Implementation of Crime Analysis using Clustering Algorithms

Crime is an unlawful act punishable by a state or other authority. Crime can be analyzed in devising solutions to crime problems, and formulating crime prevention strategies. Analysis of crime can be done in exploring the US city, Toronto's crime data via their Open Data portals. Toronto Police launched a public safety data portal to enlarge clarity between the public and officers. Accordingly, we had the chance to explore Toronto’s crimes via the Toronto Police Service Public Safety Data Portal. We especially engrossed in Major Crime Indicators (MCI) 2016 which contains a planar record of 32, 612 reports for 2016 (The only year that the data was made available). This paper integrates methods of crime analysis with its report data and process of crime .This section describes particularly on crime happening, crime analysis, data explored in year wise and generating a map for the exposed data .This can be analyzed using two clustering types: one is K-Means and the other is Hierarchical Clustering. The analysis can be done using R language. R is the most comprehensive statistical analysis package available.