A prediction model for criminal levels using socio-criminal data. (1st January 2012) - Damasceno, Marcelo; Teixeira, Jerffeson; Campos, Gustavo
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 socioeconomic 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.