An accurate understanding of the population is essential for the development of medical care and social resources. However, the development of transportation networks has increased temporal and spatial fluctuations in the population, making it difficult to accurately forecast medical care demand, especially during disaster recovery. This study examined the population movement in areas affected by the Fukushima Daiichi nuclear power plant accident using demographic data. The target area includes two cities, seven towns, and three villages that are in the evacuation zone. Using a population estimation that reflects changes in population by time of day, which was obtained from a mobile phone company (NTT DOCOMO), we applied clustering analysis to examine the population dynamics over a 4-year period. From 2019 to 2022, the population increased in eight areas and decreased in four areas. The population was further classified into five groups, identifying the unique characteristics and fluctuations of each group. Different regions had different percentages of groups reflecting the characteristics of their populations. The differences among the regions and demographic transition showed the potential to understand the challenges of recovery and to use the data to inform healthcare planning and social policies. This method, which utilizes estimated population data, is also applicable to the study of medical resources and social policies in the event of future disasters and may be useful in analyzing regional characteristics in detail.
Keywords: Fukushima Daiichi nuclear power plant accident; community; disaster; machine learning; medical care; population estimation.
© The Author(s) 2024. Published by Oxford University Press on behalf of The Japanese Radiation Research Society and Japanese Society for Radiation Oncology.