IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v063i17.html
   My bibliography  Save this article

GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models

Author

Listed:
  • Gollini, Isabella
  • Lu, Binbin
  • Charlton, Martin
  • Brunsdon, Christopher
  • Harris, Paul

Abstract

Spatial statistics is a growing discipline providing important analytical techniques in a wide range of disciplines in the natural and social sciences. In the R package GWmodel we present techniques from a particular branch of spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localized calibration provides a better description. The approach uses a moving window weighting technique, where localized models are found at target locations. Outputs are mapped to provide a useful exploratory tool into the nature of the data spatial heterogeneity. Currently, GWmodel includes functions for: GW summary statistics, GW principal components analysis, GW regression, and GW discriminant analysis; some of which are provided in basic and robust forms.

Suggested Citation

  • Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
    • Handle: RePEc:jss:jstsof:v:063:i17
    DOI: http://hdl.handle.net/10.18637/jss.v063.i17
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v063i17/v63i17.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v063i17/GWmodel_1.2-5.tar.gz
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v063i17/v63i17.R
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v063.i17?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David C Wheeler, 2009. "Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso," Environment and Planning A, , vol. 41(3), pages 722-742, March.
    2. David Wheeler & Catherine Calder, 2007. "An assessment of coefficient accuracy in linear regression models with spatially varying coefficients," Journal of Geographical Systems, Springer, vol. 9(2), pages 145-166, June.
    3. Chris Lloyd & Ian Shuttleworth, 2005. "Analysing Commuting Using Local Regression Techniques: Scale, Sensitivity, and Geographical Patterning," Environment and Planning A, , vol. 37(1), pages 81-103, January.
    4. David Wheeler & Lance Waller, 2009. "Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests," Journal of Geographical Systems, Springer, vol. 11(1), pages 1-22, March.
    5. Yan Kestens & Marius Thériault & François Des Rosiers, 2006. "Heterogeneity in hedonic modelling of house prices: looking at buyers’ household profiles," Journal of Geographical Systems, Springer, vol. 8(1), pages 61-96, March.
    6. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    7. Christopher Bitter & Gordon Mulligan & Sandy Dall’erba, 2007. "Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method," Journal of Geographical Systems, Springer, vol. 9(1), pages 7-27, April.
    8. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model," Environment and Planning A, , vol. 32(1), pages 9-32, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Löchl, Michael & Axhausen, Kay W., 2010. "Modelling hedonic residential rents for land use and transport simulation while considering spatial effects," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 39-63.
    2. A. Stewart Fotheringham & Taylor M. Oshan, 2016. "Geographically weighted regression and multicollinearity: dispelling the myth," Journal of Geographical Systems, Springer, vol. 18(4), pages 303-329, October.
    3. David C. Wheeler & Antonio Páez & Jamie Spinney & Lance A. Waller, 2014. "A Bayesian approach to hedonic price analysis," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 663-683, August.
    4. Antonio Páez & Steven Farber & David Wheeler, 2011. "A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships," Environment and Planning A, , vol. 43(12), pages 2992-3010, December.
    5. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    6. Christos Agiakloglou & Cleon Tsimbos & Apostolos Tsimpanos, 2019. "Evidence of spurious results along with spatially autocorrelated errors in the context of geographically weighted regression for two independent SAR(1) processes," Empirical Economics, Springer, vol. 57(5), pages 1613-1631, November.
    7. repec:rre:publsh:v:51:y:2021:i:2 is not listed on IDEAS
    8. M. Bárcena & P. Menéndez & M. Palacios & F. Tusell, 2014. "Alleviating the effect of collinearity in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 16(4), pages 441-466, October.
    9. Sven Müller, 2012. "Identifying spatial nonstationarity in German regional firm start-up data," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 32(2), pages 113-132, September.
    10. Maria Terres & Alan Gelfand, 2015. "Using spatial gradient analysis to clarify species distributions with application to South African protea," Journal of Geographical Systems, Springer, vol. 17(3), pages 227-247, July.
    11. Wenjie Wu & Guanpeng Dong & Wenzhong Zhang, 2017. "The puzzling heterogeneity of amenity capitalization effects on land markets," Papers in Regional Science, Wiley Blackwell, vol. 96, pages 135-153, March.
    12. Diana Gutiérrez Posada & Fernando Rubiera Morollón & Ana Viñuela, 2018. "Ageing Places in an Ageing Country: The Local Dynamics of the Elderly Population in Spain," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 109(3), pages 332-349, July.
    13. Thomas M. Fullerton & Arturo Bujanda, 2018. "Commercial property values in a border metropolitan economy," Asia-Pacific Journal of Regional Science, Springer, vol. 2(2), pages 337-360, August.
    14. Alexis Comber & Paul Harris, 2018. "Geographically weighted elastic net logistic regression," Journal of Geographical Systems, Springer, vol. 20(4), pages 317-341, October.
    15. Kim, Jinwon & Jang, Seongsoo & Kang, Sanghoon & Kim, SeungHyun (James), 2020. "Why are hotel room prices different? Exploring spatially varying relationships between room price and hotel attributes," Journal of Business Research, Elsevier, vol. 107(C), pages 118-129.
    16. David C Wheeler, 2009. "Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso," Environment and Planning A, , vol. 41(3), pages 722-742, March.
    17. Diana Gutiérrez-Posada & Fernando Rubiera-Morollon & Ana Viñuela, 2017. "Heterogeneity in the Determinants of Population Growth at the Local Level," International Regional Science Review, , vol. 40(3), pages 211-240, May.
    18. Olaru, Doina & Mulley, Corinne & Smith, Brett & Ma, Liang, 2017. "Policy-led selection of the most appropriate empirical model to estimate hedonic prices in the residential market," Journal of Transport Geography, Elsevier, vol. 62(C), pages 213-228.
    19. Rabin Chakrabortty & Subodh Chandra Pal & Mehebub Sahana & Ayan Mondal & Jie Dou & Binh Thai Pham & Ali P. Yunus, 2020. "Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1259-1294, November.
    20. Declan Curran, 2012. "British regional growth and sectoral trends: global and local spatial econometric approaches," Applied Economics, Taylor & Francis Journals, vol. 44(17), pages 2187-2201, June.
    21. Deller, Steven, 2011. "Spatial heterogeneity in the wage curve," Economics Letters, Elsevier, vol. 113(3), pages 231-233.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:063:i17. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.