Showing papers on "Spatial analysis published in 2004"

New insights from fine-scale spatial genetic structure analyses in plant populations.

Abstract: Many empirical studies have assessed fine-scale spatial genetic structure (SGS), i.e. the nonrandom spatial distribution of genotypes, within plant populations using genetic markers and spatial autocorrelation techniques. These studies mostly provided qualitative descriptions of SGS, rendering quantitative comparisons among studies difficult. The theory of isolation by distance can predict the pattern of SGS under limited gene dispersal, suggesting new approaches, based on the relationship between pairwise relatedness coefficients and the spatial distance between individuals, to quantify SGS and infer gene dispersal parameters. Here we review the theory underlying such methods and discuss issues about their application to plant populations, such as the choice of the relatedness statistics, the sampling scheme to adopt, the procedure to test SGS, and the interpretation of spatial autocorrelograms. We propose to quantify SGS by an ‘ Sp ’ statistic primarily dependent upon the rate of decrease of pairwise kinship coefficients between individuals with the logarithm of the distance in two dimensions. Under certain conditions, this statistic estimates the reciprocal of the neighbourhood size. Reanalysing data from, mostly, published studies, the Sp statistic was assessed for 47 plant species. It was found to be significantly related to the mating system (higher in selfing species) and to the life form (higher in herbs than trees), as well as to the population density (higher under low density). We discuss the necessity for comparing SGS with direct estimates of gene dispersal distances, and show how the approach presented can be extended to assess (i) the level of biparental inbreeding, and (ii) the kurtosis of the gene dispersal distribution.

Applied Spatial Statistics for Public Health Data

Abstract: Preface.Acknowledgments.1 Introduction.1.1 Why Spatial Data in Public Health?1.2 Why Statistical Methods for Spatial Data?1.3 Intersection of Three Fields of Study.1.4 Organization of the Book.2 Analyzing Public Health Data.2.1 Observational vs. Experimental Data.2.2 Risk and Rates.2.2.1 Incidence and Prevalence.2.2.2 Risk.2.2.3 Estimating Risk: Rates and Proportions.2.2.4 Relative and Attributable Risks.2.3 Making Rates Comparable: Standardized Rates.2.3.1 Direct Standardization.2.3.2 Indirect Standardization.2.3.3 Direct or Indirect?2.3.4 Standardizing to What Standard?2.3.5 Cautions with Standardized Rates.2.4 Basic Epidemiological Study Designs.2.4.1 Prospective Cohort Studies.2.4.2 Retrospective Case-Control Studies.2.4.3 Other Types of Epidemiological Studies.2.5 Basic Analytic Tool: The Odds Ratio.2.6 Modeling Counts and Rates.2.6.1 Generalized Linear Models.2.6.2 Logistic Regression.2.6.3 Poisson Regression.2.7 Challenges in the Analysis of Observational Data.2.7.1 Bias.2.7.2 Confounding.2.7.3 Effect Modification.2.7.4 Ecological Inference and the Ecological Fallacy.2.8 Additional Topics and Further Reading.2.9 Exercises.3 Spatial Data.3.1 Components of Spatial Data.3.2 An Odyssey into Geodesy.3.2.1 Measuring Location: Geographical Coordinates.3.2.2 Flattening the Globe: Map Projections and Coordinate Systems.3.2.3 Mathematics of Location: Vector and Polygon Geometry.3.3 Sources of Spatial Data.3.3.1 Health Data.3.3.2 Census-Related Data.3.3.3 Geocoding.3.3.4 Digital Cartographic Data.3.3.5 Environmental and Natural Resource Data.3.3.6 Remotely Sensed Data.3.3.7 Digitizing.3.3.8 Collect Your Own!3.4 Geographic Information Systems.3.4.1 Vector and Raster GISs.3.4.2 Basic GIS Operations.3.4.3 Spatial Analysis within GIS.3.5 Problems with Spatial Data and GIS.3.5.1 Inaccurate and Incomplete Databases.3.5.2 Confidentiality.3.5.3 Use of ZIP Codes.3.5.4 Geocoding Issues.3.5.5 Location Uncertainty.4 Visualizing Spatial Data.4.1 Cartography: The Art and Science of Mapmaking.4.2 Types of Statistical Maps.MAP STUDY: Very Low Birth Weights in Georgia Health Care District 9.4.2.1 Maps for Point Features.4.2.2 Maps for Areal Features.4.3 Symbolization.4.3.1 Map Generalization.4.3.2 Visual Variables.4.3.3 Color.4.4 Mapping Smoothed Rates and Probabilities.4.4.1 Locally Weighted Averages.4.4.2 Nonparametric Regression.4.4.3 Empirical Bayes Smoothing.4.4.4 Probability Mapping.4.4.5 Practical Notes and Recommendations.CASE STUDY: Smoothing New York Leukemia Data.4.5 Modifiable Areal Unit Problem.4.6 Additional Topics and Further Reading.4.6.1 Visualization.4.6.2 Additional Types of Maps.4.6.3 Exploratory Spatial Data Analysis.4.6.4 Other Smoothing Approaches.4.6.5 Edge Effects.4.7 Exercises.5 Analysis of Spatial Point Patterns.5.1 Types of Patterns.5.2 Spatial Point Processes.5.2.1 Stationarity and Isotropy.5.2.2 Spatial Poisson Processes and CSR.5.2.3 Hypothesis Tests of CSR via Monte Carlo Methods.5.2.4 Heterogeneous Poisson Processes.5.2.5 Estimating Intensity Functions.DATA BREAK: Early Medieval Grave Sites.5.3 K Function.5.3.1 Estimating the K Function.5.3.2 Diagnostic Plots Based on the K Function.5.3.3 Monte Carlo Assessments of CSR Based on the K Function.DATA BREAK: Early Medieval Grave Sites.5.3.4 Roles of First- and Second-Order Properties.5.4 Other Spatial Point Processes.5.4.1 Poisson Cluster Processes.5.4.2 Contagion/Inhibition Processes.5.4.3 Cox Processes.5.4.4 Distinguishing Processes.5.5 Additional Topics and Further Reading.5.6 Exercises.6 Spatial Clusters of Health Events: Point Data for Cases and Controls.6.1 What Do We Have? Data Types and Related Issues.6.2 What Do We Want? Null and Alternative Hypotheses.6.3 Categorization of Methods.6.4 Comparing Point Process Summaries.6.4.1 Goals.6.4.2 Assumptions and Typical Output.6.4.3 Method: Ratio of Kernel Intensity Estimates.DATA BREAK: Early Medieval Grave Sites.6.4.4 Method: Difference between K Functions.DATA BREAK: Early Medieval Grave Sites.6.5 Scanning Local Rates.6.5.1 Goals.6.5.2 Assumptions and Typical Output.6.5.3 Method: Geographical Analysis Machine.6.5.4 Method: Overlapping Local Case Proportions.DATA BREAK: Early Medieval Grave Sites.6.5.5 Method: Spatial Scan Statistics.DATA BREAK: Early Medieval Grave Sites.6.6 Nearest-Neighbor Statistics.6.6.1 Goals.6.6.2 Assumptions and Typical Output.6.6.3 Method: q Nearest Neighbors of Cases.CASE STUDY: San Diego Asthma.6.7 Further Reading.6.8 Exercises.7 Spatial Clustering of Health Events: Regional Count Data.7.1 What Do We Have and What Do We Want?7.1.1 Data Structure.7.1.2 Null Hypotheses.7.1.3 Alternative Hypotheses.7.2 Categorization of Methods.7.3 Scanning Local Rates.7.3.1 Goals.7.3.2 Assumptions.7.3.3 Method: Overlapping Local Rates.DATA BREAK: New York Leukemia Data.7.3.4 Method: Turnbull et al.'s CEPP.7.3.5 Method: Besag and Newell Approach.7.3.6 Method: Spatial Scan Statistics.7.4 Global Indexes of Spatial Autocorrelation.7.4.1 Goals.7.4.2 Assumptions and Typical Output.7.4.3 Method: Moran's I .7.4.4 Method: Geary's c.7.5 Local Indicators of Spatial Association.7.5.1 Goals.7.5.2 Assumptions and Typical Output.7.5.3 Method: Local Moran's I.7.6 Goodness-of-Fit Statistics.7.6.1 Goals.7.6.2 Assumptions and Typical Output.7.6.3 Method: Pearson's chi2.7.6.4 Method: Tango's Index.7.6.5 Method: Focused Score Tests of Trend.7.7 Statistical Power and Related Considerations.7.7.1 Power Depends on the Alternative Hypothesis.7.7.2 Power Depends on the Data Structure.7.7.3 Theoretical Assessment of Power.7.7.4 Monte Carlo Assessment of Power.7.7.5 Benchmark Data and Conditional Power Assessments.7.8 Additional Topics and Further Reading.7.8.1 Related Research Regarding Indexes of Spatial Association.7.8.2 Additional Approaches for Detecting Clusters and/or Clustering.7.8.3 Space-Time Clustering and Disease Surveillance.7.9 Exercises.8 Spatial Exposure Data.8.1 Random Fields and Stationarity.8.2 Semivariograms.8.2.1 Relationship to Covariance Function and Correlogram.8.2.2 Parametric Isotropic Semivariogram Models.8.2.3 Estimating the Semivariogram.DATA BREAK: Smoky Mountain pH Data.8.2.4 Fitting Semivariogram Models.8.2.5 Anisotropic Semivariogram Modeling.8.3 Interpolation and Spatial Prediction.8.3.1 Inverse-Distance Interpolation.8.3.2 Kriging.CASE STUDY: Hazardous Waste Site Remediation.8.4 Additional Topics and Further Reading.8.4.1 Erratic Experimental Semivariograms.8.4.2 Sampling Distribution of the Classical Semivariogram Estimator.8.4.3 Nonparametric Semivariogram Models.8.4.4 Kriging Non-Gaussian Data.8.4.5 Geostatistical Simulation.8.4.6 Use of Non-Euclidean Distances in Geostatistics.8.4.7 Spatial Sampling and Network Design.8.5 Exercises.9 Linking Spatial Exposure Data to Health Events.9.1 Linear Regression Models for Independent Data.9.1.1 Estimation and Inference.9.1.2 Interpretation and Use with Spatial Data.DATA BREAK: Raccoon Rabies in Connecticut.9.2 Linear Regression Models for Spatially Autocorrelated Data.9.2.1 Estimation and Inference.9.2.2 Interpretation and Use with Spatial Data.9.2.3 Predicting New Observations: Universal Kriging.DATA BREAK: New York Leukemia Data.9.3 Spatial Autoregressive Models.9.3.1 Simultaneous Autoregressive Models.9.3.2 Conditional Autoregressive Models.9.3.3 Concluding Remarks on Conditional Autoregressions.9.3.4 Concluding Remarks on Spatial Autoregressions.9.4 Generalized Linear Models.9.4.1 Fixed Effects and the Marginal Specification.9.4.2 Mixed Models and Conditional Specification.9.4.3 Estimation in Spatial GLMs and GLMMs.DATA BREAK: Modeling Lip Cancer Morbidity in Scotland.9.4.4 Additional Considerations in Spatial GLMs.CASE STUDY: Very Low Birth Weights in Georgia Health Care District 9.9.5 Bayesian Models for Disease Mapping.9.5.1 Hierarchical Structure.9.5.2 Estimation and Inference.9.5.3 Interpretation and Use with Spatial Data.9.6 Parting Thoughts.9.7 Additional Topics and Further Reading.9.7.1 General References.9.7.2 Restricted Maximum Likelihood Estimation.9.7.3 Residual Analysis with Spatially Correlated Error Terms.9.7.4 Two-Parameter Autoregressive Models.9.7.5 Non-Gaussian Spatial Autoregressive Models.9.7.6 Classical/Bayesian GLMMs.9.7.7 Prediction with GLMs.9.7.8 Bayesian Hierarchical Models for Spatial Data.9.8 Exercises.References.Author Index.Subject Index.

Geographic Patterns of (Genetic, Morphologic, Linguistic) Variation: How Barriers Can Be Detected by Using Monmonier's Algorithm

Abstract: When sampling locations are known, the association between genetic and geographic distances can be tested by spatial autocorrelation or regression methods. These tests give some clues to the possible shape of the genetic landscape. Nevertheless, correlation analyses fail when attempting to identify where genetic barriers exist, namely, the areas where a given variable shows an abrupt rate of change. To this end, a computational geometry approach is more suitable because it provides the locations and the directions of barriers and because it can show where geographic patterns of two or more variables are similar. In this frame we have implemented Monmonier's (1973) maximum difference algorithm in a new software package to identify genetic barriers. To provide a more realistic representation of the barriers in a genetic landscape, we implemented in the software a significance test by means of bootstrap matrices analysis. As a result, the noise associated with genetic markers can be visualized on a geographic map and the areas where genetic barriers are more robust can be identified. Moreover, this multiple matrices approach can visualize the patterns of variation associated with different markers in the same overall picture. This improved Monmonier's method is highly reliable and can be applied to nongenetic data whenever sampling locations and a distance matrix between corresponding data are available.

An evaluation of methods for modelling species distributions

Abstract: Aim Various statistical techniques have been used to model species probabilities of occurrence in response to environmental conditions. This paper provides a comprehensive assessment of methods and investigates whether errors in model predictions are associated to specific kinds of geographical and environmental distributions of species. Location Portugal, Western Europe. Methods Probabilities of occurrence for 44 species of amphibians and reptiles in Portugal were modelled using seven modelling techniques: Gower metric, Ecological Niche Factor Analysis, classification trees, neural networks, generalized linear models, generalized additive models and spatial interpolators. Generalized linear and additive models were constructed with and without a term accounting for spatial autocorrelation. Model performance was measured using two methods: sensitivity and Kappa index. Species were grouped according to their spatial (area of occupancy and extent of occurrence) and environmental (marginality and tolerance) distributions. Two-way comparison tests were performed to detect significant interactions between models and species groups. Results Interaction between model and species groups was significant for both sensitivity and Kappa index. This indicates that model performance varied for species with different geographical and environmental distributions. Artificial neural networks performed generally better, immediately followed by generalized additive models including a covariate term for spatial autocorrelation. Non-parametric methods were preferred to parametric approaches, especially when modelling distributions of species with a greater area of occupancy, a larger extent of occurrence, lower marginality and higher tolerance. Main conclusions This is a first attempt to relate performance of modelling techniques with species spatial and environmental distributions. Results indicate a strong relationship between model performance and the kinds of species distributions being modelled. Some methods performed generally better, but no method was superior in all circumstances. A suggestion is made that choice of the appropriate method should be contingent on the goals and kinds of distributions being modelled.

Measures of Spatial Segregation

Abstract: The measurement of residential segregation patterns and trends has been limited by a reliance on segregation measures that do not appropriately take into account the spatial patterning of population distributions. In this paper we define a general approach to measuring spatial segregation among multiple population groups. This general approach allows researchers to specify any theoretically based definition of spatial proximity desired in computing segregation measures. Based on this general approach, we develop a general spatial exposure/isolation index (), and a set of general multigroup spatial evenness/clustering indices: a spatial information theory index (), a spatial relative diversity index (), and a spatial dissimilarity index (). We review these and previously proposed spatial segregation indices against a set of eight desirable properties of spatial segregation indices. We conclude that the spatial exposure/isolation index *—which can be interpreted as a measure of the average composition of in...

Constructing the Spatial Weights Matrix Using a Local Statistic

Abstract: Spatial weights matrices are necessary elements in most regression models where a representation of spatial structure is needed. We construct a spatial weights matrix, W, based on the principle that spatial structure should be considered in a two-part framework, those units that evoke a distance effect, and those that do not. Our two-variable local statistics model (LSM) is based on the G i * local statistic. The local statistic concept depends on the designation of a critical distance, d c , defined as the distance beyond which no discernible increase in clustering of high or low values exists. In a series of simulation experiments LSM is compared to well-known spatial weights matrix specifications – two different contiguity configurations, three different inverse distance formulations, and three semi-variance models. The simulation experiments are carried out on a random spatial pattern and two types of spatial clustering patterns. The LSM performed best according to the Akaike Information Criterion, a spatial autoregressive coefficient evaluation, and Moran’s I tests on residuals. The flexibility inherent in the LSM allows for its favorable performance when compared to the rigidity of the global models.

Discovering colocation patterns from spatial data sets: a general approach

Abstract: Given a collection of Boolean spatial features, the colocation pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology data set may reveal symbiotic species. The spatial colocation rule problem is different from the association rule problem since there is no natural notion of transactions in spatial data sets which are embedded in continuous geographic space. We provide a transaction-free approach to mine colocation patterns by using the concept of proximity neighborhood. A new interest measure, a participation index, is also proposed for spatial colocation patterns. The participation index is used as the measure of prevalence of a colocation for two reasons. First, this measure is closely related to the cross-K function, which is often used as a statistical measure of interaction among pairs of spatial features. Second, it also possesses an antimonotone property which can be exploited for computational efficiency. Furthermore, we design an algorithm to discover colocation patterns. This algorithm includes a novel multiresolution pruning technique. Finally, experimental results are provided to show the strength of the algorithm and design decisions related to performance tuning.

Mapping Spatial Attributes in Survey Research for Natural Resource Management: Methods and Applications

Abstract: Traditional survey research measures attributes such as opinions, attitudes, beliefs, values, norms, and preferences. Few public surveys have attempted to map perceived spatial attributes of places and landscapes, a subject of increasing importance to environmental and natural resource management. For the past 5 years, this researcher has included spatial measures of landscape values and attributes in five separate surveys of the general public in Alaska (1998–2003). This article reviews the spatial data collection rationale behind these studies, design concepts, methods, and implementation issues when administering a general public survey that includes a spatial mapping component. A research framework for using landscape values and spatial measures in GIS planning applications is presented, including suitability analysis, gap analysis, and hot-spot identification. Spatial measure ambiguity and survey response rates will require future research attention. The mapping of psychometric attributes of place th...

Spatial statistics for urban analysis: A review of techniques with examples

Abstract: Traditionally, urban analysis has been quick to adopt and benefit from developments in technology (e.g., microcomputer, GIS) and techniques (e.g., statistics, mathematical programming). This has not been the case, however, with newer methods of spatial analysis — in particular, spatial statistics. Only recently has this situation started to change. This paper documents the confluence of spatial statistics and urban analysis by first reviewing developments in spatial statistics, and then presenting examples of recent applications in urban analysis. The developments reviewed fall under the rubric of global and local forms of spatial analysis, and cover three major technical issues: spatial association, spatial heterogeneity and the modifiable areal unit problem. The examples highlight the relevance and usefulness of the techniques reviewed for urban transportation and land-use applications. The paper concludes with conjectures concerning future developments at the intersection of spatial statistics and urban analysis.

Advances in Spatial Econometrics: Methodology, Tools and Applications

Abstract: 1 Econometrics for Spatial Models: Recent Advances.- I. Specification, Testing and Estimation.- 2 The Performance of Diagnostic Tests for Spatial Dependence in Linear Regression Models: A Meta-Analysis of Simulation Studies.- 3 Moran-Flavored Tests with Nuisance Parameters: Examples.- 4 The Influence of Spatially Correlated Heteroskedasticity on Tests for Spatial Correlation.- 5 A Taxonomy of Spatial Econometric Models for Simultaneous Equations Systems.- 6 Exploring Spatial Data Analysis Techniques Using R: The Case of Observations with No Neighbors.- II. Discrete Choice and Bayesian Approaches.- 7 Techniques for Estimating Spatially Dependent Discrete Choice Models.- 8 Probit in a Spatial Context: A Monte Carlo Analysis.- 9 Simultaneous Spatial and Functional Form Transformations.- 10 Locally Weighted Maximum Likelihood Estimation: Monte Carlo Evidence and an Application.- 11 A Family of Geographically Weighted Regression Models.- III. Spatial Externalities.- 12 Hedonic Price Functions and Spatial Dependence: Implications for the Demand for Urban Air Quality.- 13 Prediction in the Panel Data Model with Spatial Correlation.- 14 External Effects and Cost of Production.- IV. Urban Growth and Agglomeration Economies.- 15 Identifying Urban-Rural Linkages: Tests for Spatial Effects in the Carlino-Mills Model.- 16 Economic Geography and the Spatial Evolution of Wages in the United States.- 17 Endogenous Spatial Externalities: Empirical Evidence and Implications for the Evolution of Exurban Residential Land Use Patterns.- V. Trade and Economic Growth.- 18 Does Trade Liberalization Cause a Race-to-the-Bottom in Environmental Policies? A Spatial Econometric Analysis.- 19 Regional Economic Growth and Convergence: Insights from a Spatial Econometric Perspective.- 20 Growth and Externalities Across Economies: An Empirical Analysis Using Spatial Econometrics.- References.- Author Index.- List of Contributors.

Spatial language for human-robot dialogs

Abstract: In conversation, people often use spatial relationships to describe their environment, e.g., "There is a desk in front of me and a doorway behind it," and to issue directives, e.g., "go around the desk and through the doorway." In our research, we have been investigating the use of spatial relationships to establish a natural communication mechanism between people and robots, in particular, for novice users. In this paper, the work on robot spatial relationships is combined with a multimodal robot interface. We show how linguistic spatial descriptions and other spatial information can be extracted from an evidence grid map and how this information can be used in a natural, human-robot dialog. Examples using spatial language are included for both robot-to-human feedback and also human-to-robot commands. We also discuss some linguistic consequences in the semantic representations of spatial and locative information based on this work.

Spatial Statistics and Real Estate

Abstract: Real estate has historically employed statistical tools designed for independent observations while simultaneously noting the violation of these assumptions in the form of clustering of same sign residuals by neighborhood, along roads, and near facilities such as airports. Spatial statistics takes these dependencies into account to provide more realistic inference (OLS has biased standard errors), better prediction, and more efficient parameter estimation. This article provides an overview of the field and directs readers to the relevant literature and software.

Exploiting spectral and spatial information in hyperspectral urban data with high resolution

Abstract: Very high resolution hyperspectral data should be very useful to provide detailed maps of urban land cover. In order to provide such maps, both accurate and precise classification tools need, however, to be developed. In this letter, new methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple classifications and spatial analysis to improve mapping accuracy in urban areas. In particular, we compare spatial reclassification and mathematical morphology approaches. We show results for classification of DAIS data over the town of Pavia, in northern Italy. Classification maps of two test areas are given, and the overall and individual class accuracies are analyzed with respect to the parameters of the proposed classification procedures.

A Spatial Econometric Approach to the Economics of Site-Specific Nitrogen Management in Corn Production

Abstract: The objective of this study is to determine the potential for using spatial econometric analysis of combine yield monitor data to estimate the site-specific crop response functions. The specific case study is for site-specific nitrogen (N) application to corn production in Argentina. Spatial structure of the yield data is modeled with landscape variables, spatially autoregressive error and groupwise heteroskedasticity. Results suggest that N response differs by landscape position, and that site-specific application may be modestly profitable. Profitability depends on the model specification used, with all spatial models consistently indicating profitability, whereas the nonspatial models do not.

An improved sampling-based DBSCAN for large spatial databases

Abstract: Spatial data clustering is one of the important data mining techniques for extracting knowledge from large amount of spatial data collected in various applications, such as remote sensing, GIS, computer cartography, environmental assessment and planning, etc. Several useful and popular spatial data clustering algorithms have been proposed in the past decade. DBSCAN is one of them, which can discover clusters of any arbitrary shape and can handle the noise points effectively. However, DBSCAN requires large volume of memory support because it operates on the entire database. This paper presents an improved sampling-based DBSCAN which can cluster large-scale spatial databases effectively. Experimental results included to establish that the proposed sampling-based DBSCAN outperforms DBSCAN as well as its other counterparts, in terms of execution time, without losing the quality of clustering.

Multi feature path modeling for video surveillance

Abstract: This paper proposes a novel method for detecting nonconforming trajectories of objects as they pass through a scene. Existing methods mostly use spatial features to solve this problem. Using only spatial information is not adequate; we need to take into consideration velocity and curvature information of a trajectory along with the spatial information for an elegant solution. Our method has the ability to distinguish between objects traversing spatially dissimilar paths, or objects traversing spatially proximal paths but having different spatio-temporal characteristics. The method consists of a path building training phase and a testing phase. During the training phase, we use graph-cuts for clustering the trajectories, where the Hausdorff distance metric is used to calculate the edge weights. Each cluster represents a path. An envelope boundary and an average trajectory are computed for each path. During the testing phase we use three features for trajectory matching in a hierarchical fashion. The first feature measures the spatial similarity while the second feature compares the velocity characteristics of trajectories. Finally, the curvature features capture discontinuities in velocity, acceleration, and position of the trajectory. We use real-world pedestrian sequences to demonstrate the practicality of our method.

Mapping fuels and fire regimes using remote sensing, ecosystem simulation, and gradient modeling

Abstract: Maps of fuels and fire regimes are essential for understanding ecological relationships between wildland fire and landscape structure, composition, and function, and for managing wildland fire hazard and risk with an ecosystem perspective. While critical for successful wildland fire management, there are no standard methods for creating these maps, and spatial data representing these important characteristics of wildland fire are lacking in many areas. We present an integrated approach for mapping fuels and fire regimes using extensive field sampling, remote sensing, ecosystem simulation, and biophysical gradient modeling to create predictive landscape maps of fuels and fire regimes. A main objective was to develop a standardized, repeatable system for creating these maps using spatial data describing important landscape gradients along with straightforward statistical methods. We developed a hierarchical approach to stratifying field sampling to ensure that samples represented variability in a wide variety of ecosystem processes. We used existing and derived spatial layers to develop a modeling database within a Geographic Information System that included 38 mapped variables describing gradients of physiography, spectral characteristics, weather, and biogeochemical cycles for a 5830-km 2 study area in north- western Montana. Using general linear models, discriminant analysis, classification and regression trees, and logistic regression, we created maps of fuel load, fuel model, fire interval, and fire severity based on spatial predictive variables and response variables measured in the field. Independently evaluated accuracies ranged from 51 to 80%. Direct gradient modeling improved map accuracy significantly compared to maps based solely on indirect gradients. By focusing efforts on direct as opposed to indirect gradient modeling, our approach is easily adaptable to mapping potential future conditions under a range of possible management actions or climate scenarios. Our methods are an example of a standard yet flexible approach for mapping fuels and fire regimes over broad areas and at multiple scales. The resulting maps provide fine-grained, broad-scale information to spatially assess both ecosystem integrity and the hazards and risks of wildland fire when making decisions about how best to restore forests of the western United States to within historical ranges and variability.

Analysis of determinants of mammalian species richness in South America using spatial autoregressive models

Abstract: Classically, hypotheses concerning the distribution of species have been explored by evaluating the relationship between species richness and environmental variables using ordinary least squares (OLS) regression. However, environmental and ecological data generally show spatial autocorrelation, thus violating the assumption of independently distributed errors. When spatial autocorrelation exists, an alternative is to use autoregressive models that assume spatially autocorrelated errors. We examined the relationship between mammalian species richness in South America and environmental variables, thereby evaluating the relative importance of four competing hypotheses to explain mammalian species richness. Additionally, we compared the results of ordinary least squares (OLS) regression and spatial autoregressive models using Conditional and Simultaneous Autoregressive (CAR and SAR, respectively) models. Variables associated with productivity were the most important at determining mammalian species richness at the scale analyzed. Whereas OLS residuals between species richness and environmental variables were strongly autocorrelated, those from autoregressive models showed less spatial autocorrelation, particularly the SAR model, indicating its suitability for these data. Autoregressive models also fit the data better than the OLS model (increasing R2 by 5-14%), and the relative importance of the explanatory variables shifted under CAR and SAR models. These analyses underscore the importance of controlling for spatial autocorrelation in biogeographical studies.

Spatial Analysis of Employment and Population Density: The Case of the Agglomeration of Dijon 1999

Abstract: The aim of this paper is to analyze the intraurban spatial distributions of population and employment in the agglomeration of Dijon (regional capital of Burgundy, France). We study whether this agglomeration has followed the general tendency of job decentralization observed in most urban areas or whether it is still characterized by a monocentric pattern. To that purpose, we use a sample of 136 observations at the communal and at the IRIS (infraurban statistical area) levels with 1999 census data and the employment database SIRENE (INSEE). First, we study the spatial pattern of total employment and employment density using exploratory spatial data analysis. Apart from the CBD, few IRIS are found to be statistically significant, a result contrasting with those found using standard methods of subcenter identification with employment cut-offs. Next, in order to examine the spatial distribution of residential population density, we estimate and compare different specifications: exponential negative, spline-exponential, and multicentric density functions. Moreover, spatial autocorrelation, spatial heterogeneity, and outliers are controlled for by using the appropriate maximum likelihood, generalized method of moments, and Bayesian spatial econometric techniques. Our results highlight again the monocentric character of the agglomeration of Dijon.

Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks

Abstract: SUMMARY 1. We used stream fish and decapod spatial occurrence data extracted from a national database and recent surveys with geospatial landuse data, geomorphologic, climatic, and spatial data in a geographical information system (GIS) to model fish and decapod occurrence in the Wellington Region, New Zealand. 2. To predict the occurrence of each species at a site from a common set of predictor variables we used a multi-response, artificial neural network (ANN), to produce a single model that predicted the entire fish and decapod assemblage in one procedure. 3. The predictions from the ANN using this landscape scale data proved very accurate based on evaluation metrics that are independent of species abundance or probability thresholds. The important variables contributing to the predictions included the latitudinal and elevational position of the site reach, catchment area, average air temperature, the vegetation type, landuse proportions of the catchment, and catchment geology. 4. Geospatial data available for the entire regional river network were then used to create a habitat-suitability map for all 14 species over the regional river network using a GIS. This prediction map has many potential uses including: monitoring and predicting temporal changes in fish communities caused by human activities and shifts in climate, identifying areas in need of protection, biodiversity hotspots, and areas suitable for the reintroduction of endangered or rare species.

A partial join approach for mining co-location patterns

Abstract: Spatial co-location patterns represent the subsets of events whose instances are frequently located together in geographic space. We identified the computational bottleneck in the execution time of a current co-location mining algorithm. A large fraction of the join-based co-location miner algorithm is devoted to computing joins to identify instances of candidate co-location patterns. We propose a novel partial-join approach for mining co-location patterns efficiently. It transactionizes continuous spatial data while keeping track of the spatial information not modeled by transactions. It uses a transaction-based Apriori algorithm as a building block and adopts the instance join method for residual instances not identified in transactions. We show that the algorithm is correct and complete in finding all co-location rules which have prevalence and conditional probability above the given thresholds. An experimental evaluation using synthetic datasets and a real dataset shows that our algorithm is computationally more efficient than the join-based algorithm.

Quantifying scale in ecology: lessons from awave-swept shore

Abstract: Understanding the role of scale is critical to ecologists' ability to make appropriate measurements, to ''scale up'' from local, short-term experiments to larger areas and longer times, to formulate models of community structure, and to address important conservation problems. Although these issues have received increased attention in recent years, empirical measurements of the scales of ecologically important variables are still rare. Here, we measure the spatial and temporal scales of variability of 15 ecologically relevant physical and biological processes in the wave-swept intertidal zone at Mussel Point, near Hopkins Marine Station in California. We analyze temporal variability in wave height, ocean temperature, upwelling intensity, solar irradiance, and body temperature for periods ranging from ten minutes to fifty years. In addition, we measure spatial variation in shoreline topography, wave force, wave-induced disturbance, body temperature, species diversity, recruitment, primary productivity, and the abundances of grazers, predators, and the competitive dominant occupier of space. Each of these spatial variables is measured along three horizontal transects in the upper intertidal zone: a short transect (44 m long with sampling locations spaced at ;0.5-m intervals), a medium transect (175 m long with sampling locations spaced at ;1.7-m intervals), and a long transect (334 m long with sampling locations spaced at ;3.4-m intervals). Six different methods are used to quantify the scale of each variable. Distinct scales are evident in all but one of our temporal variables, demonstrating that our methods for quantifying scale can work effectively with relatively simple, periodic phenomena. However, our spatial results reveal basic problems that arise when attempting to measure the scale of variability for more complex phenomena. For a given variable and length of transect, different methods of calculating scale seldom agree, and in some cases estimates differ by more than an order of magnitude. For a given variable and method of calculating spatial scale, measurements are sensitive to the length of a transect; the longer the transect, the larger the estimate of scale. We propose that the ''1/ f noise'' nature of the data can explain both the variability among methods for calculating scale and the length dependence of spatial scales of variation, and that the 1/ f noise character of the data may be driven by the fractal geometry of shoreline topography. We conclude that it may not be possible to define a meaningful spatial scale of variation in this system. As an alternative to the boiled-down concept of ''scale,'' we suggest that it is more appropriate to examine explicitly the pattern in which variability changes with the extent of measurement (e.g., the spectrum). Knowledge of this pattern can provide useful ecological scaling ''rules'' even when a well-defined scale (or hierarchy of scales) cannot be discerned.

Communities, street guns and homicide trajectories in chicago, 1980–1995: merging methods for examining homicide trends across space and time*

Abstract: We merge Exploratory Spatial Data Analysis (ESDA) and a semi-parametric, group-based trajectory procedure (TRAJ) to classify communities in Chicago by violence trajectories across space. Total, street gun and other weapon homicide trajectories are identified across 831 census tracts between 1980 and 1995. We find evidence consistent with a weapon substitution effect in violent neighborhoods that are proximate to one another, a defensive diffusion effect of exclusively street gun-specific homicide increases in neighborhoods bordering the most violent areas, and a spatial decay effect of temporal homicide trends in which the most violent areas are buffered from the least violent by places experiencing mid-range levels of lethal violence over time. In merging these two methods of data analysis, we provide a more efficient way to describe both spatial and temporal trends and make significant advances in furthering applications of space-time methodologies.

Integrated spectral and spatial information mining in remote sensing imagery

Abstract: Most existing remote sensing image retrieval systems allow only simple queries based on sensor, location, and date of image capture. This approach does not permit the efficient retrieval of useful hidden information from large image databases. This paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies. Land cover information corresponding to spectral characteristics is identified by supervised classification based on support vector machines with automatic model selection, while textural features characterizing spatial information are extracted using Gabor wavelet coefficients. Within identified land cover categories, textural features are clustered to acquire search-efficient space in an object-oriented database with associated images in an image database. Interesting patterns are then retrieved using a query-by-example approach. The evaluation of the study results using coverage and novelty measures validates the effectiveness of the proposed remote sensing image information mining framework, which is potentially useful for applications such as agricultural and environmental monitoring.

Employment Densities, Spatial Autocorrelation, and Subcenters in Large Metropolitan Areas

Abstract: Employment density functions are estimated for 62 large metropolitan areas. Estimated gradients are statistically significant for distance from the nearest subcenter as well as for distance from the traditional central business district. Lagrange Multiplier (LM) tests imply significant spatial autocorrelation under highly restrictive ordinary least squares (OLS) specifications. The LM test statistics fall dramatically when the models are estimated using flexible parametric and nonparametric methods. The results serve as a warning that functional form misspecification causes spatial autocorrelation.