The invention relates to a method for predicting city traffic accidents based on time-space distribution characteristics. The method comprises: first, in combination of the case information and the space information, creating a case space
database and performing pretreatment to the data; then, based on surface area statistics, analyzing the traffic accidents' time-space distribution characteristics; using the global and local self-
correlation method to realize the analyzing of the aggregate state; based on the case happening
point data, analyzing the traffic accidents' time-space distribution characteristics; through the
hierarchical clustering analysis, expressing the
distribution rule of the cases hierarchically; through the
nuclear density estimation method, expressing the continuous changes and accurate gathering center of the traffic accidents' happening distribution; and finally, utilizing the BP neural network prediction
algorithm, using the time-space distribution characteristics of the already happened cases to predict the time-space distribution areas of traffic accidents in the future. According to the invention, in combination with the time-space distribution and through the utilization of big date excavation BP neural network prediction
algorithm and the time-space distribution characteristics of the already happened cases to predict the time-space distribution areas of traffic accidents in the future, it is possible to increase the precision, the timeliness and reduce the manual cost.