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Analysing commuting using local regression techniques: scale, sensitivity, and geographical patterning


  • Chris Lloyd
  • Ian Shuttleworth


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, Pion Ltd, London, vol. 37(1), pages 81-103, January.
  • Handle: RePEc:pio:envira:v:37:y:2005:i:1:p:81-103

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    Cited by:

    1. Jensen, Tomas & Deller, Steven, 2007. "Spatial Modeling of the Migration of Older People with a Focus on Amenities," The Review of Regional Studies, Southern Regional Science Association, vol. 37(3), pages 303-343.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Wynen, Jan, 2013. "Explaining travel distance during same-day visits," Tourism Management, Elsevier, vol. 36(C), pages 133-140.
    8. Yonghua Zou, 2015. "Re-examining the Neighborhood Distribution of Higher Priced Mortgage Lending: Global versus Local Methods," Growth and Change, Wiley Blackwell, vol. 46(4), pages 654-674, December.

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