Spatial associations with estimated net migration in Osaka, 2015–2020: A grid-level machine learning approach

Source abstract

As Japan faces rapid depopulation, distinguishing net migration from natural population changes is important for understanding local population dynamics at fine spatial scales. This study proposes a data-driven framework to identify factors associated with census-derived estimates of net migration in Osaka Prefecture at a high-resolution 500 m grid level over the period 2015–2020. Methodologically, we first refined the census survival-ratio method by introducing calibration parameters fitted to prefecture-level Basic Resident Register migration totals, thereby improving age-specific aggregate alignment. Subsequently, spatial associations were analyzed using a machine learning pipeline based on LightGBM. To address the multicollinearity inherent in spatial socioeconomic data—a persistent challenge in urban modeling—we integrated variable clustering with Owen values, a game-theoretic attribution method. This approach allowed us to estimate model-attributed contributions while accounting for groups of highly correlated variables. The analysis revealed “mosaic-like” dynamics characterized by sharp intra-regional contrasts. Specifically, we identified spatial patterns among younger populations that were consistent with the “return to the city” phenomenon during the study period and strongly associated with high-rise condominiums, contrasting with estimated net outflows from aging suburban public housing districts. These findings demonstrate that this high-resolution, explainable AI approach can capture complex, non-uniform spatial patterns of estimated net migration and provide an exploratory basis for identifying area characteristics that warrant further investigation.