Dataset for visitations of public green spaces in Shanghai, China.

GreeMove is a dataset for visitations of public green spaces in Shanghai, China, constructed as a bipartite dynamic mobility network between residential polygons and parks. GreenMove utilizes mobile phone data from a telecom operator in Shanghai from January to April 2014, encompassing 10 million anonymized mobile phone users. We identify individual stays via spatial clustering from raw mobile records and categorize them as home, work, or other activities. We then recognize the other locations those are geographically overlapped to parks to identify the park visitations.
And we redefine a division approach for the Shanghai metropolitan area by using cell towers to partition it into Voronoi grids, serving as residential polygons. It can be characterized as a partition with the finest resolution, resulting in a total of 38,055 residential polygons, which are intricately linked to the mobile source data we utilized.
GreenMove comprises a daily dynamic network , as well as a more granular daily time segmented network sustained over four months. On a given day or at a given time, if visitation does exist from residential polygons to parks, an edge is established between the two nodes. The daily dynamic network is stored within the "daily_network_exp_8-4_geometry" folder, while the daily time-segmented network is contained in the "daily_segment_network_exp_8-4_geometry" folder.
Additionally, a comprehensive super-network spanning a four-month period is saved in the file "4month_network_exp_8-4_geometry.pkl". Furthermore, a CSV file is provided, serving as the input for the GBM model to predict pairwise flow.
Code examples for working with the dataset, including the implementation of the GBM model, are available in the GitHub repository.