Showing papers on "Spatial analysis published in 2011"

PASSaGE: Pattern Analysis, Spatial Statistics and Geographic Exegesis. Version 2

Abstract: Summary 1. Spatial analysis has become increasingly popular in the biological sciences, particularly in disciplines such as landscape ecology and landscape genetics. However, many statistical functions for performing spatial analysis are not readily available (except in the most limited manner) in common, easy-to-use statistical packages or geographic information systems (GIS) software. 2. Over the last decade, the software package Pattern Analysis, Spatial Statistics and Geographic Exegesis (PASSaGE) has been popular tool for conducting spatial statistics. PASSaGE is completely free and has a user-friendly graphical user interface. A new version of PASSaGE, rewritten from the ground up, has now been released and is available for download. 3. PASSaGE 2 is significantly more user friendly than the original release and provides an excellent platform for both scientific analysis and classroom training. PASSaGE 2 includes a broad array of spatial statistical analyses not commonly found in other software packages or GIS software, all in an easy-to-use framework. It includes support for one-, two- and three-dimensional spatial analysis, including a number of unique and newly developed approaches.

The spatial and temporal signatures of word production components: a critical update

Abstract: In the first decade of neurocognitive word production research the predominant approach was brain mapping, i.e. investigating the regional cerebral brain activation patterns correlated with word production tasks, such as picture naming and word generation. Indefrey and Levelt (2004) conducted a comprehensive meta-analysis of word production studies that used this approach and combined the resulting spatial information on neural correlates of component processes of word production with information on the time course of word production provided by behavioral and electromagnetic studies. In recent years, neurocognitive word production research has seen a major change towards a hypothesis testing approach. This approach is characterized by the design of experimental variables modulating single component processes of word production and testing for predicted effects on spatial or temporal neurocognitive signatures of these components. This change was accompanied by the development of a broader spectrum of measurement and analysis techniques. The article reviews the findings of recent studies using the new approach. The time course assumptions of Indefrey and Levelt (2004) have largely been confirmed requiring only minor adaptations. Adaptations of the brain structure/function relationships proposed by Indefrey and Levelt (2004) include the precise role of subregions of the left inferior frontal gyrus as well as a probable, yet to date unclear role of the inferior parietal cortex in word production.

New Directions in Spatial Econometrics

Abstract: 1 New Directions in Spatial Econometrics: Introduction.- 1.1 Introduction.- 1.2 Spatial Effects in Regression Models.- 1.2.1 Specification of Spatial Dependence.- 1.2.2 Spatial Data and Model Transformations.- 1.3 Spatial Effects in Limited Dependent Variable Models.- 1.4 Heterogeneity and Dependence in Space-Time Models.- 1.5 Future Directions.- References.- I-A: Spatial Effects in Linear Regression Models Specification of Spatial Dependence.- 2 Small Sample Properties of Tests for Spatial Dependence in Regression Models: Some Further Results.- 2.1 Introduction.- 2.2 Tests for Spatial Dependence.- 2.2.1 Null and Alternative Hypotheses.- 2.2.2 Tests for Spatial Error Dependence.- 2.2.3 Tests for Spatial Lag Dependence.- 2.3 Experimental Design.- 2.4 Results of Monte Carlo Experiments.- 2.4.1 Empirical Size of the Tests.- 2.4.2 Power of Tests Against First Order Spatial Error Dependence.- 2.4.3 Power of Tests Against Spatial Autoregressive Lag Dependence.- 2.4.4 Power of Tests Against Second Order Spatial Error Dependence.- 2.4.5 Power of Tests Against a SARMA (1,1) Process.- 2.5 Conclusions.- Acknowledgements.- References.- Appendix 1: Tables.- 3 Spatial Correlation: A Suggested Alternative to the Autoregressive Model.- 3.1 Introduction.- 3.2 The Spatial AR Model of Autocorrelation.- 3.3 The Singularity of (I - pM).- 3.3.1 Theoretical Issues.- 3.3.2 Independent Corroborative Evidence.- 3.4 The Parameter Space.- 3.5 A Suggested Variation of the Spatial AR Model.- 3.5.1 The Suggested Model.- 3.5.2 Some Limiting Correlations.- 3.5.3 A Generalization.- 3.6 Suggestions for Further Work.- Acknowledgements.- References.- Appendix 1: Spatial Weighting Matrices.- 4 Spatial Autoregressive Error Components in Travel Flow Models: An Application to Aggregate Mode Choice.- 4.1 Introduction.- 4.2 The First-Order Spatially Autoregressive Error Components Formulation.- 4.3 Estimation Issues.- 4.4 Empirical Example.- 4.4.1 An Illustration Based on Synthetic Data.- 4.5 Conclusions.- References.- I-B: Spatial Effects in Linear Regression Models Spatial Data and Model Transformations.- 5 The Impacts of Misspecified Spatial Interaction in Linear Regression Models.- 5.1 Introduction.- 5.2 Aggregation and the Identification of Spatial Interaction.- 5.3 Experimental Design.- 5.3.1 Sample Size.- 5.3.2 Spatial Interaction Structures.- 5.3.3 Spatial Models and Parameter Space.- 5.3.4 Test Statistics and Estimators.- 5.3.5 Forms of Misspecification.- 5.4 Empirical Results.- 5.4.1 Size of Tests Under the Null.- 5.4.2 Power of Tests.- 5.4.3 Misspecification Effects on the Power of Tests for Spatial Dependence.- 5.4.4 Sensitivity of Parameter Estimation to Specification of Weight Matrix.- 5.4.5 Impact of Misspecification of Weight Matrix on Estimation.- 5.5 General Inferences References.- 6 Computation of Box-Cox Transform Parameters: A New Method and its Application to Spatial Econometrics.- 6.1 Introduction.- 6.2 The Elasticity Method: Further Elaboration.- 6.2.1 Linearization Bias.- 6.2.2 Discretization Bias.- 6.2.3 Specification Bias.- 6.3 The One Exogenous Variable Test.- 6.4 An Application to Spatial Econometrics.- 6.5 The Multiple Exogenous Variable Computation.- 6.6 Conclusions.- References.- 7 Data Problems in Spatial Econometric Modeling.- 7.1 Introduction.- 7.2 Data for Spatial Econometric Analysis.- 7.3 Data Problems in Spatial Econometrics.- 7.4 Methodologies for Handling Data Problems.- 7.4.1 Influential Cases in the Standard Regression Model.- 7.4.2 Influential Cases in a Spatial Regression Model.- 7.4.3 An Example.- 7.5 Implementing Methodologies.- References.- 8 Spatial Filtering in a Regression Framework: Examples Using Data on Urban Crime, Regional Inequality, and Government Expenditures.- 8.1 Introduction.- 8.2 Rationale for a Spatial Filter.- 8.3 The Gi Statistic.- 8.4 The Filtering Procedure.- 8.5 Filtering Variables: Three Examples.- 8.5.1 Example 1: Urban Crime.- 8.5.2 Example 2: Regional Inequality.- 8.5.3 Example 3: Government Expenditures.- >8.6 Conclusions.- >Acknowledgments.- References.- II: Spatial Effects in Limited Dependent Variable Models.- 9 Spatial Effects in Probit Models: A Monte Carlo Investigation.- 9.1 Introduction.- 9.2 Sources of Heteroscedasticity.- 9.3 Heteroscedastic Probit.- 9.4 Monte Carlo Design.- 9.5 Tests.- 9.6 Monte Carlo Results.- 9.7 Conclusions.- References.- Appendix 1: Monte Carlo Results.- Appendix 2: Heteroscedastic Probit Computer Programs.- Appendix 3: Monte Carlo Computer Programs.- 10 Estimating Logit Models with Spatial Dependence.- 10.1 Introduction.- 10.1.1 Model.- 10.2 Simulation Example.- 10.3 Conclusions.- >References.- Appendix 1: Gauss Program for Finding ML Estimates.- Appendix 2: Gauss Program to Estimate Asymptotic Variances of ML Estimates.- 11 Utility Variability within Aggregate Spatial Units and its Relevance to Discrete Models of Destination Choice.- 11.1 Introduction.- 11.2 Theoretical Background.- 11.3 Estimation of the Maximum Utility Model.- 11.4 Model Specifications and Simulations.- 11.4.1 Specification Issues.- 11.4.2 Description of Simulation Method.- 11.4.3 Results.- 11.5 Conclusions.- Acknowledgement.- References.- III: Heterogeneity and Dependence in Space-Time Models.- 12 The General Linear Model and Spatial Autoregressive Models.- 12.1 Introduction.- 12.2 The GLM.- 12.3 Data Preprocessing.- 12.3.1 Analysis of the 1964 Benchmark Data.- 12.3.2 Evaluation of Missing USDA Values Estimation.- >12.4 Implementation of the Spatial Statistical GLM.- 12.4.1 Preliminary Spatial Analysis of Milk Yields: AR Trend Surface GLMs.- 12.4.2 AR GLM Models for the Repeated Measures Case.- 12.4.3 A Spatially Adjusted Canonical Correlation Analysis of the Milk Production Data.- 12.5 Conclusions.- >References.- >Appendix 1: SAS Computer Code to Compute the Popular Spatial Autocorrelation Indices.- Appendix 2: SAS Code for Estimating Missing Values in the 1969 Data Set.- Appendix 3: SAS Code for 1969 USDA Data Analysis.- 13 Econometric Models and Spatial Parametric Instability: Relevant Concepts and an Instability Index.- 13.1 Introduction.- 13.2 The Expansion Method.- 13.3 Parametric Instability.- 13.3.1 Example.- 13.4 Conclusions.- 13.4.1 Instability Measures: Scope.- 13.4.2 Instability Measures: Significance.- References.- 14 Bayesian Hierarchical Forecasts for Dynamic Systems: Case Study on Backcasting School District Income Tax Revenues.- 14.1 Introduction.- 14.2 Literature Review.- 14.3 The C-MSKF Model: Time Series Prediction with Spatial Adjustments.- 14.3.1 Multi-State Kaiman Filter.- 14.3.2 Spatial Adjustment via Hierarchical Random Effects Model.- 14.3.3 CIHM Method.- 14.3.4 C-MSKF.- 14.4 Case Study and Observational Setting.- 14.4.1 Data.- 14.4.2 Treatments.- 14.5 Results.- >14.6 Conclusions.- >References.- Appendix 1: Poolbayes Program.- 15 A Multiprocess Mixture Model to Estimate Space-Time Dimensions of Weekly Pricing of Certificates of Deposit.- 15.1 Introduction.- 15.2 A Dynamic Targeting Model of CD Rate-Setting Behavior.- 15.2.1 The Model.- 15.2.2 The Decision Rule.- 15.3 The Spatial Econometric Model.- 15.3.1 Spatial Time-Varying Parameters.- 15.3.2 Parameter Estimation.- 15.3.3 Testing Hypotheses with the Model.- 15.4 Implementing the Model.- 15.4.1 The Data.- 15.4.2 Prior Information.- 15.4.3 Empirical Results.- 15.5 Conclusions.- Acknowledgements.- References.- Appendix 1: FORTRAN Program for the Spatial Mixture.- Author Index.- Contributors.

SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics

Abstract: Summary: SPREAD is a user-friendly, cross-platform application to analyze and visualize Bayesian phylogeographic reconstructions incorporating spatial–temporal diffusion. The software maps phylogenies annotated with both discrete and continuous spatial information and can export high-dimensional posterior summaries to keyhole markup language (KML) for animation of the spatial diffusion through time in virtual globe software. In addition, SPREAD implements Bayes factor calculation to evaluate the support for hypotheses of historical diffusion among pairs of discrete locations based on Bayesian stochastic search variable selection estimates. SPREAD takes advantage of multicore architectures to process large joint posterior distributions of phylogenies and their spatial diffusion and produces visualizations as compelling and interpretable statistical summaries for the different spatial projections. Availability: SPREAD is licensed under the GNU Lesser GPL and its source code is freely available as a GitHub repository:

Determinants of the distribution of nitrogen-cycling microbial communities at the landscape scale

Abstract: Little information is available regarding the landscape-scale distribution of microbial communities and its environmental determinants. However, a landscape perspective is needed to understand the relative importance of local and regional factors and land management for the microbial communities and the ecosystem services they provide. In the most comprehensive analysis of spatial patterns of microbial communities to date, we investigated the distribution of functional microbial communities involved in N-cycling and of the total bacterial and crenarchaeal communities over 107 sites in Burgundy, a 31 500 km2 region of France, using a 16 × 16 km2 sampling grid. At each sampling site, the abundance of total bacteria, crenarchaea, nitrate reducers, denitrifiers- and ammonia oxidizers were estimated by quantitative PCR and 42 soil physico-chemical properties were measured. The relative contributions of land use, spatial distance, climatic conditions, time, and soil physico-chemical properties to the spatial distribution of the different communities were analyzed by canonical variation partitioning. Our results indicate that 43–85% of the spatial variation in community abundances could be explained by the measured environmental parameters, with soil chemical properties (mostly pH) being the main driver. We found spatial autocorrelation up to 739 km and used geostatistical modelling to generate predictive maps of the distribution of microbial communities at the landscape scale. The present study highlights the potential of a spatially explicit approach for microbial ecology to identify the overarching factors driving the spatial heterogeneity of microbial communities even at the landscape scale.

Image retrieval with geometry-preserving visual phrases

Abstract: The most popular approach to large scale image retrieval is based on the bag-of-visual-word (BoV) representation of images. The spatial information is usually re-introduced as a post-processing step to re-rank the retrieved images, through a spatial verification like RANSAC. Since the spatial verification techniques are computationally expensive, they can be applied only to the top images in the initial ranking. In this paper, we propose an approach that can encode more spatial information into BoV representation and that is efficient enough to be applied to large-scale databases. Other works pursuing the same purpose have proposed exploring the word co-occurrences in the neighborhood areas. Our approach encodes more spatial information through the geometry-preserving visual phrases (GVP). In addition to co-occurrences, the GVP method also captures the local and long-range spatial layouts of the words. Our GVP based searching algorithm increases little memory usage or computational time compared to the BoV method. Moreover, we show that our approach can also be integrated to the min-hash method to improve its retrieval accuracy. The experiment results on Oxford 5K and Flicker 1M dataset show that our approach outperforms the BoV method even following a RANSAC verification.

Modeling species distribution and change using random forest [Chapter 8]

Abstract: Although inference is a critical component in ecological modeling, the balance between accurate predictions and inference is the ultimate goal in ecological studies (Peters 1991; De’ath 2007). Practical applications of ecology in conservation planning, ecosystem assessment, and bio-diversity are highly dependent on very accurate spatial predictions of ecological process and spatial patterns (Millar et al. 2007). However, the complex nature of ecological systems hinders our ability to generate accurate models using the traditional frequentist data model (Breiman 2001a; Austin 2007). Well-defined issues in ecological modeling, such as complex non-linear interactions, spatial autocorrelation, high-dimensionality, non-stationary, historic signal, anisotropy, and scale contribute to problems that the frequentist data model has difficulty addressing (Olden et al. 2008). When one critically evaluates data used in ecological models, rarely do the data meet assumptions of independence, homoscedasticity, and multivariate normality (Breiman 2001a). This has caused constant reevaluation of modeling approaches and the effects of reoccurring issues such as spatial autocorrelation.

National satellite-based land-use regression: NO2 in the United States.

Abstract: Land-use regression models (LUR) estimate outdoor air pollution at high spatial resolution. Previous LURs have generally focused on individual cities. Here, we present an LUR for year-2006 ground-level NO2 concentrations throughout the contiguous United States. Our approach employs ground- and satellite-based NO2 measurements, and geographic characteristics such as population density, land-use (based on satellite data), and distance to major and minor roads. The results provide reliable estimates of ambient NO2 air pollution as measured by the U.S. EPA (R2 = 0.78; bias = 22%) at a spatial resolution (∼30 m) that is capable of capturing within-urban and near-roadway gradients in NO2. We explore several aspects of temporal (time-of-day; day-of-week; season) and spatial (urban versus rural; U.S. region) variability in the model. Results are robust to spatial autocorrelation, to selection of an alternative input data set, and to minor perturbations in input data (using 90% of the data to predict the remaining...

Geographically weighted principal components analysis

Abstract: Principal components analysis (PCA) is a widely used technique in the social and physical sciences. However in spatial applications, standard PCA is frequently applied without any adaptation that accounts for important spatial effects. Such a naive application can be problematic as such effects often provide a more complete understanding of a given process. In this respect, standard PCA can be (a) replaced with a geographically weighted PCA (GWPCA), when we want to account for a certain spatial heterogeneity; (b) adapted to account for spatial autocorrelation in the spatial process; or (c) adapted with a specification that represents a mixture of both (a) and (b). In this article, we focus on implementation issues concerning the calibration, testing, interpretation and visualisation of the location-specific principal components from GWPCA. Here we initially consider the basics of (global) principal components, then consider the development of a locally weighted PCA (for the exploration of local subsets in...

Identifying patterns in spatial information: a survey of methods

Abstract: Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and implicit spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns. In this paper, we explore the emerging field of spatial data mining, focusing on different methods to extract patterns from spatial information. We conclude with a look at future research needs. C

Ordinary kriging for function-valued spatial data

Abstract: In various scientific fields properties are represented by functions varying over space. In this paper, we present a methodology to make spatial predictions at non-data locations when the data values are functions. In particular, we propose both an estimator of the spatial correlation and a functional kriging predictor. We adapt an optimization criterion used in multivariable spatial prediction in order to estimate the kriging parameters. The curves are pre-processed by a non-parametric fitting, where the smoothing parameters are chosen by cross-validation. The approach is illustrated by analyzing real data based on soil penetration resistances.

Spatial Data Analysis: Models, Methods and Techniques

Abstract: Preface.- Part A: The Analysis of Geostatical Data.- Part B: The Analysis of Area Data.- Part C: The Analysis of Spatial Interaction Data.- Subject Index.- Author Index.

Bayesian inference for general gaussian graphical models with application to multivariate lattice data

Abstract: We introduce efficient Markov chain Monte Carlo methods for inference and model determination in multivariate and matrix-variate Gaussian graphical models. Our framework is based on the G-Wishart prior for the precision matrix associated with graphs that can be decomposable or non-decomposable. We extend our sampling algorithms to a novel class of conditionally autoregressive models for sparse estimation in multivariate lattice data, with a special emphasis on the analysis of spatial data. These models embed a great deal of flexibility in estimating both the correlation structure across outcomes and the spatial correlation structure, thereby allowing for adaptive smoothing and spatial autocorrelation parameters. Our methods are illustrated using a simulated example and a real-world application which concerns cancer mortality surveillance. Supplementary materials with computer code and the datasets needed to replicate our numerical results together with additional tables of results are available online.

Spatial perspectives in state-and-transition models: a missing link to land management?

Abstract: Summary 1. State-and-transition models (STMs) synthesize and communicate knowledge about the alternative states of an ecosystem and causes of state transitions. Data supported narrative descriptions within STMs are used to select or justify management actions. State transitions are characteristically heterogeneous in space and time, but spatial heterogeneity is seldom described in STMs, thereby limiting their utility. 2. We conducted a review that indicates how spatially explicit data can be used to improve STMs. We first identified three spatial scales at which spatial patterns and processes are manifest: patches, sites and landscapes. We then identified three classes of spatial processes that govern heterogeneity in state transitions at each scale and that can be considered in empirical studies, STM narratives and management interpretations. 3. First, spatial variations in land-use driver history (e.g. grazing use) can explain differences in the occurrence of state transitions within land areas that are otherwise uniform. Secondly, spatial dependence in response to drivers imposed by variations in soils, landforms and climate can explain how the likelihood of state transition varies along relatively static environmental gradients. Thirdly, state transition processes can be contagious, under control of vegetation-environment feedbacks, such that the spatiotemporal evolution of state transitions is predictable. 4. We suggest a strategy for considering each of the three spatial processes in the development of STM narratives. We illustrate how spatial data can be employed for describing early warning indicators of state transition, identifying areas that are most susceptible to state transitions, and designing and implementing monitoring schemes. 5. Synthesis and applications. State-and-transition models are increasingly important tools for guiding land-management activities. However, failure to adequately represent spatial processes in STMs can limit their ability to identify the initiation, risk and causes of state transitions and, therefore, the appropriate management responses. We suggest that multi-scaled studies targeted to different kinds of ecosystems can be used to uncover evidence of spatial processes. Such evidence should be included in STM narratives and can lead to novel interpretations of land change and improved management.

The Hausman test in a Cliff and Ord panel model

Abstract: Summary This paper studies the random effects model and the fixed effects model for spatial panel data. The model includes a Cliff and Ord type spatial lag of the dependent variable as well as a spatially lagged one-way error component structure, accounting for both heterogeneity and spatial correlation across units. We discuss instrumental variable estimation under both the fixed and the random effects specifications and propose a spatial Hausman test which compares these two models accounting for spatial autocorrelation in the disturbances. We derive the large sample properties of our estimation procedures and show that the test statistic is asymptotically chi-square distributed. A small Monte Carlo study demonstrates that this test works well even in small panels.

Groundwater quality mapping using geographic information system (GIS): A case study of Gulbarga City, Karnataka, India

Abstract: Spatial variations in ground water quality in the corporation area of Gulbarga City located in the northern part of Karnataka State, India, have been studied using geographic information system (GIS) technique. GIS, a tool which is used for storing, analyzing and displaying spatial data is also used for investigating ground water quality information. For this study, water samples were collected from 76 of the bore wells and open wells representing the entire corporation area. The water samples were analyzed for physico-chemical parameters like TDS, TH, Cl- and NO3-, using standard techniques in the laboratory and compared with the standards. The ground water quality information maps of the entire study area have been prepared using GIS spatial interpolation technique for all the above parameters. The results obtained in this study and the spatial database established in GIS will be helpful for monitoring and managing ground water pollution in the study area. Mapping was coded for potable zones, in the absence of better alternate source and non-potable zones in the study area, in terms of water quality. Key words: Groundwater pollution, drinking-water, physico-chemical parameters, spatial interpolation

The emergence of spatial cyberinfrastructure

Abstract: Cyberinfrastructure integrates advanced computer, information, and communication technologies to empower computation-based and data-driven scientific practice and improve the synthesis and analysis of scientific data in a collaborative and shared fashion. As such, it now represents a paradigm shift in scientific research that has facilitated easy access to computational utilities and streamlined collaboration across distance and disciplines, thereby enabling scientific breakthroughs to be reached more quickly and efficiently. Spatial cyberinfrastructure seeks to resolve longstanding complex problems of handling and analyzing massive and heterogeneous spatial datasets as well as the necessity and benefits of sharing spatial data flexibly and securely. This article provides an overview and potential future directions of spatial cyberinfrastructure. The remaining four articles of the special feature are introduced and situated in the context of providing empirical examples of how spatial cyberinfrastructure is extending and enhancing scientific practice for improved synthesis and analysis of both physical and social science data. The primary focus of the articles is spatial analyses using distributed and high-performance computing, sensor networks, and other advanced information technology capabilities to transform massive spatial datasets into insights and knowledge.

Using spatial principles to optimize distributed computing for enabling the physical science discoveries.

Abstract: Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century.

What spatial data do we need to develop global mammal conservation strategies

Abstract: Spatial data on species distributions are available in two main forms, point locations and distribution maps (polygon ranges and grids). The first are often temporally and spatially biased, and too discontinuous, to be useful (untransformed) in spatial analyses. A variety of modelling approaches are used to transform point locations into maps. We discuss the attributes that point location data and distribution maps must satisfy in order to be useful in conservation planning. We recommend that before point location data are used to produce and/or evaluate distribution models, the dataset should be assessed under a set of criteria, including sample size, age of data, environmental/geographical coverage, independence, accuracy, time relevance and (often forgotten) representation of areas of permanent and natural presence of the species. Distribution maps must satisfy additional attributes if used for conservation analyses and strategies, including minimizing commission and omission errors, credibility of the source/assessors and availability for public screening. We review currently available databases for mammals globally and show that they are highly variable in complying with these attributes. The heterogeneity and weakness of spatial data seriously constrain their utility to global and also sub-global scale conservation analyses.

SOWL: a framework for handling spatio-temporal information in OWL 2.0

Abstract: We propose SOWL, an ontology for representing and reasoning over spatio-temporal information in OWL. Building upon well established standards of the semantic web (OWL 2.0, SWRL) SOWL enables representation of static as well as of dynamic information based on the 4D-fluents (or, equivalently, on the N-ary) approach. Both RCC- 8 topological and cone-shaped directional relations are integrated in SOWL. Representing both qualitative temporal and spatial information (i.e., information whose temporal or spatial extents are unknown such as "left-of" for spatial and "before" for temporal relations) in addition to quantitative information (i.e., where temporal and spatial information is defined precisely) is a distinctive feature of SOWL. The SOWL reasoner is capable of inferring new relations and checking their consistency, while retaining soundness, completeness, and tractability over the supported sets of relations.

Advanced Spatial Statistics: Special Topics in the Exploration of Quantitative Spatial Data Series

Abstract: 1. Introduction to spatial statistics and data handling.- 1.1. A brief historical background.- 1.2. The principal problem of spatial statistics.- 1.3. Spatial sampling perspectives.- 1.4. Models of spatial autocorrelation.- 1.5. Towards a theory of spatial statistics.- 1.6 References.- Appendix 1A: Derivation of the expected value of MC.- Appendix 1B: Derivation of the expected value of GR.- 2. Developing a theory of spatial statistics.- 2.1. The small sample size problem.- 2.2. Finite versus infinite surfaces.- 2.3. Data transformations.- 2.4. Multivariate analysis.- 2.5. Higher order autoregressive models.- 2.6. Concluding comments.- 2.7. References.- 3. Areal unit configuration and locational information.- 3.1. Planar tessellations.- 3.2. Eigenfunction analysis of areal unit configuration tessellations.- 3.3. Selected applications of the principal eigenfunctions of matrix C.- 3.4. The modifiable areal unit problem.- 3.5. The importance of configurational information: a case study of Toronto.- 3.5.1. Generalized canonical correlation analysis.- 3.5.2. Land use structure.- 3.5.3. Social area structure.- 3.5.4. Spatial interaction structure.- 3.5.5. Spatial infrastructure.- 3.5.6. The generalized canonical correlation solution for the Toronto data.- 3.6. Implications.- 3.7. References.- 4. Reformulating classical linear statistical models.- 4.1. Autocorrelated errors models.- 4.2. Autocorrelated bivariate models.- 4.3. A spatially adjusted ANOVA model.- 4.4. The two-groups discriminant function model.- 4.5. Hypothesis testing and spatial dependence.- 4.6. Efficiency of spatial statistics estimators.- 4.7. Consistency of spatial statistics estimators.- 4.8. Conclusions.- 4.9. References.- 5. Spatial autocorrelation and spectral analysis.- 5.1. A brief background for spectral analysis.- 5.2. Relationships between autoregressive and spectral models.- 5.3. Defining the covariance matrix of a conditional spatial model using the spectral density function.- 5.4. Spectral analysis and two-dimensional shape measurement.- 5.5. Concluding comments.- 5.6. References.- 6. The missing data problem of a two-dimensional surface.- 6.1. The incomplete data problem statement.- 6.2. Background.- 6.3. Solutions available in commercial statistical packages.- 6.4. The spatial data problem.- 6.5. Properties of the conditional model when data are incomplete.- 6.6. An algorithm for the conditional spatial case.- 6.6.1. COMMON block arguments.- 6.6.2. Input.- 6.6.3. Subroutines.- 6.6.4. Output.- 6.6.5. Working space and library subroutines.- 6.7. Constrained MLEs.- 6.8. Concluding comments.- 6.9. References.- Appendix 6A: FORTRAN subroutine.- 7. Correcting for edge effects in spatial statistical analyses.- 7.1. Problem statement.- 7.2. Major proposed solutions.- 7.3. An evaluation of the major proposed solutions.- 7.4. Conclusions and implications.- 7.5. References.- 8. Multivariate models of spatial dependence.- 8.1. A multivariate normal probability density function with spatial autocorrelation.- 8.2. Discerning latent structure in multivariate spatial data.- 8.3. Estimation problems.- 8.4. Selected empirical examples.- 8.4.1. An empirical example: 1981 Buffalo crime data.- 8.4.2. An empirical example: 1969 agricultural production in Puerto Rico.- 8.5. Extensions to multivariate models in general.- 8.6. Concluding comments.- 8.7. References.- Appendix 8A: Rules for Kronecker products.- 9: Simulation experimentation in spatial analysis.- 9.1. Testing a null hypothesis of zero spatial autocorrelation.- 9.2. Generating autocorrelated pseudo-random numbers for two-dimensional surfaces.- 9.3. Background.- 9.4. Quality of the pseudo-random numbers.- 9.5. Variance reduction techniques.- 9.6. Selecting the number of replications r.- 9.7. Analysis of the simulation results for Chapter 6.- 9.8. Concluding comments.- 9.9. References.- 10. Summary and conclusions.- 10.1. Summary.- 10.2 Conclusions.- 10.3 References.

Accounting for Spatial Effects in Economic Models of Land Use: Recent Developments and Challenges Ahead

Abstract: The marked increase in the availability of spatial data has forced researchers engaged in land use modeling to directly confront the question of space and the theoretical and methodological challenges involved in developing spatial models. Advances have come from multiple disciplines, most notably through the development and application of spatial theory and methods from regional science, geography, urban economics and more recently, theoretical and applied econometrics. The main goal of this paper is to summarize the econometric challenges of spatial data and to highlight spatial models and methods with a particular focus on models of land markets and land use change. We also discuss the data and modeling challenges associated with modeling the underlying spatial economic mechanisms that give rise to land use patterns and the complexities involved in modeling land use as a coupled economic-ecological system.