Population migration,especially population inflow from epidemic areas,is a key source of the risk related to the coronavirus disease 2019(COVID-19)epidemic.This paper selects Guangdong Province,China,for a case study.It utilizes big data on population mi-gration and the geospatial analysis technique to develop a model to achieve spatiotemporal analysis of COVID-19 risk.The model takes into consideration the risk differential between the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities of population migration.It further incorporates a time-lag process based on the time distribution of the onset of the imported cases.In theory,the model will be able to predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and provide support for advanced planning and targeted prevention measures.The research findings indicate the following:(1)The COVID-19 epidemic in Guangdong Province reached a turning point on January 29,2020,after which it showed a gradual decreasing trend.(2)Based on the time-lag analysis of the onset of the imported cases,it is common fora time interval to exist between case importation and illness onset,and the proportion of the cases with an interval of 1-14 days is relatively high.(3)There is evident spatial heterogeneity in the epidemic risk;the risk varies signifi-cantly between different areas based on their imported risk,susceptibility risk,and ability to prevent the spread.(4)The degree of connectedness and the scale of population migration between Guangdong’s prefecture-level cities and their counterparts in the source regions of the epidemic,as well as the transportation and location factors of the cities in Guangdong,have a significant impact on the risk classification of the cities in Guangdong.The first-tier