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Spatial Regression Analysis Using Eigenvector Spatial Filtering

  • 1st Edition - September 14, 2019
  • Authors: Daniel Griffith, Yongwan Chun, Bin Li
  • Language: English
  • Paperback ISBN:
    9 7 8 - 0 - 1 2 - 8 1 5 0 4 3 - 6
  • eBook ISBN:
    9 7 8 - 0 - 1 2 - 8 1 5 6 9 2 - 6

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering… Read more

Spatial Regression Analysis Using Eigenvector Spatial Filtering

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Spatial Regression Analysis Using Eigenvector Spatial Filtering 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.

This 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. Spatial Regression Analysis Using Eigenvector Spatial Filtering 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.