交通暴露与土地利用模式对社区COVID-19传播风险的影响

为揭示交通暴露和土地利用层面各因素对新型冠状病毒肺炎（COVID-19）疫情在社区尺度传播风险的影响，以A市315个小区的1 947例COVID-19确诊数据为研究样本，利用ArcGIS平台的地理编码、核密度分析、空间统计和网络分析等方法，基于道路网络、公交网络、城市POI和中国GDP/人口空间分布的公里网格数据，获取有病例小区500 m缓冲区范围内表征交通暴露和土地利用的14项具体指标。在此基础上，采用小区的COVID-19确诊人数作为解释变量，同时考虑交通暴露层面变量（路网密度、设施邻近度）和土地利用层面变量（混合度、使用强度），运用经典全局泊松回归和变系数地理加权泊松回归（GWPR）2种方法建立模型并进行实证对比分析。研究结果表明：考虑空间异质性的GWPR模型具有更高拟合优度和解释度；道路密度、公交线网密度、CBD邻近度、建筑密度、人口密度和土地价值与小区COVID-19传播风险呈显著正相关；出入口邻近度、绿地公园邻近度和土地利用混合度变量则在GWR模型中表现出随空间位置的改变呈现显著正负2种影响效果；人口密度、土地价值、绿地公园邻近度和土地利用混合度对小区COVID-19传播风险的影响程度要高于其他变量。因此，城市空间要素不仅会影响非传染性的病发风险，同时也与传染性疾病的传播风险显著相关，所得结果可为通过土地利用、城市交通规划等手段降低流行病发生的潜在风险提供参考。

Impact of Traffic Exposure and Land Use Patterns on the Risk of COVID-19 Spread at the Community Level

This study explores how traffic exposure and land use patterns affect the spread of coronavirus disease 2019 (COVID-19) at the community level. Using the data collected from 1 947 confirmed COVID-19 cases and 315 neighborhoods in A City, this study applied geocodes, kernel density estimation, spatial statistics, and network analysis approaches to obtain 14 indicators related to traffic exposure and land use in a 500 m buffer for each confirmed community. Data from the road network, public transit network, points of interest (POI), and the spatial distribution of national gross domestic product and population in a 1 km×1 km grid in 2015 were used. A classical global Poisson regression model and a geographically weighted Poisson regression model with variable coefficients were adapted to estimate the complex relationships between traffic exposure variables (road network density, facility proximity), land use variables (mixture, intensity), and the spread of COVID-19 at the community level. The results show that the geographically weighted Poisson regression model obtains a better result when the spatial heterogeneity of traffic exposure variables and land use variables is considered. Despite this, road density, public transit density, building density, population density, central business district (CBD) proximity, and land value have a positive impact on the spread of COVID-19 at the community level. However, entrance (exit) proximity, green park proximity, and land use mixture have both positive and negative effects on the spread of COVID-19, and the spatial effects vary significantly. The effects of population density, land value, green park proximity, and land use mixture on the spread of COVID-19 are much higher than those of other variables. This study illustrates that urban space elements have an impact on both communicable and noncommunicable diseases. These findings provide insight for controlling the outbreak of an epidemic in the context of transportation planning and land use.