Revealing Critical Characteristics of Mobility Patterns in New York City During the Onset of COVID-19 Pandemic
Revealing Critical Characteristics of Mobility Patterns in New York City During the Onset of COVID-19 Pandemic
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DOI：
10.3389/fbuil.2021.654409
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Akhil Anil Rajput;Qingchun Li;Xinyu Gao;A. Mostafavi
中科院分区：
文献类型：
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作者：
Akhil Anil Rajput;Qingchun Li;Xinyu Gao;A. Mostafavi
由于持续的危机，纽约已成为受影响最严重的疫情热点之一，也是大流行的中心。本文通过分析纽约市宏观和微观层面的多个可用数据集，确定了大流行的影响以及政府政策对人员流动的有效性。利用与人口密度、人口流动总量、公共轨道交通使用、车辆使用、热点和非热点移动模式以及人类活动集聚相关的数据源，在行政区层面上对纽约市的区际和区内移动进行了分析。我们还评估了2020年3月和4月热点和非热点兴趣点之间的节间人口流动。结果显示，从3月中旬开始，该市的人口流动性下降了约80%。进出曼哈顿的人流对公共交通和道路交通的影响最大。该市于2020年3月1日发现了第一例病例，但只有在3月的第二周之后，当避难所的订单生效时，才能看到流动性的中断。由于人们在家工作，遵守居家命令，曼哈顿对区际和区内运动的干扰最大。但事实证明，由于与热点相关的人员流动较多，感染在曼哈顿传播的风险很高。居家限制还导致布鲁克林和皇后区的人口密度增加，因为人们不再往返于曼哈顿。从这项研究中获得的见解将有助于决策者更好地理解人类行为以及他们对新闻和政府政策的反应。
New York has become one of the worst-affected COVID-19 hotspots and a pandemic epicenter due to the ongoing crisis. This paper identifies the impact of the pandemic and the effectiveness of government policies on human mobility by analyzing multiple datasets available at both macro and micro levels for New York City. Using data sources related to population density, aggregated population mobility, public rail transit use, vehicle use, hotspot and non-hotspot movement patterns, and human activity agglomeration, we analyzed the inter-borough and intra-borough movement for New York City by aggregating the data at the borough level. We also assessed the internodal population movement amongst hotspot and non-hotspot points of interest for the month of March and April 2020. Results indicate a drop of about 80% in people’s mobility in the city, beginning in mid-March. The movement to and from Manhattan showed the most disruption for both public transit and road traffic. The city saw its first case on March 1, 2020, but disruptions in mobility can be seen only after the second week of March when the shelter in place orders was put in effect. Owing to people working from home and adhering to stay-at-home orders, Manhattan saw the largest disruption to both inter- and intra-borough movement. But the risk of spread of infection in Manhattan turned out to be high because of higher hotspot-linked movements. The stay-at-home restrictions also led to an increased population density in Brooklyn and Queens as people were not commuting to Manhattan. Insights obtained from this study would help policymakers better understand human behavior and their response to the news and governmental policies.