Sustainable design of running friendly streets: Environmental exposures predict runnability by Volunteered Geographic Information and multilevel model approaches

Abstract

Understanding the influence of built environment on running behavior is a significant step towards developing related landscape strategies. This paper adopted a volunteered geographic information (VGI) approach to measure urban runnability by quantifying environmental features that encourage or hinder running activities. The GPS-based routes collected from Strava were used to compute the running intensity of street segments in Helsinki, Finland. We applied multilevel regression models to assess the spatial-varied impacts of street environment on running intensity, so as to elicit runner's preferences and investigate their associations with sociodemographic characteristics at higher hierarchical level (neighbourhood). The results showed street greenery assessed by Green View Index (GVI) represented the greenness exposure to runners better than top-down greenness assessed by Normalized Difference Vegetation Index (NDVI), and thus can be considered as a more reliable predictor for running behaviours. Blue space density was the predominant factor and associated with running intensity positively. Running intensity negatively correlated with urban density, connectivity and traffic accidents, and positively correlated with traffic noise and air pollution. Population density and income level were positively associated with running intensity. These results provide profound insights and boost our capacity to design more attractive and sustainable streets for physical activity.