The week's pick
Random Articles
Reseach Article
Analyzing and Displaying of Crime Hotspots using Fuzzy Mapping Method
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 103 - Number 1 |
| Year of Publication: 2014 |
| Authors: Ranbir Kaur, Sukhjit Singh Sehra |
10.5120/18039-8914
|
Ranbir Kaur, Sukhjit Singh Sehra . Analyzing and Displaying of Crime Hotspots using Fuzzy Mapping Method. International Journal of Computer Applications. 103, 1 ( October 2014), 25-28. DOI=10.5120/18039-8914
Abstract
Pattern detection is one of the essential challenges in crime mapping and analysis. Data mining can be used to explore crime detection problems. A cluster technique is an effective method for determining areas with high concentrations of localized events. Conversely, it remains a particularly demanding task to detect hotspots with mapping methods in view of the vulnerability connected with the suitable number of groups to create and additionally securing significance of individual clusters identified. Fuzzy clustering means algorithm was used for identifying hotspots of Chicago police department's citizen law enforcement analysis and reporting system data. In fuzzy clustering, a membership value to each data is assigned, which indicate the strength of relationship between that data points and a specific cluster. In this study each cluster represented the group of global positioning system data points having latitude and longitude as their co-ordinates. The findings from this study were expected to aware the public about crime hotspots. Law enforcement agencies can take prior steps to prevent crime with the use of detected crime hotspots.
References
- Brantingham, P. 1981, Environmental Criminology, Sage Publications, Los Angeles.
- Bezdek, J. C. 1974, Cluster validity with fuzzy sets, IEEE Journal of Cybernetics, Vol. 8, 3, pp. 58–73.
- Bezdek, J. C. 1981, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York.
- Bezdek, J. C. , Ehrlich, R. and Full, W. 1984, The fuzzy C-means clustering algorithm, Computers and Geosciences Vol. 10, pp. 191–203.
- CLEAR (Citizen Law Enforcement Analysis and Reporting, City of Chicago , Data Portal, 2011, (https://data. cityofchicago. org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2 )
- Cohen, L. and Felson, M. 1979, Social change and crime rate trends: a routine activity approach, American Sociological Review Vol. 44, pp. 588–608.
- CMRC (Crime Geographic Mapping Research Centre), Hotspot project, 1998, (www. ojp. usdoj. gov/cmrc/whatsnew/hotspot/toc. html).
- Craglia, M. , Haining R. and Wiles, P. ,2000, A comparative evaluation of approaches to urban crime pattern analysis, Urban Studies, Vol. 37, 4, pp. 711–729.
- Greenburg, S. and Rohe, W. , 1984, Neighborhood design and crime, Journal of the American Planning Association Vol. 50, pp. 48–61.
- Gordon, A. D. , 1998, How many clusters? An investigation of five procedures for detecting nested cluster structure, in: Data Science, Classification and Related Methods, C. Hayashi, N. Ohsumi, K. Yajima, Y. Tanaka, H. Bock and Y. Baba, eds, Springer-Verlag, Tokyo, pp. 109–116.
- Harries, K. , 1999, Geographic Mapping Crime: Principle and Practice, National Institute of Justice, Washington DC.
- Levine, N. , 1999, CrimeStat: A spatial statistics program for the analysis of crime incident locations, version 1. 1, Ned Levine and Associates, National Institute of Justice, Washington DC.
- Sneath, P. H. A. , 1957, The application of computers to taxonomy, Journal of General Microbiology, Vol. 17, pp. 201–226.
- Wang, F. , 2005, Geographical Information System and Crime Analysis, Idea Group Publishing, Hershey.
Index Terms
Keywords