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Spatial Modeling (spatial + modeling)
Selected AbstractsAnalyzing the Relationship Between Smoking and Coronary Heart Disease at the Small Area Level: A Bayesian Approach to Spatial ModelingGEOGRAPHICAL ANALYSIS, Issue 2 2006Jane Law We model the relationship between coronary heart disease and smoking prevalence and deprivation at the small area level using the Poisson log-linear model with and without random effects. Extra-Poisson variability (overdispersion) is handled through the addition of spatially structured and unstructured random effects in a Bayesian framework. In addition, four different measures of smoking prevalence are assessed because the smoking data are obtained from a survey that resulted in quite large differences in the size of the sample across the census tracts. Two of the methods use Bayes adjustments of standardized smoking ratios (local and global adjustments), and one uses a nonparametric spatial averaging technique. A preferred model is identified based on the deviance information criterion. Both smoking and deprivation are found to be statistically significant risk factors, but the effect of the smoking variable is reduced once the confounding effects of deprivation are taken into account. Maps of the spatial variability in relative risk, and the importance of the underlying covariates and random effects terms, are produced. We also identify areas with excess relative risk. [source] Modélisation spatiale de la pauvretéà Montréal: apport méthodologique de la régression géographiquement pondéréeTHE CANADIAN GEOGRAPHER/LE GEOGRAPHE CANADIEN, Issue 4 2007PHILIPPE APPARICIO Spatial Modeling of Poverty in Montréal: Methodological Contribution of the Geographically Weighted Regression The Island of Montréal is particularly concerned with the issue of poverty. In 2000, 29 percent of its inhabitants lived under the low income cut-offs as defined by Statistics Canada. However, poverty is not a homogeneous phenomenon at the intra-urban scale, and identifying and categorizing spaces of poverty has become a main concern for ongoing researches. According to this way of thinking, this paper proposes an analysis of the factors influencing the geographical distribution of poverty on the Island of Montréal. To be able to identify properly the various profiles of poverty, this analysis uses a specific methodology, the geographically weighted regression (GWR), and compares its results with the ones of a classical regression model. At the global level, the most important factors to explain poverty are in order: unemployment, lone-parent families, one person households, recent immigrants, part time or part year workers, school dropouts. At the local level, L'île de Montréal est particulièrement touchée par la pauvreté, puisqu'en 2000 29 pour cent de sa population vivait sous le seuil de faible revenu tel que défini par Statistique Canada. La pauvreté ne constituant pas toutefois un phénomène homogène à l'échelle intra-urbaine, l'identification et la qualification des zones de pauvreté deviennent des enjeux de recherche de première importance. Dans cette perspective, cet article propose une analyse des facteurs qui déterminent la distribution spatiale de la pauvreté au niveau des secteurs de recensement de l'île de Montréal. Pour ce faire, l'analyse mobilise un outil méthodologique particulier: la régression géographiquement pondérée, et en compare les résultats avec un modèle de régression multiple global. Au niveau global, on constate que les facteurs classiques conduisant à la pauvreté sont à l',uvre sur le territoire de l'île de Montréal. Dans l'ordre, ces facteurs sont: le chômage, la monoparentalité, le fait de vivre seul, le fait d'être un immigrant récent, le travail atypique et la non-fréquentation scolaire des jeunes de 15 à 24 ans. Au niveau local, s'il est vrai we observe that variables employment and lone-parents families play significantly in almost all the census tracts, the four other factors are significant only in some census tracts in the center of the Island. At the end of this analysis, the advantages of the GWR methodology appear clearly, as its capacity to take into account the geographical variations of the phenomenon allows a better identification and categorization of poverty areas in Montréal. que le chômage et la monoparentalité agissent significativement dans presque tous les secteurs, les quatre autres facteurs sont uniquement significatifs dans certains secteurs du centre de l'île. Au terme de l'analyse, les avantages de la régression géographiquement pondérée apparaissent clairement, sa plus grande sensibilité aux variations spatiales du phénomène permettant de mieux identifier et qualifier les zones de pauvreté montréalaises. [source] Joint Spatial Modeling of Recurrent Infection and Growth with Processes under Intermittent ObservationBIOMETRICS, Issue 2 2010F. S. Nathoo Summary In this article, we present a new statistical methodology for longitudinal studies in forestry, where trees are subject to recurrent infection, and the hazard of infection depends on tree growth over time. Understanding the nature of this dependence has important implications for reforestation and breeding programs. Challenges arise for statistical analysis in this setting with sampling schemes leading to panel data, exhibiting dynamic spatial variability, and incomplete covariate histories for hazard regression. In addition, data are collected at a large number of locations, which poses computational difficulties for spatiotemporal modeling. A joint model for infection and growth is developed wherein a mixed nonhomogeneous Poisson process, governing recurring infection, is linked with a spatially dynamic nonlinear model representing the underlying height growth trajectories. These trajectories are based on the von Bertalanffy growth model and a spatially varying parameterization is employed. Spatial variability in growth parameters is modeled through a multivariate spatial process derived through kernel convolution. Inference is conducted in a Bayesian framework with implementation based on hybrid Monte Carlo. Our methodology is applied for analysis in an 11-year study of recurrent weevil infestation of white spruce in British Columbia. [source] Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial DatasetsBIOMETRICS, Issue 2 2009Andrew O. Finley Summary This article expands upon recent interest in Bayesian hierarchical models in quantitative genetics by developing spatial process models for inference on additive and dominance genetic variance within the context of large spatially referenced trial datasets. Direct application of such models to large spatial datasets are, however, computationally infeasible because of cubic-order matrix algorithms involved in estimation. The situation is even worse in Markov chain Monte Carlo (MCMC) contexts where such computations are performed for several iterations. Here, we discuss approaches that help obviate these hurdles without sacrificing the richness in modeling. For genetic effects, we demonstrate how an initial spectral decomposition of the relationship matrices negate the expensive matrix inversions required in previously proposed MCMC methods. For spatial effects, we outline two approaches for circumventing the prohibitively expensive matrix decompositions: the first leverages analytical results from Ornstein,Uhlenbeck processes that yield computationally efficient tridiagonal structures, whereas the second derives a modified predictive process model from the original model by projecting its realizations to a lower-dimensional subspace, thereby reducing the computational burden. We illustrate the proposed methods using a synthetic dataset with additive, dominance, genetic effects and anisotropic spatial residuals, and a large dataset from a Scots pine (Pinus sylvestris L.) progeny study conducted in northern Sweden. Our approaches enable us to provide a comprehensive analysis of this large trial, which amply demonstrates that, in addition to violating basic assumptions of the linear model, ignoring spatial effects can result in downwardly biased measures of heritability. [source] Regional Spatial Modeling of Topsoil GeochemistryBIOMETRICS, Issue 1 2009C. A. Calder Summary Geographic information about the levels of toxics in environmental media is commonly used in regional environmental health studies when direct measurements of personal exposure is limited or unavailable. In this article, we propose a statistical framework for analyzing the spatial distribution of topsoil geochemical properties, including the concentrations of various toxicants. Due to the small-scale heterogeneity of most geochemical topsoil processes, direct measurements of the processes themselves only provide highly localized information; it is thus financially prohibitive to study the spatial patterns of these processes across a large region using traditional geostatistical analyses of point-referenced topsoil data. Instead, it is standard practice to assess geochemical patterns at a regional scale using point-referenced measurements collected in stream sediment because, unlike topsoil data, individual stream sediment geochemical measurements are representative of the surrounding area. We propose a novel multiscale soils (MSS) model that formally synthesizes data collected in topsoil and stream sediment and allows the richer stream sediment information to inform about the topsoil process, which in environmental health studies is typically more relevant. Our model accommodates the small-scale heterogeneity of topsoil geochemical processes by modeling spatial dependence at an aggregate resolution corresponding to hydrologically similar regions known as watersheds. We present an analysis of the levels of arsenic, a toxic heavy metal, in topsoil across the midwestern United States using the MSS model and show that this model has better predictive abilities than alternative approaches using more conventional statistical models for point-referenced spatial data. [source] Spatial Modeling of Wetland Condition in the U.S. Prairie Pothole RegionBIOMETRICS, Issue 2 2002J. Andrew Royle Summary. We propose a spatial modeling framework for wetland data produced from a remote-sensing-based waterfowl habitat survey conducted in the U.S. Prairie Pothole Region (PPR). The data produced from this survey consist of the area containing water on many thousands of wetland basins (i.e., prairie potholes). We propose a two-state model containing wet and dry states. This model provides a concise description of wet probability, i.e., the probability that a basin contains water, and the amount of water contained in wet basins. The two model components are spatially linked through a common latent effect, which is assumed to be spatially correlated. Model fitting and prediction is carried out using Markov chain Monte Carlo methods. The model primarily facilitates mapping of habitat conditions, which is useful in varied monitoring and assessment capacities. More importantly, the predictive capability of the model provides a rigorous statistical framework for directing management and conservation activities by enabling characterization of habitat structure at any point on the landscape. [source] Evaluation of Uncertainties Associated with Geocoding TechniquesCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2004Hassan A. Karimi Geocoded data play a major role in numerous engineering applications such as transportation and environmental studies where geospatial information systems (GIS) are used for spatial modeling and analysis as they contain spatial information (e.g., latitude and longitude) about objects. The information that a GIS produces is impacted by the quality of the geocoded data (e.g., coordinates) stored in its database. To make appropriate and reasonable decisions using geocoded data, it is important to understand the sources of uncertainty in geocoding. There are two major sources of uncertainty in geocoding, one related to the database that is used as a reference data set to geocode objects and one related to the interpolation technique used. Factors such as completeness, correctness, consistency, currency, and accuracy of the data in the reference database contribute to the uncertainty of the former whereas the specific logic and assumptions used in an interpolation technique contribute to the latter. The primary purpose of this article is to understand uncertainties associated with interpolation techniques used for geocoding. In doing so, three geocoding algorithms were used and tested and the results were compared with the data collected by the Global Positioning System (GPS). The result of the overall comparison indicated no significant differences between the three algorithms. [source] Computation of Likelihood Ratios in Fingerprint Identification for Configurations of Any Number of MinutiæJOURNAL OF FORENSIC SCIENCES, Issue 1 2007Cédric Neumann M.Sc. ABSTRACT: Recent court challenges have highlighted the need for statistical research on fingerprint identification. This paper proposes a model for computing likelihood ratios (LRs) to assess the evidential value of comparisons with any number of minutiæ. The model considers minutiae type, direction and relative spatial relationships. It expands on previous work on three minutiae by adopting a spatial modeling using radial triangulation and a probabilistic distortion model for assessing the numerator of the LR. The model has been tested on a sample of 686 ulnar loops and 204 arches. Features vectors used for statistical analysis have been obtained following a preprocessing step based on Gabor filtering and image processing to extract minutiae data. The metric used to assess similarity between two feature vectors is based on an Euclidean distance measure. Tippett plots and rates of misleading evidence have been used as performance indicators of the model. The model has shown encouraging behavior with low rates of misleading evidence and a LR power of the model increasing significantly with the number of minutiæ. The LRs that it provides are highly indicative of identity of source on a significant proportion of cases, even when considering configurations with few minutiæ. In contrast with previous research, the model, in addition to minutia type and direction, incorporates spatial relationships of minutiæ without introducing probabilistic independence assumptions. The model also accounts for finger distortion. [source] | ||||||||||