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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.
- Reviews the uses of ESF across linear regression, generalized linear regression, spatial autocorrelation measurement, and spatially varying coefficient models
- Includes computer code and template datasets for further modeling
- Provides comprehensive coverage of related concepts in spatial data analysis and spatial statistics
Graduate students and researchers worldwide working in spatial econometrics, spatial statistics, urban and regional economics, spatial data analysis, and more broadly from geography, GIS science, ecology, regional science, epidemiology and public health, economics, demography, applied statistics, remote sensing, urban and regional planning, transportation, and crime mapping
1. Spatial autocorrelation
2. An introduction to spectral analysis
3. MESF and linear regression
4. Software implementation for constructing an ESF, with special reference to linear regression
5. MESF and generalized linear regression
6. Modeling spatial heterogeneity with MESF
7. Spatial interaction modeling
8. Space-time modeling
9. MESF and multivariate statistical analysis
10. Concluding comments: Toy dataset implementation demonstrations
- No. of pages:
- © Academic Press 2019
- 14th September 2019
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Daniel A. Griffith is an Ashbel Smith Professor of Geospatial Information Sciences at the University of Texas at Dallas, affiliated professor in the College of Public Health at the University of South Florida, and adjunct professor in the Department of Resource Economics and Environmental Sociology at the University of Alberta. He holds degrees in Mathematics, Statistics, and Geography, and arguably is the inventor of Moran eigenvector spatial filtering. He is a two-time Fulbright Senior Specialist, an AAG Distinguished Research Honors awardee, and an elected fellow of the Royal Society of Canada, UCGIS, AAG, American Association for the Advancement of Science, American Statistical Association, Regional Science Association International, and Spatial Econometrics Association.
University of Texas at Dallas, Texas, USA
Yongwan Chun is an Associate Professor of Geospatial Information Sciences at the University of Texas at Dallas. His research interests lie in spatial statistics and GIS, focusing on urban issues, including population movement, environment, health, and crime. His research has been supported by the US National Science Foundation, and the US National Institutes of Health, among others. He has over 50 publications, including books, journal articles, book chapters, and conference proceedings.
University of Texas at Dallas, Texas, USA
Bin Li is a Professor of Geography at Central Michigan University. His academic interests are in GIS, cartography, spatial statistics, and economic geography. His research publications range from high performance processing geographic information services, to environmental modeling, geovisualization, and spatial statistics. He is a member of the editorial board of the Journal of Geospatial Information Science. He is active in international collaborations, serving as the co-director of the International Collaborative Center for Geocomputation Studies at Wuhan University, China.
Central Michigan University, Mt. Pleasant, MI, USA
"Provides an overview of traditional linear multivariate statistics applied to geospatial data, with an emphasis on SA, its data analytic impacts, and its representation by eigenvector spatial filters. " --Journal of Economic Literature
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