Showing papers on "Spatial analysis published in 2010"

The Analysis of Spatial Association by Use of Distance Statistics

Abstract: Introduced in this paper is a family of statistics, G, that can be used as a measure of spatial association in a number of circumstances. The basic statistic is derived, its properties are identified, and its advantages explained. Several of the G statistics make it possible to evaluate the spatial association of a variable within a specified distance of a single point. A comparison is made between a general G statistic and Moran’s I for similar hypothetical and empirical conditions. The empirical work includes studies of sudden infant death syndrome by county in North Carolina and dwelling unit prices in metropolitan San Diego by zip-code districts. Results indicate that G statistics should be used in conjunction with I in order to identify characteristics of patterns not revealed by the I statistic alone and, specifically, the G i and G i ∗ statistics enable us to detect local “pockets” of dependence that may not show up when using global statistics.

Local Spatial Autocorrelation Statistics: Distributional Issues and an Application

Abstract: The statistics Gi(d) and Gi*(d), introduced in Getis and Ord (1992) for the study of local pattern in spatial data, are extended and their properties further explored. In particular, nonbinary weights are allowed and the statistics are related to Moran's autocorrelation statistic, I. The correlations between nearby values of the statistics are derived and verified by simulation. A Bonferroni criterion is used to approximate significance levels when testing extreme values from the set of statistics. An example of the use of the statistics is given using spatial-temporal data on the AIDS epidemic centering on San Francisco. Results indicate that in recent years the disease is intensifying in the counties surrounding the city.

Mapping Species Distributions: Spatial Inference and Prediction

Abstract: Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, researchers and practitioners in a broad range of environmental sciences including ecology, geography, conservation biology, and natural resources management.

Handbook of spatial statistics

Abstract: Introduction Historical Introduction, Peter J. Diggle Continuous Spatial Variation Continuous Parameter Stochastic Process Theory, Tilmann Gneiting and Peter Guttorp Classical Geostatistical Methods, Dale L. Zimmerman and Michael Stein Likelihood-Based Methods, Dale L. Zimmerman Spectral Domain, Montserrat Fuentes and Brian Reich Asymptotics for Spatial Processes, Michael Stein Hierarchical Modeling with Spatial Data, Christopher K. Wikle Low Rank Representations for Spatial Processes, Christopher K. Wikle Constructions for Nonstationary Spatial Processes, Paul D. Sampson Monitoring Network Design, James V. Zidek and Dale L. Zimmerman Non-Gaussian and Nonparametric Models for Continuous Spatial Data, Mark F.J. Steel and Montserrat Fuentes Discrete Spatial Variation Discrete Spatial Variation, Havard Rue and Leonard Held Conditional and Intrinsic Autoregressions, Leonhard Held and Havard Rue Disease Mapping, Lance Waller and Brad Carlin Spatial Econometrics, R. Kelley Pace and James LeSage Spatial Point Patterns Spatial Point Process Theory, Marie-Colette van Lieshout Spatial Point Process Models, Valerie Isham Nonparametric Methods, Peter J. Diggle Parametric Methods, Jesper Moller Modeling Strategies, Adrian Baddeley Multivariate and Marked Point Processes, Adrian Baddeley Point Process Models and Methods in Spatial Epidemiology, Lance Waller Spatio-Temporal Processes Continuous Parameter Spatio-Temporal Processes, Tilmann Gneiting and Peter Guttorp Dynamic Spatial Models Including Spatial Time Series, Dani Gamerman Spatio-Temporal Point Processes, Peter J. Diggle and Edith Gabriel Modeling Spatial Trajectories, David R. Brillinger Data Assimilation, Douglas W. Nychka and Jeffrey L. Anderson Additional Topics Multivariate Spatial Process Models, Alan E. Gelfand and Sudipto Banerjee Misaligned Spatial Data: The Change of Support Problem, Alan E. Gelfand Spatial Aggregation and the Ecological Fallacy, Jonathan Wakefield and Hilary Lyons Spatial Gradients and Wombling, Sudipto Banerjee Index

Regression analysis of spatial data

Abstract: Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Estimating and controlling for spatial structure in the study of ecological communities

Abstract: Aim Variation partitioning based on canonical analysis is the most commonly used analysis to investigate community patterns according to environmental and spatial predictors. Ecologists use this method in order to understand the pure contribution of the environment independent of space, and vice versa, as well as to control for inflated type I error in assessing the environmental component under spatial autocorrelation. Our goal is to use numerical simulations to compare how different spatial predictors and model selection procedures perform in assessing the importance of the spatial component and in controlling for type I error while testing environmental predictors. Innovation We determine for the first time how the ability of commonly used (polynomial regressors) and novel methods based on eigenvector maps compare in the realm of spatial variation partitioning. We introduce a novel forward selection procedure to select spatial regressors for community analysis. Finally, we point out a number of issues that have not been previously considered about the joint explained variation between environment and space, which should be taken into account when reporting and testing the unique contributions of environment and space in patterning ecological communities. Main conclusions In tests of species-environment relationships,spatial autocorrelation is known to inflate the level of type I error and make the tests of significance invalid. First, one must determine if the spatial component is significant using all spatial predictors (Moran’s eigenvector maps). If it is, consider a model selection for the set of spatial predictors (an individual-species forward selection procedure is to be preferred) and use the environmental and selected spatial predictors in a partial regression or partial canonical analysis scheme. This is an effective way of controlling for type I error in such tests. Polynomial regressors do not provide tests with a correct level of type I error.

Extended profiles with morphological attribute filters for the analysis of hyperspectral data

Abstract: Extended attribute profiles and extended multi-attribute profiles are presented for the analysis of hyperspectral high-resolution images. These extended profiles are based on morphological attribute filters and, through a multi-level analysis, are capable of extracting spatial features that can better model the spatial information, with respect to conventional extended morphological profiles. The features extracted by the proposed extended profiles were considered for a classification task. Two hyperspectral high-resolution datasets acquired for the city of Pavia, Italy, were considered in the analysis. The effectiveness of the introduced operators in modelling the spatial information was proved by the higher classification accuracies obtained with respect to those achieved by a conventional extended morphological profile.

A Spatial Autocorrelation Approach for Examining the Effects of Urban Greenspace on Residential Property Values

Abstract: This paper presents spatially explicit analyses of the greenspace contribution to residential property values in a hedonic model. The paper utilizes data from the housing market near downtown Los Angeles. We first used a standard hedonic model to estimate greenspace effects. Because the residuals were spatially autocorrelated, we implemented a spatial lag model as indicated by specification tests. Our results show that neighborhood greenspace at the immediate vicinity of houses has a significant impact on house prices even after controlling for spatial autocorrelation. The different estimation results from non-spatial and spatial models provide useful bounds for the greenspace effect. Greening of inner city areas may provide a valuable policy instrument for elevating depressed housing markets in those areas.

Spatial Statistics in ArcGIS

Abstract: With over a million software users worldwide, and installations at over 5,000 universities, Environmental Systems Research Institute, Inc. (ESRI), established in 1969, is a world leader for the design and development of Geographic Information Systems (GIS) software. GIS technology allows the organization, manipulation, analysis, and visualization of spatial data, often uncovering relationships, patterns, and trends. It is an important tool for urban planning (Maantay and Ziegler 2006), public health (Cromley and McLafferty 2002), law enforcement (Chainey and Ratcliffe 2005), ecology (Johnston 1998), transportation (Thill 2000), demographics (Peters and MacDonald 2004), resource management (Pettit et al. 2008), and many other industries (see http://www.esri.com/industries.html). Traditional GIS analysis techniques include spatial queries, map overlay, buffer analysis, interpolation, and proximity calculations (Mitchell 1999). Along with basic cartographic and data management tools, these analytical techniques have long been a foundation for geographic information software. Tools to perform spatial analysis have been extended over the years to include geostatistical techniques (Smith et al. 2006), raster analysis (Tomlin 1990), analytical methods for business (Pick 2008), 3D analysis (Abdul-Rahman et al. 2006), network analytics (Okabe et al. 2006), space-time dynamics (Peuquet 2002), and techniques specific to a variety of industries (e.g., Miller and Shaw 2001). In 2004, a new set of spatial statistics tools designed to describe feature patterns was added to ArcGIS 9. This chapter focuses on the methods and models found in the Spatial Statistics toolbox.

Issues in spatial data analysis

Abstract: Misspecified functional forms tend to produce biased estimates and spatially correlated errors. Imposing less structure than standard spatial lag models while being more amenable to large datasets, nonparametric and semiparametric methods offer significant advantages for spatial modeling. Fixed effect estimators have significant advantages when spatial effects are constant within well-defined zones, but their flexibility can produce variable, inefficient estimates while failing to account adequately for smooth spatial trends. Though estimators that are designed to measure treatment effects can potentially control for unobserved variables while eliminating the need to specify a functional form, they may be biased if the variables are not constant within discrete zones.

Towards quality metrics for OpenStreetMap

Abstract: Volunteered Geographic Information (VGI) is currently a "hot topic" in the GIS community. The OpenStreetMap (OSM) project is one of the most popular and well supported examples of VGL Traditional measures of spatial data quality are often not applicable to OSM as in many cases it is not possible to access ground-truth spatial data for all regions mapped by OSM. We investigate to develop measures of quality for OSM which operate in an unsupervised manner without reference to a "trusted" source of ground-truth data. We provide results of analysis of OSM data from several European countries. The results highlight specific quality issues in OSM. Results of comparing OSM with ground-truth data for Ireland are also presented.

A Moving Average Approach for Spatial Statistical Models of Stream Networks

Abstract: In this article we use moving averages to develop new classes of models in a flexible modeling framework for stream networks. Streams and rivers are among our most important resources, yet models with autocorrelated errors for spatially continuous stream networks have been described only recently. We develop models based on stream distance rather than on Euclidean distance. Spatial autocovariance models developed for Euclidean distance may not be valid when using stream distance. We begin by describing a stream topology. We then use moving averages to build several classes of valid models for streams. Various models are derived depending on whether the moving average has a “tail-up” stream, a “tail-down” stream, or a “two-tail” construction. These models also can account for the volume and direction of flowing water. The data for this article come from the Ecosystem Health Monitoring Program in Southeast Queensland, Australia, an important national program aimed at monitoring water quality. We model two w...

Utility of computer simulations in landscape genetics

Abstract: Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to processes by combining complex and realistic life histories, behaviours, landscape features and genetic data. Central to landscape genetics is the connection of spatial patterns of genetic variation to the usually highly stochastic space-time processes that create them over both historical and contemporary time periods. The field should benefit from a shift to computer simulation approaches, which enable incorporation of demographic and environmental stochasticity. A key role of simulations is to show how demographic processes such as dispersal or reproduction interact with landscape features to affect probability of site occupancy, population size, and gene flow, which in turn determine spatial genetic structure. Simulations could also be used to compare various statistical methods and determine which have correct type I error or the highest statistical power to correctly identify spatio-temporal and environmental effects. Simulations may also help in evaluating how specific spatial metrics may be used to project future genetic trends. This article summarizes some of the fundamental aspects of spatial-temporal population genetic processes. It discusses the potential use of simulations to determine how various spatial metrics can be rigorously employed to identify features of interest, including contrasting locus-specific spatial patterns due to micro-scale environmental selection.

Associations between street connectivity and active transportation.

Abstract: Past studies of associations between measures of the built environment, particularly street connectivity, and active transportation (AT) or leisure walking/bicycling have largely failed to account for spatial autocorrelation of connectivity variables and have seldom examined both the propensity for AT and its duration in a coherent fashion. Such efforts could improve our understanding of the spatial and behavioral aspects of AT. We analyzed spatially identified data from Los Angeles and San Diego Counties collected as part of the 2001 California Health Interview Survey. Principal components analysis indicated that ~85% of the variance in nine measures of street connectivity are accounted for by two components representing buffers with short blocks and dense nodes (PRIN1) or buffers with longer blocks that still maintain a grid like structure (PRIN2). PRIN1 and PRIN2 were positively associated with active transportation (AT) after adjustment for diverse demographic and health related variables. Propensity and duration of AT were correlated in both Los Angeles (r = 0.14) and San Diego (r = 0.49) at the zip code level. Multivariate analysis could account for the correlation between the two outcomes. After controlling for demography, measures of the built environment and other factors, no spatial autocorrelation remained for propensity to report AT (i.e., report of AT appeared to be independent among neighborhood residents). However, very localized correlation was evident in duration of AT, particularly in San Diego, where the variance of duration, after accounting for spatial autocorrelation, was 5% smaller within small neighborhoods (~0.01 square latitude/longitude degrees = 0.6 mile diameter) compared to within larger zip code areas. Thus a finer spatial scale of analysis seems to be more appropriate for explaining variation in connectivity and AT. Joint analysis of the propensity and duration of AT behavior and an explicitly geographic approach can strengthen studies of the built environment and physical activity (PA), specifically AT. More rigorous analytical work on cross-sectional data, such as in the present study, continues to support the need for experimental and longitudinal study designs including the analysis of natural experiments to evaluate the utility of environmental interventions aimed at increasing PA.

Spatially explicit inference for open populations: estimating demographic parameters from camera-trap studies.

Abstract: We develop a hierarchical capture-recapture model for demographically open populations when auxiliary spatial information about location of capture is obtained. Such spatial capture-recapture data arise from studies based on camera trapping, DNA sampling, and other situations in which a spatial array of devices records encounters of unique individuals. We integrate an individual-based formulation of a Jolly-Seber type model with recently developed spatially explicit capture-recapture models to estimate density and demographic parameters for survival and recruitment. We adopt a Bayesian framework for inference under this model using the method of data augmentation which is implemented in the software program WinBUGS. The model was motivated by a camera trapping study of Pampas cats Leopardus colocolo from Argentina, which we present as an illustration of the model in this paper. We provide estimates of density and the first quantitative assessment of vital rates for the Pampas cat in the High Andes. The precision of these estimates is poor due likely to the sparse data set. Unlike conventional inference methods which usually rely on asymptotic arguments, Bayesian inferences are valid in arbitrary sample sizes, and thus the method is ideal for the study of rare or endangered species for which small data sets are typical.

Dynamic Grouping of Hippocampal Neural Activity During Cognitive Control of Two Spatial Frames

Abstract: Cognitive control is the ability to coordinate multiple streams of information to prevent confusion and select appropriate behavioral responses, especially when presented with competing alternatives. Despite its theoretical and clinical significance, the neural mechanisms of cognitive control are poorly understood. Using a two-frame place avoidance task and partial hippocampal inactivation, we confirmed that intact hippocampal function is necessary for coordinating two streams of spatial information. Rats were placed on a continuously rotating arena and trained to organize their behavior according to two concurrently relevant spatial frames: one stationary, the other rotating. We then studied how information about locations in these two spatial frames is organized in the action potential discharge of ensembles of hippocampal cells. Both streams of information were represented in neuronal discharge—place cell activity was organized according to both spatial frames, but almost all cells preferentially represented locations in one of the two spatial frames. At any given time, most coactive cells tended to represent locations in the same spatial frame, reducing the risk of interference between the two information streams. An ensemble's preference to represent locations in one or the other spatial frame alternated within a session, but at each moment, location in the more behaviorally relevant spatial frame was more likely to be represented. This discharge organized into transient groups of coactive neurons that fired together within 25 ms to represent locations in the same spatial frame. These findings show that dynamic grouping, the transient coactivation of neural subpopulations that represent the same stream of information, can coordinate representations of concurrent information streams and avoid confusion, demonstrating neural-ensemble correlates of cognitive control in hippocampus.

Spatial autocorrelation and the scaling of species-environment relationships.

Abstract: Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in the literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena.

The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets

Abstract: Increasingly, the geographically weighted regression (GWR) model is being used for spatial prediction rather than for inference. Our study compares GWR as a predictor to (a) its global counterpart of multiple linear regression (MLR); (b) traditional geostatistical models such as ordinary kriging (OK) and universal kriging (UK), with MLR as a mean component; and (c) hybrids, where kriging models are specified with GWR as a mean component. For this purpose, we test the performance of each model on data simulated with differing levels of spatial heterogeneity (with respect to data relationships in the mean process) and spatial autocorrelation (in the residual process). Our results demonstrate that kriging (in a UK form) should be the preferred predictor, reflecting its optimal statistical properties. However the GWR-kriging hybrids perform with merit and, as such, a predictor of this form may provide a worthy alternative to UK for particular (non-stationary relationship) situations when UK models cannot be reliably calibrated. GWR predictors tend to perform more poorly than their more complex GWR-kriging counterparts, but both GWR-based models are useful in that they provide extra information on the spatial processes generating the data that are being predicted.

Geographical patterns of micro-organismal community structure: are diatoms ubiquitously distributed across boreal streams?

Abstract: A topic under intensive study in community ecology and biogeography is the degree to which microscopic, as well as macroscopic organisms, show spatially-structured variation in community characteristics. In general, unicellular microscopic organisms are regarded as ubiquitously distributed and, therefore, without a clear biogeographic signal. This view was summarized 75 years ago by Baas-Becking, who stated ‘‘everything is everywhere, but, the environment selects’’. Within the context of metacommunity theory, this hypothesis is congruent with the species sorting model. By using a broad-scale dataset on stream diatom communities and environmental predictor variables across most of Finland, our main aim was to test this hypothesis. Patterns of spatial autocorrelation were evaluated by Moran’s I based correlograms, whereas partial regression analysis and partial redundancy analysis were used to quantify the relative importance of environmental and spatial factors on total species richness and on community composition, respectively. Significant patterns of spatial autocorrelation were found for all environmental variables, which also varied widely. Our main results were clear-cut. In general, pure spatial effects clearly overcame those of environmental effects, with the former explaining much more variation in species richness and community composition. Most likely, missing environmental variables cannot explain the higher predictive power of spatial variables, because we measured key factors that have previously been found to be the most important variables (e.g. pH, conductivity, colour, phosphorus, nitrogen) shaping the structure of diatom communities. Therefore, our results provided only limited support for the Baas-Becking hypothesis and the species sorting perspective of metacommunity theory.

Geotagging with local lexicons to build indexes for textually-specified spatial data

Abstract: The successful execution of location-based and feature-based queries on spatial databases requires the construction of spatial indexes on the spatial attributes. This is not simple when the data is unstructured as is the case when the data is a collection of documents such as news articles, which is the domain of discourse, where the spatial attribute consists of text that can be (but is not required to be) interpreted as the names of locations. In other words, spatial data is specified using text (known as a toponym) instead of geometry, which means that there is some ambiguity involved. The process of identifying and disambiguating references to geographic locations is known as geotagging and involves using a combination of internal document structure and external knowledge, including a document-independent model of the audience's vocabulary of geographic locations, termed its spatial lexicon. In contrast to previous work, a new spatial lexicon model is presented that distinguishes between a global lexicon of locations known to all audiences, and an audience-specific local lexicon. Generic methods for inferring audiences' local lexicons are described. Evaluations of this inference method and the overall geotagging procedure indicate that establishing local lexicons cannot be overlooked, especially given the increasing prevalence of highly local data sources on the Internet, and will enable the construction of more accurate spatial indexes.

Getis-Ord Spatial Statistics to Identify Hot Spots by Using Incident Management Data

Abstract: Traditionally, data have been collected to measure and improve the performance of incident management (IM). While these data are less detailed than crash records, they are timelier and contain useful attributes typically not reported in the crash database. This paper proposes the use of Getis-Ord (Gi*) spatial statistics to identify hot spots on freeways from an IM database while selected impact attributes are incorporated into the analysis. The Gi* spatial statistics jointly evaluate the spatial dependency effect of the frequency and attribute values within the framework of the conceptualized spatial relationship. The application of the method was demonstrated through a case study by using the incident database from the Houston, Texas, Transportation Management Center (TranStar). The method successfully identified the clusters of high-impact accidents from more than 30,000 accident records from 2006 to 2008. The accident duration was used as a proxy measure of its impact. The proposed method could be mod...

Handbook of applied spatial analysis : software tools, methods and applications

Abstract: GI Software Tools- Spatial Statistics in ArcGIS- Spatial Statistics in SAS- Spatial Econometric Functions in R- GeoDa: An Introduction to Spatial Data Analysis- STARS: Space-Time Analysis of Regional Systems- Space-Time Intelligence System Software for the Analysis of Complex Systems- Geostatistical Software- GeoSurveillance: GIS-based Exploratory Spatial Analysis Tools for Monitoring Spatial Patterns and Clusters- Web-based Analytical Tools for the Exploration of Spatial Data- PySAL: A Python Library of Spatial Analytical Methods- Spatial Statistics and Geostatistics- The Nature of Georeferenced Data- Exploratory Spatial Data Analysis- Spatial Autocorrelation- Spatial Clustering- Spatial Filtering- The Variogram and Kriging- Spatial Econometrics- Spatial Econometric Models- Spatial Panel Data Models- Spatial Econometric Methods for Modeling Origin-Destination Flows- Spatial Econometric Model Averaging- Geographically Weighted Regression- Expansion Method, Dependency, and Multimodeling- Multilevel Modeling- The Analysis of Remotely Sensed Data- ARTMAP Neural Network Multisensor Fusion Model for Multiscale Land Cover Characterization- Model Selection in Markov Random Fields for High Spatial Resolution Hyperspectral Data- Geographic Object-based Image Change Analysis- Applications in Economic Sciences- The Impact of Human Capital on Regional Labor Productivity in Europe- Income Distribution Dynamics and Cross-Region Convergence in Europe- A Multi-Equation Spatial Econometric Model, with Application to EU Manufacturing Productivity Growth- Applications in Environmental Sciences- A Fuzzy -Means Classification and a Bayesian Approach for Spatial Prediction of Landslide Hazard- Incorporating Spatial Autocorrelation in Species Distribution Models- A Web-based Environmental Decision Support System for Environmental Planning and Watershed Management- Applications in Health Sciences- Spatio-Temporal Patterns of Viral Meningitis in Michigan, 1993 - 2001- Space-Time Visualization and Analysis in the Cancer Atlas Viewer- Exposure Assessment in Environmental Epidemiology

Verifying Forecasts Spatially

Abstract: Numerous new methods have been proposed for using spatial information to better quantify and diagnose forecast performance when forecasts and observations are both available on the same grid. The majority of the new spatial verification methods can be classified into four broad categories (neighborhood, scale separation, features based, and field deformation), which themselves can be further generalized into two categories of filter and displacement. Because the methods make use of spatial information in widely different ways, users may be uncertain about what types of information each provides, and which methods may be most beneficial for particular applications. As an international project, the Spatial Forecast Verification Methods Inter-Comparison Project (ICP; www.ral.ucar.edu/projects/icp) was formed to address these questions. This project was coordinated by NCAR and facilitated by the WMO/World Weather Research Programme (WWRP) Joint Working Group on Forecast Verification Research. An overview of t...