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Reseach Article

Spatial Clustering Simulation on Analysis of Spatial-Temporal Crime Hotspot for Predicting Crime activities

by M. Vijaya Kumar, Dr. C. Chandrasekar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 35 - Number 3
Year of Publication: 2011
Authors: M. Vijaya Kumar, Dr. C. Chandrasekar
10.5120/4384-6072

M. Vijaya Kumar, Dr. C. Chandrasekar . Spatial Clustering Simulation on Analysis of Spatial-Temporal Crime Hotspot for Predicting Crime activities. International Journal of Computer Applications. 35, 3 ( December 2011), 36-43. DOI=10.5120/4384-6072

@article{ 10.5120/4384-6072,
author = { M. Vijaya Kumar, Dr. C. Chandrasekar },
title = { Spatial Clustering Simulation on Analysis of Spatial-Temporal Crime Hotspot for Predicting Crime activities },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 35 },
number = { 3 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 36-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume35/number3/4384-6072/ },
doi = { 10.5120/4384-6072 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:21:04.773966+05:30
%A M. Vijaya Kumar
%A Dr. C. Chandrasekar
%T Spatial Clustering Simulation on Analysis of Spatial-Temporal Crime Hotspot for Predicting Crime activities
%J International Journal of Computer Applications
%@ 0975-8887
%V 35
%N 3
%P 36-43
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a spatial‐temporal prediction of crime that allows forecasting of the criminal activity behavior in a particular district by using structured crime classification algorithm. The quantity of each crime is understood as the forecasted enhance or reduce the particular moment in time and location of the criminal activity. The proposed algorithm used for forecasting crime is based on one year crime reports. It is proposed a new structured crime classification algorithm which improves the prediction performance on the studied dataset of criminal activity. It execute the following analyses: To find the exact hotspot location and disposition analysis, which shows that it is possible to predict crime location promptly, in a specific space and time, and highest percentage of effectiveness in the prediction of the position of crime. The usage of the said algorithm is to identify the particular crime from number of crimes.

References
  1. Amir, M., Patterns in forcible rape. Chicago: University of Chicago Press, 1971.
  2. Baldwin, J., Bottoms, A. The urban criminal: A study in Sheffield. London: Tavistock Publications, 1976.
  3. S. Godoy‐Calderón, Hiram Calvo., The CR‐Ω+ Classification Algorithm for Spatio‐Temporal Prediction of Criminal Activity, vol 8 No1 April 2010.
  4. Block, C., STAC hot‐spot areas: A statistical tool for law enforcement decisions. In Block, C. R., Dabdoub, M., & Fregly, S. (Eds.), Crime analysis through computer mapping. Washington, DC: Police Executive Research Forum, p. 15−32, 1995.
  5. S. Guha, R. Rastogi, and K. Shim, “An efficient clustering algorithm for large databases”, Management of Data, SIGMOD’98, Seattle, 1998, pp: 73-84.
  6. T. Zhang, R. Ramakrishnan, and M. Livny, “An efficient data clustering method for large databases”. Management of Data,Seattle,1996,pp:103-114..
  7. M. Ester, H. P. Kriegel, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases”, Knowledge Discovery and Data Mining, Portland, 1996, pp: 226-231.
  8. M. nkerst, M. breunig, H. P. Kriegel, and J. Sander, “Ordering points to identify the clustering structure”, Management of Data, Philadelphia, 1999, pp: 49-60
  9. Xiang Zhang; Zhiang Hu; Rong Li; Zheng Zheng; Detecting and mapping crime hot spots based on improved attribute oriented induce clustering Geoinformatics,2010, 18 th International conference,Beijing, pp: 1-5.
  10. A. H. Pilevar, M. Sukumar, “GCHL: A grid- clustering algorithm for high-dimensional very large spatial data bases”, Pattern Recognition Letters, 2005, pp: 999-1010.
  11. L. Kaufman and P. Jrousseeuw, Finding Group in Data: An Introduction to Cluster Analysis, 1990, New York.
  12. R. Ng., J. Han, “Efficient and effective clustering method for spatial data mining”, Very Large Data Bases, Santiago, 1994, pp: 144-155.
  13. S. Guha, R. Rastogi, and K. Shim, “An efficient clustering algorithm for large databases”, Management of Data, SIGMOD’98, Seattle, 1998, pp: 73-84.
  14. T. Zhang, R. Ramakrishnan, and M. Livny, “An efficient data clustering method for large databases”, Management of Data, Seattle, 1996, pp: 103-114.
  15. M. Ester, H. P. Kriegel, and X. Xu, “A density- based algorithm for discovering clusters in large spatial databases”, Knowledge Discovery and Data Mining, Portland, 1996, pp: 226-231.
  16. E. Jefferis, “A Multi-Method Exploration of Crime Hot Spots: A Summary of Findings,” National Institute of Justice, Washington, pp: 8, 1999.
  17. CRIME REVIEW TAMIL NADU 2007. State Crime Records Bureau Crime CID, Chennai Tamil Nadu.
  18. Tamil Nadu Police Website http://www.tnpolice.gov.in/
  19. Levine, N., “Hot Spot” analysis using CrimeStat kernel density interpolation. Presentation at the Annual Meeting of the Academy of Criminal Justice Sciences, Albuquerque, NM, March 10 –14, 1998.
  20. Chennai City Map: http://www.mapsofindia.com/maps/tamilnadu/chennai-map.htm
Index Terms

Computer Science
Information Sciences

Keywords

Crime hot spots structured classification crime Pattern Theory optimization algorithm spatial clustering