ABSTRACT
ABSTRACT: A city is a mutant, dynamic, living body where deep differences exist and need to be managed on behalf of its population’s quality of life. Over the last 20 years, the increase in crime has become a problem in the majority of the world’s largest cities. Crimes are social nuisance and cost our societies dealing with in several ways. In urban management, law enforcement implies police force, which is responsible for the maintenance of law and order. Most law enforcement agencies today are faced with enormous quantities of data that must be processed and turned into useful information. This paper attempts to implement clustering algorithm as a data mining approach to assist detecting the crime patterns and speed up the process of responding to the crime. We have looked at k-means clustering with some enhancements to aid the process of identification of crime patterns. In this paper the benefits of using Geospatial Information Systems (GIS) to study the high potential crime risk areas has been successfully tested in Tehran using spatial data mining. The achieved results illustrated partitioning of the study area into difference levels of crime-prone locations to be monitored by police force.
