Multiscale Geographically Weighted Regression (MGWR)
Arizona State University · University of St Andrews
Abstract
Scale is a fundamental geographic concept, and a substantial literature exists discussing the various roles that scale plays in different geographical contexts. Relatively little work exists, though, that provides a means of measuring the geographic scale over which different processes operate. Here we demonstrate how geographically weighted regression (GWR) can be adapted to provide such measures. GWR explores the potential spatial nonstationarity of relationships and provides a measure of the spatial scale at which processes operate through the determination of an optimal bandwidth. Classical GWR assumes that all of the processes being modeled operate at the same spatial scale, however. The work here relaxes…
Citation impact
- FWCI
- 140.76
- Percentile
- 100%
- References
- 31
Authors
3Topics & keywords
- Scale (ratio)
- Geographically Weighted Regression
- Computer science
- Data mining
- Spatial ecology
- Spatial analysis
- Spatial heterogeneity
- Geography