skip to main content
10.1145/3038884.3038908acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmedpraiConference Proceedingsconference-collections
research-article

Analyzing Socio-economic and Geographical factors for Crime Incidents using Heat maps and Hotspots

Authors Info & Claims
Published:22 November 2016Publication History

ABSTRACT

Spatio-temporal data mining techniques are used for crime analysis for their knowledge oriented and meaningful visual representation of crime incidents. Visual representation of crime patterns assist analysts with in-depth understanding of crime behavior with time and location. The representation can be made more knowledgeable and perceptible by incorporating details of socio-economic factor and areafis geographical information providing insights to features that actually play role in certain crime pattern. To analyze the impact of these factors, two of the best density calculation clustering techniques i.e. Heat Maps and Hot Spots analysis are performed for Crime Against Person and Crime Against Property. The analysis demonstrated that Crimes Against Persons are more frequent in rural and sub-urban areas with mostly low socio-economic conditions; whereas, Crimes Against Property are mostly in commercial areas with mix socio-economic conditions.

References

  1. Luc Anselin, Jacqueline Cohen, David Cook, Wilpen Gorr, and George Tita. 2000. Spatial analyses of crime. Criminal justice 4, 2 (2000), 213--262.Google ScholarGoogle Scholar
  2. SS Santhosh Baboo and others. 2011. Enhanced Algorithms to Identify Change in Crime Patterns. International Journal of Combinatorial Optimization Problems and Informatics 2, 3 (2011), 32.Google ScholarGoogle Scholar
  3. Donald E Brown, Hua Liu, and Yifei Xue. 2001. Mining Preferences from Spatial-Temporal Data.. In SDM. SIAM, 1--17.Google ScholarGoogle Scholar
  4. Christopher W Bruce and N Ouellette. 2008. Closing the Gap Between Analysis and Response. The Police Chief 75, 9 (2008), 30--32.Google ScholarGoogle Scholar
  5. Lawrence E Cohen and Marcus Felson. 1979. Social change and crime rate trends: A routine activity approach. American sociological review (1979), 588--608.Google ScholarGoogle Scholar
  6. Francis T Cullen and Pamela Wilcox. 2010. Encyclopedia of criminological theory. Vol. 1. Sage.Google ScholarGoogle Scholar
  7. John Eck, Spencer Chainey, James Cameron, and R Wilson. 2005. Mapping crime: Understanding hotspots. (2005).Google ScholarGoogle Scholar
  8. Herman Goldstein. 1979. Improving policing: A problem-oriented approach. Crime & delinquency 25, 2 (1979), 236--258.Google ScholarGoogle Scholar
  9. Tony H Grubesic and Elizabeth A Mack. 2008. Spatio-temporal interaction of urban crime. Journal of Quantitative Criminology 24, 3 (2008), 285--306.Google ScholarGoogle ScholarCross RefCross Ref
  10. Keith Harries. 2006. Extreme spatial variations in crime density in Baltimore County, MD. Geoforum 37, 3 (2006), 404--416.Google ScholarGoogle ScholarCross RefCross Ref
  11. Kent A Harries. 1999. Mapping crime: Principle and practice. Technical Report.Google ScholarGoogle Scholar
  12. T Kalaikumaran, S Karthik, and others. 2012. Criminals and crime hotspot detection using data mining algorithms: clustering and classification. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 1, 10 (2012), pp-225.Google ScholarGoogle Scholar
  13. Konstantin Krivoruchko. 2011. Spatial statistical data analysis for GIS users. Esri Press Redlands.Google ScholarGoogle Scholar
  14. Aravindan Mahendiran, Michael Shuffett, Sathappan Muthiah, Rimy Malla, and Gaoqiang Zhang. 2011. Forecasting Crime Incidents using Cluster Analysis and Bayesian Belief Networks. (2011).Google ScholarGoogle Scholar
  15. Sara McLafferty, Doug Williamson, and PG McGuire. 2000. Identifying crime hot spots using kernel smoothing. V. Goldsmith. PO McGuire, JH Mollenkopf and TA Ross CRIME MAPPING AND THE TRAINING NEEDS OF LAW ENFORCEMENT 127 (2000).Google ScholarGoogle Scholar
  16. Jeffrey D Morenoff, Robert J Sampson, and Stephen WRaudenbush. 2001. Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence*. Criminology 39, 3 (2001), 517--558.Google ScholarGoogle ScholarCross RefCross Ref
  17. Andrew A Reid, Richard Frank, Natalia Iwanski, Vahid Dabbaghian, and Patricia Brantingham. 2014. Uncovering the spatial patterning of crimes a criminal movement model (CriMM). Journal of research in crime and delinquency 51, 2 (2014), 230--255.Google ScholarGoogle ScholarCross RefCross Ref
  18. Dennis W Roncek and Ralph Bell. 1981. Bars, blocks, and crimes. Journal of Environmental Systems 11, 1 (1981), 35--47.Google ScholarGoogle ScholarCross RefCross Ref
  19. Robert E Roth, Kevin S Ross, Benjamin G Finch, Wei Luo, and Alan M MacEachren. 2013. Spatiotemporal crime analysis in US law enforcement agencies: Current practices and unmet needs. Government Information Quarterly 30, 3 (2013), 226--240.Google ScholarGoogle ScholarCross RefCross Ref
  20. Shashi Shekhar, Pusheng Zhang, Yan Huang, and Ranga Raju Vatsavai. 2003. Trends in spatial data mining. Data mining: Next generation challenges and future directions (2003), 357--380.Google ScholarGoogle Scholar
  21. Jian Song, Valerie Spicer, and Patricia Brantingham. 2013. The edge effect: Exploring high crime zones near residential neighborhoods. In Intelligence and Security Informatics (ISI), 2013 IEEE International Conference on. IEEE, 245--250.Google ScholarGoogle ScholarCross RefCross Ref
  22. Hadi Fanaee Tork. 2012. Spatio-temporal clustering methods classification. In Doctoral Symposium on Informatics Engineering. 199--209.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    MedPRAI-2016: Proceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence
    November 2016
    163 pages
    ISBN:9781450348768
    DOI:10.1145/3038884
    • General Chairs:
    • Chawki Djeddi,
    • Imran Siddiqi,
    • Akram Bennour,
    • Program Chairs:
    • Youcef Chibani,
    • Haikal El Abed

    Copyright © 2016 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 22 November 2016

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited
  • Article Metrics

    • Downloads (Last 12 months)29
    • Downloads (Last 6 weeks)7

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader