Journal
GEO-SPATIAL INFORMATION SCIENCE
Volume 17, Issue 2, Pages 85-101Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10095020.2014.917453
Keywords
principal components analysis; semi-parametric GW regression; discriminant analysis; Monte Carlo tests; election data
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Funding
- Science Foundation Ireland under the National Development Plan [07/SRC/I1168]
- BBSRC [BBS/E/C/00005198, BBS/E/C/00005190] Funding Source: UKRI
- NERC [NE/J011568/1] Funding Source: UKRI
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In this study, we present a collection of local models, termed geographically weighted (GW) models, which can be found within the GWmodel R package. A GW model suits situations when spatial data are poorly described by the global form, and for some regions the localized fit provides a better description. The approach uses a moving window weighting technique, where a collection of local models are estimated at target locations. Commonly, model parameters or outputs are mapped so that the nature of spatial heterogeneity can be explored and assessed. In particular, we present case studies using: (i) GW summary statistics and a GW principal components analysis; (ii) advanced GW regression fits and diagnostics; (iii) associated Monte Carlo significance tests for non-stationarity; (iv) a GW discriminant analysis; and (v) enhanced kernel bandwidth selection procedures. General Election data-sets from the Republic of Ireland and US are used for demonstration. This study is designed to complement a companion GWmodel study, which focuses on basic and robust GW models.
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