Crafting a jogging-friendly city: Harnessing big data to evaluate the runnability of urban streets

Abstract

Jogging, historically marginalized in the realms of urban and transportation strategy since it is not about commuting, is garnering appreciation for its health-related merits. The growing public focus on health underscores the urgent need for planning and infrastructure to support outdoor physical activities, yet current evaluations of urban environments' friendliness toward such activities are insufficient. This investigation unveils a runnability evaluation framework predicated on accessible geospatial big data. Initial steps involved delineating potential metrics from the built environment, pedestrian perceptions, and the natural setting, as informed by literature. This was followed by constructing a backward stepwise regression analysis, utilizing jogging frequency as the response variable against the identified metrics as predictors. The ensuing model retained certain variables, which were then deemed valid metrics, and their regression coefficients were appropriated as weights to compute a runnability index for individual street segments. This framework was applied in Guangzhou, affirming the model's objectivity and validity. The introduced framework furnishes researchers and urban planners with an objective and reproducible tool for the evaluation of runnability and possesses the versatility for an extension to assess walkability and bikeability. This study encourages the attention and support of jogging activities.