Property Crime Forecast Based on Improved GA-BP Neural Network Model

To discover the spatial-temporal distribution and estimate the occurrence of crimes is an important method of improving policing strategies and preventing and controlling crimes effectively. In this paper, a prediction model for property crimes is first established based on the analysis of the spatial-temporal distribution of property crimes by using the BP(back propagation) neural network to train and learn the non-linear relationship between factors and crimes automatically. Aiming at the defects of BP neural network model of easily trapping in local optimum and instability, an improved GA-BP neural network model is then put forward, which uses the genetic algorithm (GA) to select the optimal initial weights and parameters for BP neural network model so as to learn and train the historical data. Finally, in order to evaluate whether our improved GA-BP neural network model is better than the BP neural network model in forecasting property crimes, a comparative experiment between those two predictive models is carried out with the data of 35 comprehensive impact factors from 2007 to 2012, such as property crimes, population, GDP, land utilization and so on. According to the study results, the improved GA-BP neural network model overcomes the defects of BP model successfully and shows a better performance in predicting property crimes. On the one hand, the minimum number of convergent iteration is reduced from 117 to 8. On the other hand, the maximum error of the ten times of calculation of the iterations is reduced from 370 to 5. Additionally, the prediction precision RMES is improved from 0.043 0 to 0.019 95.