Abstract. Urban traffic analysis has acted an important role in the process of urban development, which can provide insights for urban planning, traffic management and resource allocation. Meanwhile, the advancement of Intelligent Transportation Systems has produced a variety of traffic-related data from sensors and cameras to monitor urban traffic conditions in high spatio-temporal resolution. This research applies spatial regression models combined with computer vision and deep learning to analyse traffic flow distributions via various factors in the urban areas and traffic flow data. We include road characteristics and surrounding environments such as land use/cover, nearby points of interest (POI) and Google Street View images. The results show that the daily average traffic flow on main roads is much higher than smaller roads, and nearby POIs numbers have positive effect on traffic flows. The impact of land cover type is insignificant in the linear regression model, while demonstrates significant contribution to traffic flows in spatial regression models. Although the spatial autocorrelation still exists after the spatial regression, the spatial error model generates a better fit on the dataset. Further analysis will focus on extend the current model with the time parameters and understand what influence the changes of traffic flow in the different spatio-temporal scales.