Abstract: With applications of new generation of information technologies in the field of transportation,such as broadband mobile communication and Internet of Things,studies of driving behavior for traffic safety in the environment of Mobile Internet attract more attention.In order to complement previous studies on the spatial analysis of risky driving behavior and Connected Vehicle,risky driving behaviors are identified and extracted on basis of On-board Diagnosis (OBD)data.Spatial distribution of risky driving behavior is thus analyzed in terms of Traffic Analysis Zone (TAZ).To study the mechanism of spatial discrepancy on risky driving behavior,Point of Interests (POI)data are utilized to meas-ure the built environment of cities.Variables of significant effects on risky driving behavior are identified by an ordinary least square (OLS)regression model.Moreover,the coefficients of different environmental variables on risky driving be-haviors are evaluated by a geographically weighted regression (GWR)model.The results show that the GWR model is superior to the OLS regression method,which is able to effectively present spatial-temporal characteristics of the relation-ship between built environment and risky driving behavior.Study results can be used to support decision-making related to traffic safety improvement program of traffic management and planning agencies.