Work

Spatiotemporal Heterogeneity of Influencing Factors for Urban Spaces Suitable for Running Workouts Based on Multi-Source Big Data

Year: 2025

Type: article

Abstract: With the growing emphasis on running in urban health initiatives, understanding the spatiotemporal dynamics of running behavior has become essential for smart city development. This study harnesses multi-source big data—including running trajectories, points of interest (POIs), and remote sensing data—to systematically analyze factors influencing running space selection. Through stepwise regression analysis, we identify 16 significant variables encompassing accessibility, diversity, and comfort dimensions. The Geographical and Temporally Weighted Regression (GTWR) model is then employed to uncover distinct spatiotemporal heterogeneity patterns, demonstrating how these factors variably influence running activities across different urban zones and time periods. The methodology and findings contribute to geospatial analysis in urban health studies while providing practical guidance for creating more inclusive, runner-friendly urban environments.

Source: ISPRS International Journal of Geo-Information

DOI: 10.3390/ijgi14090366

Authors: Xinyu Di, Jun Zhang

Institution: Northeast Forestry University

Language: English

Topic: Urban Transport and Accessibility

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,292