Crime pattern recognition in Chicago city using hadoop multinode cluster
Vanita Jain, Arun Kumar Dubey, Achin Jain, Mohit Malhotra, Shikha Rastogi · Journal of Information and Optimization Sciences · 2019
Crimes are a social annoyance and harm our society very much in many ways. In this paper we have designed a Map Reduce algorithm to aid in the process of identification of crimes in Chicago city at different levels of abstractions. Analysis is divided into three phases. Twenty-nine different kinds of crimes have been identified in Chicago city in the first phase. In the second phase crimes are segregated into four zones namely east, west, south and north. In the third phase frequency of crime at specific location is determined. We have used approximately 40 GB of crime data from Chicago Crime Portal. The finding of this research is helpful for law enforcement officers, police and other law practitioners to improve the productivity and substantially sublime the policies so that a significant decrease in crime can be seen. The paper also compares the efficiency of multimode hadoop clusters of different sizes.