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Analysis of Crime Data Using Neighbourhood Rough Sets

Analysis of Crime Data Using Neighbourhood Rough Sets

Lydia J. Gnanasigamani, Seetha Hari
Copyright: © 2020 |Volume: 15 |Issue: 3 |Pages: 15
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781799805298|DOI: 10.4018/IJITWE.2020070104
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MLA

Gnanasigamani, Lydia J., and Seetha Hari. "Analysis of Crime Data Using Neighbourhood Rough Sets." IJITWE vol.15, no.3 2020: pp.61-75. http://doi.org/10.4018/IJITWE.2020070104

APA

Gnanasigamani, L. J. & Hari, S. (2020). Analysis of Crime Data Using Neighbourhood Rough Sets. International Journal of Information Technology and Web Engineering (IJITWE), 15(3), 61-75. http://doi.org/10.4018/IJITWE.2020070104

Chicago

Gnanasigamani, Lydia J., and Seetha Hari. "Analysis of Crime Data Using Neighbourhood Rough Sets," International Journal of Information Technology and Web Engineering (IJITWE) 15, no.3: 61-75. http://doi.org/10.4018/IJITWE.2020070104

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Abstract

Crime analysis has been carried out to find out patterns and associations in crime incidents. A few of the different latitudes that research has been carried out are the prediction of crime rate, sociological impacts of crime, the contribution of socio-economic factors to the crime and finding the places where the frequency of crime is unusually high. GIS and spatial information have evolved as an inherent part of the crime data as the information is made public by the policing agencies. ‘Crime mapping' refers to mapping a crime to a particular place. Geography or the spatial information of crime plays an important role in the analysis of crime. Previous research have documented the spatial importance in identifying the hotspots and showing crime distribution in a particular geography. This work intends to identify the similarity between regions in the geographical area using Rough Set methodology. By doing so, we can prepare similar crime-fighting strategies for the neighbours and alleviate the crime.

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