Crime Prediction & Monitoring Framework Based on Spatial Analysis

摘要

Crimes are treacherous and common social problem faced worldwide. Crimes affect the quality of life, economic growth, and reputation of a nation. There has been an enormous increase in crime rate in the last few years. In order to reduce the crime rate, the law enforcements need to take the preventive measures. With the aim of securing the society from crimes, there is a need for advanced systems and new approaches for improving the crime analytics for protecting their communities. Accurate real-time crime predictions help to reduce the crime rate but remains challenging problem for the scientific community as crime occurrences depend on many complex factors. In this work, various visualizing techniques and machine learning algorithms are adopted for predicting the crime distribution over an area. In the first step, the raw datasets were processed and visualized based on the need. Afterwards, machine learning algorithms were used to extract the knowledge out of these large datasets and discover the hidden relationships among the data which is further used to report and discover the crime patterns that is valuable for crime analysts to analyse these crime networks by the means of various interactive visualizations for crime prediction and hence is supportive in prevention of crimes.