一座城市，两种叙事：利用移动网络理解COVID-19大流行期间社区的韧性与脆弱性
One City, Two Tales: Using Mobility Networks to Understand Neighborhood Resilience and Fragility during the COVID-19 Pandemic
AI总结：
本研究利用纽约市两年移动数据构建每周移动网络，发现社会经济与地理属性均能预测社区对COVID-19居家政策的适应能力，为提升城市抗疫韧性的规划提供参考。
摘要：
什么因素能够预测社区对旨在遏制COVID-19有害传播的基本公共卫生政策和居家隔离规定的韧性与适应能力？为回答这一问题，本文提出了将人类移动模式与人类行为应用于网络场景的一种新颖方法。我们分析了2019年1月至2020年12月这两年间纽约市的移动数据，通过聚合兴趣点（Point of Interest）层面的到访模式，构建了人口普查街区组（Census Block Groups）之间的每周移动网络。研究结果表明，社区的社会经济属性和地理属性都能显著预测其对当时实施的居家隔离政策的适应能力。具体而言，我们的研究发现和模拟结果显示，除了种族、教育程度和收入等因素外，诸如社区内是否有满足社区需求的便利设施这类地理属性，也是预测社区适应能力和COVID-19传播情况的同等重要因素。本研究结果提供的洞见可优化助力疫情缓解工作的城市规划策略，进而可能帮助城市地区对COVID-19大流行这类外生冲击具备更强的韧性。
英文摘要：
What predicts a neighborhood's resilience and adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, in this paper we present a novel application of human mobility patterns and human behavior in a network setting. We analyze mobility data in New York City over two years, from January 2019 to December 2020, and create weekly mobility networks between Census Block Groups by aggregating Point of Interest level visit patterns. Our results suggest that both the socioeconomic and geographic attributes of neighborhoods significantly predict neighborhood adaptability to the shelter-in-place policies active at that time. That is, our findings and simulation results reveal that in addition to factors such as race, education, and income, geographical attributes such as access to amenities in a neighborhood that satisfy community needs were equally important factors for predicting neighborhood adaptability and the spread of COVID-19. The results of our study provide insights that can enhance urban planning strategies that contribute to pandemic alleviation efforts, which in turn may help urban areas become more resilient to exogenous shocks such as the COVID-19 pandemic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
