Wu Lingling, Shimizu Tetsuo
Graduate School of Urban Environmental Sciences, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japan.
Graduate School of Urban Environmental Sciences, Tokyo Metropolitan University, Japan.
Cities. 2022 Aug;127:103751. doi: 10.1016/j.cities.2022.103751. Epub 2022 May 18.
To curb the spread of the COVID-19 pandemic, countries around the world have imposed restrictions on their population. This study quantitatively assessed the impact of non-compulsory measures on human mobility in Japan during the COVID-19 pandemic, through the analysis of large-scale anonymized mobile-phone data. The non-negative matrix factorization (NMF) method was used to analyze mobile statistics data from the Tokyo area. The results confirmed the suitability of the NMF method for extracting behavior patterns from aggregated mobile statistics data. Data analysis results indicated that although non-pharmaceutical interventions (NPIs) measures adopted by the Japanese government are non-compulsory and rely largely on requests for voluntary self-restriction, they are effective in reducing population mobility and motivating people to practice social distancing. In addition, the current study compared the mobility change in three cities (i.e., Tokyo, Osaka, and Hiroshima), and discussed their similarity and difference in behavior pattern changes during the pandemic. It is expected that the analytical tool proposed in this study can be used to monitor mobility changes in real-time during the pandemic, as well as the long-term evolution of population mobility patterns in the post-pandemic phase.
为遏制新冠疫情的传播，世界各国都对本国人口实施了限制措施。本研究通过分析大规模匿名手机数据，定量评估了新冠疫情期间日本非强制性措施对人员流动的影响。采用非负矩阵分解（NMF）方法分析东京地区的移动统计数据。结果证实了NMF方法适用于从汇总的移动统计数据中提取行为模式。数据分析结果表明，尽管日本政府采取的非药物干预（NPIs）措施是非强制性的，且很大程度上依赖于自愿自我限制的要求，但这些措施在减少人口流动和促使人们保持社交距离方面是有效的。此外，本研究比较了三个城市（即东京、大阪和广岛）的流动变化，并讨论了它们在疫情期间行为模式变化方面的异同。预计本研究提出的分析工具可用于在疫情期间实时监测流动变化，以及疫情后阶段人口流动模式的长期演变。
