Extreme coefficients in Geographically Weighted Regression and their effects on mapping
AbstractThis study deals with the issue of extreme coefficients in geographically weighted regression (GWR) and their effects on mapping coefficients using three datasets with different spatial resolutions. We found that although GWR yields extreme coefficients regardless of the resolution of the dataset or types of kernel function, 1) the GWR tends to generate extreme coefficients for less spatially dense datasets, 2) coefficient maps based on polygon data representing aggregated areal units are more sensitive to extreme coefficients, and 3) coefficient maps using bandwidths generated by a fixed calibration procedure are more vulnerable to the extreme coefficients than adaptive calibration.
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Bibliographic InfoPaper provided by Agricultural and Applied Economics Association in its series 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin with number 49117.
Date of creation: 2009
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extreme coefficient; fixed and adaptive calibrations; geographically weighted regression; Mapping; Research Methods/ Statistical Methods;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-05-02 (All new papers)
- NEP-ECM-2009-05-02 (Econometrics)
- NEP-GEO-2009-05-02 (Economic Geography)
- NEP-URE-2009-05-02 (Urban & Real Estate Economics)
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