| 摘要: |
| 院前急救系统是城市公共医疗系统
的重要组成部分，急救效率直接决定了患者
的生存结果。为精确评估城市急救服务效率
与公平，以“北上广深”四个一线城市为
例，构建人居空间矩阵结合开放地图应用编
程接口的急救时空可达性研究模型，并通过
泰尔指数及线性回归模型分析城市急救服务
的“空间公平性”以及“群体公平性”。结果
显示，在交通通畅期，四个城市人均急救反
应时间均不超过12.5 min（12.24~12.49 min）；
但交通高峰期急救可达性显著降低。同时，
四个城市均存在显著的城区差异，且低收入
群体急救面临更严重的急救延迟。研究证明，
中国一线城市院前急救系统在公平建设中仍
存在很大不足，研究结果对城市院前急救系
统的全面评价与持续性建设具有现实意义。 |
| 关键词: 院前急救服务 多源大数据 可达
性 公平性 线性回归 |
| DOI：10.13791/j.cnki.hsfwest.20240924002 |
| 分类号: |
| 基金项目:国家重点研发计划（2022YFC3006201） |
|
| A Study on the accessibility and equity of pre-hospital emergency medical services in firsttiercities of China based on multi-source big data |
|
ZHU Haolin,XU Mo,ZHU Luying,CUI Tong
|
| Abstract: |
| Pre-hospital Emergency Medical Services (EMS) represent the most time-critical
component of urban public healthcare systems, as response speed and spatial coverage directly
determine survival outcomes in acute and life-threatening conditions. With China experiencing rapid
urbanization and unprecedented population mobility, cities are facing increasing pressure to maintain
timely and effective emergency response. Using four first-tier cities as case studies, this research aims
to construct a high-resolution, multi – source, data-driven evaluation framework to systematically
quantify EMS accessibility and equity across urban regions. This study develops an advanced EMS
accessibility model that integrates a 100-meter-resolution human settlement matrix, dynamic traffic
conditions, and more than four million navigation-based origin – destination (OD) samples. Using
these datasets across four representative traffic periods, the model produces high-precision
spatiotemporal estimates of emergency accessibility for the four cities. In parallel, spatial equity and
group equity are assessed using Theil index decomposition and income-linked linear regression
models. Within this analytical framework, two core performance indicators are established: the
population-weighted average EMS response time and the 12-minute isochrone population coverage
rate. Collectively, these metrics allow for fine-scale comparisons across cities, districts, and urbanrural
gradients, offering an integrated picture of service efficiency. To evaluate fairness, the study
applies a Theil index decomposition approach to quantify spatial equity across administrative
boundaries and uses linear regression—linked with spatialized income data derived from over 59 000
rental records—to measure group equity among socioeconomic strata. In addition, the integration of
multi-source demographic, socioeconomic, and network-based mobility data enables cross-validation
of urban EMS performance, ensuring that the model captures not only static spatial configurations but
also dynamic behavioral patterns of urban residents. This significantly strengthens the robustness and
transferability of the analytical framework.The results reveal clear spatiotemporal differences in EMS
accessibility across the four megacities. During off-peak periods(00: 00), the cities show tightly
clustered performance, with average response times ranging from 12.24 to 12.49 minutes and 12-
minute isochrone coverage rates between 49.18% and 55.92%. However, congestion during peak
periods dramatically reduces accessibility: response times rise to 13.49~15.64 minutes, and
population coverage drops sharply to 25.05%~41.62%. Among all cities, Shenzhen exhibits the
steepest decline, reflecting the combined impact of rapid population growth, dense road networks, and
high traffic pressure. Spatial equity results show that inequalities remain deeply embedded in EMS
service distribution. Shanghai exhibits the highest spatial inequity, with a total Theil index of 0.0949
at 00: 00, 94%~98% of which is attributed to pronounced differences between central districts and
peripheral areas. Guangzhou displays a similar pattern of inequity, albeit at slightly lower intensity.Beijing and Shenzhen show more moderate inequity, yet both continue to demonstrate substantial disparities between urban cores, peri-urban districts, and rural
fringes—particularly in response times exceeding 16 minutes in some outer-suburban zones.Urban-rural comparisons further highlight systemic disadvantage
for townships and outer districts. At midnight, Shanghai’s urban-rural response-time gap reaches 2.08 minutes, followed by Guangzhou and Shenzhen,
indicating structural rather than incidental inequality. Even in Beijing—the most balanced city—peri-urban gaps remain evident, reflecting supply-demand
imbalances, road-network limitations, and geographic constraints.Group equity analysis demonstrates a robust and statistically significant association (P<0.001)
between income levels and EMS accessibility across all four cities. In Shanghai, each 1% decline in income corresponds to a 0.054-minute increase in response
time—the steepest gradient among the four. Guangzhou (0.042 minutes) and Beijing (0.040 minutes) follow closely, while Shenzhen shows the smallest but
still meaningful gradient (0.016 minutes). These results suggest that—even in highly developed megacities—lower-income populations systematically face
longer waiting times for emergency care, highlighting persistent socioeconomic vulnerability in the EMS system.Taken together, the findings reveal that while
the EMS systems of China’s first-tier cities have achieved commendable progress in improving efficiency, they continue to exhibit substantial shortcomings in
terms of equitable allocation. Persistent disparities across districts, between urban and rural areas, and among income groups indicate that efficiency-oriented
planning alone cannot guarantee equitable emergency access.The study argues that single-indicator evaluation frameworks—such as Shanghai's well-known 12-
minute citywide benchmark—are insufficient for capturing the multidimensional nature of EMS performance. Instead, a comprehensive evaluation system that
integrates efficiency, spatial justice, and socioeconomic equity is urgently needed. The spatiotemporal modeling approach developed in this study—linking APIbased
dynamic travel data, human settlement matrices, and inequality diagnostics—offers a transferable analytical tool that can be extended beyond EMS to
other critical public-service systems such as healthcare delivery, firefighting response, disaster-relief coordination, and urban safety management.Ultimately, by
revealing the structural mechanisms behind unequal EMS access, this study provides essential scientific evidence to support the optimization of China’s
emergency response networks. It offers actionable insights for policymakers seeking to promote more equitable, resilient, and people-centered urban
development in the context of rapid urbanization and increasing mobility demands. |
| Key words: pre-hospital emergency medical services multi-source big data accessibility equity linear regression |
