Code for the BRT model.

Several associations between the built environment and COVID-19 case distribution have been identified in previous studies. However, few studies have explored the non-linear associations between the built environment and COVID-19 at the community level. This study employed the March 2022 Shanghai COVID-19 pandemic as a case study to examine the association between built-environment characteristics and the incidence of COVID-19. A non-linear modeling approach, namely the boosted regression tree model, was used to investigate this relationship. A multi-scale study was conducted at the community level based on buffers of 5-minute, 10-minute, and 15-minute walking distances. The main findings are as follows: (1) Relationships between built environment variables and COVID-19 case distribution vary across scales of analysis at the neighborhood level. (2) Significant non-linear associations exist between built-environment characteristics and COVID-19 case distribution at different scales. Population, housing price, normalized difference vegetation index, Shannon’s diversity index, number of bus stops, floor–area ratio, and distance from the city center played important roles at different scales. These non-linear results provide a more refined reference for pandemic responses at different scales from an urban planning perspective and offer useful recommendations for a sustainable COVID-19 post-pandemic response.

既往研究已证实建成环境（built environment）与新冠疫情病例分布之间存在多种关联。然而，鲜有研究在社区尺度下探讨建成环境与新冠疫情间的非线性关联。
本研究以2022年3月上海新冠疫情为研究案例，旨在探究建成环境特征与新冠感染发病率之间的关联。研究采用提升回归树（boosted regression tree）模型这一非线性建模方法对二者的关系展开分析，并基于5分钟、10分钟、15分钟步行距离的缓冲区，在社区尺度下开展多尺度研究。
主要研究结论如下：
（1）在街区尺度下，建成环境变量与新冠病例分布间的关联随分析尺度的不同而存在差异；
（2）不同尺度下，建成环境特征与新冠病例分布间均存在显著的非线性关联。
人口、房价、归一化差分植被指数（normalized difference vegetation index）、香农多样性指数、公交站点数量、容积率以及距城市中心的距离等变量在不同尺度下均发挥了重要作用。本研究的非线性分析结果从城市规划视角为不同尺度下的疫情防控工作提供了更为精细化的参考依据，并为新冠疫情后可持续的防控策略制定提供了有益建议。