Crime Analysis of spatial-temporal distribution based on KNN Algorithm

With the rapid advance of global urbanization, the problem of urban crime is becoming more and more serious, which brings a great challenge to the police all over the world. How to use big data to drive police work has become the hot and difficult point of crime research. In this paper, the research object is theft, battery, narcotics and criminal damage in Chicago. The research method is to use visualization technology and machine learning algorithm to predict the spatial distribution of crime. Firstly, the spatial distribution characteristics of crime occurrence are analyzed by the methods of neighborhood repetition and spatial analysis. Then, the spatial distribution map of aggregated data and the crime distribution heat map are visualized. Finally, we combine the theory of crime distribution to further analyze the spatial-temporal distribution features of several crimes and criminal symbiosis.