Local Analysis of Spatial Relationships: A Comparison of GWR and the Expansion Method
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TL;DR
The results suggest that, on average, spatial variability in G WR is not a consequence of the calibration procedure, and that GWR is sufficiently flexible to reproduce the type of map patterns used in the simulation experiment.
Abstract
Considerable attention has been paid in recent years to the use and development of local forms of spatial analysis, including the method known as geographically weighted regression (GWR). GWR is a simple, yet conceptually appealing approach for exploring spatial non-stationarity that has been described as a natural evolution of the expansion method. The objective of the present paper is to compare, by means of a simulation exercise, these two local forms of spatial analysis. Motivation for the exercise derives from two basic research questions: Is spatial non-stationarity in GWR an artifact of the way the model is calibrated? And, how well does GWR capture spatial variability? The results suggest that, on average, spatial variability in GWR is not a consequence of the calibration procedure, and that GWR is sufficiently flexible to reproduce the type of map patterns used in the simulation experiment.