IDEAS home Printed from https://ideas.repec.org/a/sae/envira/v37y2005i1p81-103.html
   My bibliography  Save this article

Analysing Commuting Using Local Regression Techniques: Scale, Sensitivity, and Geographical Patterning

Author

Listed:
  • Chris Lloyd
  • Ian Shuttleworth

Abstract

In this paper, two forms of local regression are employed in the analysis of relations between out-commuting distance and other socioeconomic variables in Northern Ireland. The two regression approaches used are moving window regression (MWR) and geographically weighted regression (GWR). For the first approach different window sizes are applied and changes in results assessed. For the second approach, a Gaussian kernel is used and its bandwidth varied. Seven independent variables are utilised, although a single variable (deprivation) provides the main analytical focus. Differences in results obtained with use of the two approaches are discussed. The relationship between window size or bandwidth size and observed spatial patterning is discussed and elucidated. The results support previous work that indicated severe limitations in using global regressions to examine relationships between socioeconomic variables. Also, the utility of comparing results obtained from MWR and GWR is assessed and the benefits of both approaches are outlined.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:envira:v:37:y:2005:i:1:p:81-103
    DOI: 10.1068/a36116
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/a36116
    Download Restriction: no
    File URL: https://libkey.io/10.1068/a36116?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dimitrios Tsiotas & George Aspridis & Ioannis Gavardinas & Labros Sdrolias & Dagmar Škodová-Parmová, 2019. "Gravity modeling in social science: the case of the commuting phenomenon in Greece," Evolutionary and Institutional Economics Review, Springer, vol. 16(1), pages 139-158, June.
    2. Tong, Zhaomin & An, Rui & Zhang, Ziyi & Liu, Yaolin & Luo, Minghai, 2022. "Exploring non-linear and spatially non-stationary relationships between commuting burden and built environment correlates," Journal of Transport Geography, Elsevier, vol. 104(C).
    3. Mark D. Partridge & Dan S. Rickman & Kamar Ali & M. Rose Olfert, 2008. "The Geographic Diversity of U.S. Nonmetropolitan Growth Dynamics: A Geographically Weighted Regression Approach," Land Economics, University of Wisconsin Press, vol. 84(2), pages 241-266.
    4. Wynen, Jan, 2013. "Explaining travel distance during same-day visits," Tourism Management, Elsevier, vol. 36(C), pages 133-140.
    5. Ian Shuttleworth & Myles Gould, 2010. "Distance between Home and Work: A Multilevel Analysis of Individual Workers, Neighbourhoods, and Employment Sites in Northern Ireland," Environment and Planning A, , vol. 42(5), pages 1221-1238, May.
    6. repec:rre:publsh:v:37:y:2007:i:3:p:303-43 is not listed on IDEAS
    7. Chiou, Yu-Chiun & Jou, Rong-Chang & Yang, Cheng-Han, 2015. "Factors affecting public transportation usage rate: Geographically weighted regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 161-177.
    8. Zhao, Pengjun & Cao, Yushu, 2020. "Commuting inequity and its determinants in Shanghai: New findings from big-data analytics," Transport Policy, Elsevier, vol. 92(C), pages 20-37.
    9. 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.
    10. Christopher D Lloyd & Ian Shuttleworth, 2012. "Residential Segregation in Northern Ireland in 2001: Assessing the Value of Exploring Spatial Variations," Environment and Planning A, , vol. 44(1), pages 52-67, January.
    11. 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).

    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:sae:envira:v:37:y:2005:i:1:p:81-103. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: SAGE Publications (email available below). General contact details of provider: .

    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.