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A massive geographically weighted regression model of walking-environment relationships

Author

Listed:
  • Feuillet, T.
  • Commenges, H.
  • Menai, M.
  • Salze, P.
  • Perchoux, C.
  • Reuillon, R.
  • Kesse-Guyot, E.
  • Enaux, C.
  • Nazare, J.-A.
  • Hercberg, S.
  • Simon, C.
  • Charreire, H.
  • Oppert, J.M.

Abstract

Many studies aim at identifying environmental correlates of walking in order to identify specific potential levers for tackling the medical burden of physical inactivity. The links between environmental characteristics and walking behaviors are usually context specific. While local studies fail to embrace a global overview of these contexts, global studies hide the context scale patterns. In this study, we applied a geographically weighted logistic regression (GWR) on a large area (whole of France) to explore spatial variations of the relations between five environmental variables and walking for leisure and errands purposes among 40,480 French adults. This approach allowed us to adopt a global view of local patterns of relations and to highlight spatial contexts (defined through a clustering of GWR odds ratios) where combinations of correlates varied. Specifically, clustering algorithms on the GWR odds ratios led to 9 and 6 clusters for walking for leisure and errands, respectively. Some clusters were characterized by a particularly strong effect of population density, whereas others exhibited low effect of vegetation cover rate. Chi-squared tests indicated that these clusters were associated with type of urban areas (Paris, major urban poles, periurban areas, small urban poles, isolated areas) for the two types of walking. Beyond its methodological contribution - providing a method to handle large data samples into GWR analyses - this study offers key elements to practitioners and policy makers to target relevant contexts and environmental features for promoting daily walking.

Suggested Citation

  • Feuillet, T. & Commenges, H. & Menai, M. & Salze, P. & Perchoux, C. & Reuillon, R. & Kesse-Guyot, E. & Enaux, C. & Nazare, J.-A. & Hercberg, S. & Simon, C. & Charreire, H. & Oppert, J.M., 2018. "A massive geographically weighted regression model of walking-environment relationships," Journal of Transport Geography, Elsevier, vol. 68(C), pages 118-129.
    • Handle: RePEc:eee:jotrge:v:68:y:2018:i:c:p:118-129
    DOI: 10.1016/j.jtrangeo.2018.03.002
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    3. Charreire, H. & Roda, C. & Feuillet, T. & Piombini, A. & Bardos, H. & Rutter, H. & Compernolle, S. & Mackenbach, J.D. & Lakerveld, J. & Oppert, J.M., 2021. "Walking, cycling, and public transport for commuting and non-commuting travels across 5 European urban regions: Modal choice correlates and motivations," Journal of Transport Geography, Elsevier, vol. 96(C).
    4. Shen Zhao & Yong Xu, 2019. "Exploring the Spatial Variation Characteristics and Influencing Factors of PM 2.5 Pollution in China: Evidence from 289 Chinese Cities," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
    5. Yang Liu & Yanjie Ji & Zhuangbin Shi & Liangpeng Gao, 2018. "The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    6. Yupei Jiang & Honghu Sun, 2021. "Exploring the Characteristics and Influencing Factors of Leisure Walking Based on the Demand of Behavior," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    7. Guzman, Luis A. & Peña, Javier & Carrasco, Juan Antonio, 2020. "Assessing the role of the built environment and sociodemographic characteristics on walking travel distances in Bogotá," Journal of Transport Geography, Elsevier, vol. 88(C).
    8. Qinglin Jia & Tao Zhang & Long Cheng & Gang Cheng & Minjie Jin, 2022. "The Impact of the Neighborhood Built Environment on the Walking Activity of Older Adults: A Multi-Scale Spatial Heterogeneity Analysis," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
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