| 摘要: |
| 绿地是城市居民健身活动的重要载体，但传统的绿地网络分析方法难以反映居民的实际使用模式。以上海市内环区域为例，利用健身轨迹数据构建城市
绿地网络，并运用社会网络分析方法，从节点、子群和整体3个层次分析了绿地健身网络的结构特征。研究显示：1)绿地节点呈现出“核心-边缘”结构，节点中
心度受绿地内部道路长度、周围写字楼面积等内外部环境因素影响；2)健身网络存在显著的区域隔离，利用结构洞识别到了工业用地阻隔、滨水空间私有化、里
弄绿化缺失、交通干线阻隔4类典型的绿地隔离情景；3)健身网络整体连接稀疏且同质性较高，稳定性较低。本研究基于“真实连通性”视角，为构建支持人群活
动的城市绿地网络提供数据驱动的规划支持 |
| 关键词: 风景园林 社会网络分析 绿地网络 健康城市 健身轨迹数据 数据驱动 |
| DOI：10.19775/j.cla.2025.04.0023 |
| 投稿时间：2024-10-01修订日期：2025-01-09 |
| 基金项目:国家自然科学基金面上项目(52178050) |
|
| Social Network Analysis of Urban Green Space Fitness Network Characteristics: A Case Study ofShanghai's Inner Ring Area |
| LIU Song,,BAI Zhaocheng,,LIU Dizi,,SHEN Peiyu |
| Abstract: |
| Urban green spaces serve as essential venues for residents' fitness
activities, contributing significantly to urban public health and well-being. In
response to the national strategy of "Healthy China 2030", cities have begun
to emphasize the integration of fitness activities with green space systems.
For instance, Shanghai proposes the concept of "integrating sports and green
spaces" and aims to construct "an interconnected, accessible urban green
leisure network that links green spaces and penetrates communities". However,
traditional green space network analyses, primarily based on accessibility metrics
and ecological connectivity, often fail to reflect actual usage patterns and network
characteristics. While urban green space network construction has shifted from
focusing on ecological functions to supporting human activities, research on actual
connectivity based on residents' dynamic behavior patterns remains insufficient.
This gap is particularly evident in high-density urban areas where the efficient
integration of green spaces with residents' fitness activities becomes crucial for
promoting public health. Taking Shanghai's Inner Ring Area as a case study,
this research employs social network analysis (SNA) to examine the structural
characteristics of urban green space fitness networks from three hierarchical
levels: node importance, regional differences, and overall stability. The study
analyzed 152 green spaces, categorized into large parks, community parks, and
pocket parks. Using fitness trajectory data from the "2bulu" platform, we collected
4,561 valid walking and running trajectories between March and April 2024 to
construct the network. Node centrality indices and environmental factors were
analyzed using geographical detector analysis, while community detection and
structural hole analysis were applied to identify regional patterns and connectivity
barriers. The research specifically focused on understanding how different scales
of green spaces function within the fitness network and how environmental
factors influence their usage patterns. The findings reveal 3 key characteristics of
the urban green space fitness network. First, the node analysis shows a distinct
"core-periphery" structure, with node centrality significantly influenced by both
internal factors (pathway length, green coverage) and external factors (surrounding
office building area, commercial facilities). The geographical detector analysis
identifies internal road length as the most critical factor affecting node importance,
highlighting the significance of pathway design in promoting fitness activities.
Second, the network demonstrates notable regional isolation, with structural hole
analysis identifying four typical disconnection scenarios: industrial land barriers,
privatized waterfront spaces, absence of traditional neighborhood greenery,
and transportation corridor blockages. Three distinct fitness patterns emerged:
community living circle mode, linear greenway mode, and intensive connection
mode, each serving different user groups and activity purposes. Third, the overall
network exhibits sparse connectivity and moderate homogeneity, suggesting
potential stability issues. Simulation results indicate that strategic placement
of new green spaces could significantly improve network stability and reduce
regional isolation. Four specific modes for new green space development were
identified: infrastructure greening, brownfield regeneration, affiliated green space
construction, and waterfront greenway integration. Based on these findings, we
propose three optimization strategies for constructing human-oriented urban
green space networks: 1) implementing fitness-oriented network enhancement
based on centrality rather than traditional size-based hierarchies; 2) improving
critical connection points, particularly in waterfront areas; and 3) developing a
diverse, multi-level green space network through strategic node deployment.
This research advances the theoretical transition from ecological networks to
"human-centered networks" in urban green space planning, moving beyond
conventional accessibility analyses to examine how green spaces are actively
used and connected through human activities. The methodology developed in this
study provides a reproducible framework for analyzing green space networks in
other urban contexts. Furthermore, it offers valuable data-driven planning insights
for healthy city development and suggests new directions for integrating human
behavioral data into urban green space planning. |
| Key words: Social Network Analysis of Urban Green Space Fitness Network Characteristics: A Case Study of
Shanghai's Inner Ring Area |
