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Investigating Crime Rate Prediction Using Street-Level Images and Siamese Convolutional Neural Networks

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Computational Neuroscience (LAWCN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 720))

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Abstract

The analysis of the environment for crime prediction is based on the premise that criminal behavior is influenced by the nature of the environment in which occurs. Street-level images are the closest digital depiction available of the urban environment, in which most street crimes take place. This work proposes a crime rate prediction model that uses street-level images to classify street crimes into low or high crime rate levels. For that, we use a 4-Cardinal Siamese Convolution Neural Network (4-CSCNN) and train and test our analytic model in two regions of Rio de Janeiro, Brazil, that showed high street crime concentrations between the years of 2007 and 2016. With this preliminary experiment, we investigate the use of convolutional neural networks (CNN) for the task of crime rating through visual scene analysis and found possibilities towards automatic crime rate predictions using CNN models.

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Notes

  1. 1.

    http://www.wikicrimes.org.

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Acknowledgemnts

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research. This work is supported by the Research Initiation Scholarship Program - Doctorate in Progress (PBIP-DA) from Federal University of Pelotas (UFPel).

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Authors and Affiliations

  1. Federal University of Pelotas, Pelotas, RS, Brazil

    Virginia O. Andersson, Marco A. F. Birck & Ricardo M. Araujo

Authors
  1. Virginia O. Andersson
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  2. Marco A. F. Birck
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  3. Ricardo M. Araujo
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Corresponding author

Correspondence to Virginia O. Andersson .

Editor information

Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Dante Augusto Couto Barone

  2. Federal Institute of Education, Science and Technology of Bahia, Camacari, Brazil

    Eduardo Oliveira Teles

  3. Federal Institute of Education, Science and Technology Farroupilha, Santa Maria, Brazil

    Christian Puhlmann Brackmann

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Andersson, V.O., Birck, M.A.F., Araujo, R.M. (2017). Investigating Crime Rate Prediction Using Street-Level Images and Siamese Convolutional Neural Networks. In: Barone, D., Teles, E., Brackmann, C. (eds) Computational Neuroscience. LAWCN 2017. Communications in Computer and Information Science, vol 720. Springer, Cham. https://doi.org/10.1007/978-3-319-71011-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-71011-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71010-5

  • Online ISBN: 978-3-319-71011-2

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