IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v505y2018icp435-443.html
   My bibliography  Save this article

Crime prediction through urban metrics and statistical learning

Author

Listed:
  • Alves, Luiz G.A.
  • Ribeiro, Haroldo V.
  • Rodrigues, Francisco A.

Abstract

Understanding the causes of crime is a longstanding issue in researcher’s agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multicollinearity in urban indicators, it is common to find controversial conclusions about the influence of some urban indicators on crime. Machine learning ensemble-based algorithms can handle well such problems. Here, we use a random forest regressor to predict crime and quantify the influence of urban indicators on homicides. Our approach can have up to 97% of accuracy on crime prediction, and the importance of urban indicators is ranked and clustered in groups of equal influence, which are robust under slightly changes in the data sample analyzed. Our results determine the rank of importance of urban indicators to predict crime, unveiling that unemployment and illiteracy are the most important variables for describing homicides in Brazilian cities. We further believe that our approach helps in producing more robust conclusions regarding the effects of urban indicators on crime, having potential applications for guiding public policies for crime control.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:505:y:2018:i:c:p:435-443
    DOI: 10.1016/j.physa.2018.03.084
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118304059
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.03.084?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Edward L. Glaeser & Bruce Sacerdote & José A. Scheinkman, 1996. "Crime and Social Interactions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(2), pages 507-548.
    2. Raphael, Steven & Winter-Ember, Rudolf, 2001. "Identifying the Effect of Unemployment on Crime," Journal of Law and Economics, University of Chicago Press, vol. 44(1), pages 259-283, April.
    3. Gary S. Becker, 1974. "Crime and Punishment: An Economic Approach," NBER Chapters, in: Essays in the Economics of Crime and Punishment, pages 1-54, National Bureau of Economic Research, Inc.
    4. Ehrlich, Isaac, 1975. "The Deterrent Effect of Capital Punishment: A Question of Life and Death," American Economic Review, American Economic Association, vol. 65(3), pages 397-417, June.
    5. Morgan Kelly, 2000. "Inequality And Crime," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 530-539, November.
    6. Luiz G A Alves & Renio S Mendes & Ervin K Lenzi & Haroldo V Ribeiro, 2015. "Scale-Adjusted Metrics for Predicting the Evolution of Urban Indicators and Quantifying the Performance of Cities," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    7. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    8. Hyeon-Woo Kang & Hang-Bong Kang, 2017. "Prediction of crime occurrence from multi-modal data using deep learning," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-19, April.
    9. Luís M A Bettencourt & José Lobo & Deborah Strumsky & Geoffrey B West, 2010. "Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-9, November.
    10. Fajnzylber, Pablo & Lederman, Daniel & Loayza, Norman, 2002. "What causes violent crime?," European Economic Review, Elsevier, vol. 46(7), pages 1323-1357, July.
    11. M. B. Gordon & J. R. Iglesias & V. Semeshenko & J. P. Nadal, 2009. "Crime and punishment: the economic burden of impunity," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 68(1), pages 133-144, March.
    12. Entorf, Horst & Spengler, Hannes, 2000. "Socioeconomic and demographic factors of crime in Germany: Evidence from panel data of the German states," International Review of Law and Economics, Elsevier, vol. 20(1), pages 75-106, March.
    13. Alves, Luiz G.A. & Ribeiro, Haroldo V. & Mendes, Renio S., 2013. "Scaling laws in the dynamics of crime growth rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(11), pages 2672-2679.
    14. Alves, L.G.A. & Ribeiro, H.V. & Lenzi, E.K. & Mendes, R.S., 2014. "Empirical analysis on the connection between power-law distributions and allometries for urban indicators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 409(C), pages 175-182.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ganguly, Prasangsha & Mukherjee, Sayanti, 2021. "A multifaceted risk assessment approach using statistical learning to evaluate socio-environmental factors associated with regional felony and misdemeanor rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    2. Wang, Jia & Hu, Jun & Shen, Shifei & Zhuang, Jun & Ni, Shunjiang, 2020. "Crime risk analysis through big data algorithm with urban metrics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    3. Danlin Yu & Chuanglin Fang, 2022. "How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luiz G A Alves & Haroldo V Ribeiro & Ervin K Lenzi & Renio S Mendes, 2013. "Distance to the Scaling Law: A Useful Approach for Unveiling Relationships between Crime and Urban Metrics," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    2. Denis Fougère & Francis Kramarz & Julien Pouget, 2009. "Youth Unemployment and Crime in France," Journal of the European Economic Association, MIT Press, vol. 7(5), pages 909-938, September.
    3. Paolo Buonanno, 2003. "The Socioeconomic Determinants of Crime. A Review of the Literature," Working Papers 63, University of Milano-Bicocca, Department of Economics, revised Nov 2003.
    4. Montolio, Daniel, 2018. "The effects of local infrastructure investment on crime," Labour Economics, Elsevier, vol. 52(C), pages 210-230.
    5. Wang, Jia & Hu, Jun & Shen, Shifei & Zhuang, Jun & Ni, Shunjiang, 2020. "Crime risk analysis through big data algorithm with urban metrics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    6. Kausik Chaudhuri & Payel Chowdhury & Subal Kumbhakar, 2015. "Crime in India: specification and estimation of violent crime index," Journal of Productivity Analysis, Springer, vol. 43(1), pages 13-28, February.
    7. Tripathi, Jai Prakash & Bugalia, Sarita & Burdak, Kavita & Abbas, Syed, 2021. "Dynamical analysis and effects of law enforcement in a social interaction model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    8. Kangoh Lee, 2018. "Unemployment and crime: the role of apprehension," European Journal of Law and Economics, Springer, vol. 45(1), pages 59-80, February.
    9. GholamReza Keshavarz Haddad & Hamed Markazi Moghadam, 2011. "The socioeconomic and demographic determinants of crime in Iran (a regional panel study)," European Journal of Law and Economics, Springer, vol. 32(1), pages 99-114, August.
    10. Paolo Buonanno, 2003. "Identifying the Effect of Education on Crime. Evidence from the Italian Regions," Working Papers 65, University of Milano-Bicocca, Department of Economics, revised Nov 2003.
    11. Nilsson, Anna, 2004. "Income Inequality and Crime: The Case of Sweden," Research Papers in Economics 2004:3, Stockholm University, Department of Economics.
    12. Nilsson, Anna, 2004. "Income inequality and crime: The case of Sweden," Working Paper Series 2004:6, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    13. Demombynes, Gabriel & Ozler, Berk, 2005. "Crime and local inequality in South Africa," Journal of Development Economics, Elsevier, vol. 76(2), pages 265-292, April.
    14. Altindag, Duha T., 2012. "Crime and unemployment: Evidence from Europe," International Review of Law and Economics, Elsevier, vol. 32(1), pages 145-157.
    15. Sebastian Leitner, 2015. "Effects of Income Inequality on Population Health and Social Outcomes at the Regional Level in the EU," wiiw Working Papers 113, The Vienna Institute for International Economic Studies, wiiw.
    16. Entorf, Horst & Winkler, Peter, 2001. "The economics of crime: investigating the drugs-crime channel: empirical evidence from panel data of the German states," ZEW Discussion Papers 01-37, ZEW - Leibniz Centre for European Economic Research.
    17. Povilas Lastauskas & Eirini Tatsi, 2017. "Spatial Nexus in Crime and Unemployement in Times of Crisis," Bank of Lithuania Working Paper Series 39, Bank of Lithuania.
    18. Abbas, Syed & Tripathi, Jai Prakash & Neha, A.A., 2017. "Dynamical analysis of a model of social behavior: Criminal vs non-criminal population," Chaos, Solitons & Fractals, Elsevier, vol. 98(C), pages 121-129.
    19. Paolo Buonanno, 2006. "Crime, Education and Peer Pressure," Rivista di Politica Economica, SIPI Spa, vol. 96(5), pages 89-110, September.
    20. Kausik Chaudhuri & Payel Chowdhury & Kevin Reilly, 2013. "A New Perspective on Violent Crime Burden Index: Evidence from Indian Districts," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(2), pages 771-789, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:505:y:2018:i:c:p:435-443. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.