Pan Jiayu, Bardhan Ronita
The Martin Centre for Architecture, Department of Architecture, University of Cambridge, Cambridge CB2 1PX, UK.
Urban For Urban Green. 2022 Aug;74:127648. doi: 10.1016/j.ufug.2022.127648. Epub 2022 Jun 14.
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年初由严重急性呼吸综合征冠状病毒2(SARS-CoV-2,即新冠病毒)引发的疫情,使得人们大多只能待在室内,仅允许在绿地进行一些社交互动和日常锻炼性质的休闲活动。了解并衡量在城市公共绿地(PUGS)游玩期间感染新冠病毒的风险,对于减少病毒传播和改善健康状况至关重要。本研究构建了一个数据融合风险评估模型,以评估伦敦城市公共绿地的游玩风险。该模型使用三个参数进行风险评估:中层超级输出区(MSOA)层面的新增病例数、各公共绿地的可达性以及下层超级输出区(LSOA)层面的多重贫困指数。该模型对1357个城市公共绿地进行了评估,并根据两步聚类分析的结果,将风险分为高、中、低三个等级。评估结果展示了全市风险的空间变异性。风险评估能够为个人层面决定前往哪个绿地游玩,以及行政区层面如何实施绿地访问限制措施提供更好的决策指标。