nach oben

Journal of Geographical Systems

Erschienen in:

01.10.2016 | Original Article

Geographically weighted regression and multicollinearity: dispelling the myth

verfasst von: A. Stewart Fotheringham, Taylor M. Oshan

Erschienen in: Journal of Geographical Systems | Ausgabe 4/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Geographically weighted regression (GWR) extends the familiar regression framework by estimating a set of parameters for any number of locations within a study area, rather than producing a single parameter estimate for each relationship specified in the model. Recent literature has suggested that GWR is highly susceptible to the effects of multicollinearity between explanatory variables and has proposed a series of local measures of multicollinearity as an indicator of potential problems. In this paper, we employ a controlled simulation to demonstrate that GWR is in fact very robust to the effects of multicollinearity. Consequently, the contention that GWR is highly susceptible to multicollinearity issues needs rethinking.

Nächster Artikel Spatial representation in the social interaction potential metric: an analysis of scale and parameter sensitivity

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Bãrcena MJ, Menãndez P, Palacios MB, Tusell F (2014) Alleviating the effect of collinearity in geographically weighted regression. J Geogr Syst 16(4):441–466. doi:10.1007/s10109-014-0199-6 CrossRef

Belsey DA, Kuh E, Welsch RE (1980) Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New YorkCrossRef

Bonferroni CE (1935) Il calcolo delle assicurazioni su gruppi di teste. In: Studi in Onore del Professor Salvatore Ortu Carboni, Rome, pp 13–60

Brown S, Versace VL, Laurenson L, Ierodiaconou D, Fawcett J, Salzman S (2011) Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression. Environ Model Assess 17(3):241–254. doi:10.1007/s10666-011-9289-8. http://link.springer.com.ezproxy1.lib.asu.edu/article/10.1007/s10666-011-9289-8

Brunsdon C, Charlton M, Harris P (2012) Living with collinearity in local regression models. http://eprints.maynoothuniversity.ie/5755/

Byrne G, Charlton M, Fotheringham S (2009) Multiple dependent hypothesis tests in geographically weighted regression. In: Lees BG, Laffan SW (eds) Proceedings of the 10th international conference on geocomputation, University of New South Wales. http://eprints.maynoothuniversity.ie/5768/

Cahill M, Mulligan G (2007) Using geographically weighted regression to explore local crime patterns. Soc Sci Comput Rev 25(2):174–193. doi:10.1177/0894439307298925. http://ssc.sagepub.com.ezproxy1.lib.asu.edu/content/25/2/174

da Silva AR, Fotheringham AS (2015) The multiple testing issue in geographically weighted regression: the multiple testing issue in gwr. Geogr Anal. doi:10.1111/gean.12084

Finley AO (2011) Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence: spatially-varying coefficients models. Methods Ecol Evol 2(2):143–154. doi:10.1111/j.2041-210X.2010.00060.x CrossRef

Fotheringham AS (1982) Multicollinearity and parameter estimates in a linear model. Geogr Anal 14(1):64–71. doi:10.1111/j.1538-4632.1982.tb00055.x. http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.1982.tb00055.x/abstract

Fotheringham AS, Brunsdon C (1999) Local forms of spatial analysis. Geogr Anal 31(4):340–358. doi:10.1111/j.1538-4632.1999.tb00989.x. http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.1999.tb00989.x/abstract

Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, New York

Fotheringham AS, Crespo R, Yao J (2015) Geographical and temporal weighted regression (GTWR). Geogr Anal 47(4):431–452. doi:10.1111/gean.12071. http://onlinelibrary.wiley.com/doi/10.1111/gean.12071/abstract

Gilbert A, Chakraborty J (2011) Using geographically weighted regression for environmental justice analysis: cumulative cancer risks from air toxics in Florida. Soc Sci Res 40(1):273–286. doi:10.1016/j.ssresearch.2010.08.006. http://www.sciencedirect.com/science/article/pii/S0049089X10001754

Miller JA, Hanham RQ (2011) Spatial nonstationarity and the scale of species environment relationships in the Mojave Desert, California. USA. Int J Geogr Inf Sci 25(3):423–438. doi:10.1080/13658816.2010.518147 CrossRef

Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis, 5th edn. Wiley, New Jersey

O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690. doi:10.1007/s11135-006-9018-6 CrossRef

Páez A, Farber S, Wheeler D (2011) A simulation-based study of geographically weighted regression as a method for investigating spatially varying relationships. Environ Plan A 43(12):2992–3010. doi:10.1068/a44111. http://epn.sagepub.com/content/43/12/2992

Waller LA, Zhu L, Gotway CA, Gorman DM, Gruenewald PJ (2007) Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models. Stoch Environ Res Risk Assess 21(5):573–588. doi:10.1007/s00477-007-0139-9 CrossRef

Wheeler DC (2007) Diagnostic tools and a remedial method for collinearity in geographically weighted regression. Environ Plan A 39(10):2464–2481. doi:10.1068/a38325. http://epn.sagepub.com/content/39/10/2464

Wheeler DC (2009) Simultaneous coefficient penalization and model selection in geographically weighted regression: the geographically weighted lasso. Environ Plan A 41(3):722–742. doi:10.1068/a40256. http://epn.sagepub.com/content/41/3/722

Wheeler DC (2010) Visualizing and diagnosing coefficients from geographically weighted regression models. In: Jiang B, Yao X (eds) Geospatial analysis and modelling of urban structure and dynamics, vol 99. Springer, Dordrecht, pp 415–436. http://link.springer.com/10.1007/978-90-481-8572-621

Wheeler DC, Calder CA (2007) An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. J Geogr Syst 9(2):145–166. doi:10.1007/s10109-006-0040-y CrossRef

Wheeler D, Tiefelsdorf M (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 7(2):161–187. doi:10.1007/s10109-005-0155-6 CrossRef

Wheeler DC, Waller LA (2009) Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests. J Geogr Syst 11(1):1–22. doi:10.1007/s10109-008-0073-5. http://search.proquest.com.ezproxy1.lib.asu.edu/docview/230073360/abstract

Williams VSL, Jones LV, Tukey JW (1999) Controlling error in multiple comparisons, with examples from state-to-state differences in educational achievement. J Educ Behav Stat 24(1):42–69CrossRef

Titel: Geographically weighted regression and multicollinearity: dispelling the myth
verfasst von: A. Stewart Fotheringham
Taylor M. Oshan
Publikationsdatum: 01.10.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Geographical Systems / Ausgabe 4/2016
Print ISSN: 1435-5930
Elektronische ISSN: 1435-5949
DOI: https://doi.org/10.1007/s10109-016-0239-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2016

Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data

Formulas for precisely and efficiently estimating the bias and variance of the length measurements

Spatial representation in the social interaction potential metric: an analysis of scale and parameter sensitivity

The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics

Premium Partner