Crime pattern recognition in Chicago city using hadoop multinode cluster

Abstract 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.

[1]  Antony Rowstron,et al.  Nobody ever got fired for using Hadoop on a cluster , 2012, HotCDP '12.

[3]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[4]  Manoj B. Chandak.  Role of big-data in classification and novel class detection in data streams , 2016, Journal of Big Data.

[5]  Chuck Lam,et al.  Hadoop in Action , 2010 .

[6]  Priti Dimri,et al.  The Implications of Big Data in Indian Stock Market , 2014 .

[7]  Ronald V. Clarke,et al.  Crime Analysis for Problem Solvers in 60 Small Steps , 2005 .

[8]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[9]  M. Divya,et al.  Comparison of Hadoop multiple node cluster performance over physical and virtual nodes using inverted index data structure for search over Wikipedia data set , 2014 .

[10]  David Bernstein.  The Emerging Hadoop, Analytics, Stream Stack for Big Data , 2014, IEEE Cloud Computing.

[11]  Song Guo,et al.  Big Data Analytics for Emergency Communication Networks: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[12]  Feng Liu,et al.  Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop , 2014, IEEE Network.

[13]  Alicja Kwasniewska,et al.  Edge Assisted Efficient Data Annotation for Realtime Video Big Data , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[14]  Li Shuai,et al.  Big Data security and privacy: A review , 2014, China Communications.

[15]  Shyam Varan Nath,et al.  Crime Pattern Detection Using Data Mining , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops.