Utilizing Multi-Source Geospatial Big Data to Examine How Environmental Factors Attract Outdoor Jogging Activities

Abstract

In the post-pandemic era, outdoor jogging has become an increasingly popular form of exercise due to the growing emphasis on health. It is essential to comprehensively analyze the factors influencing the spatial distribution of outdoor jogging activities and to propose planning strategies with practical guidance. Using multi-source geospatial big data and multiple models, this study constructs a comprehensive analytical framework to examine the association between environmental variables and the frequency of outdoor jogging activities in Guangzhou. Firstly, outdoor jogging trajectory data were collected from a fitness app, and potential influencing factors were selected based on multi-source big data from the perspectives of the built environment, street perception, and natural environment. For example, using the street-view imagery, objective environmental elements such as greenery and subjective elements such as safety perception were extracted from a human-centric perspective. Secondly, the framework included three models: a backward stepwise regression, an optimal parameters-based geographical detector, and a geographically weighted regression (GWR) model. These models served, to screen significant variables, identify the synergistic effects among the variables, and quantify the spatial heterogeneity of the effects, respectively. Finally, the study area was clustered based on the results of the GWR model to propose urban planning strategies with clear spatial positions and practical significance. The results indicated the following: (1) Factors related to the built environment and street perception significantly influence jogging frequency distribution. (2) Public sports facilities, the level of greenery, and safety perception were identified as key factors influencing jogging activities, representing the three aspects of service facilities, objective perception, and subjective perception, respectively. (3) Specifically, the influence of each factor on jogging activities displayed significant spatial variation. For instance, sports facilities and greenery level were positively correlated with jogging frequency in the city center. (4) Lastly, the study area was divided into four clusters, each representing different local associative characteristics between variables and jogging activities. The zonal planning recommendations have significant implications for urban planners and policymakers aiming to create jogging-friendly environments.