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\u003cp\u003e\u003ci\u003eSpatial Regression Analysis Using Eigenvector Spatial Filtering\u003c/i\u003e provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. \u003c/p\u003e \u003cp\u003eThis book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. \u003ci\u003eSpatial Regression Analysis Using Eigenvector Spatial Filtering\u003c/i\u003e is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre.\u003c/p\u003e\u003cul\u003e \u003cli\u003eReviews the uses of ESF across linear regression, generalized linear regression, spatial autocorrelation measurement, and spatially varying coefficient models\u003c/li\u003e \u003cli\u003eIncludes computer code and template datasets for further modeling\u003c/li\u003e \u003cli\u003eProvides comprehensive coverage of related concepts in spatial data analysis and spatial statistics\u003c/li\u003e\u003c/ul\u003e |
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