The spatiotemporal influence of socioeconomic factors on the number of OpenStreetMap contributors in China

Abstract The number of OpenStreetMap (OSM) contributors is closely related to both the quality of OSM data and various socioeconomic factors. This study applies the Geographically and Temporally Weighted Regression (GTWR) model to explore the spatiotemporal impacts of urban socioeconomic variables on OSM contributor numbers. Using empirical data from mainland China, covering OSM contributor statistics and national socioeconomic indicators from 2013 to 2021, we demonstrate the effectiveness of the GTWR model. In comparison with traditional methods like Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR), the GTWR model shows significantly improved performance. Key influencing factors include Urban Construction Land Area, Gross Regional Domestic Product (GRDP), the Number of Employees, Employees in Education, and the Number of Internet Users. Furthermore, we select three representative cities with different urban characteristics to examine the temporal variation in these factors. This understanding will fundamentally contribute to ensuring the long-term development of OSM.