Research And System Development Of Crime Early Warning Based On Data Mining

Crime is the inevitable outcome of the development of human society, which is the reflection of contradiction between man and society as well as among people. With the development of the society, there are more kinds of crime, and it is hard for people to keep alert for all the crime all the time. When people are confronted with crime, crime is inevitable, leading to personal and property losses. However, if we can predict the potential crimes based on existing criminal information, and warn people, then preventive measures can be resorted to prevent the encounter with crimes. On the other side, as the construction of the National Public Security Information System, the Public Security Organization has accumulated a plenty of criminal records which provide data support for crime early warning. As an intelligent technology, data mining can extract implicit knowledge from large amounts of data, and possess formidable ability to data analysis and process. It provides theoretical and technical support for finding rules and trends in criminal data, discovering potential crimes and realizing crime early warning.This dissertation discovers the rules of crime based on data mining through the analysis of the Public Security Organization’s criminal records, and carries out the forecast and early warning of potential crimes timely based on mobile client real-time positioning technology through the real-time monitoring of users’ geographic location, guiding people to travel safely.Firstly, this dissertation analyzes the psychology and motive of criminals, and finds seven characteristics of crime, then analyzes the problems crime early warning to solve, and summarizes six requirements for the method to solve the crime early warning problem. Then, based on this, this dissertation presents a method to realize crime early warning, which uses data mining and real-time positioning technology.After that, in this dissertation, two classification algorithms, C4.5 and Naive Bayes algorithm, and two clustering algorithms, K-Means and EM algorithm are applied to detect rules of crime, predict potential crimes, and create the prediction model. Meanwhile, their effectiveness are compared and analyzed. The analysis result shows that the clustering analysis method of data mining is more suitable to solve the crime early warning problem. The method proposed in this dissertation achieves prediction and early warning task by mobile phones client. Mobile phones client downloadsforecast model containing criminal rules from server, which can predict the security situation of user’s next position, and give user early warning if necessary.At last, in order to test and verify the feasibility and effectiveness of this study’s method, we design and develop an urban crime early warning system based on data mining. This dissertation introduces "Urban Crime Early Warning System Based on Data Mining" summarily, including its framework design, the main function modules, program design, database design and critical codes. The application result shows that the method to so lve the crime early warning problem based on data mining and real-time positioning technology is feasible and effective.