations are central questions in ecology and can be answered by analyzing presence--absence data. Often, both the sampling of sites and the desired area of inference involve neighboring sites; thus, positive spatial autocorrelation between these sites is expected. Using survey data for the Southern Ground Hornbill (Bucorvus leadbeateri) from the Southern African Bird Atlas Project, we compared advantages and disadvantages of three increasingly complex models for species occupancy: an occupancy model that accounted for nondetection but assumed all sites were independent, and two spatial occupancy models that accounted for both nondetection and spatial autocorrelation. We modeled the spatial autocorrelation with an intrinsic conditional autoregressive (ICAR) model and with a restricted spatial regression (RSR) model. Both spatial models can readily be applied to any other gridded, presence--absence data set using a newly introduced R package. The RSR model provided the best inference and was able to capture small-scale variation that the other models did not. It showed that ground hornbills are strongly dependent on protected areas in the north of their South African range, but less so further south. The ICAR models did not capture any spatial autocorrelation in the data, and they took an order, of magnitude longer than the RSR models to run. Thus, the RSR occupancy model appears to be an attractive choice for modeling occurrences at large spatial domains, while accounting for imperfect detection and spatial autocorrelation.
Accounting for spatial effects in land use regression for urban air pollution modeling.
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.
Hoffmann, H.; Zhao, G.; Bussel, van L.G.J.
2015-01-01
Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield
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.
Turner, D.P.; Dodson, R.; Marks, D.
1996-01-01
Spatially distributed biogeochemical models may be applied over grids at a range of spatial resolutions, however, evaluation of potential errors and loss of information at relatively coarse resolutions is rare. In this study, a georeferenced database at the 1-km spatial resolution was developed to initialize and drive a process-based model (Forest-BGC) of water and carbon balance over a gridded 54976 km2 area covering two river basins in mountainous western Oregon. Corresponding data sets were also prepared at 10-km and 50-km spatial resolutions using commonly employed aggregation schemes. Estimates were made at each grid cell for climate variables including daily solar radiation, air temperature, humidity, and precipitation. The topographic structure, water holding capacity, vegetation type and leaf area index were likewise estimated for initial conditions. The daily time series for the climatic drivers was developed from interpolations of meteorological station data for the water year 1990 (1 October 1989-30 September 1990). Model outputs at the 1-km resolution showed good agreement with observed patterns in runoff and productivity. The ranges for model inputs at the 10-km and 50-km resolutions tended to contract because of the smoothed topography. Estimates for mean evapotranspiration and runoff were relatively insensitive to changing the spatial resolution of the grid whereas estimates of mean annual net primary production varied by 11%. The designation of a vegetation type and leaf area at the 50-km resolution often subsumed significant heterogeneity in vegetation, and this factor accounted for much of the difference in the mean values for the carbon flux variables. Although area wide means for model outputs were generally similar across resolutions, difference maps often revealed large areas of disagreement. Relatively high spatial resolution analyses of biogeochemical cycling are desirable from several perspectives and may be particularly important in the
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.
A spatially-averaged mathematical model of kidney branching morphogenesis
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.
A spatially-averaged mathematical model of kidney branching morphogenesis
Zubkov, V.S.; Combes, A.N.; Short, K.M.; Lefevre, J.; Hamilton, N.A.; Smyth, I.M.; Little, M.H.; Byrne, H.M.
2015-01-01
© 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.
Scaling-up spatially-explicit ecological models using graphics processors
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
Towards a computational spatial knowledge acquisition model in architectural space
Lyu, J.; Vries, de B.; Sun, C.; Sun, C.; Zhang, J.
2013-01-01
Abstract. Existing research which is related to spatial knowledge acquisition often shows a limited scope because of the complexity in the cognition process. Research in spatial representation such as space syntax presumes that vision drives movement. This assumption is only true under certain
Including spatial data in nutrient balance modelling on dairy farms
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
Muška, Milan; Tušer, Michal; Frouzová, Jaroslava; Mrkvička, Tomáš; Ricard, Daniel; Seďa, Jaromír; Morelli, Federico; Kubečka, Jan
2018-03-29
Understanding spatial distribution of organisms in heterogeneous environment remains one of the chief issues in ecology. Spatial organization of freshwater fish was investigated predominantly on large-scale, neglecting important local conditions and ecological processes. However, small-scale processes are of an essential importance for individual habitat preferences and hence structuring trophic cascades and species coexistence. In this work, we analysed the real-time spatial distribution of pelagic freshwater fish in the Římov Reservoir (Czechia) observed by hydroacoustics in relation to important environmental predictors during 48 hours at 3-h interval. Effect of diurnal cycle was revealed of highest significance in all spatial models with inverse trends between fish distribution and predictors in day and night in general. Our findings highlighted daytime pelagic fish distribution as highly aggregated, with general fish preferences for central, deep and highly illuminated areas, whereas nighttime distribution was more disperse and fish preferred nearshore steep sloped areas with higher depth. This turnover suggests prominent movements of significant part of fish assemblage between pelagic and nearshore areas on a diel basis. In conclusion, hydroacoustics, GIS and spatial modelling proved as valuable tool for predicting local fish distribution and elucidate its drivers, which has far reaching implications for understanding freshwater ecosystem functioning.
Hybrid Spatial Data Model for Indoor Space: Combined Topology and Grid
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Zhiyong Lin
2017-11-01
Full Text Available The construction and application of an indoor spatial data model is an important prerequisite to meet the requirements of diversified indoor spatial location services. The traditional indoor spatial topology model focuses on the construction of topology information. It has high path analysis and query efficiency, but ignores the spatial location information. The grid model retains the plane position information by grid, but increases the data volume and complexity of the model and reduces the efficiency of the model analysis. This paper presents a hybrid model for interior space based on topology and grid. Based on the spatial meshing and spatial division of the interior space, the model retains the position information and topological connectivity information of the interior space by establishing the connection or affiliation between the grid subspace and the topological subspace. The model improves the speed of interior spatial analysis and solves the problem of the topology information and location information updates not being synchronized. In this study, the A* shortest path query efficiency of typical daily indoor activities under the grid model and the hybrid model were compared for the indoor plane of an apartment and a shopping mall. The results obtained show that the hybrid model is 43% higher than the A* algorithm of the grid model as a result of the existence of topology communication information. This paper provides a useful idea for the establishment of a highly efficient and highly available interior spatial data model.
Environmental Impacts of Large Scale Biochar Application Through Spatial Modeling
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
Phase transition in a spatial Lotka-Volterra model
International Nuclear Information System (INIS)
Szabo, Gyorgy; Czaran, Tamas
2001-01-01
Spatial evolution is investigated in a simulated system of nine competing and mutating bacterium strains, which mimics the biochemical war among bacteria capable of producing two different bacteriocins (toxins) at most. Random sequential dynamics on a square lattice is governed by very symmetrical transition rules for neighborhood invasions of sensitive strains by killers, killers by resistants, and resistants by sensitives. The community of the nine possible toxicity/resistance types undergoes a critical phase transition as the uniform transmutation rates between the types decreases below a critical value P c above that all the nine types of strains coexist with equal frequencies. Passing the critical mutation rate from above, the system collapses into one of three topologically identical (degenerated) states, each consisting of three strain types. Of the three possible final states each accrues with equal probability and all three maintain themselves in a self-organizing polydomain structure via cyclic invasions. Our Monte Carlo simulations support that this symmetry-breaking transition belongs to the universality class of the three-state Potts model
Parameterizing the Spatial Markov Model from Breakthrough Curve Data Alone
Sherman, T.; Bolster, D.; Fakhari, A.; Miller, S.; Singha, K.
2017-12-01
The spatial Markov model (SMM) uses a correlated random walk and has been shown to effectively capture anomalous transport in porous media systems; in the SMM, particles' future trajectories are correlated to their current velocity. It is common practice to use a priori Lagrangian velocity statistics obtained from high resolution simulations to determine a distribution of transition probabilities (correlation) between velocity classes that govern predicted transport behavior; however, this approach is computationally cumbersome. Here, we introduce a methodology to quantify velocity correlation from Breakthrough (BTC) curve data alone; discretizing two measured BTCs into a set of arrival times and reverse engineering the rules of the SMM allows for prediction of velocity correlation, thereby enabling parameterization of the SMM in studies where Lagrangian velocity statistics are not available. The introduced methodology is applied to estimate velocity correlation from BTCs measured in high resolution simulations, thus allowing for a comparison of estimated parameters with known simulated values. Results show 1) estimated transition probabilities agree with simulated values and 2) using the SMM with estimated parameterization accurately predicts BTCs downstream. Additionally, we include uncertainty measurements by calculating lower and upper estimates of velocity correlation, which allow for prediction of a range of BTCs. The simulated BTCs fall in the range of predicted BTCs. This research proposes a novel method to parameterize the SMM from BTC data alone, thereby reducing the SMM's computational costs and widening its applicability.
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
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......, 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...... with discrete time processes in the setting of the present paper as well as other spatial-temporal situations....
Duan, L L; Szczesniak, R D; Wang, X
2017-11-01
Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization.
Duan, L. L.; Szczesniak, R. D.; Wang, X.
2018-01-01
Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization. PMID:29576735
Research on the decision-making model of land-use spatial optimization
He, Jianhua; Yu, Yan; Liu, Yanfang; Liang, Fei; Cai, Yuqiu
2009-10-01
Using the optimization result of landscape pattern and land use structure optimization as constraints of CA simulation results, a decision-making model of land use spatial optimization is established coupled the landscape pattern model with cellular automata to realize the land use quantitative and spatial optimization simultaneously. And Huangpi district is taken as a case study to verify the rationality of the model.
Chu, Chunlei; Stoffa, Paul L.
2012-01-01
sampled models onto vertically nonuniform grids. We use a 2D TTI salt model to demonstrate its effectiveness and show that the nonuniform grid implicit spatial finite difference method can produce highly accurate seismic modeling results with enhanced
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.
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
Modeling Spatial Data within Object Relational-Databases
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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.
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.
Hiebeler, David E; Millett, Nicholas E
2011-06-21
We investigate a spatial lattice model of a population employing dispersal to nearest and second-nearest neighbors, as well as long-distance dispersal across the landscape. The model is studied via stochastic spatial simulations, ordinary pair approximation, and triplet approximation. The latter method, which uses the probabilities of state configurations of contiguous blocks of three sites as its state variables, is demonstrated to be greatly superior to pair approximations for estimating spatial correlation information at various scales. Correlations between pairs of sites separated by arbitrary distances are estimated by constructing spatial Markov processes using the information from both approximations. These correlations demonstrate why pair approximation misses basic qualitative features of the model, such as decreasing population density as a large proportion of offspring are dropped on second-nearest neighbors, and why triplet approximation is able to include them. Analytical and numerical results show that, excluding long-distance dispersal, the initial growth rate of an invading population is maximized and the equilibrium population density is also roughly maximized when the population spreads its offspring evenly over nearest and second-nearest neighboring sites. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Sohair F Higazi
2013-02-01
Full Text Available Regression analysis depends on several assumptions that have to be satisfied. A major assumption that is never satisfied when variables are from contiguous observations is the independence of error terms. Spatial analysis treated the violation of that assumption by two derived models that put contiguity of observations into consideration. Data used are from Egypt's 2006 latest census, for 93 counties in middle delta seven adjacent Governorates. The dependent variable used is the percent of individuals classified as poor (those who make less than 1$ daily. Predictors are some demographic indicators. Explanatory Spatial Data Analysis (ESDA is performed to examine the existence of spatial clustering and spatial autocorrelation between neighboring counties. The ESDA revealed spatial clusters and spatial correlation between locations. Three statistical models are applied to the data, the Ordinary Least Square regression model (OLS, the Spatial Error Model (SEM and the Spatial Lag Model (SLM.The Likelihood Ratio test and some information criterions are used to compare SLM and SEM to OLS. The SEM model proved to be better than the SLM model. Recommendations are drawn regarding the two spatial models used.
Velazco, Julio G.; Rodríguez-Álvarez, María Xosé; Boer, Martin P.; Jordan, David R.; Eilers, Paul H.C.; Malosetti, Marcos; Eeuwijk, van Fred A.
2017-01-01
Key message: A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials. Abstract: Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and
J.G. Velazco (Julio G.); M.X. Rodríguez-Álvarez (María Xosé); M.P. Boer (Martin); D.R. Jordan (David R.); P.H.C. Eilers (Paul); M. Malosetti (Marcos); F. van Eeuwijk (Fred)
2017-01-01
markdownabstract_Key message: A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials._ __Abstract:__ Adjustment for spatial trends in plant breeding field trials is essential for
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Mansour Halimi
2014-02-01
Full Text Available Objective: To model spatial relationship between climatic conditions and annual parasite incidence (API of malaria in southern part of Sistan&Balouchistan Province of Iran using spatial statistic models . Methods: A geographical weighted regression model was applied for predicting API by 3 climatic factors in order to model the spatial API of malaria in Sistan&Baluchistan Province of Iran. Results: The results indicated that most important climatic factor for explaining API in Sistan&Baluchistan was annual rainfall being of more importance in southern part of study area such as Chabahar, and Nikshar. The temperature and relative humidity are of the second and third priority respectively. The importance of these two climatic factors is higher in northern part of the studied region. The spatial autocorrelation (Moran ’s I for standard residual of applied geographical weighted regression model is -0.022 which indicated no spatial patterns. Conclusions: This model explained only 0.51 of API spatial variation (R2=0.51. Thus, the nonclimatic factors such as socioeconomic, lifestyle and the neighborhood position of this province with Afghanistan, and Pakistan also should be considered in epidemiological survey of malaria in Sistan&Baluchistan.
Unemployment estimation: Spatial point referenced methods and models
Pereira, Soraia; Turkman, Kamil Feridun; Correia, Luis; Rue, Haavard
2017-01-01
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
MODEL OF SPATIAL EVALUATION FOR TOURISM ECO-RENT
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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.
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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.
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.
Modelling malaria treatment practices in Bangladesh using spatial statistics
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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.
Analysing the distribution of synaptic vesicles using a spatial point process model
DEFF Research Database (Denmark)
Khanmohammadi, Mahdieh; Waagepetersen, Rasmus; Nava, Nicoletta
2014-01-01
functionality by statistically modelling the distribution of the synaptic vesicles in two groups of rats: a control group subjected to sham stress and a stressed group subjected to a single acute foot-shock (FS)-stress episode. We hypothesize that the synaptic vesicles have different spatial distributions...... in the two groups. The spatial distributions are modelled using spatial point process models with an inhomogeneous conditional intensity and repulsive pairwise interactions. Our results verify the hypothesis that the two groups have different spatial distributions....
Bayesian spatial modeling of HIV mortality via zero-inflated Poisson models.
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.
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
Spatial models for context-aware indoor navigation systems: A survey
Directory of Open Access Journals (Sweden)
Imad Afyouni
2012-06-01
Full Text Available This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS, and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, considering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation purposes, and by taking into account the context dimensions.
A spatial error model with continuous random effects and an application to growth convergence
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.
Research on spatial Model and analysis algorithm for nuclear weapons' damage effects
International Nuclear Information System (INIS)
Liu Xiaohong; Meng Tao; Du Maohua; Wang Weili; Ji Wanfeng
2011-01-01
In order to realize the three dimension visualization of nuclear weapons' damage effects. Aiming at the characteristics of the damage effects data, a new model-MRPCT model is proposed, and this model can carry out the modeling of the three dimension spatial data of the nuclear weapons' damage effects. For the sake of saving on the memory, linear coding method is used to store the MRPCT model. On the basis of Morton code, spatial analysis of the damage effects is completed. (authors)
A spatial-dynamic value transfer model of economic losses from a biological invasion
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...
Towards models of strategic spatial choice behaviour: theory and application issues
Han, Q.; Timmermans, H.J.P.
2005-01-01
Models of spatial choice behaviour have been around in urban planning for decades to assess the feasibility of planning actions or to predict external (competition) effects on existing destinations. The well known spatial interaction models of the 1970s have gradually been replaced by discrete
Computer Games versus Maps before Reading Stories: Priming Readers' Spatial Situation Models
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…
DEFF Research Database (Denmark)
Demirel, Mehmet C.; Mai, Juliane; Mendiguren Gonzalez, Gorka
2018-01-01
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...... 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...
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.
Applying Spatial-Temporal Model and Game Theory to Asymmetric Threat Prediction
National Research Council Canada - National Science Library
Wei, Mo; Chen, Genshe; Cruz, Jr., Jose B; Haynes, Leonard; Kruger, Martin
2007-01-01
.... In most Command and Control "C2" applications, the existing techniques, such as spatial-temporal point models for ECOA prediction or Discrete Choice Model "DCM", assume that insurgent attack features...
Huser, Raphaë l; Opitz, Thomas; Thibaud, Emeric
2018-01-01
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
Improving the spatial representation of basin hydrology and flow processes in the SWAT model
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.
Spatial Mapping of Agricultural Water Productivity Using the SWAT Model
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
China’s Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model
Directory of Open Access Journals (Sweden)
Qilong Cao
2017-09-01
Full Text Available Background: Air pollution has become an important factor restricting China’s economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods: Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM2.5. Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results: It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM2.5 pollutions in the control of other variables. Conclusions: Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables.
China's Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model.
Cao, Qilong; Liang, Ying; Niu, Xueting
2017-09-18
Background : Air pollution has become an important factor restricting China's economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods : Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM 2.5 . Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results : It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM 2.5 pollutions in the control of other variables. Conclusions : Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables.
China’s Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model
Cao, Qilong; Liang, Ying; Niu, Xueting
2017-01-01
Background: Air pollution has become an important factor restricting China’s economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods: Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM2.5. Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results: It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM2.5 pollutions in the control of other variables. Conclusions: Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables. PMID:28927016
Periodicity in spatial data and geostatistical models: autocorrelation between patches
Volker C. Radeloff; Todd F. Miller; Hong S. He; David J. Mladenoff
2000-01-01
Several recent studies in landscape ecology have found periodicity in correlograms or semi-variograms calculated, for instance, from spatial data of soils, forests, or animal populations. Some of the studies interpreted this as an indication of regular or periodic landscape patterns. This interpretation is in disagreement with other studies that doubt whether such...
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 ...
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 ...
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 ...
Social dynamics interest groups in a model of spatial competition
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
Using 3D Geometric Models to Teach Spatial Geometry Concepts.
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)
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
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...... that spatial models can be used to re-examine classic questions regarding the presence and strength of density dependence in wild populations Read More: http://www.esajournals.org/doi/abs/10.1890/14-0739.1...
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.
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
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.
Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models
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...
Thematic and spatial resolutions affect model-based predictions of tree species distribution.
Liang, Yu; He, Hong S; Fraser, Jacob S; Wu, ZhiWei
2013-01-01
Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.
Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior
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 (...
Spatial modelling of population at risk and PM 2.5 exposure index: A ...
African Journals Online (AJOL)
However, monitoring, spatial representation and development of associated risk indicators have been major problems undermining formulation of relevant policy on air quality. This study used ... to environmental health. Key Words: Population at risk, PM2.5; Spatial modeling, GIS, Exposure index, environmental health ...
Climate Change and Agricultural Productivity in Sub-Saharan Africa: A Spatial Sample Selection Model
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
Spatial prediction models for landslide hazards: review, comparison and evaluation
Directory of Open Access Journals (Sweden)
A. Brenning
2005-01-01
Full Text Available The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting 'present' and 'future' landslides are estimated within and outside the training area. In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities. The evaluation outside the training area reveals that tree-based methods tend to overfit the data.
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.
Koike, A.; Tavasszy, L.; Sato, K.
2009-01-01
The authors apply the RAEM-Light model to analyze the distribution of social benefits from expressway network projects from the viewpoint of spatial equity. The RAEM-Light model has some innovative features. The spatial behavior of producers and consumers is explicitly described and is endogenously
Schumann, Andreas; Oppel, Henning
2017-04-01
To represent the hydrological behaviour of catchments a model should reproduce/reflect the hydrologically most relevant catchment characteristics. These are heterogeneously distributed within a watershed but often interrelated and subject of a certain spatial organisation. Since common models are mostly based on fundamental assumptions about hydrological processes, the reduction of variance of catchment properties as well as the incorporation of the spatial organisation of the catchment is desirable. We have developed a method that combines the idea of the width-function used for determination of the geomorphologic unit hydrograph with information about soil or topography. With this method we are able to assess the spatial organisation of selected catchment characteristics. An algorithm was developed that structures a watershed into sub-basins and other spatial units to minimise its heterogeneity. The outcomes of this algorithm are used for the spatial setup of a semi-distributed model. Since the spatial organisation of a catchment is not bound to a single characteristic, we have to embed information of multiple catchment properties. For this purpose we applied a fuzzy-based method to combine the spatial setup for multiple single characteristics into a union, optimal spatial differentiation. Utilizing this method, we are able to propose a spatial structure for a semi-distributed hydrological model, comprising the definition of sub-basins and a zonal classification within each sub-basin. Besides the improved spatial structuring, the performed analysis ameliorates modelling in another way. The spatial variability of catchment characteristics, which is considered by a minimum of heterogeneity in the zones, can be considered in a parameter constrained calibration scheme in a case study both options were used to explore the benefits of incorporating the spatial organisation and derived parameter constraints for the parametrisation of a HBV-96 model. We use two benchmark
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.
Lehodey, Patrick; Senina, Inna; Murtugudde, Raghu
2008-09-01
An enhanced version of the spatial ecosystem and population dynamics model SEAPODYM is presented to describe spatial dynamics of tuna and tuna-like species in the Pacific Ocean at monthly resolution over 1° grid-boxes. The simulations are driven by a bio-physical environment predicted from a coupled ocean physical-biogeochemical model. This new version of SEAPODYM includes expanded definitions of habitat indices, movements, and natural mortality based on empirical evidences. A thermal habitat of tuna species is derived from an individual heat budget model. The feeding habitat is computed according to the accessibility of tuna predator cohorts to different vertically migrating and non-migrating micronekton (mid-trophic) functional groups. The spawning habitat is based on temperature and the coincidence of spawning fish with presence or absence of predators and food for larvae. The successful larval recruitment is linked to spawning stock biomass. Larvae drift with currents, while immature and adult tuna can move of their own volition, in addition to being advected by currents. A food requirement index is computed to adjust locally the natural mortality of cohorts based on food demand and accessibility to available forage components. Together these mechanisms induce bottom-up and top-down effects, and intra- (i.e. between cohorts) and inter-species interactions. The model is now fully operational for running multi-species, multi-fisheries simulations, and the structure of the model allows a validation from multiple data sources. An application with two tuna species showing different biological characteristics, skipjack ( Katsuwonus pelamis) and bigeye ( Thunnus obesus), is presented to illustrate the capacity of the model to capture many important features of spatial dynamics of these two different tuna species in the Pacific Ocean. The actual validation is presented in a companion paper describing the approach to have a rigorous mathematical parameter optimization
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
Stochastic Geometric Models with Non-stationary Spatial Correlations in Lagrangian Fluid Flows
Gay-Balmaz, François; Holm, Darryl D.
2018-01-01
Inspired by spatiotemporal observations from satellites of the trajectories of objects drifting near the surface of the ocean in the National Oceanic and Atmospheric Administration's "Global Drifter Program", this paper develops data-driven stochastic models of geophysical fluid dynamics (GFD) with non-stationary spatial correlations representing the dynamical behaviour of oceanic currents. Three models are considered. Model 1 from Holm (Proc R Soc A 471:20140963, 2015) is reviewed, in which the spatial correlations are time independent. Two new models, called Model 2 and Model 3, introduce two different symmetry breaking mechanisms by which the spatial correlations may be advected by the flow. These models are derived using reduction by symmetry of stochastic variational principles, leading to stochastic Hamiltonian systems, whose momentum maps, conservation laws and Lie-Poisson bracket structures are used in developing the new stochastic Hamiltonian models of GFD.
Stochastic Geometric Models with Non-stationary Spatial Correlations in Lagrangian Fluid Flows
Gay-Balmaz, François; Holm, Darryl D.
2018-06-01
Inspired by spatiotemporal observations from satellites of the trajectories of objects drifting near the surface of the ocean in the National Oceanic and Atmospheric Administration's "Global Drifter Program", this paper develops data-driven stochastic models of geophysical fluid dynamics (GFD) with non-stationary spatial correlations representing the dynamical behaviour of oceanic currents. Three models are considered. Model 1 from Holm (Proc R Soc A 471:20140963, 2015) is reviewed, in which the spatial correlations are time independent. Two new models, called Model 2 and Model 3, introduce two different symmetry breaking mechanisms by which the spatial correlations may be advected by the flow. These models are derived using reduction by symmetry of stochastic variational principles, leading to stochastic Hamiltonian systems, whose momentum maps, conservation laws and Lie-Poisson bracket structures are used in developing the new stochastic Hamiltonian models of GFD.
Thogmartin, W.E.; Knutson, M.G.
2007-01-01
Much of what is known about avian species-habitat relations has been derived from studies of birds at local scales. It is entirely unclear whether the relations observed at these scales translate to the larger landscape in a predictable linear fashion. We derived habitat models and mapped predicted abundances for three forest bird species of eastern North America using bird counts, environmental variables, and hierarchical models applied at three spatial scales. Our purpose was to understand habitat associations at multiple spatial scales and create predictive abundance maps for purposes of conservation planning at a landscape scale given the constraint that the variables used in this exercise were derived from local-level studies. Our models indicated a substantial influence of landscape context for all species, many of which were counter to reported associations at finer spatial extents. We found land cover composition provided the greatest contribution to the relative explained variance in counts for all three species; spatial structure was second in importance. No single spatial scale dominated any model, indicating that these species are responding to factors at multiple spatial scales. For purposes of conservation planning, areas of predicted high abundance should be investigated to evaluate the conservation potential of the landscape in their general vicinity. In addition, the models and spatial patterns of abundance among species suggest locations where conservation actions may benefit more than one species. ?? 2006 Springer Science+Business Media B.V.
Assessing NARCCAP climate model effects using spatial confidence regions
Directory of Open Access Journals (Sweden)
J. P. French
2017-07-01
Full Text Available We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.
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
Redding, David W; Lucas, Tim C D; Blackburn, Tim M; Jones, Kate E
2017-01-01
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 for spatial
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
Jun, Mikyoung; Park, Eun Sug
2013-01-01
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
Tran, Quoc Quan; Willems, Patrick; Pannemans, Bart; Blanckaert, Joris; Pereira, Fernando; Nossent, Jiri; Cauwenberghs, Kris; Vansteenkiste, Thomas
2015-04-01
Based on an international literature review on model structures of existing rainfall-runoff and hydrological models, a generalized model structure is proposed. It consists of different types of meteorological components, storage components, splitting components and routing components. They can be spatially organized in a lumped way, or on a grid, spatially interlinked by source-to-sink or grid-to-grid (cell-to-cell) routing. The grid size of the model can be chosen depending on the application. The user can select/change the spatial resolution depending on the needs and/or the evaluation of the accuracy of the model results, or use different spatial resolutions in parallel for different applications. Major research questions addressed during the study are: How can we assure consistent results of the model at any spatial detail? How can we avoid strong or sudden changes in model parameters and corresponding simulation results, when one moves from one level of spatial detail to another? How can we limit the problem of overparameterization/equifinality when we move from the lumped model to the spatially distributed model? The proposed approach is a step-wise one, where first the lumped conceptual model is calibrated using a systematic, data-based approach, followed by a disaggregation step where the lumped parameters are disaggregated based on spatial catchment characteristics (topography, land use, soil characteristics). In this way, disaggregation can be done down to any spatial scale, and consistently among scales. Only few additional calibration parameters are introduced to scale the absolute spatial differences in model parameters, but keeping the relative differences as obtained from the spatial catchment characteristics. After calibration of the spatial model, the accuracies of the lumped and spatial models were compared for peak, low and cumulative runoff total and sub-flows (at downstream and internal gauging stations). For the distributed models, additional
Housing price prediction: parametric versus semi-parametric spatial hedonic models
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.
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...... 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...... 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...
Spatial aspects affecting acidification factors in European acidification modelling
Bellekom, S.; Hettelingh, J. -P.; Aben, J.
Plain linear models have recently been used in methodologies to model fate and transport for assessing acidification in life cycle impact assessment (LCIA), or in support of air pollution abatement policies. These models originate from a statistical analysis of the relationship between inputs and
Spatial modeling on the upperstream of the Citarum watershed: An application of geoinformatics
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.
Modeling of spatial dependence in wind power forecast uncertainty
DEFF Research Database (Denmark)
Papaefthymiou, George; Pinson, Pierre
2008-01-01
It is recognized today that short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. When considering different areas covering a region, they are produced independently, and thus...... neglect the interdependence structure of prediction errors, induced by movement of meteorological fronts, or more generally by inertia of meteorological systems. This issue is addressed here by describing a method that permits to generate interdependent scenarios of wind generation for spatially...... distributed wind power production for specific look-ahead times. The approach is applied to the case of western Denmark split in 5 zones, for a total capacity of more than 2.1 GW. The interest of the methodology for improving the resolution of probabilistic forecasts, for a range of decision-making problems...
The Spatial Fay-Herriot Model in Poverty Estimation
Directory of Open Access Journals (Sweden)
Wawrowski Łukasz
2016-12-01
Full Text Available Counteracting poverty is one of the objectives of the European Commission clearly emphasized in the Europe 2020 strategy. Conducting appropriate social policy requires knowledge of the extent of this phenomenon. Such information is provided through surveys on living conditions conducted by, among others, the Central Statistical Office (CSO. Nevertheless, the sample size in these surveys allows for a precise estimation of poverty rate only at a very general level - the whole country and regions. Small sample size at the lower level of spatial aggregation results in a large variance of obtained estimates and hence lower reliability. To obtain information in sparsely represented territorial sections, methods of small area estimation are used. Through using the information from other sources, such as censuses and administrative registers, it is possible to estimate distribution parameters with smaller variance than in the case of direct estimation.
Whidden, E.; Roulet, N.
2003-04-01
Interpretation of a site average terrestrial flux may be complicated in the presence of inhomogeneities. Inhomogeneity may invalidate the basic assumptions of aerodynamic flux measurement. Chamber measurement may miss or misinterpret important temporal or spatial anomalies. Models may smooth over important nonlinearities depending on the scale of application. Although inhomogeneity is usually seen as a design problem, many sites have spatial variance that may have a large impact on net flux, and in many cases a large homogeneous surface is unrealistic. The sensitivity and validity of a site average flux are investigated in the presence of an inhomogeneous site. Directional differences are used to evaluate the validity of aerodynamic methods and the computation of a site average tower flux. Empirical and modelling methods are used to interpret the spatial controls on flux. An ecosystem model, Ecosys, is used to assess spatial length scales appropriate to the ecophysiologic controls. A diffusion model is used to compare tower, chamber, and model data, by spatially weighting contributions within the tower footprint. Diffusion model weighting is also used to improve tower flux estimates by producing footprint averaged ecological parameters (soil moisture, soil temperature, etc.). Although uncertainty remains in the validity of measurement methods and the accuracy of diffusion models, a detailed spatial interpretation is required at an inhomogeneous site. Flux estimation between methods improves with spatial interpretation, showing the importance to an estimation of a site average flux. Small-scale temporal and spatial anomalies may be relatively unimportant to overall flux, but accounting for medium-scale differences in ecophysiological controls is necessary. A combination of measurements and modelling can be used to define the appropriate time and length scales of significant non-linearity due to inhomogeneity.
Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance
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.
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.
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.
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...
Spatial age-length key modelling using continuation ratio logits
DEFF Research Database (Denmark)
Berg, Casper W.; Kristensen, Kasper
2012-01-01
-called age-length key (ALK) is then used to obtain the age distribution. Regional differences in ALKs are not uncommon, but stratification is often problematic due to a small number of samples. Here, we combine generalized additive modelling with continuation ratio logits to model the probability of age...
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...... of this study was to look at, and improve, various approaches for modelling the land-surface energy uxes at different spatial scales. The work was done using physically-based Two-Source Energy Balance (TSEB) approach as well as semi-empirical \\Triangle" approach. The TSEB-based approach was the main focus...
E. Garcia; C.L. Tague; J. Choate
2013-01-01
Most spatially explicit hydrologic models require estimates of air temperature patterns. For these models, empirical relationships between elevation and air temperature are frequently used to upscale point measurements or downscale regional and global climate model estimates of air temperature. Mountainous environments are particularly sensitive to air temperature...
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...
Transverse tripolar stimulation of peripheral nerve: a modelling study of spatial selectivity
Deurloo, K.E.I.; Holsheimer, J.; Boom, H.B.K.
1998-01-01
Various anode-cathode configurations in a nerve cuff are modelled to predict their spatial selectivity characteristics for functional nerve stimulation. A 3D volume conductor model of a monofascicular nerve is used for the computation of stimulation-induced field potentials, whereas a cable model of
A spatially explicit scenario-driven model of adaptive capacity to global change in Europe
Acosta, L.; Klein, R.J.T.; Reidsma, P.; Metzger, M.J.; Rounsevell, M.D.A.; Leemans, R.
2013-01-01
Traditional impact models combine exposure in the form of scenarios and sensitivity in the form of parameters, providing potential impacts of global change as model outputs. However, adaptive capacity is rarely addressed in these models. This paper presents the first spatially explicit
Alan K. Swanson; Solomon Z. Dobrowski; Andrew O. Finley; James H. Thorne; Michael K. Schwartz
2013-01-01
The uncertainty associated with species distribution model (SDM) projections is poorly characterized, despite its potential value to decision makers. Error estimates from most modelling techniques have been shown to be biased due to their failure to account for spatial autocorrelation (SAC) of residual error. Generalized linear mixed models (GLMM) have the ability to...
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
Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.
Liu, Da; Li, Jianxun
2016-12-16
Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.
Modelling H5N1 in Bangladesh across spatial scales: Model complexity and zoonotic transmission risk
Directory of Open Access Journals (Sweden)
Edward M. Hill
2017-09-01
Full Text Available Highly pathogenic avian influenza H5N1 remains a persistent public health threat, capable of causing infection in humans with a high mortality rate while simultaneously negatively impacting the livestock industry. A central question is to determine regions that are likely sources of newly emerging influenza strains with pandemic causing potential. A suitable candidate is Bangladesh, being one of the most densely populated countries in the world and having an intensifying farming system. It is therefore vital to establish the key factors, specific to Bangladesh, that enable both continued transmission within poultry and spillover across the human–animal interface. We apply a modelling framework to H5N1 epidemics in the Dhaka region of Bangladesh, occurring from 2007 onwards, that resulted in large outbreaks in the poultry sector and a limited number of confirmed human cases. This model consisted of separate poultry transmission and zoonotic transmission components. Utilising poultry farm spatial and population information a set of competing nested models of varying complexity were fitted to the observed case data, with parameter inference carried out using Bayesian methodology and goodness-of-fit verified by stochastic simulations. For the poultry transmission component, successfully identifying a model of minimal complexity, which enabled the accurate prediction of the size and spatial distribution of cases in H5N1 outbreaks, was found to be dependent on the administration level being analysed. A consistent outcome of non-optimal reporting of infected premises materialised in each poultry epidemic of interest, though across the outbreaks analysed there were substantial differences in the estimated transmission parameters. The zoonotic transmission component found the main contributor to spillover transmission of H5N1 in Bangladesh was found to differ from one poultry epidemic to another. We conclude by discussing possible explanations for
Spatial Modelling of Land Price in The Semarang City
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.
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.
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.
Measuring the value of air quality: application of the spatial hedonic model.
Kim, Seung Gyu; Cho, Seong-Hoon; Lambert, Dayton M; Roberts, Roland K
2010-03-01
This study applies a hedonic model to assess the economic benefits of air quality improvement following the 1990 Clean Air Act Amendment at the county level in the lower 48 United States. An instrumental variable approach that combines geographically weighted regression and spatial autoregression methods (GWR-SEM) is adopted to simultaneously account for spatial heterogeneity and spatial autocorrelation. SEM mitigates spatial dependency while GWR addresses spatial heterogeneity by allowing response coefficients to vary across observations. Positive amenity values of improved air quality are found in four major clusters: (1) in East Kentucky and most of Georgia around the Southern Appalachian area; (2) in a few counties in Illinois; (3) on the border of Oklahoma and Kansas, on the border of Kansas and Nebraska, and in east Texas; and (4) in a few counties in Montana. Clusters of significant positive amenity values may exist because of a combination of intense air pollution and consumer awareness of diminishing air quality.
Multiphase modelling of vascular tumour growth in two spatial dimensions
Hubbard, M.E.; Byrne, H.M.
2013-01-01
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
Sandpile models with and without an underlying spatial structure
International Nuclear Information System (INIS)
Christensen, K.; Olami, Z.
1993-01-01
We present a simple mean-field model for the sandpile model introduced by Bak, Tang, and Wiesenfeld (BTW) [Phys. Rev. Lett. 59, 381 (1987)]. In the mean-field model we are able to pinpoint the process of self-organization as well as the emerging scale invariance displayed as a power-law distribution of avalanche sizes. We discuss the BTW sandpile model on a lattice and show that the dynamical behavior can be expressed as a transport problem. This implies that the average avalanche size scales with the system size, and additional heuristic arguments related to the transport properties more than indicate the origin of the power-law behavior. We review recent work in which scaling relations and additional constraints between the various critical exponents are addressed. We demonstrate that some of the proposed relations are inconsistent. We present a coherent ''theory'' in which the scaling relations along with additional constraints leave only one exponent unknown
Spatial distribution of emissions to air – the SPREAD model
DEFF Research Database (Denmark)
Plejdrup, Marlene Schmidt; Gyldenkærne, Steen
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...... 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...
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.
What spatial scales are believable for climate model projections of sea surface temperature?
Kwiatkowski, Lester; Halloran, Paul R.; Mumby, Peter J.; Stephenson, David B.
2014-09-01
Earth system models (ESMs) provide high resolution simulations of variables such as sea surface temperature (SST) that are often used in off-line biological impact models. Coral reef modellers have used such model outputs extensively to project both regional and global changes to coral growth and bleaching frequency. We assess model skill at capturing sub-regional climatologies and patterns of historical warming. This study uses an established wavelet-based spatial comparison technique to assess the skill of the coupled model intercomparison project phase 5 models to capture spatial SST patterns in coral regions. We show that models typically have medium to high skill at capturing climatological spatial patterns of SSTs within key coral regions, with model skill typically improving at larger spatial scales (≥4°). However models have much lower skill at modelling historical warming patters and are shown to often perform no better than chance at regional scales (e.g. Southeast Asian) and worse than chance at finer scales (coral bleaching frequency and other marine processes linked to SST warming.
Calibration of a distributed hydrologic model using observed spatial patterns from MODIS data
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
Spatial modelling of disease using data- and knowledge-driven approaches.
Stevens, Kim B; Pfeiffer, Dirk U
2011-09-01
The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which are not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution. Copyright © 2011. Published by Elsevier Ltd.
Accounting for and predicting the influence of spatial autocorrelation in water quality modeling
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
Harnessing Big Data to Represent 30-meter Spatial Heterogeneity in Earth System Models
Chaney, N.; Shevliakova, E.; Malyshev, S.; Van Huijgevoort, M.; Milly, C.; Sulman, B. N.
2016-12-01
Terrestrial land surface processes play a critical role in the Earth system; they have a profound impact on the global climate, food and energy production, freshwater resources, and biodiversity. One of the most fascinating yet challenging aspects of characterizing terrestrial ecosystems is their field-scale (˜30 m) spatial heterogeneity. It has been observed repeatedly that the water, energy, and biogeochemical cycles at multiple temporal and spatial scales have deep ties to an ecosystem's spatial structure. Current Earth system models largely disregard this important relationship leading to an inadequate representation of ecosystem dynamics. In this presentation, we will show how existing global environmental datasets can be harnessed to explicitly represent field-scale spatial heterogeneity in Earth system models. For each macroscale grid cell, these environmental data are clustered according to their field-scale soil and topographic attributes to define unique sub-grid tiles. The state-of-the-art Geophysical Fluid Dynamics Laboratory (GFDL) land model is then used to simulate these tiles and their spatial interactions via the exchange of water, energy, and nutrients along explicit topographic gradients. Using historical simulations over the contiguous United States, we will show how a robust representation of field-scale spatial heterogeneity impacts modeled ecosystem dynamics including the water, energy, and biogeochemical cycles as well as vegetation composition and distribution.
Seasonal and spatial variation in broadleaf forest model parameters
Groenendijk, M.; van der Molen, M. K.; Dolman, A. J.
2009-04-01
Process based, coupled ecosystem carbon, energy and water cycle models are used with the ultimate goal to project the effect of future climate change on the terrestrial carbon cycle. A typical dilemma in such exercises is how much detail the model must be given to describe the observations reasonably realistic while also be general. We use a simple vegetation model (5PM) with five model parameters to study the variability of the parameters. These parameters are derived from the observed carbon and water fluxes from the FLUXNET database. For 15 broadleaf forests the model parameters were derived for different time resolutions. It appears that in general for all forests, the correlation coefficient between observed and simulated carbon and water fluxes improves with a higher parameter time resolution. The quality of the simulations is thus always better when a higher time resolution is used. These results show that annual parameters are not capable of properly describing weather effects on ecosystem fluxes, and that two day time resolution yields the best results. A first indication of the climate constraints can be found by the seasonal variation of the covariance between Jm, which describes the maximum electron transport for photosynthesis, and climate variables. A general seasonality we found is that during winter the covariance with all climate variables is zero. Jm increases rapidly after initial spring warming, resulting in a large covariance with air temperature and global radiation. During summer Jm is less variable, but co-varies negatively with air temperature and vapour pressure deficit and positively with soil water content. A temperature response appears during spring and autumn for broadleaf forests. This shows that an annual model parameter cannot be representative for the entire year. And relations with mean annual temperature are not possible. During summer the photosynthesis parameters are constrained by water availability, soil water content and
Directory of Open Access Journals (Sweden)
Jingyi Zhang
2018-06-01
Full Text Available This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF method to estimate ground PM2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM2.5 analysis and prediction.
Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng
2018-06-11
This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.
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.
DEFF Research Database (Denmark)
Bastardie, Francois; Nielsen, J. Rasmus; Eigaard, Ole Ritzau
2015-01-01
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...... various constraints. Interlinked spatial, technical, and biological dynamics of vessels and stocks in the scenarios result in stable profits, which compensate for the additional costs from effort displacement and release pressure on the fish stocks. The effort is further redirected away from sensitive...
Modeling spatial-temporal operations with context-dependent associative memories.
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.
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.
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
Linear models for multivariate, time series, and spatial data
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...
Validation of elk resource selection models with spatially independent data
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...
How to handle spatial heterogeneity in hydrological models.
Loritz, Ralf; Neuper, Malte; Gupta, Hoshin; Zehe, Erwin
2017-04-01
The amount of data we observe in our environmental systems is larger than ever. This leads to a new kind of problem where hydrological modelers can have access to large datasets with various quantitative and qualitative observations but are uncertain about the information content with respect to the hydrological functioning of a landscape. For example digital elevation models obviously contain plenty of information about the topography of a landscape; however the question of relevance for Hydrology is how much of this information is important for the hydrological functioning of a landscape. This kind of question is not limited to topography and we can ask similar questions when handling distributed rainfall data or geophysical images. In this study we would like to show how one can separate dominant patterns in the landscape from idiosyncratic system details. We use a 2D numerical hillslope model in combination with an extensive research data set to test a variety of different model setups that are built upon different landscape characteristics and run by different rainfalls measurements. With the help of information theory based measures we can identify and learn how much heterogeneity is really necessary for successful hydrological simulations and how much of it we can neglect.
Multiphase modelling of vascular tumour growth in two spatial dimensions
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.
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.
Booij, Martijn J.
2002-01-01
Important characteristics of an appropriate river basin model, intended to study the effect of climate change on basin response, are the spatial and temporal resolution of the model and the rainfall input. The effects of input and model resolution on extreme discharge of a large river basin are
The importance of spatial ability and mental models in learning anatomy
Chatterjee, Allison K.
As a foundational course in medical education, gross anatomy serves to orient medical and veterinary students to the complex three-dimensional nature of the structures within the body. Understanding such spatial relationships is both fundamental and crucial for achievement in gross anatomy courses, and is essential for success as a practicing professional. Many things contribute to learning spatial relationships; this project focuses on a few key elements: (1) the type of multimedia resources, particularly computer-aided instructional (CAI) resources, medical students used to study and learn; (2) the influence of spatial ability on medical and veterinary students' gross anatomy grades and their mental models; and (3) how medical and veterinary students think about anatomy and describe the features of their mental models to represent what they know about anatomical structures. The use of computer-aided instruction (CAI) by gross anatomy students at Indiana University School of Medicine (IUSM) was assessed through a questionnaire distributed to the regional centers of the IUSM. Students reported using internet browsing, PowerPoint presentation software, and email on a daily bases to study gross anatomy. This study reveals that first-year medical students at the IUSM make limited use of CAI to study gross anatomy. Such studies emphasize the importance of examining students' use of CAI to study gross anatomy prior to development and integration of electronic media into the curriculum and they may be important in future decisions regarding the development of alternative learning resources. In order to determine how students think about anatomical relationships and describe the features of their mental models, personal interviews were conducted with select students based on students' ROT scores. Five typologies of the characteristics of students' mental models were identified and described: spatial thinking, kinesthetic approach, identification of anatomical structures
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.
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.
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.
Spatial-temporal data model and fractal analysis of transportation network in GIS environment
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.
A Deep Similarity Metric Learning Model for Matching Text Chunks to Spatial Entities
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.
DEFF Research Database (Denmark)
Denton, Paul W.; Søgaard, Ole Schmeltz; Tolstrup, Martin
2016-01-01
Research challenges associated with understanding HIV persistence during antiretroviral therapy can be categorized as temporal, spatial and combinatorial. Temporal research challenges relate to the timing of events during establishment and maintenance of HIV persistence. Spatial research challeng...... for directly addressing these research challenges. The aim of this manuscript is to provide a comprehensive review of these recent translational advances made in animal models of HIV persistence....... will improve our understanding of HIV persistence and move the field closer to achieving eradication of persistent HIV. Given that humanized mice and non-human primate HIV models permit rigorous control of experimental conditions, these models have been used extensively as in vivo research platforms...
The stock-flow model of spatial data infrastructure development refined by fuzzy logic.
Abdolmajidi, Ehsan; Harrie, Lars; Mansourian, Ali
2016-01-01
The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development.
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....
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.
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.
Evaluating the Value of High Spatial Resolution in National Capacity Expansion Models using ReEDS
Energy Technology Data Exchange (ETDEWEB)
Krishnan, Venkat; Cole, Wesley
2016-07-18
This poster is based on the paper of the same name, presented at the IEEE Power & Energy Society General Meeting, July18, 2016. Power sector capacity expansion models (CEMs) have a broad range of spatial resolutions. This paper uses the Regional Energy Deployment System (ReEDS) model, a long-term national scale electric sector CEM, to evaluate the value of high spatial resolution for CEMs. ReEDS models the United States with 134 load balancing areas (BAs) and captures the variability in existing generation parameters, future technology costs, performance, and resource availability using very high spatial resolution data, especially for wind and solar modeled at 356 resource regions. In this paper we perform planning studies at three different spatial resolutions - native resolution (134 BAs), state-level, and NERC region level - and evaluate how results change under different levels of spatial aggregation in terms of renewable capacity deployment and location, associated transmission builds, and system costs. The results are used to ascertain the value of high geographically resolved models in terms of their impact on relative competitiveness among renewable energy resources.
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...... 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....
Mesoscopic model of temporal and spatial heterogeneity in aging colloids
DEFF Research Database (Denmark)
Becker, Nikolaj; Sibani, Paolo; Boettcher, Stefan
2014-01-01
We develop a simple and effective description of the dynamics of dense hard sphere colloids in the aging regime deep in the glassy phase. Our description complements the many efforts to understand the onset of jamming in low density colloids, whose dynamics is still time-homogeneous. Based...... scattering function and particle mean-square displacements for jammed colloidal systems, and we predict a growth for the peak of the χ4 mobility correlation function that is logarithmic in waiting-time. At the same time, our model suggests a novel unified description for the irreversible aging dynamics...
A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates
Lindström, Johan; Szpiro, Adam A; Sampson, Paul D; Oron, Assaf P; Richards, Mark; Larson, Tim V; Sheppard, Lianne
2013-01-01
The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implem...
We used a spatially explicit population model of wolves (Canis lupus) to propose a framework for defining rangewide recovery priorities and finer-scale strategies for regional reintroductions. The model predicts that Yellowstone and central Idaho, where wolves have recently been ...
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, ...
Spatial agent-based models for socio-ecological systems: challenges and prospects
de Filatova, T.; Verburg, P.H.; Parker, D.C.; Stannard, S.R.
2013-01-01
Departing from the comprehensive reviews carried out in the field, we identify the key challenges that agent-based methodology faces when modeling coupled socio-ecological systems. Focusing primarily on the papers presented in this thematic issue, we review progress in spatial agent-based models
Eric J. Gustafson; L. Jay Roberts; Larry A. Leefers
2006-01-01
Forest management planners require analytical tools to assess the effects of alternative strategies on the sometimes disparate benefits from forests such as timber production and wildlife habitat. We assessed the spatial patterns of alternative management strategies by linking two models that were developed for different purposes. We used a linear programming model (...
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
Spatial competition for biogas production using insights from retail location models
DEFF Research Database (Denmark)
Bojesen, Mikkel; Birkin, M.; Clarke, G.
2014-01-01
production sector. Through a two-step approach by combining a location-allocation model with a production constrained spatial interaction model, this paper addresses the dual problem of determining optimal location and production capacity. What-if scenarios, in combination with the strategic economic...
Timmermans, H.J.P.; Borgers, A.W.J.; Waerden, van der P.J.H.J.
1992-01-01
The purpose of this article is to compare a set of multinomial logit models derived from revealed choice data and a decompositional choice model derived from experimental data in terms of predictive success in the context of consumer spatial shopping behavior. Data on consumer shopping choice
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.
Evaluation of Spatial Uncertainties In Modeling of Cadastral Systems
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.
Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador
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.
Filgueira, Ramon; Grant, Jon; Strand, Øivind
2014-06-01
Shellfish carrying capacity is determined by the interaction of a cultured species with its ecosystem, which is strongly influenced by hydrodynamics. Water circulation controls the exchange of matter between farms and the adjacent areas, which in turn establishes the nutrient supply that supports phytoplankton populations. The complexity of water circulation makes necessary the use of hydrodynamic models with detailed spatial resolution in carrying capacity estimations. This detailed spatial resolution also allows for the study of processes that depend on specific spatial arrangements, e.g., the most suitable location to place farms, which is crucial for marine spatial planning, and consequently for decision support systems. In the present study, a fully spatial physical-biogeochemical model has been combined with scenario building and optimization techniques as a proof of concept of the use of ecosystem modeling as an objective tool to inform marine spatial planning. The object of this exercise was to generate objective knowledge based on an ecosystem approach to establish new mussel aquaculture areas in a Norwegian fjord. Scenario building was used to determine the best location of a pump that can be used to bring nutrient-rich deep waters to the euphotic layer, increasing primary production, and consequently, carrying capacity for mussel cultivation. In addition, an optimization tool, parameter estimation (PEST), was applied to the optimal location and mussel standing stock biomass that maximize production, according to a preestablished carrying capacity criterion. Optimization tools allow us to make rational and transparent decisions to solve a well-defined question, decisions that are essential for policy makers. The outcomes of combining ecosystem models with scenario building and optimization facilitate planning based on an ecosystem approach, highlighting the capabilities of ecosystem modeling as a tool for marine spatial planning.
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.
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
Spatial interaction models facility location using game theory
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.
Fuzzy modeling with spatial information for geographic problems
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 ...
Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A
2014-08-01
Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate
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
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)
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)
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.
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.
Modeling Alveolar Epithelial Cell Behavior In Spatially Designed Hydrogel Microenvironments
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.
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.
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.
Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology.
Schaff, James C; Gao, Fei; Li, Ye; Novak, Igor L; Slepchenko, Boris M
2016-12-01
Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available. Here we describe fundamentals of a general-purpose spatial hybrid method. The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator, Smoldyn. Rigorous validation of the algorithm is detailed, using a simple model of calcium 'sparks' as a testbed. The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity. The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell.
Analysis of the impact of immigration on labour market using spatial models
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.
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.
International Nuclear Information System (INIS)
Chang, Qiang; Zuo, Li
2013-01-01
Spatial gradients of surrounding chemoattractants are the key factors in determining the directionality of eukaryotic cell movement. Thus, it is important for cells to accurately measure the spatial gradients of surrounding chemoattractants. Here, we study the precision of sensing the spatial gradients of multiple chemoattractants using cooperative receptor clusters. Cooperative receptors on cells are modeled as an Ising chain of Monod–Wyman–Changeux clusters subject to multiple chemical-gradient fields to study the physical limits of multiple chemoattractants spatial gradients sensing. We found that eukaryotic cells cannot sense each chemoattractant gradient individually. Instead, cells can only sense a weighted sum of surrounding chemical gradients. Moreover, the precision of sensing one chemical gradient is signicantly affected by coexisting chemoattractant concentrations. These findings can provide a further insight into the role of chemoattractants in immune response and help develop novel treatments for inflammatory diseases. (paper)
Wantz, Andrea L; Lobmaier, Janek S; Mast, Fred W; Senn, Walter
2017-08-01
Recent research put forward the hypothesis that eye movements are integrated in memory representations and are reactivated when later recalled. However, "looking back to nothing" during recall might be a consequence of spatial memory retrieval. Here, we aimed at distinguishing between the effect of spatial and oculomotor information on perceptual memory. Participants' task was to judge whether a morph looked rather like the first or second previously presented face. Crucially, faces and morphs were presented in a way that the morph reactivated oculomotor and/or spatial information associated with one of the previously encoded faces. Perceptual face memory was largely influenced by these manipulations. We considered a simple computational model with an excellent match (4.3% error) that expresses these biases as a linear combination of recency, saccade, and location. Surprisingly, saccades did not play a role. The results suggest that spatial and temporal rather than oculomotor information biases perceptual face memory. Copyright © 2016 Cognitive Science Society, Inc.
Predictive spatial modelling for mapping soil salinity at continental scale
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 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.
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.
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.
Comparing spatial diversification and meta-population models in the Indo-Australian Archipelago.
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.
Comparing spatial diversification and meta-population models in the Indo-Australian Archipelago
Chalmandrier, Loïc; Albouy, Camille; Descombes, Patrice; Sandel, Brody; Faurby, Soren; Svenning, Jens-Christian; Zimmermann, Niklaus E.
2018-01-01
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. PMID:29657753
Quantifying measurement uncertainty and spatial variability in the context of model evaluation
Choukulkar, A.; Brewer, A.; Pichugina, Y. L.; Bonin, T.; Banta, R. M.; Sandberg, S.; Weickmann, A. M.; Djalalova, I.; McCaffrey, K.; Bianco, L.; Wilczak, J. M.; Newman, J. F.; Draxl, C.; Lundquist, J. K.; Wharton, S.; Olson, J.; Kenyon, J.; Marquis, M.
2017-12-01
In an effort to improve wind forecasts for the wind energy sector, the Department of Energy and the NOAA funded the second Wind Forecast Improvement Project (WFIP2). As part of the WFIP2 field campaign, a large suite of in-situ and remote sensing instrumentation was deployed to the Columbia River Gorge in Oregon and Washington from October 2015 - March 2017. The array of instrumentation deployed included 915-MHz wind profiling radars, sodars, wind- profiling lidars, and scanning lidars. The role of these instruments was to provide wind measurements at high spatial and temporal resolution for model evaluation and improvement of model physics. To properly determine model errors, the uncertainties in instrument-model comparisons need to be quantified accurately. These uncertainties arise from several factors such as measurement uncertainty, spatial variability, and interpolation of model output to instrument locations, to name a few. In this presentation, we will introduce a formalism to quantify measurement uncertainty and spatial variability. The accuracy of this formalism will be tested using existing datasets such as the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign. Finally, the uncertainties in wind measurement and the spatial variability estimates from the WFIP2 field campaign will be discussed to understand the challenges involved in model evaluation.
Automating an integrated spatial data-mining model for landfill site selection
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.
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
Lattice Three-Species Models of the Spatial Spread of Rabies among FOXES
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.
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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
Habyarimana, Faustin; Zewotir, Temesgen; Ramroop, Shaun
2018-03-01
The main objective of this study was to assess the risk factors and spatial correlates of domestic violence against women of reproductive age in Rwanda. A structured spatial approach was used to account for the nonlinear nature of some covariates and the spatial variability on domestic violence. The nonlinear effect was modeled through second-order random walk, and the structured spatial effect was modeled through Gaussian Markov Random Fields specified as an intrinsic conditional autoregressive model. The data from the Rwanda Demographic and Health Survey 2014/2015 were used as an application. The findings of this study revealed that the risk factors of domestic violence against women are the wealth quintile of the household, the size of the household, the husband or partner's age, the husband or partner's level of education, ownership of the house, polygamy, the alcohol consumption status of the husband or partner, the woman's perception of wife-beating attitude, and the use of contraceptive methods. The study also highlighted the significant spatial variation of domestic violence against women at district level.
Modelling the soil microclimate: does the spatial or temporal resolution of input parameters matter?
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Anna Carter
2016-01-01
Full Text Available The urgency of predicting future impacts of environmental change on vulnerable populations is advancing the development of spatially explicit habitat models. Continental-scale climate and microclimate layers are now widely available. However, most terrestrial organisms exist within microclimate spaces that are very small, relative to the spatial resolution of those layers. We examined the effects of multi-resolution, multi-extent topographic and climate inputs on the accuracy of hourly soil temperature predictions for a small island generated at a very high spatial resolution (<1 m2 using the mechanistic microclimate model in NicheMapR. Achieving an accuracy comparable to lower-resolution, continental-scale microclimate layers (within about 2–3°C of observed values required the use of daily weather data as well as high resolution topographic layers (elevation, slope, aspect, horizon angles, while inclusion of site-specific soil properties did not markedly improve predictions. Our results suggest that large-extent microclimate layers may not provide accurate estimates of microclimate conditions when the spatial extent of a habitat or other area of interest is similar to or smaller than the spatial resolution of the layers themselves. Thus, effort in sourcing model inputs should be focused on obtaining high resolution terrain data, e.g., via LiDAR or photogrammetry, and local weather information rather than in situ sampling of microclimate characteristics.
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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.
Analysis and Research on Spatial Data Storage Model Based on Cloud Computing Platform
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.
Schliep, E. M.; Gelfand, A. E.; Holland, D. M.
2015-12-01
There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.
Modelling the spatial distribution of Fasciola hepatica in dairy cattle in Europe
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Els Ducheyne
2015-03-01
Full Text Available A harmonized sampling approach in combination with spatial modelling is required to update current knowledge of fasciolosis in dairy cattle in Europe. Within the scope of the EU project GLOWORM, samples from 3,359 randomly selected farms in 849 municipalities in Belgium, Germany, Ireland, Poland and Sweden were collected and their infection status assessed using an indirect bulk tank milk (BTM enzyme-linked immunosorbent assay (ELISA. Dairy farms were considered exposed when the optical density ratio (ODR exceeded the 0.3 cut-off. Two ensemble-modelling techniques, Random Forests (RF and Boosted Regression Trees (BRT, were used to obtain the spatial distribution of the probability of exposure to Fasciola hepatica using remotely sensed environmental variables (1-km spatial resolution and interpolated values from meteorological stations as predictors. The median ODRs amounted to 0.31, 0.12, 0.54, 0.25 and 0.44 for Belgium, Germany, Ireland, Poland and southern Sweden, respectively. Using the 0.3 threshold, 571 municipalities were categorized as positive and 429 as negative. RF was seen as capable of predicting the spatial distribution of exposure with an area under the receiver operation characteristic (ROC curve (AUC of 0.83 (0.96 for BRT. Both models identified rainfall and temperature as the most important factors for probability of exposure. Areas of high and low exposure were identified by both models, with BRT better at discriminating between low-probability and high-probability exposure; this model may therefore be more useful in practise. Given a harmonized sampling strategy, it should be possible to generate robust spatial models for fasciolosis in dairy cattle in Europe to be used as input for temporal models and for the detection of deviations in baseline probability. Further research is required for model output in areas outside the eco-climatic range investigated.
Tang, G.; Bartlein, P. J.
2012-01-01
Water balance models of simple structure are easier to grasp and more clearly connect cause and effect than models of complex structure. Such models are essential for studying large spatial scale land surface water balance in the context of climate and land cover change, both natural and anthropogenic. This study aims to (i) develop a large spatial scale water balance model by modifying a dynamic global vegetation model (DGVM), and (ii) test the model's performance in simulating actual evapotranspiration (ET), soil moisture and surface runoff for the coterminous United States (US). Toward these ends, we first introduced development of the "LPJ-Hydrology" (LH) model by incorporating satellite-based land covers into the Lund-Potsdam-Jena (LPJ) DGVM instead of dynamically simulating them. We then ran LH using historical (1982-2006) climate data and satellite-based land covers at 2.5 arc-min grid cells. The simulated ET, soil moisture and surface runoff were compared to existing sets of observed or simulated data for the US. The results indicated that LH captures well the variation of monthly actual ET (R2 = 0.61, p 0.46, p 0.52) with observed values over the years 1982-2006, respectively. The modeled spatial patterns of annual ET and surface runoff are in accordance with previously published data. Compared to its predecessor, LH simulates better monthly stream flow in winter and early spring by incorporating effects of solar radiation on snowmelt. Overall, this study proves the feasibility of incorporating satellite-based land-covers into a DGVM for simulating large spatial scale land surface water balance. LH developed in this study should be a useful tool for studying effects of climate and land cover change on land surface hydrology at large spatial scales.
Spatial scale effects in environmental risk-factor modelling for diseases
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Ram K. Raghavan
2013-05-01
Full Text Available Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD and Kansas Gap Analysis Program (KS GAP around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed.
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Chaogui Kang
Full Text Available We generalized the recently introduced "radiation model", as an analog to the generalization of the classic "gravity model", to consolidate its nature of universality for modeling diverse mobility systems. By imposing the appropriate scaling exponent λ, normalization factor κ and system constraints including searching direction and trip OD constraint, the generalized radiation model accurately captures real human movements in various scenarios and spatial scales, including two different countries and four different cities. Our analytical results also indicated that the generalized radiation model outperformed alternative mobility models in various empirical analyses.
Spatially explicit models for inference about density in unmarked or partially marked populations
Chandler, Richard B.; Royle, J. Andrew
2013-01-01
Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5–10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19–1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating
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
Role of spatial inhomogenity in GPCR dimerisation predicted by receptor association-diffusion models
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.
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)
Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model
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Joon‐young Jung
2018-02-01
Full Text Available This paper proposes a hierarchical dual filtering (HDF algorithm to estimate the spatial region between a Cloud of Things (CoT gateway and an Internet of Things (IoT device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM with a raw Bluetooth received signal strength indicator (RSSI. However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high‐frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.
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.
Elhorst, J. Paul; Freret, Sandy
2009-01-01
This research proposes a two-regime spatial Durbin model with spatial and time-period fixed effects to test for political yardstick competition and exclude any other explanation that might produce spatial interaction effects among the dependent variable, the independent variables, or the error term.
Spatially explicit modeling of particulate nutrient flux in Large global rivers
Cohen, S.; Kettner, A.; Mayorga, E.; Harrison, J. A.
2017-12-01
Water, sediment, nutrient and carbon fluxes along river networks have undergone considerable alterations in response to anthropogenic and climatic changes, with significant consequences to infrastructure, agriculture, water security, ecology and geomorphology worldwide. However, in a global setting, these changes in fluvial fluxes and their spatial and temporal characteristics are poorly constrained, due to the limited availability of continuous and long-term observations. We present results from a new global-scale particulate modeling framework (WBMsedNEWS) that combines the Global NEWS watershed nutrient export model with the spatially distributed WBMsed water and sediment model. We compare the model predictions against multiple observational datasets. The results indicate that the model is able to accurately predict particulate nutrient (Nitrogen, Phosphorus and Organic Carbon) fluxes on an annual time scale. Analysis of intra-basin nutrient dynamics and fluxes to global oceans is presented.
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
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
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.
[Application of Land-use Regression Models in Spatial-temporal Differentiation of Air Pollution].
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.
Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning
Officer, Rick; Clarke, Maurice; Reid, David G.; Brophy, Deirdre
2017-01-01
Boosted Regression Trees. Excellent for data-poor spatial management but hard to use 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. BRTs automated and simplified for accessible general use with rich feature set 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. Gbm
Li, Ping; Jiang, Li Jun; Bagci, Hakan
2018-01-01
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.
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.
Integrated Spatial Modeling using Geoinformatics: A Prerequisite for Natural Resources Management
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.
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.
Madani, N.; Kimball, J. S.; Running, S. W.
2014-12-01
Remote sensing based light use efficiency (LUE) models, including the MODIS (MODerate resolution Imaging Spectroradiometer) MOD17 algorithm are commonly used for regional estimation and monitoring of vegetation gross primary production (GPP) and photosynthetic carbon (CO2) uptake. A common model assumption is that plants in a biome matrix operate at their photosynthetic capacity under optimal climatic conditions. A prescribed biome maximum light use efficiency parameter defines the maximum photosynthetic carbon conversion rate under prevailing climate conditions and is a large source of model uncertainty. Here, we used tower (FLUXNET) eddy covariance measurement based carbon flux data for estimating optimal LUE (LUEopt) over a North American domain. LUEopt was first estimated using tower observed daily carbon fluxes, meteorology and satellite (MODIS) observed fraction of photosynthetically active radiation (FPAR). LUEopt was then spatially interpolated over the domain using empirical models derived from independent geospatial data including global plant traits, surface soil moisture, terrain aspect, land cover type and percent tree cover. The derived LUEopt maps were then used as primary inputs to the MOD17 LUE algorithm for regional GPP estimation; these results were evaluated against tower observations and alternate MOD17 GPP estimates determined using Biome-specific LUEopt constants. Estimated LUEopt shows large spatial variability within and among different land cover classes indicated from a sparse North American tower network. Leaf nitrogen content and soil moisture are two important factors explaining LUEopt spatial variability. GPP estimated from spatially explicit LUEopt inputs shows significantly improved model accuracy against independent tower observations (R2 = 0.76; Mean RMSE plant trait information can explain spatial heterogeneity in LUEopt, leading to improved GPP estimates from satellite based LUE models.
Simulation of spatial and temporal properties of aftershocks by means of the fiber bundle model
Monterrubio-Velasco, Marisol; Zúñiga, F. R.; Márquez-Ramírez, Victor Hugo; Figueroa-Soto, Angel
2017-11-01
The rupture processes of any heterogeneous material constitute a complex physical problem. Earthquake aftershocks show temporal and spatial behaviors which are consequence of the heterogeneous stress distribution and multiple rupturing following the main shock. This process is difficult to model deterministically due to the number of parameters and physical conditions, which are largely unknown. In order to shed light on the minimum requirements for the generation of aftershock clusters, in this study, we perform a simulation of the main features of such a complex process by means of a fiber bundle (FB) type model. The FB model has been widely used to analyze the fracture process in heterogeneous materials. It is a simple but powerful tool that allows modeling the main characteristics of a medium such as the brittle shallow crust of the earth. In this work, we incorporate spatial properties, such as the Coulomb stress change pattern, which help simulate observed characteristics of aftershock sequences. In particular, we introduce a parameter ( P) that controls the probability of spatial distribution of initial loads. Also, we use a "conservation" parameter ( π), which accounts for the load dissipation of the system, and demonstrate its influence on the simulated spatio-temporal patterns. Based on numerical results, we find that P has to be in the range 0.06 sequences. This means that the system requires a small difference in the spatial distribution of initial stress, and a very particular fraction of load transfer in order to generate realistic aftershocks.
DEFF Research Database (Denmark)
Lühr, Armin; Löck, Steffen; Jakobi, Annika
2017-01-01
PURPOSE: Objectives of this work are (1) to derive a general clinically relevant approach to model tumor control probability (TCP) for spatially variable risk of failure and (2) to demonstrate its applicability by estimating TCP for patients planned for photon and proton irradiation. METHODS AND ...
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...
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…
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.
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…
A contextual ICA stakeholder model approach for the Namibian spatial data infrastructure (NamSDI)
CSIR Research Space (South Africa)
Sinvula, KM
2013-08-01
Full Text Available In 2011, the Namibian parliament presented and promulgated the Namibian Spatial Data Infrastructure (NamSDI) with the aim of promoting the sharing and improved access and use of geospatial data and services across Namibia. Notable SDI models...
Probing interaction and spatial curvature in the holographic dark energy model
International Nuclear Information System (INIS)
Li, Miao; Li, Xiao-Dong; Wang, Shuang; Wang, Yi; Zhang, Xin
2009-01-01
In this paper we place observational constraints on the interaction and spatial curvature in the holographic dark energy model. We consider three kinds of phenomenological interactions between holographic dark energy and matter, i.e., the interaction term Q is proportional to the energy densities of dark energy (ρ Λ ), matter (ρ m ), and matter plus dark energy (ρ m +ρ Λ ). For probing the interaction and spatial curvature in the holographic dark energy model, we use the latest observational data including the type Ia supernovae (SNIa) Constitution data, the shift parameter of the cosmic microwave background (CMB) given by the five-year Wilkinson Microwave Anisotropy Probe (WMAP5) observations, and the baryon acoustic oscillation (BAO) measurement from the Sloan Digital Sky Survey (SDSS). Our results show that the interaction and spatial curvature in the holographic dark energy model are both rather small. Besides, it is interesting to find that there exists significant degeneracy between the phenomenological interaction and the spatial curvature in the holographic dark energy model
Modelling the spatial distribution of SO2 and NO(x) emissions in Ireland
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.
A mixed integer program to model spatial wildfire behavior and suppression placement decisions
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...
Li, Y.; McDougall, T. J.
2016-02-01
Coarse resolution ocean models lack knowledge of spatial correlations between variables on scales smaller than the grid scale. Some researchers have shown that these spatial correlations play a role in the poleward heat flux. In order to evaluate the poleward transport induced by the spatial correlations at a fixed horizontal position, an equation is obtained to calculate the approximate transport from velocity gradients. The equation involves two terms that can be added to the quasi-Stokes streamfunction (based on temporal correlations) to incorporate the contribution of spatial correlations. Moreover, these new terms do not need to be parameterized and is ready to be evaluated by using model data directly. In this study, data from a high resolution ocean model have been used to estimate the accuracy of this HRM approach for improving the horizontal property fluxes in coarse-resolution ocean models. A coarse grid is formed by sub-sampling and box-car averaging the fine grid scale. The transport calculated on the coarse grid is then compared to the transport on original high resolution grid scale accumulated over a corresponding number of grid boxes. The preliminary results have shown that the estimate on coarse resolution grids roughly match the corresponding transports on high resolution grids.
S. Salazar; M. Mendoza; A. M. Tejeda
2006-01-01
A spatial model is presented to explain the concentration of heavy metals (Fe, Cu, Zn, Ni, Cr, Co and Pb), in the soils around the industrial complex near the Port of Veracruz, Mexico. Unexpected low concentration sites where then tested to detect woody plant species that may have the capability to hiperacumulate these contaminants, hence having a potential for...
Kronecker-ARX models in identifying (2D) spatial-temporal systems
Sinquin, B.; Verhaegen, M.H.G.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri
2017-01-01
In this paper we address the identification of (2D) spatial-temporal dynamical systems governed by the Vector Auto-Regressive (VAR) form. The coefficient-matrices of the VAR model are parametrized as sums of Kronecker products. When the number of terms in the sum is small compared to the size of
Using Online Modelled Spatial Constraints for Pose Estimation in an Industrial Setting
DEFF Research Database (Denmark)
Meyer, Kenneth Korsgaard; Wolniakowski, Adam; Hagelskjær, Frederik
2017-01-01
We introduce a vision system that is able to on-line learn spatial constraints to improve pose estimation in terms of correct recognition as well as computational speed. By making use of a simulated industrial robot system performing various pick and place tasks, we show the effect of model...
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...
A large-scale multi-species spatial depletion model for overwintering waterfowl
Baveco, J.M.; Kuipers, H.; Nolet, B.A.
2011-01-01
In this paper, we develop a model to evaluate the capacity of accommodation areas for overwintering waterfowl, at a large spatial scale. Each day geese are distributed over roosting sites. Based on the energy minimization principle, the birds daily decide which surrounding fields to exploit within
Application of spatial models to the stopover ecology of trans-Gulf migrants
Theodore R. Simons; Scott M. Pearson; Frank R. Moore
2000-01-01
Studies at migratory stopover sites along the northern coast of the Gulf of Mexico are providing an understanding of how weather, habitat, and energetic factors combine to shape the stopover ecology of trans-Gulf migrants. We are coupling this understanding with analyses of landscape-level patterns of habitat availability by using spatially explicit models to simulate...
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…
Modelling the potential spatial distribution of mosquito species using three different techniques
Cianci, D.; Hartemink, N.; Ibáñez-Justicia, A.
2015-01-01
Background: Models for the spatial distribution of vector species are important tools in the assessment of the risk of establishment and subsequent spread of vector-borne diseases. The aims of this study are to define the environmental conditions suitable for several mosquito species through species
Spatial modelling and monitoring of natural landscapes with cases in the Amsterdam Waterworks Dunes
Droesen, W.
1999-01-01
The utilisation of geographic information systems and digital image processing techniques for the construction of digital landscape models necessitate for a reconsideration of the classical concepts for landscape ecological mapping. In this thesis, some methods are presented for the spatial
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.
Benjankar, Rohan; Burke, Michael; Yager, Elowyn; Tonina, Daniele; Egger, Gregory; Rood, Stewart B; Merz, Norm
2014-12-01
Dam operations have altered flood and flow patterns and prevented successful cottonwood seedling recruitment along many rivers. To guide reservoir flow releases to meet cottonwood recruitment needs, we developed a spatially-distributed, GIS-based model that analyzes the hydrophysical requirements for cottonwood recruitment. These requirements are indicated by five physical parameters: (1) annual peak flow timing relative to the interval of seed dispersal, (2) shear stress, which characterizes disturbance, (3) local stage recession after seedling recruitment, (4) recruitment elevation above base flow stage, and (5) duration of winter flooding, which may contribute to seedling mortality. The model categorizes the potential for cottonwood recruitment in four classes and attributes a suitability value at each individual spatial location. The model accuracy was estimated with an error matrix analysis by comparing simulated and field-observed recruitment success. The overall accuracies of this Spatially-Distributed Cottonwood Recruitment model were 47% for a braided reach and 68% for a meander reach along the Kootenai River in Idaho, USA. Model accuracies increased to 64% and 72%, respectively, when fewer favorability classes were considered. The model predicted areas of similarly favorable recruitment potential for 1997 and 2006, two recent years with successful cottonwood recruitment. This model should provide a useful tool to quantify impacts of human activities and climatic variability on cottonwood recruitment, and to prescribe instream flow regimes for the conservation and restoration of riparian woodlands. Copyright © 2014 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Kukrika Milan
2008-01-01
Full Text Available This article gives a simple and brief scope of structure and usage of location-allocation models in territory planning of retail network, trying to show the main shortage of some given models and the primary direction of their future improving. We give an inspection of theirs main usage and give an explanation of basic factors that models take in consideration during the process of demand allocation. Location-allocation models are an important segment of development of spatial retail network optimization process. Their future improvement is going towards their approximation and integration with spatial-interaction models. In this way, much better methodology of planning and directing spatial development of trade general. Methodology which we have used in this research paper is based on the literature and research projects in the area. Using this methodology in analyzing parts of Serbian territory through usage of location-allocation models, showed the need for creating special software for calculating matrix with recursions. Considering the fact that the integration of location-allocation models with GIS still didn't occur, all the results acquired during the calculation of methaformula has been brought into ArcGIS 9.2 software and presented as maps.
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
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
Revealing the regime of shallow coral reefs at patch scale by continuous spatial modeling
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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.
Towards Linking 3D SAR and Lidar Models with a Spatially Explicit Individual Based Forest Model
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
Speciation in the Derrida-Higgs model with finite genomes and spatial populations
de Aguiar, Marcus A. M.
2017-02-01
The speciation model proposed by Derrida and Higgs demonstrated that a sexually reproducing population can split into different species in the absence of natural selection or any type of geographic isolation, provided that mating is assortative and the number of genes involved in the process is infinite. Here we revisit this model and simulate it for finite genomes, focusing on the question of how many genes it actually takes to trigger neutral sympatric speciation. We find that, for typical parameters used in the original model, it takes the order of 105 genes. We compare the results with a similar spatially explicit model where about 100 genes suffice for speciation. We show that when the number of genes is small the species that emerge are strongly segregated in space. For a larger number of genes, on the other hand, the spatial structure of the population is less important and the species distribution overlap considerably.
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
Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo
2017-03-01
The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the
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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.
Spatial structures in a simple model of population dynamics for parasite-host interactions
Energy Technology Data Exchange (ETDEWEB)
Dong, J. J.; Skinner, B.; Breecher, N.; Schmittmann, B.; Zia, R. K. P.
2015-08-01
Spatial patterning can be crucially important for understanding the behavior of interacting populations. Here we investigate a simple model of parasite and host populations in which parasites are random walkers that must come into contact with a host in order to reproduce. We focus on the spatial arrangement of parasites around a single host, and we derive using analytics and numerical simulations the necessary conditions placed on the parasite fecundity and lifetime for the populations long-term survival. We also show that the parasite population can be pushed to extinction by a large drift velocity, but, counterintuitively, a small drift velocity generally increases the parasite population.
A Spatial Faithful Cooperative System Based on Mixed Presence Groupware Model
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.
Predicting detection performance with model observers: Fourier domain or spatial domain?
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.
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
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
A Matérn model of the spatial covariance structure of point rain rates
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.
A Matérn model of the spatial covariance structure of point rain rates
Sun, Ying; Bowman, Kenneth P.; Genton, Marc G.; Tokay, Ali
2014-01-01
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.
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.
Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain
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.
Brakebill, J.W.; Wolock, D.M.; Terziotti, S.E.
2011-01-01
Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based/statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling. ?? 2011 American Water Resources Association. This article is a U.S. Government work and is in the public domain in the USA.
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.
Spatial Impairment and Memory in Genetic Disorders: Insights from Mouse Models
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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.
Scanning of speechless comics changes spatial biases in mental model construction.
Román, Antonio; Flumini, Andrea; Santiago, Julio
2018-08-05
The mental representation of both time and number shows lateral spatial biases, which can be affected by habitual reading and writing direction. However, this effect is in place before children begin to read. One potential early cause is the experiences of looking at picture books together with a carer, as those images also follow the directionality of the script. What is the underlying mechanism for this effect? In the present study, we test the possibility that such experiences induce spatial biases in mental model construction, a mechanism which is a good candidate to induce the biases observed with numbers and times. We presented a speechless comic in either standard (left-to-right) or mirror-reversed (right-to-left) form to adult Spanish participants. We then asked them to draw the scene depicted by sentences like 'the square is between the cross and the circle'. The position of the lateral objects in these drawings reveals the spatial biases at work when building mental models in working memory. Under conditions of highly consistent directionality, the mirror comic changed pre-existing lateral biases. Processes of mental model construction in working memory stand as a potential mechanism for the generation of spatial biases for time and number.This article is part of the theme issue 'Varieties of abstract concepts: development, use and representation in the brain'. © 2018 The Author(s).
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.
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.
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
Kim, Dohyeong; Galeano, M Alicia Overstreet; Hull, Andrew; Miranda, Marie Lynn
2008-12-01
Preventive approaches to childhood lead poisoning are critical for addressing this longstanding environmental health concern. Moreover, increasing evidence of cognitive effects of blood lead levels system-based childhood lead exposure risk models, especially if executed at highly resolved spatial scales, can help identify children most at risk of lead exposure, as well as prioritize and direct housing and health-protective intervention programs. However, developing highly resolved spatial data requires labor-and time-intensive geocoding and analytical processes. In this study we evaluated the benefit of increased effort spent geocoding in terms of improved performance of lead exposure risk models. We constructed three childhood lead exposure risk models based on established methods but using different levels of geocoded data from blood lead surveillance, county tax assessors, and the 2000 U.S. Census for 18 counties in North Carolina. We used the results to predict lead exposure risk levels mapped at the individual tax parcel unit. The models performed well enough to identify high-risk areas for targeted intervention, even with a relatively low level of effort on geocoding. This study demonstrates the feasibility of widespread replication of highly spatially resolved childhood lead exposure risk models. The models guide resource-constrained local health and housing departments and community-based organizations on how best to expend their efforts in preventing and mitigating lead exposure risk in their communities.
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.
The use of spatial empirical models to estimate soil erosion in arid ecosystems.
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.
Philbin, R.; Jun, M.
2015-01-01
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.
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.
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
Non-monotonic resonance in a spatially forced Lengyel-Epstein model
Energy Technology Data Exchange (ETDEWEB)
Haim, Lev [Physics Department, Ben-Gurion University of the Negev, Beer-Sheva 84105 (Israel); Department of Oncology, Soroka University Medical Center, Beer-Sheva 84101 (Israel); Hagberg, Aric [Center for Nonlinear Studies, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States); Meron, Ehud [Physics Department, Ben-Gurion University of the Negev, Beer-Sheva 84105 (Israel); Department of Solar Energy and Environmental Physics, BIDR, Ben-Gurion University of the Negev, Sede Boqer Campus, Midreshet Ben-Gurion 84990 (Israel)
2015-06-15
We study resonant spatially periodic solutions of the Lengyel-Epstein model modified to describe the chlorine dioxide-iodine-malonic acid reaction under spatially periodic illumination. Using multiple-scale analysis and numerical simulations, we obtain the stability ranges of 2:1 resonant solutions, i.e., solutions with wavenumbers that are exactly half of the forcing wavenumber. We show that the width of resonant wavenumber response is a non-monotonic function of the forcing strength, and diminishes to zero at sufficiently strong forcing. We further show that strong forcing may result in a π/2 phase shift of the resonant solutions, and argue that the nonequilibrium Ising-Bloch front bifurcation can be reversed. We attribute these behaviors to an inherent property of forcing by periodic illumination, namely, the increase of the mean spatial illumination as the forcing amplitude is increased.
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
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.
Demand prediction model for regional railway services considering spatial effects between stations
Energy Technology Data Exchange (ETDEWEB)
Cordera Piñera, R.; Sañudo, R.; Olio, L. Dell'
2016-07-01
The railways are a priority transport mode for the European Union given their safety record and environmental sustainability. Therefore it is important to have quantitative models available which allow passenger demand for rail travel to be simulated for planning purposes and to evaluate different policies. The aim of this article is to specify and estimate trip distribution models between railway stations by considering the most influential demand variables. Two types of models were estimated: Poisson regression and gravity. The input data were the ticket sales on a regional line in Cantabria (Spain) which were provided by the Spanish railway infrastructure administrator (ADIF – RAM). The models have also considered the possible existence of spatial effects between train stations. The results show that the models have a good fit to the available data, especial the gravity models constrained by origins and destinations. Furthermore, the gravity models which considered the existence of spatial effects between stations had a significantly better fit than the Poisson models and the gravity models that did not consider this phenomenon. The proposed models have therefore been shown to be good support tools for decision making in the field of railway planning. (Author)
Schneider, K.
2001-12-01
Carbon, water and nitrogen fluxes are closely coupled. They interact and have many feedbacks. Human interference, in particular through land use management and global change strongly modifies these fluxes. Increasing demands and conflicting interests result in an increasing need for regulation targeting different aspects of the system. Without being their main target, many of these measures directly affect water quantity, quality and availability. Improved management and planning of our water resources requires the development of integrated tools, in particular since interactions of the involved environmental and social systems often lead to unexpected or adverse results. To investigate the effect of plant growth, land use management and global change on water fluxes and quality, the PROcess oriented Modular EnvironmenT and Vegetation Model (PROMET-V) was developed. PROMET-V models the spatial patterns and temporal course of water, carbon and nitrogen fluxes using process oriented and mechanistic model components. The hydrological model is based on the Penman-Monteith approach, it uses a plant-physiological model to calculate the canopy conductance, and a multi-layer soil water model. Plant growth for different vegetation is modelled by calculating canopy photosynthesis, respiration, phenology and allocation. Plant growth and water fluxes are coupled directly through photosynthesis and transpiration. Many indirect feedbacks and interactions occur due to their mutual dependency upon leaf area, root distribution, water and nutrient availability for instance. PROMET-V calculates nitrogen fluxes and transformations. The time step used depends upon the modelled process and varies from 1 hour to 1 day. The kernel model is integrated in a raster GIS system for spatially distributed modelling. PROMET-V was tested in a pre-alpine landscape (Ammer river, 709 km**2, located in Southern Germany) which is characterized by small scale spatial heterogeneities of climate, soil and
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 he