Applied Machine Learning in Social Sciences

Neural Networks and Crime Prediction

Bibliographic Data

ID5933445
AuthorsRicardo Francisco Reier Forradellas (0000-0002-4790-0351, Catholic University of Ávila, corresponding author), Sergio Luis Náñez Alonso (0000-0001-5353-2017, Catholic University of Ávila), Javier Jorge-Vázquez (0000-0003-3654-7203, Catholic University of Ávila), Marcela Laura Rodríguez (Catholic University of Ávila)
Year2020
Volume10
Issue1
Pages4
Publication date2020-12-29
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Sciences (JOURNAL)
Journal identifiersISSN: 2076-0760 • E-ISSN: 2076-0760
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/socsci10010004
OpenAlexW3117549921
LanguageEN
Citations received5
References cited27

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

Artificial neural network · Machine learning · Python (programming language · Sample (material · Standardization · Anomaly Detection Techniques and Applications · Computer Science · Crime Patterns and Interventions · Artificial Intelligence

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