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International Journal of Information Technology

, Volume 11, Issue 4, pp 799–805 | Cite as

Analysis and prediction of crime patterns using big data

  • Ravi Kumar
  • Bharti NagpalEmail author
Original Article

Abstract

Nowadays crimes are increasing at a high rate which is a great challenge for the police department of a city. A huge amount of data on different types of crimes taking place in different geographic locations is collected and stored annually. It is highly essential to analyze data so that potential solutions for solving and mitigating the crime incidents and predicting similar incident patterns for future becomes possible. Then it can be carried out using big data and various machine learning techniques in conjunction. The paper introduced a solution to the crime prediction problem using Naive Bayes classifier, which includes finding the most likely criminal of a particular crime incident when the history of similar crime incidents has been provided with the incident-level crime data. The incident-level crime data is provided as a crime dataset which includes incident date and location, crime type, criminal ID and the acquaintances are the attributes or crime parameters. The acquaintances are the suspects whose names are either directly involved in the incident or indirectly the acquaintances of the criminal. Acquiring a real-time crime dataset is a difficult process in practice due to confidentiality principle. So, crime dataset are used for the inputs using the state of the art methods. The proposed system is tested for the crime prediction problem using the data learning, and the experimental results show that the proposed system provides better results and finding of the potential solutions and crime patterns.

Keywords

Crime patterns Big data Naive Bayes classifier Incident-level crime data 

References

  1. 1.
    Gera P, Vohra R (2014) City crime profiling using cluster analysis. Int J Comput Sci Inf Technol 5(4):5145–5148Google Scholar
  2. 2.
    Vural S, Gok M, Yetgin Z (2013) Generating incident-level artificial data using GIS-based crime simulation. International Conference on IEEE Electronics, Computer, and Computation (ICECCO’ 2013), pp 239–242Google Scholar
  3. 3.
    Brunsdon C, Corcoran J, Higgs G (2007) Visualising space and time in crime patterns: a comparison of methods. Comput Environ Urban Syst 31(1):52–75CrossRefGoogle Scholar
  4. 4.
    Xiang Y, Chau M, Atabakhsh H, Chen H (2005) Visualizing criminal relationships: comparison of a hyperbolic tree and a hierarchical list. Decis Support Syst 41(1):69–83CrossRefGoogle Scholar
  5. 5.
    Jain LC, Seera M, Lim CP, Balasubramaniam P (2014) A review of online learning in supervised neural networks. Neural Comput Appl 25(3):491–509CrossRefGoogle Scholar
  6. 6.
    Corsini P, Lazzerini B, Marcelloni F (2006) Combining supervised and unsupervised learning for data clustering. Neural Comput Appl 15(3):289–297CrossRefGoogle Scholar
  7. 7.
    Enzmann D, Podana Z (2010) Official crime statistics and survey data: comparing trends of youth violence between 2000 and 2006 in cities of the Czech Republic, Germany, Poland, Russia, and Slovenia. Eur J Crim Policy Res 16:191–205CrossRefGoogle Scholar
  8. 8.
    Vural MS, Gok M, Yetgin Z (2014) Analysis of incident-level crime data using clustering with hybrid metrics. GAUJ Appl Soc Sci 6:8–20Google Scholar
  9. 9.
    Kiani R, Mahdavi S, Keshavarzi A (2015) Analysis and prediction of crimes by clustering and classification. Int J Adv Res Artif Intell (IJARAI) 4(8):11–17Google Scholar
  10. 10.
    Yamuna S, Sudha Bhuvaneswari N (2012) Data mining techniques to analyze and predict crimes. Int J Eng Sci (IJES) 1(2):243–247Google Scholar
  11. 11.
    Deshmukh SR, Dalvi AS, Bhalerao TJ, Dahale AA, Bharati RS, Kadam CR (2015) Crime investigation using data mining. Int J Adv Res Comput Commun Eng (IJARCCE) 4(3):22–24CrossRefGoogle Scholar
  12. 12.
    Awal MA, Rabbi J, Hossain SI, and Hashem MMA (2016) Using linear regression to forecast future trends in crime of Bangladesh. In 5th International Conference on Informatics, Electronics, and Vision (ICIEV)Google Scholar
  13. 13.
    Keyvanpoura M, Javidehb M, Ebrahimia MR (2011) Detecting and investigating crime by means of data mining: a general crime matching framework. Proc Comput Sci 3:872–880CrossRefGoogle Scholar
  14. 14.
    Almanie T, Mirza R, Lor E (2015) Crime prediction based on crime types and using spatial and temporal criminal hotspots. Int J Data Min Knowl Manag Process (IJDKP) 5(4):1–19CrossRefGoogle Scholar
  15. 15.
    Malathi A, Baboo SS (2011) An enhanced algorithm to predict a future crime using data mining. Int J Comput Appl 21(1):1–6Google Scholar
  16. 16.
    Saeed U, Sarim M, Usmani A, Mukhtar A, Shaikh AB, Raffat SK (2015) Application of machine learning algorithms in crime classification and classification rule mining. Res J Recent Sci (ISCA) 4(3):106–114Google Scholar
  17. 17.
    McClendon L, Natarajan Meghanathan N (2015) Using machine learning algorithms to analyze crime data in machine learning and applications. Int J (MLAIJ) 2(1):1–12Google Scholar
  18. 18.
    Hussain S, Lee S (2015) Visualization and descriptive analytics of wellness data through Big Data. IEEE Conf Digital Inf Manag (ICDIM).  https://doi.org/10.1109/icdim.2015.7381878 CrossRefGoogle Scholar
  19. 19.
    Venkataraman S, Yang Z, Liu D, Liang E, Falaki H, Meng X, Xin R, Ghodsi A, Franklin M, Stoica I, Zaharia M (2016) SparkR: Scaling R Programs with Spark SIGMODGoogle Scholar
  20. 20.
    Vural MS, Gok M (2016) Criminal prediction using Naive Bayes theory. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2205-z CrossRefGoogle Scholar
  21. 21.
    Jung YG, Kim KT, Lee B, Youn HY (2016) Enhanced Naive Bayes Classifier for real-time sentiment analysis. In: International Conference on IEEE information and communication technology convergence (ICTC)Google Scholar
  22. 22.
    Krishnamurthy R, Satheesh Kumar J (2012) Survey of data mining techniques on crime data analysis. Int J Data Min Tech Appl 01(02):117–120Google Scholar
  23. 23.
    Mena J (2003) Investigative data mining for security and criminal detection. Butterworth-Heinemann Press, Oxford, pp 15–16Google Scholar
  24. 24.
    Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai DB, Amde M, Owen S, Xin D, Xin R, Franklin MJ, Zadeh R, Zaharia M, Talwalkar A (2016) MLlib: machine learning in Apache Spark. Journal of Mach Learn Res 17(34):1–7MathSciNetzbMATHGoogle Scholar
  25. 25.
    Poulsen E, Kennedy LW (2004) Using dasymetric mapping for spatially aggregated crime data. J Quant Criminol 20(3):243–262CrossRefGoogle Scholar
  26. 26.
    Kumar S, Toshniwal D (2016) A novel framework to analyze road accident time series data. J Big Data 3:8.  https://doi.org/10.1186/s40537-016-0044-5 CrossRefGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceAmbedkar Institute of Advanced Communication Technologies and ResearchDelhiIndia

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