Assessing internal displacement patterns in Ukraine during the beginning of the Russian invasion in 2022.

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..

Do open data impact citizens' behavior? Assessing face mask panic buying behaviors during the Covid-19 pandemic.

Data are essential for digital solutions and supporting citizens' everyday behavior. Open data initiatives have expanded worldwide in the last decades, yet investigating the actual usage of open data and evaluating their impacts are insufficient. Thus, in this paper, we examine an exemplary use case of open data during the early stage of the Covid-19 pandemic and assess its impacts on citizens. Based on quasi-experimental methods, the study found that publishing local stores' real-time face mask stock levels as open data may have influenced people's purchase behaviors. Results indicate a reduced panic buying behavior as a consequence of the openly accessible information in the form of an online mask map. Furthermore, the results also suggested that such open-data-based countermeasures did not equally impact every citizen and rather varied among socioeconomic conditions, in particular the education level.