Crime Level Prediction using Stacked Maps with Deep Convolutional Autoencoder

N. Esquivel, B. Peralta, O. Nicolis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Chicago is denoted one of the most dangerous cities in the United States and its crime data are free available on the web portal of the town. This information has been often used to analyze and predict crime events, using statistical and machine learning space time models. Given the recent widespread success of deep neural networks, we believe that these tools could be used for producing high quality predictions of the criminality. In particular, in this work we propose a deep convolutional autoencoder neural network adapted to multiple inputs of a temporary nature for the crime prediction on a particular day. First, a preliminary analysis of data, based on the Principal Component analisis (PCA) and correlations between variables are provided. Then, the proposed prediction model is applied to forecast the nexday crime events in the town of Chicago. Finally, multiple metrics are reported to evaluate the quality of the proposed deep neural network model. By comparing the results for different scenarios (tasks), the best model has been obtained using two days events as inputs for predicting the third day. In this case the coefficient of determination reached the 97% in the validation set. Despite the use of temporal data, the deep convolutional model has shown a great predictive capacity, being this particular case trough six convolutions. As a future work, we are going to consider different real datasets, with a greater number of historical events. On the methodological part, we are going to incorporate temporary models such as the Long short-term memory (LSTM).

Original languageEnglish
Title of host publicationIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728131856
DOIs
Publication statusPublished - Nov 2019
Event2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019 - Valparaiso, Chile
Duration: 13 Nov 201927 Nov 2019

Publication series

NameIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019

Conference

Conference2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
Country/TerritoryChile
CityValparaiso
Period13/11/1927/11/19

Keywords

  • Convolutional Autoencoder
  • Crime Prediction
  • Deep Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Energy Engineering and Power Technology

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