CFP last date
20 December 2024
Reseach Article

Mitigating Serial Hot Spots on Crime Data using Interpolation Method and Graph Measures

by S. Sivaranjani, S. Sivakumari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 126 - Number 7
Year of Publication: 2015
Authors: S. Sivaranjani, S. Sivakumari
10.5120/ijca2015906088

S. Sivaranjani, S. Sivakumari . Mitigating Serial Hot Spots on Crime Data using Interpolation Method and Graph Measures. International Journal of Computer Applications. 126, 7 ( September 2015), 17-25. DOI=10.5120/ijca2015906088

@article{ 10.5120/ijca2015906088,
author = { S. Sivaranjani, S. Sivakumari },
title = { Mitigating Serial Hot Spots on Crime Data using Interpolation Method and Graph Measures },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 126 },
number = { 7 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 17-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume126/number7/22563-2015906088/ },
doi = { 10.5120/ijca2015906088 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:16:49.355265+05:30
%A S. Sivaranjani
%A S. Sivakumari
%T Mitigating Serial Hot Spots on Crime Data using Interpolation Method and Graph Measures
%J International Journal of Computer Applications
%@ 0975-8887
%V 126
%N 7
%P 17-25
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Crime detection is the vital and emerging research field in the real world environment which aims to prevent the number of crimes happening in the world. The nature of crime differs in different places based on location, age, religion, habitual characteristics and so on. Mitigating the serial crimes which are identical to each other is the most important scenario to be concerned in the real world. There may be a problem arise while mitigating the hot spots in the different crime locations due to missing values of some important features. Prediction of similar types of crimes also becomes the complex process where the temporal features are scattered. To solve the problem in this work the triangulation based interpolation methodology (TIM) and the graph measures were introduced. The TIM tends to find the missing value among the set of values based on the average level of the most nearer points where the data points are scattered unevenly.And the similarity measures assure the selection of the most nearest neighbour solutions. The similarity measures that are used in this work for predicting the most nearest location with same type of crime behaviour are Distance Measure (DM), Centrality Measure (CM) and Graph Assortativity (GA) measure. The performance evaluations were conducted with the help of spatio temporal data sets where the list of crimes and the location, behaviour are depicted properly. The experimental tests conducted proves that the proposed methodology in this work can mitigate the serial crime hot spots more accurately.

References
  1. Hsinchun Chen, Wingyan Chung, Yi Qin, Michael Chau, Jennifer Jie Xu, Gang Wang, Rong Zheng, Homa Atabakhsh, “Crime Data Mining: An Overview and Case Studies”, AI Lab, University of Arizona, proceedings National Conference on Digital Government Research, 2003, available at: http://ai.bpa.arizona.edu/
  2. Smith T.R., Menon, S., Starr, J.E., (1987). Requirement and Principles for the implementation and construction of large scale geographic information system.International Journal of Geographic Information System
  3. Sahu and Peeyush (2011) Effective crime control using GIS gisdevelopment.net.
  4. Guta R., Rajitha K., Basu S. and Mittal S.; Application of GIS in Crime Analysis: A Gateway to Safe City, India Geospatial Forum, 2012
  5. http://aurangabadcitypolice.gov.in/police_juridiction.php , 19-09-2013.
  6. M. Vijaya Kumar and Dr. C. Charasekar; Spatial Statistical Analysis of burglary Crime in Chennai City Promoters Apartments: A Case Study, IJETT, 2011.
  7. http://www.gisresources.com/types-interpolation -methods_3 /; accessed on 29-04-2014.
  8. Jitendra Kumar, Sripati Mishra and Neeraj Tiwari; Identification of Hotspots and Safe ones of Crime in Uttar Pradesh, India: Geo-spatial Analysis Approach; IJRSA, 2012.
  9. Shyam Varan Nath, S.,2007, “ Crime data mining, Advances and innovations in systems, K. Elleithy (ed.),” Computing Sciences and Software Engineering, PP. 405-409
  10. Devesh Bajpai, 2012, “Emerging Trends in Utilization of Data Mining in Criminal Investigation: An Overview”, Journal of Environmental Science, Computer Science and Engineering & Technology, Vol.1, Issue.2, PP.124-131
  11. John David Elijah Sandig, Ruby Mae Somoba, Ma. Beth Concepcion and Bobby D. Gerardo, 2013,” Mining Online GIS for Crime Rate and Models based on Frequent Pattern Analysis”, Proceedings of the World Congress on Engineering and Computer Science, Vol.2, PP.23-27
  12. TongWang , Cynthia Rudin, Daniel Wagner and Rich Sevieri, 2013, “Detecting Patterns of Crime with Series Finder”, Twenty-Seventh AAAI Conference on Artificial Intelligence, PP.140-142.
  13. Kate Bowers,J., Shane Johnson,D., and Ken Pease ,2004,“Prospective Hot-Spotting The Future of Crime Mapping?” Brit J Criminol, vol: 44, PP.641-658
  14. Arbind Kumar Singh., Manimannan, G., 2013,“Detecting Hot Spots on Crime Data Using Data Mining and Geographical Information System” ,Int J of Statistika and Mathematika, ISSN: 2277- 2790 E-ISSN: 2249-8605, Volume 8, Issue 1, PP 05-09.
  15. Jitendra Kumar, Sripati Mishra and Neeraj Tiwari, 2012, “ Identification of Hotspots and Safe Zones of Crime in Uttar Pradesh, India: Geo-spatial Analysis Approach”, Int J Remote Sensing Applications, Vol.2, Issue.1, PP.15-19.
  16. Xiang Zhang et al., 2010, “Detecting and mapping crime hot spots based on improved attribute oriented induce clustering,” IEEE International conference on Geoinformatics, PP.1-5.
  17. Timothy, C., Hart Paul ,A., and Zandbergen,2012,”Effects of Data Quality on Predictive Hotspot Mapping” ,National Institute of Justice ,US Department of Justice,Office of Justice Programs,United States of America.
  18. Kadhim ,B and Swadi Al-Janabi, 2011, “A Proposed Framework for Analyzing Crime Data Set Using Decision Tree and Simple K-Means Mining Algorithms”, Journal of Kufa for Mathematics and Computer, Vol. 1, Issue.3, PP.8-24.
  19. Aniruddha Kshirsagar, Lalit Dole, 2014, “A Review On Data Mining Methods For Identity Crime Detection,” International Journal of Electrical, Electronics and Computer Systems, Vol.2, Issue.1, PP. 51-55.
  20. Grubesic, Tony H., and Alan T. Murray. "Detecting hot spots using cluster analysis and GIS." Proceedings from the Fifth Annual International Crime Mapping Research Conference. Vol. 26. 2001
  21. David A. Bader, Shiva Kintali, Kamesh Madduri, and Milena Mihail, “Approximating Betweenness Centrality” Proceedings of the 5th international conference on Algorithms and models for the web-graph, Pages 124-137, 2007
  22. Aaron Clauset, “Homophily and assortative mixing”, Network Analysis and Modeling, 2013
  23. https://en.wikipedia.org/wiki/Assortativity
  24. E. A. Leicht, Petter Holme, and M. E. J. Newman, “Vertex similarity in networks”, Disordered Systems and Neural Networks (cond-mat.dis-nn); Data Analysis, Statistics and Probability
  25. M. Vijaya Kumar and Dr. C. Charasekar; Spatial Clustering Simulation on Analysis of Spatial-Temporal Crime Hotspot for Predicting Crime activities, IJCA, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Spatio temporal data Interpolation Graph distance measures hot spots.