| 摘要: |
| 韧性城市建设是在高风险阶段响应
不确定压力、保障城市安全的重要对策。韧
性城市规划的目的是压力扰动下保障城市功
能的空间实现，应急避难空间是应对压力扰
动的安全底线。以郑州市为实证，判析其避
难空间网络簇群的网络结构特征；通过随机
攻击和蓄意攻击模拟网络簇群韧性的动态变
化；总结出中心性结构、分布式结构、简单
结构等三类典型网络簇群的空间结构模式及
其韧性响应机制和潜在风险，并提出针对性
韧性提升策略。结果表明：第一，塑造有效
的网络结构是提升避难空间系统韧性的关
键；第二，城市韧性具有尺度特征以及“网
络簇群”是避难空间系统韧性的基本组织单
元；第三，建成环境的差异致使避难空间系
统形成分异的簇群并表现出差异化的韧性特
征。研究为增强城市避难空间系统韧性提供
切实有效的规划对策。 |
| 关键词: 应急避难空间 韧性单元 网络簇
群 网络韧性 规划提升 |
| DOI：10.13791/j.cnki.hsfwest.20240910001 |
| 分类号: |
| 基金项目:国家社会科学基金面上项目（22BRK020） |
|
| Research on network resilience and planning improvement of urban emergency shelterspace: A case study of Zhengzhou |
|
WANG Jiwu,MAO Danyi,SHEN Yuyan,WANG Chenhao
|
| Abstract: |
| Against the background of increasingly frequent and unpredictable disaster risks faced by
contemporary metropolises, resilience planning has emerged as a critical strategy for safeguarding
urban security and sustainable development. As essential infrastructure constituting the “safety
bottom line” of a city, urban emergency shelter spaces—specifically their spatial organization modes
and network synergy efficacy—directly determine a city’s capacity for absorption, buffering,
adaptation, and recovery under high-intensity perturbation scenarios. In this context, taking the main
urban area of Zhengzhou City as the empirical research region, this study conducts a systematic
investigation revolving around the structural characteristics, resilience measurement, and optimization
strategies of the urban emergency shelter space network. The research aims to achieve three primary
objectives: first, to identify the spatial organizational units and structural attributes of the emergency
shelter space network; second, to construct a dual-scenario resilience measurement system
encompassing both static and dynamic dimensions and conduct empirical assessments; and third, to
propose resilience enhancement strategies based on a network perspective, thereby realizing the
operationalization of planning interventions. Methodologically, the study adopts a complex network
perspective to construct a weighted directed network composed of emergency shelter points. It
designates three categories of shelter spaces defined by the planning system as network nodes, and
establishes effective collaborative associations based on the maximum spatial-temporal linkage radius
as network edges. Furthermore, a gravity model integrating shelter capacity and nodal distance is
employed to construct edge weights, accurately reflecting the intensity of synergy between nodes. The
study utilizes the Gephi modularity method to identify network clustering units (hereinafter referred to
as “network clusters”). Structural characteristics are characterized at both the node and cluster levels
using quantitative indicators such as weighted centrality and weighted clustering coefficients.
Regarding resilience measurement, the study distinguishes between “static resilience” and “dynamic
resilience”. The former primarily reflects the initial resilience level under existing facility scale and
layout conditions, while the latter evaluates the system’s response and retention capabilities amidst
node destruction based on the evolutionary trajectory of network efficiency under perturbation
scenarios. Perturbation simulations are conducted using MATLAB to construct two distinct attack
scenarios: “deliberate attacks”, which prioritize node removal based on centrality or clustering
importance (simulating terrorist attacks or targeted destruction), and “random attacks”, which
disregard node importance (simulating natural disasters or sudden failures). The dynamic resilience is
characterized by the trend of network efficiency variation and the area under the curve following node
failures. The empirical results identify three typical network cluster structural types “centralized
structure”, “distributed structure”, and “simple structure”, which exhibit significant heterogeneity in
spatial distribution, nodal composition, network tightness, and resilience performance. Specifically,centralized structure clusters are predominantly located in older urban districts, characterized by high node density. They rely on a few high-grade, large-scale
shelters serving as hubs connecting numerous small and medium-sized nodes, exhibiting high clustering effects and short average association distances.
However, they are highly dependent on key hub nodes and show the most distinct decline in resilience under deliberate attack scenarios. Distributed structure
clusters are mainly found in new urban districts, featuring balanced node scales and strong synergistic capabilities. They possess comprehensive resilience
advantages under deliberate attack scenarios, though their larger overall spatial scale necessitates high-quality linkage channels to guarantee synergy. Simple
structure clusters are mostly situated in urban fringe areas, characterized by fewer nodes and incompletely formed structures; their resilience performance is
constrained by connectivity and supply capacity, showing significant variability. Based on network modularity analysis, a total of 18 clusters were identified
with an overall modularity coefficient Q=0.757, indicating good structural independence of the partition. Simulation results demonstrate that network efficiency
declines significantly faster under deliberate attacks compared to random attacks. Furthermore, static resilience is found to be primarily correlated with capacity
scale and density, whereas dynamic resilience is more influenced by network structural morphology, with a weak correlation observed between the two. |
| Key words: emergency shelter resilience unit network cluster network resilience planning promotion |
