
Spatial Regression Analysis Using Eigenvector Spatial Filtering
Description
Key Features
- 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
Readership
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
Table of Contents
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
Product details
- No. of pages: 286
- Language: English
- Copyright: © Academic Press 2019
- Published: September 14, 2019
- Imprint: Academic Press
- eBook ISBN: 9780128156926
- Paperback ISBN: 9780128150436
About the Authors
Daniel Griffith
Affiliations and Expertise
Yongwan Chun
Affiliations and Expertise
Bin Li
Affiliations and Expertise
Ratings and Reviews
There are currently no reviews for "Spatial Regression Analysis Using Eigenvector Spatial Filtering"