Title: A prediction model for criminal levels using socio-criminal data
Authors: Marcelo Damasceno; Jerffeson Teixeira; Gustavo Campos
Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN) – Campus Macau, R. das Margaridas, 350, Macau-RN, Brazil.
State University of Ceará, Av. Paranjana, 1700, Fortaleza-CE, Brazil.
State University of Ceará, Av. Paranjana, 1700, Fortaleza-CE, Brazil
Abstract: The increase in violence around the world is becoming a major problem, causing severe damages to society: material, social and physical ones. The government needs effective tools to fight against crime, and therefore, some tools are necessary to assist in the prevention of further crimes, in the allocation of its resources and visualisation of geographic areas with high crime concentrations. This paper proposes a model of data mining, predicting criminal levels in geographic areas. The model was proposed to work using specifically criminal and socio-economic data. This work shows the approach proposed to face the problems of this social phenomenon, as a unified process. A case study was used to validate the proposed procedure. The data used were crime and socio-economic data of the metropolitan region of Fortaleza - Brazil (RMF). The case study proved that the process is useful and effective in building a predictor of criminal levels.
Keywords: prediction modelling; crime levels; data mining; data modelling; Brazil; predicting crime; crime forecasting; law enforcement; geographic areas; socioeconomic data; metropolitan regions.
Int. J. of Electronic Security and Digital Forensics, 2012 Vol.4, No.2/3, pp.201 - 214
Submission date: 16 Oct 2011
Date of acceptance: 15 Mar 2012
Available online: 05 Aug 2012