Li Jiulin, Lin Wenhui, Chu Jinlong
School of Architecture and Planning, Anhui Jianzhu University, Hefei, China.
Anhui Collaborative Innovation Center for Urbanization Construction, Hefei, China.
PLoS One. 2025 Jun 18;20(6):e0325908. doi: 10.1371/journal.pone.0325908. eCollection 2025.
The spatial patterns of population mobility serve as a critical indicator for urban network characterization, providing an essential foundation for resilience assessment. Based on the complex network theory, this study constructs urban networks using the Baidu Migration Big Data of the Jiangsu-Zhejiang-Shanghai region in 2023 to analyze the spatiotemporal dynamics of population flow. Through a research framework of "structure measurement-scenario simulation-resilience assessment," the study systematically reveals the response mechanisms of urban networks. The static characteristics of network resilience in the normal scenario and the dynamic characteristics in the disruption scenario were analyzed. The results are as follows: (1) Population flow is dense in the central region and sparse in the north and south. Network clusters exhibit dual characteristics of "administrative boundary constraints" and "economic gravity dominance", forming more easily among developed cities across provinces or adjacent cities within the same province. The overall connection intensity of the network during holidays is markedly higher than that in the daily period. However, daily contact between developed cities is more frequent than that during holidays, indicating strong intercity commuting and routine movement. (2) In the normal scenario, core cities possess prominent centrality, while the hierarchy of the network is less pronounced. The agglomeration among nodes is moderate but features evident asymmetric connections. The transmission efficiency is relatively high. (3) In the disruption scenario, both the network transmission efficiency and the path connectivity experience phased changes, and the impact of deliberate disturbances on resilience is more significant than that of random disturbances. A handful of cities with crucial influence constitute the core network. This research aims to reveal the resilience characteristics and response mechanisms of population flow networks, offering insights into regional spatial coordination and sustainable development.
人口流动的空间格局是城市网络特征的关键指标,为韧性评估提供了重要基础。基于复杂网络理论,本研究利用2023年江浙沪地区的百度迁徙大数据构建城市网络,分析人口流动的时空动态。通过“结构测度—情景模拟—韧性评估”的研究框架,系统揭示城市网络的响应机制。分析了正常情景下网络韧性的静态特征和扰动情景下的动态特征。结果如下:(1)人口流动中部密集、南北稀疏。网络聚类呈现“行政边界约束”和“经济引力主导”双重特征,更易在跨省发达城市或省内相邻城市间形成。节假日期间网络的整体连接强度明显高于日常时段。然而,发达城市间的日常联系比节假日更频繁,表明城际通勤和日常流动较强。(2)在正常情景下,核心城市具有突出的中心性,而网络层级不太明显。节点间的集聚程度适中,但存在明显的非对称连接。传输效率相对较高。(3)在扰动情景下,网络传输效率和路径连通性均发生阶段性变化,蓄意干扰对韧性的影响比随机干扰更显著。少数具有关键影响力的城市构成核心网络。本研究旨在揭示人口流动网络的韧性特征和响应机制,为区域空间协调和可持续发展提供见解。