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Predicting Safe Parking Spaces: A Machine Learning Approach to Geospatial Urban and Crime Data

Author

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  • Irina Matijosaitiene

    (Data Science Institute, Saint Peter’s University, Jersey City, NJ 07306, USA
    Centre for Smart Cities and Infrastructure, Kaunas University of Technology, Kaunas 44249, Lithuania)

  • Anthony McDowald

    (Data Science Institute, Saint Peter’s University, Jersey City, NJ 07306, USA)

  • Vishal Juneja

    (Amazon Robotics, Boston, MA 01864, USA)

Abstract

This research aims to identify spatial and time patterns of theft in Manhattan, NY, to reveal urban factors that contribute to thefts from motor vehicles and to build a prediction model for thefts. Methods include time series and hot spot analysis, linear regression, elastic-net, Support vector machines SVM with radial and linear kernels, decision tree, bagged CART, random forest, and stochastic gradient boosting. Machine learning methods reveal that linear models perform better on our data (linear regression, elastic-net), specifying that a higher number of subway entrances, graffiti, and restaurants on streets contribute to higher theft rates from motor vehicles. Although the prediction model for thefts meets almost all assumptions (five of six), its accuracy is 77%, suggesting that there are other undiscovered factors making a contribution to the generation of thefts. As an output demonstrating final results, the application prototype for searching safer parking in Manhattan, NY based on the prediction model, has been developed.

Suggested Citation

  • Irina Matijosaitiene & Anthony McDowald & Vishal Juneja, 2019. "Predicting Safe Parking Spaces: A Machine Learning Approach to Geospatial Urban and Crime Data," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2848-:d:232494
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    References listed on IDEAS

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    2. Alves, Luiz G.A. & Ribeiro, Haroldo V. & Rodrigues, Francisco A., 2018. "Crime prediction through urban metrics and statistical learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 435-443.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    4. Stafford, M. & Chandola, T. & Marmot, M., 2007. "Association between fear of crime and mental health and physical functioning," American Journal of Public Health, American Public Health Association, vol. 97(11), pages 2076-2081.
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    1. Saba Inam & Azhar Mahmood & Shaheen Khatoon & Majed Alshamari & Nazia Nawaz, 2022. "Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction," Sustainability, MDPI, vol. 14(12), pages 1-21, June.

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