Geographically Weighted Regression in Geospatial Analysis



Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial non-stationarity. The assumption in GWR is that observations nearby have a greater influence on parameter estimates than observations at a greater distance. This is very close to Tobler’s first law of geography—everything is related to everything else, but near things are more related than distant things (Tobler 1970). GWR was developed on the basis of the traditional regression framework which incorporates local spatial relationships into the framework in an intuitive and explicit manner (Brunsdon et al. 1996; Fotheringham and Brunsdon 1999; Fotheringham et al. 2002).


Geographically Weighted Regression Akaike Information Criterion Ordinary Little Square Model Landscape Fragmentation Geographically Weighted Regression Model 
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  1. Atkinson PM, German SE, Sear DA, Clark MJ (2003) Exploring the relations between riverbank erosion and geomorphological controls using geographically weighted logistic regression. Geogr Anal 35:58–82CrossRefGoogle Scholar
  2. Bivand R, Yu D (2011) Package ‘spgwr’. Accessed 6 July 2011
  3. Brunsdon C (2011) Geographically weighted analysis: review and prospect. Accessed 22 July 2011
  4. Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: a method for exploring spatial non-stationarity. Geogr Anal 28:281–298CrossRefGoogle Scholar
  5. Clement F, Orange D, Williams M, Mulley C, Epprecht M (2009) Drivers of afforestation in Northern Vietnam: assessing local variations using geographically weighted regression. Appl Geogr 29:561–576CrossRefGoogle Scholar
  6. Cleveland WS, Devlin SJ (1988) Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83:596–610CrossRefGoogle Scholar
  7. Fotheringham AS, Brunsdon C (1999) Local forms of spatial analysis. Geogr Anal 31:340–358CrossRefGoogle Scholar
  8. Fotheringham AS, Brunsdon C, Charlton ME (1998) Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environ Plann A 30:1905–1927CrossRefGoogle Scholar
  9. Fotheringham AS, Brunsdon C, Charlton ME (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, ChichesterGoogle Scholar
  10. Gao J, Li S (2011) Detecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using geographically weighted regression. Appl Geogr 31:292–302CrossRefGoogle Scholar
  11. Jaimes NBP, Sendra JB, Delgado MJ, Plata RF (2010) Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression. Appl Geogr 30:576–591CrossRefGoogle Scholar
  12. LeSage J (2010) Econometrics toolbox for MATLAB.
  13. Li S, Zhao Z, Miaomiao X, Wang Y (2010) Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression. Environ Model Software 25:1789–1800CrossRefGoogle Scholar
  14. Lin CH, Wen TH (2011) Using Geographically Weighted Regression (GWR) to explore spatial varying relationships of immature mosquitoes and human densities with the incidence of Dengue. Int J Environ Res Public Health 8:2798–2815CrossRefGoogle Scholar
  15. Luo J, Wei YHD (2009) Modeling spatial variations of urban growth patterns in Chinese cities: the case of Nanjing. Landsc Urban Plann 91:51–64CrossRefGoogle Scholar
  16. Mei CL, Wang N, Zhang WX (2006) Testing the importance of the explanatory variables in a mixed geographically weighted regression model. Environ Plann A 38:587–598CrossRefGoogle Scholar
  17. Mennis J (2006) Mapping the results of geographically weighted regression. The Cartographic Journal 43:171–179CrossRefGoogle Scholar
  18. Nakaya T, Fotheringham AS, Brunsdon C, Charlton M (2005) Geographically weighted Poisson regression for disease association mapping. Stat Med 24:2695–2717CrossRefGoogle Scholar
  19. Ogneva-Himmelberger Y, Pearsall H, Rakshit R (2009) Concrete evidence & geographically weighted regression: a regional analysis of wealth and the land cover in Massachusetts. Appl Geogr 29:478–487CrossRefGoogle Scholar
  20. Paez A, Wheeler DC (2009) Geographically weighted regression. In: Kitchin R, Thrift N (eds.) International Encyclopedia of Human Geography 1:407–414. Oxford: ElsevierGoogle Scholar
  21. Platt RV (2004) Global and local analysis of fragmentation in a mountain region of Colorado. Agric Ecosyst Environ 101:207–218CrossRefGoogle Scholar
  22. Tobler W (1970) A computer movie simulating urban growth in the Detroit region. Geogr Anal 46:234–240Google Scholar
  23. Tu J (2011) Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression. Appl Geogr 31:376–392CrossRefGoogle Scholar
  24. Tu J, Xia ZG (2008) Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation. Sci Total Environ 407:358–378CrossRefGoogle Scholar
  25. Wheeler DC, Paez A (2010) Geographically weighted regression. In: Fischer MM, Getis A (eds) Handbook of applied spatial analysis: software tools, methods and applications. Springer, Berlin, pp 461–486CrossRefGoogle Scholar
  26. World Health Organization (WHO) (2009) Dengue: guidelines for diagnosis, treatment, prevention and control, New Edition. WHO, GenevaGoogle Scholar
  27. Yu D, Wei YD (2004) Geographically weighted regression: investigation of spatially varying relationships: methods, techniques, and implementation. University of Wisconsin, Milwaukee, Paper presented during the GIS DayGoogle Scholar
  28. Zhang L, Bib H, Cheng P, Davis CJ (2004) Modeling spatial variation in tree diameter–height relationships. For Ecol Manage 189:317–329CrossRefGoogle Scholar

Copyright information

© Springer Japan 2012

Authors and Affiliations

  1. 1.Earth Observation Research Center, Space Applications Mission DirectorateJapan Aerospace Exploration Agency (JAXA)TsukubaJapan
  2. 2.Division of Spatial Information Science, Graduate School of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan
  3. 3.Don Mariano Marcos Memorial State UniversityBacnotanPhilippines

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