The pandemic caused by SARS-CoV-2 (COVID-19) at the beginning of 2020 has restricted the human population indoor with some allowance for recreation in green spaces for social interaction and daily exercise. Understanding and measuring the risk of COVID-19 infection during public urban green spaces (PUGS) visits is essential to reduce the spread of the virus and improve well-being. This study builds a data-fused risk assessment model to evaluate the risk of visiting the PUGS in London. Three parameters are used for risk evaluation: the number of new cases at the middle-layer super output area (MSOA) level, the accessibility of each public green space and the Indices of Multiple Deprivation at the lower-layer super output area (LSOA) level. The model assesses 1357 PUGS and identifies the risk in three levels, high, medium and low, according to the results of a two-step clustering analysis. The spatial variability of risk across the city is demonstrated in the evaluation. The evaluation of risk can provide a better metric to the decision-making at both the individual level, on deciding which green space to visit, and the borough level, on how to implement restricting measures on green space access.

2020 年初由 SARS-CoV-2 (COVID-19) 引起的大流行限制了室内人口，允许在绿色空间进行社交互动和日常锻炼。了解和衡量在公共城市绿地 (PUGS) 访问期间感染 COVID-19 的风险对于减少病毒传播和改善福祉至关重要。本研究建立了一个数据融合风险评估模型来评估访问伦敦 PUGS 的风险。风险评估采用三个参数：中层超输出区（MSOA）层级的新增病例数、各公共绿地的可达性以及下层超输出区（LSOA）的多重剥夺指数（Index of Multiple Deprivation）等级。该模型评估了1357只PUGS，并识别出高、中、低三个等级的风险，根据两步聚类分析的结果。评估中展示了整个城市风险的空间变异性。风险评估可以为个人层面的决策提供更好的衡量标准，决定访问哪个绿地，以及行政区层面如何实施绿地访问限制措施。