基于交通网络模型的长沙市医疗急救空间结构特征分析及其优化策略

Analysis and Optimization Strategy of Spatial Structure Characteristics of Medical Emergency in Changsha Based on Transportation Network Model

摘要: 本文以2024年长沙市中心城区的交通路网为基础，构建交通网络分析模型，通过对医疗急救站点的空间特征和服务分析，揭示城市医疗急救空间结构的特征。首先，论文利用基尼系数、差异系数等区域统计指标，分析了医疗急救站点POI数据与人口数据的空间分布特征，揭示了其高度集聚、区域发展不均衡的格局。其次，通过构建交通网络模型，采用两步移动搜索法和潜能模型评估了医疗急救服务的空间可达性，识别出“核心–边缘”的空间分异格局和资源分布的不公平性。最后，运用最大化覆盖范围模型和最小化设施点数模型等位置分配方法，对现有急救站点的布局进行优化，并得出结论：为满足长沙市的医疗急救需求，至少需要37个急救站点，即在现有基础上需新增3个站点。本文对于特大型城市的医疗急救站点选址具有积极作用，能识别出医疗急救站点选址的适宜区和医疗急救的空间格局特征，并基于最大化覆盖范围模型和最小化设施点数模型优化医疗急救站点的服务能力，满足研究区域的设施要求。

Abstract: Based on the traffic network of the central urban area of Changsha in 2024, this paper constructs a transportation network analysis model. By analyzing the spatial characteristics and service capacity of emergency medical sites, it explores the spatial structural features of urban emergency medical resources. Firstly, regional statistical indicators, including the Gini coefficient and coefficient of variation, are adopted to analyze the spatial distribution of POI data of emergency medical sites and population data, revealing a highly agglomerated and spatially unbalanced layout. Secondly, with the established transportation network model, the two-step floating catchment area (2SFCA) method and potential model are applied to evaluate the spatial accessibility of emergency medical services, identifying a “core-periphery” spatial differentiation pattern and inequitable resource allocation. Finally, location-allocation models, including the maximum coverage model and minimum facilities model, are used to optimize the existing layout of emergency stations. The results indicate that a minimum of 37 emergency stations is required to satisfy the emergency medical demands of Changsha, meaning three additional stations need to be built on the current basis. This research provides practical references for emergency medical site planning in megacities: it identifies suitable construction zones and spatial distribution patterns of emergency medical facilities, and optimizes service capacity via maximum coverage and minimum facilities models to match the facility demands of the study area.