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

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Data Science Institute, Saint Peter’s University, Jersey City, NJ 07306, USA
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Centre for Smart Cities and Infrastructure, Kaunas University of Technology, Kaunas 44249, Lithuania
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Amazon Robotics, Boston, MA 01864, USA
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Author to whom correspondence should be addressed.
Sustainability 2019, 11(10), 2848; https://doi.org/10.3390/su11102848
Received: 11 April 2019 / Revised: 13 May 2019 / Accepted: 13 May 2019 / Published: 19 May 2019
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. View Full-Text
Keywords: geospatial data; machine learning; Manhattan; prediction model; theft from motor vehicle; crime prevention through urban planning geospatial data; machine learning; Manhattan; prediction model; theft from motor vehicle; crime prevention through urban planning
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Matijosaitiene, I.; McDowald, A.; Juneja, V. Predicting Safe Parking Spaces: A Machine Learning Approach to Geospatial Urban and Crime Data. Sustainability 2019, 11, 2848.

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