Location-aware big data from social media have been widely used to quantitatively characterize natural disasters and disaster-induced losses. It is not clear how human activities collectively respond to a disaster. In this study, we examined the collective human activities in response to Typhoon Hato at multi spatial scales using aggregated location request data. We proposed a Multilevel Abrupt Changes Detection (MACD) methodological framework to detect and characterize the abrupt changes in location requests in response to Typhoon Hato. Results show that, at the grid level, most anomaly grids were located within a radius of 53 km around the typhoon trajectory. At the city level, there are significant spatial difference in terms of the human activity recovery duration (230 h on average). At the subnational level, the absolute magnitude of abrupt location request changes is strongly correlated with the typhoon-induced economic losses and the population affected.

来自社交媒体的可感知位置的大数据已被广泛用于定量表征自然灾害和灾害造成的损失。尚不清楚人类活动如何共同应对灾难。在这项研究中，我们使用汇总的位置请求数据检查了人类在多空间尺度上应对台风哈多造成的集体活动。我们提出了一个多级突发变化检测（MACD）方法框架，以检测和表征响应台风哈托而发生的位置请求中的突发变化。结果显示，在网格级别，大多数异常网格位于台风轨迹周围53 km的半径内。在城市一级，人类活动的恢复时间（平均230小时）存在很大的空间差异。在国家以下一级，