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Gender differences in urban recreational running: a data-driven approach

Grant McKenzie, Daniel Romm, Clara Féré and Maria Laura Guerrero Balarezo

Article (2025)

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Abstract

Exploring the dynamics of urban recreational running, this study examines the spatial and temporal patterns of running activities among men and women in two major North American cities, Montréal, Canada and Washington, DC, USA. A total of 20,446 running trajectories from a geosocial fitness tracking application were analyzed, revealing significant gender differences. These gender preferences differ in terms of location and time, highlighting significant variations between the two cities and shifts between day and night running habits. We further investigate the influence of socio-economic, demographic, and built environment factors on these different spatiotemporal patterns. Regression models show that proximity to bike lanes and parks strongly influenced running locations in both cities, with a preference for lower population density and lower median household income areas. Insights from this work are important for urban planners and public health officials, providing a data-driven foundation for developing more inclusive and safe public spaces for recreational activities. The study not only contributes to our understanding of urban recreational behaviors but also addresses broader societal concerns about gender and public space utilization.

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Department: Department of Civil, Geological and Mining Engineering
Research Center: CIRRELT - Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation
PolyPublie URL: https://publications.polymtl.ca/63414/
Journal Title: Journal of Transport Geography (vol. 124)
Publisher: Elsevier
DOI: 10.1016/j.jtrangeo.2025.104171
Official URL: https://doi.org/10.1016/j.jtrangeo.2025.104171
Date Deposited: 20 Mar 2025 11:17
Last Modified: 11 Jan 2026 10:29
Cite in APA 7: McKenzie, G., Romm, D., Féré, C., & Guerrero Balarezo, M. L. (2025). Gender differences in urban recreational running: a data-driven approach. Journal of Transport Geography, 124, 104171 (11 pages). https://doi.org/10.1016/j.jtrangeo.2025.104171

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