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International Journal of Statistika and Mathematika, ISSN: 2277- 2790 E-ISSN: 2249-8605

Volume 7, Issue 2, October 2013 pp 84-88

Research Article

Detecting Hot Spots on Crime Data Using Data Mining and Geographical Information System


Arbind Kumar Singh1, G. Manimannan2

1Senior Lecturer, Department of Statistics, S.N.S.R.K.S College Saharsa,  B. N. Mandal University, Madhepura, Bihar, INDIA.

2Assistant Professor, Department of Statistics, Madras Christian College, Chennai, Tamil Nadu, INDIA.

Academic Editor: Dr. Dase R. K.


An attempt is made to introduce a new method of mappingthe top level crime in various district/cities of Tamilnadu on the basis of crime parameters. Narrative information and crime records are stored digitally across individual police departments, enabling the collection of this data to compile a district wise database of crimes they committed in Tamilnadu. About 35 districts consist of twenty two important crime parameters from Police department database were considered for each year from 2009 to 2011 that could give different idea of the objectives and have important meaning in the literature. The unique feature of this study is the application of factor, k-mean clustering and Geographical Information System (GIS) analyses as data mining tools to develop the hidden structure present in the data for each year. Initially, factor analysis is used to uncover the patterns underlying crime parameters. The scores from extracted factors were used to find initial groups by k-mean clustering algorithm. The clusters thus obtained formed the basis for the further analyses as they inherent the structural patterns found by the factor analysis. Finally, the groups were identified as crimes belonging to High Crime Activity (HCA), Intermediate Crime Activity (ICA) and Low Crime Activity (LCA) in that order, which show the behavior of High Crime Activity cities, Intermediate Crime Activity cities and Low Crime Activity cities. From the present study it was observed that a little over 77 and more percentage of the total variation of the data was explained by the first for factors for each year. These four factors revealed the underlying structural patterns among the twenty two crimes parameter in the analysis. Also only three could be meaningfully formed clusters for each of the years. The results of this data mining and GIS could potentially be used to identify the hot spot and even prevent crime for the forth coming years.

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