Accounting for Spatial Heterogeneity Using Crowdsourced Data

Mohammad Alattar, Caitlin Cottrill, Mark Beecroft

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Given the numerous benefits of active travel (human-powered transportation), in this paper, we argue that using crowdsourced data and a spatial heterogeneity treatment enhances the predictive performance of data modelling. Using such an approach thus increases the amount of insight that can be obtained to improve active travel decision-making. In particular, we model cyclists’ route choices using data on cycling trips and street network centralities obtained from Strava and OSMnx, respectively. It was found that: i) the number of cyclist trips is spatially clustered; and ii) the spatial error model exhibits a better predictive performance than spatial lag and ordinary least squares models. The results demonstrate the ability of the fine-grained resolution of crowdsourced data to provide more insights on active travel compared to traditional data.
Original languageEnglish
Number of pages9
Early online date29 Apr 2021
Publication statusPublished - Apr 2021


  • stava
  • OSMnx
  • crowdsourced
  • active travel
  • cycling
  • spatial modelling


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