Understanding the Drivers of Mobility during the COVID-19 Pandemic in Florida, USA Using a Machine Learning Approach
Understanding the Drivers of Mobility during the COVID-19 Pandemic in Florida, USA Using a Machine Learning Approach
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DOI：
10.3390/ijgi10070440
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发表时间：
2021-06
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影响因子：
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通讯作者：
Guiming Zhu;K. Stewart;D. Niemeier;Junchuan Fan
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文献类型：
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作者：
Guiming Zhu;K. Stewart;D. Niemeier;Junchuan Fan
截至 2021 年 3 月，美国佛罗里达州约占美国 COVID-19（SARS-CoV-2 冠状病毒病）病例总数的 6.67%。这项研究的主要目的是分析 2020 年夏季三个月期间的流动模式，当时佛罗里达州迈阿密戴德县、布劳沃德县和棕榈滩县这三个县的 COVID-19 病例数非常高。为了调查与整个三县地区流动性变化相关的模式和驱动因素，我们利用社会人口、旅行和建筑环境因素以及 COVID-19 阳性病例数据构建了随机森林回归模型。从 2020 年 6 月中旬开始，当新的 COVID-19 感染开始上升时，每个县的流动模式都出现下降。虽然由于关闭，酒吧和餐馆的平均访问次数总体较低，但分析表明，即使病例有所增加，这些访问仍然是影响所有三个县流动性的首要因素。我们的建模结果表明，在 COVID-19 病例激增之前和之后的两个时间段内，各县之间存在与种族和民族相关的因素（例如，种族和民族（每个县不同人口群体的影响因素不同））以及社交距离或旅行相关因素（例如，留在家里的行为）等相关因素的流动模式差异。
As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.