Geographically Weighted Regression

International Encyclopedia of Human Geography

Volumn , Issue , 2009, Pages 407-414

  Paez A ^a   Wheeler, D C ^b  

^a MCMASTER UNIVERSITY   (Canada)

^b EMORY UNIVERSITY   (United States)

Author keywords

Bandwidth; Cross validation; Distance decay; Geographically weighted regression; Kernel function; Local forms of spatial analysis; Moving window; Multicollinearity; Nearest neighbors; Nonstationarity; Quantitative geography; Regression analysis; Regression diagnostics; Spatial statistics

Indexed keywords

EID: 84855324163     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-008044910-4.00447-8     Document Type: Chapter

References (16)

1. Atkinson P.M., German S.E., Sear D.A., Clark M.J. Exploring the relations between riverbank erosion and geomorphological controls using geographically weighted logistic regression. Geographical Analysis 2003, 35(1):58-82.
2. Brunsdon C., Fotheringham A.S., Charlton M.E. Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis 1996, 28(4):281-298.
3. Cleveland W.S., Devlin S.J. Locally weighted regression - an approach to regression analysis by local fitting. Journal of the American Statistical Association 1988, 83(403):596-610.
4. Fotheringham A.S., Brunsdon C. Local forms of spatial analysis. Geographical Analysis 1999, 31(4):340-358.
5. Fotheringham A.S., Brunsdon C., Charlton M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships 2002, Wiley, Chichester.
6. Goodchild M.F. The validity and usefulness of laws in geographic information science and geography. Annals of the Association of American Geographers 2004, 94(2):300-303.
7. LeSage J.P. A family of geographically weighted regression models. Advances in Spatial Econometrics 2004, 241-264. Springer, Berlin. L. Anselin, R.J.G.M. Florax, S.J. Rey (Eds.).
8. Leung Y., Mei C.L., Zhang W.X. Testing for spatial autocorrelation among the residuals of the geographically weighted regression. Environment and Planning A 2000, 32(5):871-890.
9. McMillen D.P. One hundred fifty years of land values in Chicago: A nonparametric approach. Journal of Urban Economics 1996, 40(1):100-124.
10. Mei C.L., He S.Y., Fang K.T. A note on the mixed geographically weighted regression model. Journal of Regional Science 2004, 44(1):143-157.
11. Páez A. Exploring contextual variations in land use and transport analysis using a probit model with geographical weights. Journal of Transport Geography 2006, 14(3):167-176.
12. Páez A., Long F., Farber S. Moving window approaches for hedonic price estimation: An empirical comparison of four techniques. Urban Studies 2008, 45(8):1565-1581.
13. Páez A., Uchida T., Miyamoto K. A general framework for estimation and inference of geographically weighted regression models: 1. Location-specific kernel bandwidths and a test for locational heterogeneity. Environment and Planning A 2002, 34(4):733-754.
14. Wheeler D., Tiefelsdorf M. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems 2005, 7(2):161-187.
15. Wheeler D.C., Calder C.A. An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. Journal of Geographical Systems 2007, 9(2):145-166.
16. WinBUGS website. http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml.