Revealing Spatial Heterogeneity and Drivers of Day–Night Mobility Differentiation Among Chinese Migrants in Seoul via Multiscale Geographically Weighted Regression

Day–night mobility differentiation provides important insights into the spatial organization of migrant activities, yet its spatial variation and underlying drivers remain insufficiently understood in Asian metropolitan areas. Using kernel density estimation (KDE), spatial autoregressive models, and multiscale geographically weighted regression (MGWR), the study examines how the built environment, socioeconomic context, economic attractiveness, and accessibility factors shape variations in migrant mobility across space among Chinese migrants in Seoul, South Korea. The results reveal pronounced spatial clustering, with higher levels of mobility differentiation concentrated in central and southeastern Seoul, whereas lower levels are observed in migrant-concentrated districts such as Guro-gu and Geumcheon-gu. Migrant stock is identified as the most influential and spatially consistent determinant, exhibiting a significant negative association across most areas. Land-use mix also negatively affects mobility differentiation, while office facilities, industrial facilities, and subway accessibility exert positive effects. Model comparison demonstrates that MGWR substantially outperforms ordinary least squares (OLS) and geographically weighted regression (GWR), achieving the highest explanatory power (R2 = 0.758; adjusted R2 = 0.705) and the lowest corrected Akaike information criterion (AICc) (763.656). Furthermore, MGWR uncovers considerable spatial heterogeneity in the effects of employment facilities, apartment concentration, and service-oriented facilities. These findings suggest that migrant day–night mobility differentiation is shaped by both citywide contextual factors and localized neighborhood characteristics, highlighting the importance of accounting for spatially varying relationships when examining migrant mobility patterns in metropolitan areas.