A spatiotemporal decay model of human mobility when facing large-scale crises.
A spatiotemporal decay model of human mobility when facing large-scale crises.
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DOI：
10.1073/pnas.2203042119
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发表时间：
2022-08-16
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
--
作者：
在发生大规模极端事件--如飓风、野火和大流行--之后，人类活动模式的变化在不同的地方以及在事件发生后的几周和几个月内可能会有很大的不同。尽管在空间和时间上存在这种差异，但识别模式可以帮助社会采取更有效的应对措施。我们的模型发现，在多种类型的事件中，尽管它们具有多样性和复杂性，但这种变化遵循可预测的双曲线模式。该模型可以帮助理解和预测未来极端事件中的运动模式。它还揭示了大规模危机后收入不平等导致的行为变化的隐藏差异。大流行、野火和大风暴等大规模极端事件的一个共同特征是，尽管它们的病因和持续时间不同，但它们显著改变了人类的常规运动模式。这些变化在规模和持续时间上有大有小，并在不同的情况下有所不同，既影响事件的后果，也影响政府采取有效应对措施的能力。基于美国9000多万人的自然跟踪、匿名流动行为，我们记录了六次大规模危机中空间和时间上的流动性差异，包括野火、主要热带风暴、冬季冰冻和流行病。我们引入了一个模型，该模型有效地捕捉了大规模极端事件后人类流动性变化的高维异质性。在五个不同的衡量标准中，无论空间分辨率如何，人类流动性行为的变化都呈现出一致的双曲线下降，我们将这种模式描述为“时空衰退”。当我们的模型应用于新冠肺炎的案例时，我们的模型也发现了流动性变化的显著差异-来自富裕地区的个人不仅在疫情开始时以更高的速度降低了流动性，而且这种变化保持的时间更长。来自低收入地区的居民表现出更快、更大的双曲线衰减，我们认为这可能有助于解释不同的新冠肺炎利率。我们的模型代表了一个强大的工具，可以了解和预测紧急情况后的流动性模式，从而帮助产生更有效的响应。
Following large-scale extreme events—such as hurricanes, wildfires, and pandemics—changes in human movement patterns can vary dramatically from place to place and in the weeks and months following the event. Identifying patterns in spite of such variations across space and over time can help societies mount more effective responses. Our model uncovers that, across multiple types of events and despite their diversity and complexity, such changes follow a predictable hyperbolic pattern. The model can help understand and forecast movement patterns in future extreme events. It also uncovers hidden disparities in behavioral changes due to income inequality post large-scale crises. A common feature of large-scale extreme events, such as pandemics, wildfires, and major storms is that, despite their differences in etiology and duration, they significantly change routine human movement patterns. Such changes, which can be major or minor in size and duration and which differ across contexts, affect both the consequences of the events and the ability of governments to mount effective responses. Based on naturally tracked, anonymized mobility behavior from over 90 million people in the United States, we document these mobility differences in space and over time in six large-scale crises, including wildfires, major tropical storms, winter freeze and pandemics. We introduce a model that effectively captures the high-dimensional heterogeneity in human mobility changes following large-scale extreme events. Across five different metrics and regardless of spatial resolution, the changes in human mobility behavior exhibit a consistent hyperbolic decline, a pattern we characterize as “spatiotemporal decay.” When applied to the case of COVID-19, our model also uncovers significant disparities in mobility changes—individuals from wealthy areas not only reduce their mobility at higher rates at the start of the pandemic but also maintain the change longer. Residents from lower-income regions show a faster and greater hyperbolic decay, which we suggest may help account for different COVID-19 rates. Our model represents a powerful tool to understand and forecast mobility patterns post emergency, and thus to help produce more effective responses.
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