Background: The coronavirus disease 2019 (COVID-19) has spread quickly among the crowd and brought serious global impact since December 2019. However, there were considerable geographical disparities in the distribution of the COVID-19 incidence among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from the geographic perspective.Methods: The official surveillance data about the COVID-19 and sociodemographic information in the 342 cities of China were collected. Local GWPR model and global GLM Poisson regression model were compared to find the optimal one for analysis. Results: A significantly lower AICc in the GWPR model was shown compared with the GLM Poisson regression model (43218.9 in GWPR vs. 61953.0 in GLM, respectively). Any spatial auto-correlations of residuals were not found in the GWPR model (global Moran’s I = -0.005, p = 0.468), representing the spatial auto-correlation had been captured by the GWPR model. These cities with higher GDP, limited health resources, and shorter distance to Wuhan, were at higher risk for COVID-19. As population density increased, the incidence of COVID-19 decreased for most of the cities, except parts of the southeastern cities. Conclusions: There are potential effects of the sociodemographic factors on the COVID-19 incidence. Furthermore, the findings and methodology in our study could be used as a guide to other countries to help understand the local transmission of COVID-19 and tailor site-specific intervention strategies.