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A geographically weighted regression model to examine the spatial variation of the socioeconomic and land-use factors associated with Strava bike activity in Austin, Texas

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  • Munira, Sirajum
  • Sener, Ipek N.

Abstract

Despite evidence showing the spatial nonstationarity of the determinants of bike activity, very few studies have addressed the phenomena, probably due to the limited sample size of the traditional count data. To address this gap, this study demonstrated the applicability of Strava bike activity data by developing a geographically weighted Poisson regression (GWPR) model that can reveal how the influence of socioeconomic and land-use factors vary across a region. The city of Austin was selected as a case study, and Strava bike volume was gleaned from 1494 intersections. The representativeness of the Strava data was first examined by comparing those data with the video-based actual bicycle volume data from 43 intersections in the study area. Despite the high deviation in several locations, Strava volume exhibited moderate linear relationships with actual volume. The GWPR model developed in this study outperformed the traditional global model and revealed significant spatial variability of nine variables related to age, income, education, transit stops, hub locations, offices, schools, trails, and sidewalk facilities. Notable spatial variations on bike activity were observed across the study area in terms of magnitude, direction, and significance of the impact for all model variables. The analysis and discussion offer guidance to practitioners and policy makers in tailoring policies and programs that consider the spatial context. The study also provides insights for understanding the potential use of crowdsourced data in examining bike activity, especially when resources are limited.

Suggested Citation

  • Munira, Sirajum & Sener, Ipek N., 2020. "A geographically weighted regression model to examine the spatial variation of the socioeconomic and land-use factors associated with Strava bike activity in Austin, Texas," Journal of Transport Geography, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:jotrge:v:88:y:2020:i:c:s096669232030942x
    DOI: 10.1016/j.jtrangeo.2020.102865
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    References listed on IDEAS

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    1. Strauss, Jillian & Miranda-Moreno, Luis F., 2013. "Spatial modeling of bicycle activity at signalized intersections," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(2), pages 47-58.
    2. Selby, Brent & Kockelman, Kara M., 2013. "Spatial prediction of traffic levels in unmeasured locations: applications of universal kriging and geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 29(C), pages 24-32.
    3. Yu, Haitao & Peng, Zhong-Ren, 2019. "Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression," Journal of Transport Geography, Elsevier, vol. 75(C), pages 147-163.
    4. Hochmair, Hartwig H. & Bardin, Eric & Ahmouda, Ahmed, 2019. "Estimating bicycle trip volume for Miami-Dade county from Strava tracking data," Journal of Transport Geography, Elsevier, vol. 75(C), pages 58-69.
    5. Yanjie Ji & Xinwei Ma & Mingyuan Yang & Yuchuan Jin & Liangpeng Gao, 2018. "Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach," Sustainability, MDPI, vol. 10(5), pages 1-23, May.
    6. Feng Xie & David Levinson, 2010. "How streetcars shaped suburbanization: a Granger causality analysis of land use and transit in the Twin Cities," Journal of Economic Geography, Oxford University Press, vol. 10(3), pages 453-470, May.
    7. Blainey, Simon, 2010. "Trip end models of local rail demand in England and Wales," Journal of Transport Geography, Elsevier, vol. 18(1), pages 153-165.
    8. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    9. Griffin, Greg Phillip & Jiao, Junfeng, 2015. "Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus," SocArXiv 5gy3u, Center for Open Science.
    10. Griswold, Julia B. & Medury, Aditya & Schneider, Robert J., 2011. "Pilot Models for Estimating Bicycle Intersection Volumes," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt380855q6, Institute of Transportation Studies, UC Berkeley.
    11. Jestico, Ben & Nelson, Trisalyn & Winters, Meghan, 2016. "Mapping ridership using crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 52(C), pages 90-97.
    12. Yang, Hongtai & Lu, Xiaozhao & Cherry, Christopher & Liu, Xiaohan & Li, Yanlai, 2017. "Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 64(C), pages 184-194.
    13. Zhang, Dapeng & Wang, Xiaokun (Cara), 2014. "Transit ridership estimation with network Kriging: a case study of Second Avenue Subway, NYC," Journal of Transport Geography, Elsevier, vol. 41(C), pages 107-115.
    14. Ryus, Paul & Ferguson, Erin & Laustsen, Kelly M. & Schneider, Robert J. & Proulx, Frank R. & Hull, Tony & Miranda-Moreno, Luis, 2014. "Guidebook on Pedestrian and Bicycle Volume Data Collection," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt11q5p33w, Institute of Transportation Studies, UC Berkeley.
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    Cited by:

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    4. Ali Al-Ramini & Mohammad A Takallou & Daniel P Piatkowski & Fadi Alsaleem, 2022. "Quantifying changes in bicycle volumes using crowdsourced data," Environment and Planning B, , vol. 49(6), pages 1612-1630, July.

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