Abstract:
Crime is one of the dangerous factors for any country. Although crimes could occur everywhere, it is common that criminals work on crime opportunities they face in most familiar areas for them. The ultimate goal of crime analysis is to identify likely targets for police intervention and prevent crime or solve past crimes by making statistical predictions. Criminals and victims follow common life patterns and most of the time overlaps in those patterns indicate an increased likelihood of crime. Geographic and temporal features influence the where and when ofthose patterns. Our proposed solution consists offour major modules namely; Hotspot Analysis Module, Offender Profiling Module, Victim Profiling Module and Predicting Suspects Module. Data related to fast crimes which is used for the analysis is collected from Department of Police, Sri Lanka. Hotspot analysis module identifies crime hotspots considering geographical data of past crime where victim profiling and suspect profiling modules identify the patterns or groups of victims who are most vulnerable and suspects who share same characteristics. First three modules are developed based on simple k-means clustering algorithm where the fourth module is based on simple k-means clustering and j48 algorithm to generate the classifier model which can be used to predict the cluster of suspects of a crime. The results of this analysis can be used by law enforcers to find general and specific crime trends, patterns, and series in an ongoing, timely manner in order to take advantage of the abundance of information existing in law enforcement agencies, the criminal justice system, and public domain, to maximize the use of limited law enforcement resources, to have an objective means to access crime problems locally, regionally, nationally within and between law enforcement agencies, to be proactive in detecting and preventing crime, to meet the law enforcement needs of a changing society and to understand the criminal behaviors.
