IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v545y2020ics0378437119320229.html

Crime risk analysis through big data algorithm with urban metrics

Author

Listed:
  • Wang, Jia
  • Hu, Jun
  • Shen, Shifei
  • Zhuang, Jun
  • Ni, Shunjiang

Abstract

Crime is pervasive all around the world. Understanding the influence of social features on crime occurrences of a city is a hot topic among researchers. Correlations between crime and other social characteristics have been studied by large amounts of statistical models, including Ordinary Least Square (OLS) linear regression model, Random Forest (RF) regression model, Artificial Neural Network (ANN) model and so on. However, results of these studies, such as the prediction accuracy, are not satisfying and many contradictory conclusions are achieved in previous research works. These controversies are triggered by several factors, including the non-Gaussian distributions and multicollinearity of urban social data, inaccuracy and inadequacy of the processed data, etc. To fill these gaps, we analyzed the influence of 18 urban indicators within 6 categories including geography, economy, education, housing, urbanization and population structure on crime risk in China’s major prefecture-level cities by year. We used the big data algorithm, Least Absolute Shrinkage and Selection Operator (LASSO) and Extremely-randomized Trees (Extra-Trees), to predict the crime risk and quantify the influence of urban parameters on crime. 83% of accuracy on crime risk prediction can be obtained from our fitted model and the importance of urban indicators is ranked. Results show that area of land used for living, number of subscribers of mobile telephone, employed population are the three main factors on the crime occurrences in China. Our research makes contributions to better understanding of the effects of urban indicators on crime in a socialist nation, and providing instructions and strategies for crime prediction and crime rate control with governments, in this big-data era.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119320229
    DOI: 10.1016/j.physa.2019.123627
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119320229
    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.2019.123627?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

    for a different version of it.

    References listed on IDEAS

    as
    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. 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. Morgan Kelly, 2000. "Inequality And Crime," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 530-539, November.
    4. 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.
    5. P. Baldi & P. Sadowski & D. Whiteson, 2014. "Searching for exotic particles in high-energy physics with deep learning," Nature Communications, Nature, vol. 5(1), pages 1-9, September.
    6. 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.
    7. 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.
    8. Adi L Tarca & Vincent J Carey & Xue-wen Chen & Roberto Romero & Sorin Drăghici, 2007. "Machine Learning and Its Applications to Biology," PLOS Computational Biology, Public Library of Science, vol. 3(6), pages 1-11, June.
    9. 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.
    10. Harries, Richard, 2003. "Modelling and predicting recorded property crime trends in England and Wales--a retrospective," International Journal of Forecasting, Elsevier, vol. 19(4), pages 557-566.
    11. Iglesias, J.R. & Semeshenko, V. & Schneider, E.M. & Gordon, M.B., 2012. "Crime and punishment: Does it pay to punish?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 3942-3950.
    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. Feng, Jiajia & Liang, Yuebing & Hao, Qi & Xu, Ke & Qiu, Waishan, 2024. "Comparing effectiveness of point of interest data and land use data in theft crime modelling: A case study in Beijing," Land Use Policy, Elsevier, vol. 147(C).

    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. 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.
    2. 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).
    3. 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.
    4. 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.
    5. repec:osf:osfxxx:s8ayp_v1 is not listed on IDEAS
    6. Nouman Khaliq & Muhammad Shabbir & Zahira Batool, 2019. "Exploring the Influence of Unemployment on Criminal Behavior in Punjab, Pakistan," Global Regional Review, Humanity Only, vol. 4(1), pages 402-409, March.
    7. Gudipudi, Ramana & Rybski, Diego & Lüdeke, Matthias K.B. & Zhou, Bin & Liu, Zhu & Kropp, Jürgen P., 2019. "The efficient, the intensive, and the productive: Insights from urban Kaya scaling," Applied Energy, Elsevier, vol. 236(C), pages 155-162.
    8. Manea, Roxana Elena & Piraino, Patrizio & Viarengo, Martina, 2023. "Crime, inequality and subsidized housing: Evidence from South Africa," World Development, Elsevier, vol. 168(C).
    9. Baumann, Florian & Friehe, Tim, 2015. "Status concerns as a motive for crime?," International Review of Law and Economics, Elsevier, vol. 43(C), pages 46-55.
    10. Rudolph, Maximilian & Starke, Peter, 2020. "How does the welfare state reduce crime? The effect of program characteristics and decommodification across 18 OECD-countries," Journal of Criminal Justice, Elsevier, vol. 68(C).
    11. Anthony FJ van Raan, 2024. "Simulating urban scaling with a term linkages network of a university," Environment and Planning B, , vol. 51(9), pages 2092-2107, November.
    12. Montolio, Daniel, 2018. "The effects of local infrastructure investment on crime," Labour Economics, Elsevier, vol. 52(C), pages 210-230.
    13. Anthony F. J. Raan & Jos J. Winnink, 2025. "Urban scaling of patents and relation with socioeconomic strength for German cities and their urban areas," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(8), pages 4339-4367, August.
    14. Yves Bettignies & Joao Meirelles & Gabriela Fernandez & Franziska Meinherz & Paul Hoekman & Philippe Bouillard & Aristide Athanassiadis, 2019. "The Scale-Dependent Behaviour of Cities: A Cross-Cities Multiscale Driver Analysis of Urban Energy Use," Sustainability, MDPI, vol. 11(12), pages 1-20, June.
    15. Qingsong He & Lingping Huang & Jing Li, 2022. "Rediscovering the Scaling Law of Urban Land from a Multi-Scale Perspective—A Case Study of Wuhan," Land, MDPI, vol. 11(6), pages 1-15, June.
    16. George F. N. Shoukry, 2016. "Criminals' Response To Changing Crime Lucre," Economic Inquiry, Western Economic Association International, vol. 54(3), pages 1464-1483, July.
    17. Andres Gomez-Lievano & Oscar Patterson-Lomba, 2018. "Estimating the drivers of urban economic complexity and their connection to economic performance," Papers 1812.02842, arXiv.org, revised Sep 2021.
    18. Geert Mesters & Victor van der Geest & Catrien Bijleveld, 2014. "Crime, Employment and Social Welfare: an Individual-level Study on Disadvantaged Males," Tinbergen Institute Discussion Papers 14-091/III, Tinbergen Institute.
    19. Tatsushi Oka, 2009. "Juvenile crime and punishment: evidence from Japan," Applied Economics, Taylor & Francis Journals, vol. 41(24), pages 3103-3115.
    20. Adenuga Fabian Adekoya & Nor Azam Abdul Razak, 2018. "Unemployment and Violence: ARDL Endogeneity Approach. (Desempleo y violencia: Enfoque de endogeneidad ARDL)," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 155-176, October.
    21. Yu Liu & Thomas M. Fullerton Jr. & Nathan J. Ashby, 2013. "Assessing The Impacts Of Labor Market And Deterrence Variables On Crime Rates In Mexico," Contemporary Economic Policy, Western Economic Association International, vol. 31(4), pages 669-690, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:545:y:2020:i:c:s0378437119320229. 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.