The University of Tokyo, Tokyo, Japan.
World Bank, Washington DC, USA.
Sci Rep. 2024 May 15;14(1):11123. doi: 10.1038/s41598-024-59814-w.
Given the worldwide increase of forcibly displaced populations, particularly internally displaced persons (IDPs), it's crucial to have an up-to-date and precise tracking framework for population movements. Here, we study how the spatial and temporal pattern of a large-scale internal population movement can be monitored using human mobility datasets by exploring the case of IDPs in Ukraine at the beginning of the Russian invasion of 2022. Specifically, this study examines the sizes and travel distances of internal displacements based on GPS human mobility data, using the combinations of mobility pattern estimation methods such as truncated power law fitting and visualizing the results for humanitarian operations. Our analysis reveals that, although the city of Kyiv started to lose its population around 5 weeks before the invasion, a significant drop happened in the second week of the invasion (4.3 times larger than the size of the population lost in 5 weeks before the invasion), and the population coming to the city increased again from the third week of the invasion, indicating that displaced people started to back to their homes. Meanwhile, adjacent southern areas of Kyiv and the areas close to the western borders experienced many migrants from the first week of the invasion and from the second to third weeks of the invasion, respectively. In addition, people from relatively higher-wealth areas tended to relocate their home locations far away from their original locations compared to those from other areas. For example, 19 % of people who originally lived in higher wealth areas in the North region, including the city of Kyiv, moved their home location more than 500 km, while only 9 % of those who originally lived in lower wealth areas in the North region moved their home location more than 500 km..
鉴于全球被迫流离失所人口的增加，特别是境内流离失所者（IDPs），拥有一个最新和精确的人口流动跟踪框架至关重要。在这里，我们通过研究 2022 年俄罗斯入侵开始时乌克兰境内境内流离失所者的情况，研究如何使用人类流动数据集来监测大规模境内人口流动的时空模式。具体来说，本研究根据 GPS 人类流动数据，通过探索移动模式估计方法（如截断幂律拟合）的组合，检查境内流离失所的规模和旅行距离，并将结果用于人道主义行动。我们的分析表明，尽管基辅市在入侵前约 5 周开始失去人口，但在入侵的第二周人口急剧下降（比入侵前 5 周失去的人口大 4.3 倍），入侵的第三周开始，来该市的人口再次增加，表明流离失所者开始返回家园。同时，基辅南部的邻近地区和靠近西部边界的地区在入侵的第一周和第二至第三周分别经历了许多移民。此外，与其他地区相比，来自相对较富裕地区的人们往往会将自己的家庭住址搬迁到远离原来住址的地方。例如，北部地区（包括基辅市）原本生活在高财富地区的 19%的人将自己的家庭住址搬迁了 500 公里以上，而原本生活在北部低财富地区的人中只有 9%的人将自己的家庭住址搬迁了 500 公里以上。
