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

Irina Matijosaitiene, Anthony McDowald and Vishal Juneja
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Irina Matijosaitiene: Data Science Institute, Saint Peter’s University, Jersey City, NJ 07306, USA
Anthony McDowald: Data Science Institute, Saint Peter’s University, Jersey City, NJ 07306, USA
Vishal Juneja: Amazon Robotics, Boston, MA 01864, USA

Sustainability, 2019, vol. 11, issue 10, 1-15

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.

Keywords: geospatial data; machine learning; Manhattan; prediction model; theft from motor vehicle; crime prevention through urban planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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