ArticleScientific data2025
Dataset for visitations of public green spaces in Shanghai, China.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Dataset for visitations of public green spaces in Shanghai, China.Scientific data · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
Research on urban green spaces remains active, with a shift towards data-driven methodologies. Leveraging mobile phone data from 10 million anonymized users in Shanghai, we 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 edges are weighted with informative metrics, including flow, commuter ratio, and distance between nodes. It bridges the "demand-supply" gap by modeling polygon-park connections and quantifies park demand and attractiveness. Beyond considering the geographic characteristics of parks and their competitive interactions, we associate comprehensive and consistent socioeconomic indicators with the network, as well as granular weather data. GreenMove serves as a dynamic topology that demonstrates the pattern of residents' enjoyment of green spaces and offers multi-dimensional insights to urban park research advancement and equity-focused planning. It particularly offers a critical temporal benchmark for understanding the longitudinal evolution of cities and vertical dynamics in how human access to parks, as well as facilitating broader fields of future sustainable city design.
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