Assessing impacts of objective features and subjective perceptions of street environment on running amount: A case study of Boston

The built environment is found to relate to running behaviors. However, the impacts of the street environment on running were less addressed due to the lack of running data in large geospatial urban regions, while the potential of semi-open data sources like Strava Heatmap for running studies is rarely verified. Moreover, how objective features and the subjective perceptions of the street environment are related to running is still largely unknown. We hypothesize that the eye-level subjective and objective streetscapes may complement the macro-scale built environment factors to better inform running amount prediction. Therefore, we evaluated the associations between running and street attributes by applying multi-sourced data, street view imagery (SVI) and artificial intelligence (AI) technologies, taking Boston as an example. We found that, first, the street environment is significantly correlated with running. Accounting for the spatial effects, the collective strength of street attributes was almost the same as the counterpart of the built environment, validating the value of including subjective and objective streetscapes measures in running studies. Second, street factors can complement built environment factors, indicating the necessity of using both macro-scale and eye-level environmental features to interpret running. Third, in addition to higher accessibility and more public transportation, the safer, wider and relatively open streets with more natural views, street lights, amenities and furniture, could promote running, while the enclosed environment, dense and overwhelming buildings, excessive interruptions on streets might hinder running. Our study provides an important example of using semi-open running data and integrating multi-sourced data and AI to bring new insights into running and urban environment studies. The findings could provide instructive suggestions for the establishment of a running-friendly urban environment and ultimately help to improve public health.

Automated summary

This study evaluated the associations between running and street attributes in Boston using multi-sourced data, street view imagery, and artificial intelligence technologies. The researchers found that the street environment is significantly correlated with running and can complement the built environment factors. Factors such as safety, width, natural views, street lights, amenities, and furniture can promote running, while the enclosed environment, dense buildings, and excessive interruptions on streets might hinder running.