Applied Machine Learning in Social Sciences: Neural Networks and Crime Prediction

Ricardo Francisco Reier Forradellas, Sergio Náñez Alonso, Javier Jorge-Vazquez and Marcela Laura Rodriguez
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Ricardo Francisco Reier Forradellas: Department of Economics-DEKIS Research Group, Catholic University of Ávila, 05005 Ávila, Spain
Javier Jorge-Vazquez: Department of Economics-DEKIS Research Group, Catholic University of Ávila, 05005 Ávila, Spain
Marcela Laura Rodriguez: Department of Economics-DEKIS Research Group, Catholic University of Ávila, 05005 Ávila, Spain

Social Sciences, 2020, vol. 10, issue 1, 1-20

Abstract: This study proposes a crime prediction model according to communes (areas or districts in which the city of Buenos Aires is divided). For this, the Python programming language is used, due to its versatility and wide availability of libraries oriented to Machine Learning. The crimes reported (period 2016–2019) that occurred in the city of Buenos Aires selected to test the model are: homicides, theft, injuries, and robberies. With this, it is possible to generate a crime prediction model according to the city area based on the SEMMA (Sample, Explore, Modify, Model, and Assess) model and after data manipulation, standardization and cleaning; clustering is performed using K-means and subsequently the neural network is generated. For prediction, it is necessary to provide the model with the information corresponding to the predictive characteristics (predict); these characteristics being according to the developed neural network model: year, month, day, time zone, commune, and type of crime.

Keywords: artificial intelligence; crime prediction; prediction model; digital administration; crime; neural networks (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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