Statistics for Spatial Data.

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Modern Applied Statistics With S

Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.

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Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

An introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models. Estimation and interpretation of these models are illustrated with an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000. These extensions to conventional regression models are useful when modeling cross-sectional regional observations or and panel data samples collected from regions over both space and time can be easily implemented using publicly available software. Use of these models for the case of non-spatial structured dependence is also discussed.

/pdf/introduction-to-spatial-econometrics-9xcsp6v7d6.pdf

Introduction to spatial econometrics

Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method’s implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing

/pdf/methods-to-account-for-spatial-autocorrelation-in-the-5fbbuizhqs.pdf

Methods to account for spatial autocorrelation in the analysis of species distributional data : a review

Hello, world: handling spatial data in R.- Classes for spatial data in R.- Visualizing spatial data.- Spatial data import and export.- Further methods for handling spatial data.- Customising spatial data classes and methods.- Spatial point pattern analysis.- Interpolation and geostatistics.- Areal data and spatial autocorrelation.- Modelling areal data.- Disease mapping.- Afterword.- References.

Applied Spatial Data Analysis with R

Classes and Methods for Spatial Data

Bindings for the Geospatial Data Abstraction Library

Recent advances in the implementation of spatial econometrics model estimation techniques have made it desirable to compare results, which should correspond between implementations across software applications for the same data. These model estimation techniques are associated with methods for estimating impacts (emanating effects), which are also presented and compared. This review constitutes an up-to-date comparison of generalized method of moments and maximum likelihood implementations now available. The comparison uses the cross-sectional US county data set provided by Drukker, Prucha, and Raciborski (2013d). The comparisons will be cast in the context of alternatives using the MATLAB Spatial Econometrics toolbox, Stata's user-written sppack commands, Python with PySAL and R packages including spdep, sphet and McSpatial.

/pdf/comparing-implementations-of-estimation-methods-for-spatial-4nlm1jr3y2.pdf

Roger Bivand

Papers

Methods to account for spatial autocorrelation in the analysis of species distributional data : a review

Applied Spatial Data Analysis with R

Classes and Methods for Spatial Data

Bindings for the Geospatial Data Abstraction Library

Comparing Implementations of Estimation Methods for Spatial Econometrics