Revealing Spatial Patterns and Environmental Influences on Jogging Volume and Speed: Insights from Crowd-Sourced GPS Trajectory Data and Random Forest

Xiao Zhen Yang · Chengbo Zhang · Yang Li

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Urban Transport and Accessibility Urban Green Space and Health Human Mobility and Location-Based Analysis

Abstract

Outdoor jogging plays a critical role in active mobility and transport-related physical activity (TPA), contributing to both urban health and sustainability. While existing studies have primarily focused on jogging participation volumes through survey data, they often overlook the real-time dynamics that shape jogging experiences. This study seeks to provide a data-driven analysis of both jogging volume and speed, exploring how environmental factors influence these behaviors. Utilizing a dataset of over 1000 crowd-sourced jogging trajectories in Shenzhen, we spatially linked these trajectories to road-section-level units to map the distribution of jogging volume and average speed. By depicting a bivariate map of both behavioral characteristics, we identified spatial patterns in jogging behavior, elucidating variations in the distribution of volume and speed. A random forest regression model was validated and employed to capture nonlinear relationships and assess the differential impacts of various environmental factors on jogging volume and speed. The results reveal distinct jogging patterns across the city, where jogging volume is shaped by the mixed interplay of natural, visual, and built environment factors, while jogging speed is primarily influenced by visual factors. Additionally, the analysis highlights nonlinear effects, particularly identifying a threshold beyond which incremental environmental improvements provide diminishing returns in jogging speed. These findings clarify the distinct roles of environmental factors in influencing jogging volume and speed, offering insights into the dynamics of active mobility. Ultimately, this study provides data-informed implications for urban planners seeking to create environments that support TPA and promote active lifestyles.

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References

2016Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study 2021To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults 2020Examining non-linear built environment effects on elderly’s walking: A random forest approach 2021Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach 2023Assessing impacts of objective features and subjective perceptions of street environment on running amount: A case study of Boston 2023Environmental factors for outdoor jogging in Beijing: Insights from using explainable spatial machine learning and massive trajectory data 2022Cluster analysis of microscopic spatio-temporal patterns of tourists’ movement behaviors in mountainous scenic areas using open GPS-trajectory data 2023Bias and precision of crowdsourced recreational activity data from Strava

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