GreenMove: Constructing a dataset for public green spaces in Shanghai

The Shanghai Key Laboratory of Urban Design and Urban Science (LOUD) has made progress in the research of urban green spaces through a collaborative effort with the MoE Key Laboratory of Artificial Intelligence at the AI Institute, Shanghai Jiao Tong University (SJTU AI Lab). Leveraging mobile phone data from 10 million anonymized users in Shanghai, LOUD and SJTU AI Lab  identify park visitations over four months and construct a real population-level daily dynamic mobility network (GreenMove) that reveals how different residential polygons and parks are connected. The research, titled Dataset for Visitations of Public Green Spaces in Shanghai, China, has been published in Scientific Data, a Nature Portfolio journal.

Fig. A schematic overview of the study.

Urban green space research continues to thrive, increasingly adopting data-driven approaches. Utilizing anonymized mobile phone data from 10 million users in Shanghai, we track park visitations over a four-month period and develop GreenMove - a real, population-level, daily dynamic mobility network that maps the connections between residential polygons and parks. These connections are enriched with meaningful metrics such as visitation flow, commuter ratios, and spatial distance between nodes. By modeling the interactions between residential areas and parks, GreenMove helps bridge the "demand-supply" gap, offering quantifiable insights into park demand and attractiveness. In addition to accounting for the geographical features and competitive dynamics among parks, the network integrates a wide range of consistent socioeconomic indicators and fine-grained weather data. GreenMove acts as a dynamic framework that captures residents’ use of green spaces and supports deeper, multidimensional analyses for advancing urban park research and promoting equity in planning. It also provides a valuable temporal baseline for tracking how cities evolve over time and for understanding the shifting patterns of park accessibility - laying the groundwork for future innovations in sustainable urban design.

The authors are Yuting Feng, Shaoyu Huang, Xiaokang Yang, and Yanyan Xu from the SJTU AI Lab and Shengze Chen, ChengHe Guan, Ying Li, and Qiaoyu Tan from LOUD, NYU Shanghai. The research is supported and funded by the Shanghai Big Data Bureau, the Shanghai Nature and Health Foundation, the Shanghai Municipal Education Commission (Key program of AI-Driven Initiative to Promote Research Paradigm Reform and Empower Disciplinary Advancement), and Envision Energy.

Citation:

Feng, Y., Huang, S., Chen, S., Guan, C., Li, Y., Tan, Q., Jin, Y., Yang, X., & Xu, Y. (2025). Dataset for visitations of public green spaces in Shanghai, China. Scientific Data, 12(1260). https://doi.org/10.1038/s41597-025-05581