Social Network Analysis Reveals Spatiotemporal Patterns of Green Space Recreational Walking Between Workdays and Rest Days
Growing concerns about the negative impacts of high-density built environments on residents’ physical and mental health have made optimizing recreational walking networks in green spaces a crucial issue for improving urban public health service efficiency. While previous studies have largely focused...
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MDPI AG
2025-04-01
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| Series: | Urban Science |
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| Online Access: | https://www.mdpi.com/2413-8851/9/4/111 |
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| author | Jiali Zhang Zhaocheng Bai |
| author_facet | Jiali Zhang Zhaocheng Bai |
| author_sort | Jiali Zhang |
| collection | DOAJ |
| description | Growing concerns about the negative impacts of high-density built environments on residents’ physical and mental health have made optimizing recreational walking networks in green spaces a crucial issue for improving urban public health service efficiency. While previous studies have largely focused on static accessibility measures, these methods cannot capture actual human recreational behaviors and temporal variations in green space usage. Our research introduces a novel social network analysis methodology using GPS trajectory data from Shanghai’s Inner Ring Area to construct and compare recreational walking networks during workdays and rest days, revealing dynamic spatiotemporal patterns that traditional methods miss. Key findings include: (1) At the node level, green spaces of different sizes play differentiated roles in the network, with large-scale spaces serving as destination hubs while pocket green spaces function as critical connecting points; (2) At the regional level, workday networks show more dispersed spatial distribution patterns with higher modularity, while rest day networks form high-density clusters in the central urban area; (3) At the overall network level, rest day networks demonstrate higher density and diversity, reflecting residents’ expanded spatial activity range and diverse recreational preferences. Green space management should focus on the social value of urban green networks. These findings provide theoretical and methodological support for transitioning from “static equity” to “dynamic justice” in green space system planning, contributing to the development of more inclusive and resilient urban green space networks. |
| format | Article |
| id | doaj-art-8b6f051ecdd6431bb717689ae706b271 |
| institution | DOAJ |
| issn | 2413-8851 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Urban Science |
| spelling | doaj-art-8b6f051ecdd6431bb717689ae706b2712025-08-20T03:13:54ZengMDPI AGUrban Science2413-88512025-04-019411110.3390/urbansci9040111Social Network Analysis Reveals Spatiotemporal Patterns of Green Space Recreational Walking Between Workdays and Rest DaysJiali Zhang0Zhaocheng Bai1School of Artificial Intelligence, Dongguan City University, Dongguan 523419, ChinaCollege of Architecture and Urban Planning, Tongji University, Shanghai 200092, ChinaGrowing concerns about the negative impacts of high-density built environments on residents’ physical and mental health have made optimizing recreational walking networks in green spaces a crucial issue for improving urban public health service efficiency. While previous studies have largely focused on static accessibility measures, these methods cannot capture actual human recreational behaviors and temporal variations in green space usage. Our research introduces a novel social network analysis methodology using GPS trajectory data from Shanghai’s Inner Ring Area to construct and compare recreational walking networks during workdays and rest days, revealing dynamic spatiotemporal patterns that traditional methods miss. Key findings include: (1) At the node level, green spaces of different sizes play differentiated roles in the network, with large-scale spaces serving as destination hubs while pocket green spaces function as critical connecting points; (2) At the regional level, workday networks show more dispersed spatial distribution patterns with higher modularity, while rest day networks form high-density clusters in the central urban area; (3) At the overall network level, rest day networks demonstrate higher density and diversity, reflecting residents’ expanded spatial activity range and diverse recreational preferences. Green space management should focus on the social value of urban green networks. These findings provide theoretical and methodological support for transitioning from “static equity” to “dynamic justice” in green space system planning, contributing to the development of more inclusive and resilient urban green space networks.https://www.mdpi.com/2413-8851/9/4/111social network analysisgreen spacetrajectory datarecreational walking |
| spellingShingle | Jiali Zhang Zhaocheng Bai Social Network Analysis Reveals Spatiotemporal Patterns of Green Space Recreational Walking Between Workdays and Rest Days Urban Science social network analysis green space trajectory data recreational walking |
| title | Social Network Analysis Reveals Spatiotemporal Patterns of Green Space Recreational Walking Between Workdays and Rest Days |
| title_full | Social Network Analysis Reveals Spatiotemporal Patterns of Green Space Recreational Walking Between Workdays and Rest Days |
| title_fullStr | Social Network Analysis Reveals Spatiotemporal Patterns of Green Space Recreational Walking Between Workdays and Rest Days |
| title_full_unstemmed | Social Network Analysis Reveals Spatiotemporal Patterns of Green Space Recreational Walking Between Workdays and Rest Days |
| title_short | Social Network Analysis Reveals Spatiotemporal Patterns of Green Space Recreational Walking Between Workdays and Rest Days |
| title_sort | social network analysis reveals spatiotemporal patterns of green space recreational walking between workdays and rest days |
| topic | social network analysis green space trajectory data recreational walking |
| url | https://www.mdpi.com/2413-8851/9/4/111 |
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