Spatial regression analysis using Eigenvector spatial filtering
Title Statements
- Full title :Spatial regression analysis using Eigenvector spatial filtering / Daniel A. Griffith, Yongwan Chun, Bin Li.
Edition Statements
- Publication Date:2019.
Publication Statements
- Publisher(s):Academic Press
- Place of Publication:Place of publication not identified
- Language:eng
- Publication Date:2019.
The Physical Object
- Description:1 online resource
Edition Identifiers
- ISBN:9780128156926, 0128156929, 0128150432, 9780128150436
- oclc:on1121293860
- LC Classification:QA278.2
- Dewey Decimal:519.536
Table of Contents
Front Cover; Spatial Regression Analysis Using Eigenvector Spatial Filtering; Copyright; Dedication; Contents; Foreword; Moran eigenvector spatial filtering: Multiple origins and convergence; A word about the theoretical background for MESF in ecology; Extensions and the future of MESF analysis; References; Preface; Data description; A preview of the book's content; References; Chapter 1: Spatial autocorrelation; 1.1. Defining SA; 1.1.1. A mathematical formularization of the first law of geography; 1.1.2. Quantifying spatial relationships: The spatial weights matrix
1.1.3. Different measurements for different data types: Quantifying SA1.1.4. The MC: Distributional theory; 1.2. Impacts of SA on attribute statistical distributions; 1.2.1. Effects of spatial dependence: Deviating from independent observations; 1.2.2. SA and the Moran scatterplot; 1.2.3. SA and histograms; 1.3. Summary; Appendix 1.A. The mean and variance of the MC for linear regression residuals; References; Chapter 2: An introduction to spectral analysis; 2.1. Representing SA in the spectral domain; 2.1.1. SA: From a spatial frequency to a spatial spectral domain
2.1.2. Eigenvalues and eigenvectors2.1.3. Principal…
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