| 摘要: |
| 利用精细化的城市数据，本研究以居住
小区为分析主体，通过多视角评估和居住单元
聚类，分析城市医疗卫生服务设施的可达性。首
先，以人为本的视角，基于现行医疗卫生服务体
系，整合了城市和社区三个层级中提供居民日常
保健服务的设施数据。其次，模拟居民视角，提
出了丰富度、便利度和基于三步搜索法（3SFCA）
的匹配度三项指标。针对南京市中心城区的实证
研究发现，各项指标的评价结果在数量分布及
空间分布具有显著差异，同时部分指标呈现出了
跨层级的相关性。通过K-means聚类方法对居住
单元进行分组，进一步揭示了医疗可达性的空间
分布模式以及与居住人口特征的关系，为提升城
市宜居性提出针对性措施。 |
| 关键词: 医疗卫生服务设施 人本视角 可达
性 城市大数据 聚类分析 |
| DOI：10.13791/j.cnki.hsfwest.20240405 |
| 分类号: |
| 基金项目:国家自然科学基金面上项目（52178008） |
|
| Assessment of multi-level urban healthcare facility accessibility from a human-centricperspective: A case study of Nanjing |
|
WANG Yujiao,LI Jinze,TANG Peng
|
| Abstract: |
| Public healthcare service is a key focus in the construction of healthy cities. The rational
allocation of urban healthcare facilities (UHCFs) is crucial for promoting urban equity and enhancing
residential satisfaction. Urban big data provides support for evaluating the accessibility of public
facilities, while numerous studies have been conducted on UHCFs, there are two main issues to be
solved. First, the healthcare service system, comprising public health services, medical services, and
pharmaceutical supply, forms a hierarchical and collaborative structure, thus the consideration of
differences and coordination across the level is required in researches on UHCFs allocation. Second,
existing studies often reflect evaluation results in larger urban units such as districts, streets, and
communities, which fail to accurately measure the accessibility of primary healthcare services and
reflect residents’ needs and living differences. Therefore, this study aims to propose a human-centric
accessibility evaluation system on multi-level UHCFs.
In advance of large-scale and high-precision new data, this study evaluates residents’
accessibility to resources from a more micro perspective with residential neighborhoods used as
the analytical unit. Based on existing types of healthcare facilities and residents’ daily healthcare
behaviors, it categorizes the facilities into three types: city hospitals, primary healthcare facilities
(including clinics, health centers, and community health service centers), and other healthcare
facilities (such as pharmacies and health service shops). It then proposes three indicators—richness,
convenience, and matching, to reflect different aspects of residents’ needs. The richness index
quantifies the diversity of resources available to residents, measured by the number of accessible
facilities around a residential area. The convenience index reflects the shortest distance from various
levels of facilities to residential points. Considering competition for high-quality resources, the
matching index assesses the alignment between resource supply and residents’ needs, using the
three-step floating catchment area (3SFCA) method. Overall, the accessibility evaluation system
comprises eight indicators at the city and community levels. This methodology was applied to
the central area of Nanjing, revealing the spatial and quantitative distribution of residential units
with varying accessibility. Subsequently, K-means clustering of residential units was employed to
understand the characteristics of multi-indicator accessibility types.
The research results indicate significant differences in the distribution and spatial patterns of various
indicators. The indicators of each dimensions are distinctly directional, revealing differences in outcomes.
For instance, in the city center where hospital distribution is dense, the high attraction of medical facilities
and population density leads to intense competition, resulting in lower matching scores. The spatial patterns
of accessibility for different types of facilities is not consistent. City hospitals in the central area of Nanjing
are well-planned spatially, showing minimal disparities in accessibility across different regions. Conversely,
the accessibility of primary healthcare facilities is significantly influenced by location. The accessibility
of other healthcare facilities, in particular, varies greatly due to market-driven factors and a lack of macrolevel
regulation. However, some indicators exhibit cross-tier correlations, with a clear relationship between
the richness of different levels of facilities. Various levels of healthcare facilities work in tandem to ensureresidents’ daily health needs. These findings validate the necessity of a multi-tiered, multi-perspective evaluation approach.
The evaluation results for the city center exhibit a gradual trend, while the peripheral areas show more drastic changes. Based on accessibility indices,
residential units were clustered into eight groups. Types 1 to 5 have relatively balanced indices, whereas Types 6 to 8 display significant deviations from the
average. These clusters exhibit distinct spatial distribution characteristics within the city. Representative residential units from each cluster were selected
to analyze the relationship between accessibility landscapes and residential population characteristics. Subsequently, targeted measures were proposed to
enhance livability for each cluster. This demonstrates the advantage of using residential units as the basis for reflecting the residents' perspective and for
conducting refined evaluations.
Overall, the evaluation method proposed in this study provides an effective means for fine-grained understanding of resource distribution, helping to
reveal disparities in public health resource accessibility among different residential groups and offering targeted improvement suggestions. This method
can be applied in future research to establish a comprehensive information platform, addressing issues such as the updating of urban medical resources,
urban area expansion, and population growth, ensuring timely review and feedback. It provides scientific basis and decision support for urban public health
resource planning and encourages residents to understand and participate in community development. |
| Key words: healthcare service facilities resident’s perspective 3SFCA urban big data clustering analysis |
