Abstract: The outbreak of COVID-19 challenged China's urban system. Exploring the spatial pattern and influencing factors of the incidence of COVID-19 (PR_covid) at the city level can provide insights into urban sustainable development. The purpose of this paper is to explore the spatial heterogeneity of the relationship between the incidence of COVID-19 and population migration and socioeconomic factors in Chinese cities. Based on the geographically weighted regression (GWR) model, we analyzed the impact of the migration rate of all other cities across the country (MR_all), migration rate of Wuhan (MR_wuhan), GDP per capita (GDP_PC), green area per capita (GA_pc), number of medical staff per capita (NMS_pc) and public expenditure per capita (PE_pc) on the PR_covid in 282 cities in China. The results show that: (1) The explanatory power of GWR model on PR_covid(overall R²=0.40) was significantly higher than that of ordinary least squares linear regression model (overall R²=0.02). (2) The impact of MR_wuhan decreased with increasing distance from Wuhan, except for parts of Northeast and Southwest China. (3) GDP_pc played a positive role in controlling the PR_covid in the more developed southeast region. (4) The indicators of GA_pc and NMS_pc only effected positively in small parts of the country, excluding cities around Wuhan. In contrast, PE_pc played a key role in controlling the PC_covid in the surrounding areas of Wuhan. In conclusion, the incidence of COVID-19 in Chinese cities and its relationship with migration / urban socioeconomic indicators showed clear spatial patterns. The impact of migration, public investment and urban greening all followed a certain spatial attenuation pattern from Wuhan, while the impact of economic level is regional-dependent.