This 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.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Length: Date of creation: 2009 Date of revision: Handle: RePEc:ags:aaea09:49117
Contact details of provider: Postal: 555 East Wells Street, Suite 1100, Milwaukee, Wisconsin 53202 Phone: (414) 918-3190 Fax: (414) 276-3349 Email: Web page: http://www.aaea.org More information through EDIRC
For technical questions regarding this item, or to correct its listing, contact: (AgEcon Search).
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.: