Dataset for visitations of public green spaces in Shanghai, China
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces GreenMove, a dynamic mobility network using mobile phone data to map urban park usage. It reveals how residents connect with green spaces, aiding urban planning and sustainable city design.
Area Of Science
- Urban Planning
- Environmental Science
- Mobility Network Analysis
Background
- Research on urban green spaces is increasingly data-driven.
- Understanding resident interactions with parks is crucial for urban planning.
Purpose Of The Study
- To construct a dynamic mobility network (GreenMove) of urban park usage in Shanghai.
- To model connections between residential areas and parks, quantifying park demand and attractiveness.
- To provide insights for equity-focused urban park planning and sustainable city design.
Main Methods
- Leveraged mobile phone data from 10 million anonymized users in Shanghai.
- Constructed a population-level daily dynamic mobility network (GreenMove) based on park visitations.
- Weighted network edges with flow, commuter ratio, and distance; incorporated socioeconomic and weather data.
Main Results
- The GreenMove network visualizes daily dynamic connections between residential polygons and parks.
- Quantified park demand and attractiveness by modeling polygon-park connections.
- Demonstrated patterns of resident green space enjoyment and park accessibility.
Conclusions
- GreenMove offers multi-dimensional insights into urban park research and equity-focused planning.
- Provides a temporal benchmark for understanding city evolution and human access to parks.
- Facilitates advancements in sustainable city design and urban environmental studies.
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