Examining Deep Learning Architectures for Crime Classification and Prediction

Panagiotis Stalidis, Theodoros Semertzidis and Petros Daras
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Panagiotis Stalidis: Center of Research and Technologies Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Theodoros Semertzidis: Center of Research and Technologies Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Petros Daras: Center of Research and Technologies Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece

Forecasting, 2021, vol. 3, issue 4, 1-22

Abstract: In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction.

Keywords: deep learning; crime prediction; spatiotemporal (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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