Abstract
The concept of smart city has been gaining public interests with the considerations of socioeconomic development and quality of life. Smart initiatives have been proposed in multiple domains, such as health, energy, and public safety. One of the key factors that impact the quality of life is the crime rate in a metropolitan area. Predicting crime patterns is a significant task to develop more efficient strategies either to prevent crimes or to improve the investigation efforts. In this research, we use machine learning techniques to solve a multinomial classification problem where the goal is to predict the crime categories with spatiotemporal data. As a case study, we use San Francisco crime data from San Francisco Police Department (SFPD). Various classification methods such as Multinomial Logistic Regression, Random Forests, Lightgbm, and Xgboost have been adopted to predict the category of crime. Feature engineering was employed to boost the model performance. The results demonstrate that our proposed classifier outperforms other published models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
G. Alperovich, Multi-class Classification Problem: Crimes in San-Francisco (2016), pp. 1–5
M. Aly, Survey on multiclass classification methods.pdf. no. November (2005), pp. 1–9
L. Breiman, J. Friedman, C.J. Stone, R.A. Olshen, Classification and Regression Trees (Taylor & Francis, 1984)
S.D. Bay, Combining nearest neighbor classifiers through multiple feature subsets, in Proceedings of the Fifteenth International Conference on Machine Learning (1998), pp. 37–45
T.J. Watson, An empirical study of the Naive Bayes classifier (2001)
J. Engel, Polytomous logistic regression. Stat. Neerl. 42(4), 233–252 (1988)
C.M. Bishop, Neural Networks for Pattern Recognition (Oxford University Press Inc., New York, NY, USA, 1995)
C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
T.M. Choi, J. Gao, J.H. Lambert, C.K. Ng, J. Wang, Optimization and Control for Systems in the Big Data Era: An Introduction, vol. 252 (2017)
T.G. Dietterich, Ensemble methods in machine learning, in Proceedings of the First International Workshop on Multiple Classifier Systems (2000), pp. 1–15
L.E.O. Breiman, Random forest(LeoBreiman).pdf (2001), pp. 5–32
Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
J. Friedman, Greedy Function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
S. Darekar, Predicting and Analysis of Crime in San Francisco pp. 1–25
J. Ke, X. Li, J. Chen, San Francisco Crime Classification. no. November (2015), pp. 1–7
C. Hale, F. Liu, CS 229 Project Report : San Francisco Crime Classification.
G.H. Larios, Case Study Report San Francisco Crime Classification (2016)
P. Date, UCLA UCLA Electronic Theses and Dissertations An Informative and Predictive Analysis of the San Francisco Police Department Crime Data (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Moeinizade, S., Hu, G. (2020). Predicting Metropolitan Crime Rates Using Machine Learning Techniques. In: Yang, H., Qiu, R., Chen, W. (eds) Smart Service Systems, Operations Management, and Analytics. INFORMS-CSS 2019. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-30967-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-30967-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30966-4
Online ISBN: 978-3-030-30967-1
eBook Packages: Business and ManagementBusiness and Management (R0)