Skip to main content
SpringerLink
Log in

Predictive Crime Analytics Using Data Science (India and the USA)

  • Conference paper
  • First Online:
3rd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

  • 294 Accesses

Abstract

Predictive analysis is concerned with the branch of data science used to predict future patterns and trends. This modelling technique can be used to aid society. In recent years, crime against women has skyrocketed, and understanding past history can help to come up with insight that describe the current state of crime and assault in these countries. This research aims to foresee the crime patterns against women in India and the USA. The studies were carried out in both countries to better understand the economic bearing, if any, in crimes committed against women. The data of the past years is studied using extensive EDA (Exploratory Data Analysis) techniques to help understand the problems women face. The data is then normalized, and Linear Regression is executed to predict future trends in crime rates. K-means is then used to create clusters of states with the highest crime rates against women in India. This chapter aims to help women and law enforcement by using technological advancements in the area of data science to predict future trends.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shamsuddin, N. H. M., Ali, N. A., & Alwee, R. (2017). An overview on crime prediction methods. In 2017 6th ICT international student project conference (ICT-ISPC), Skudai (pp. 1–5). IEEE. https://doi.org/10.1109/ICT-ISPC.2017.8075335.

    Chapter  Google Scholar 

  2. Feng, M., Zheng, J., Ren, J., Hussain, A., Li, X., Xi, Y., & Liu, Q. (2019). Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE Access, 7, 106111–106123.

    Article  Google Scholar 

  3. Vaidya, O., Mitra, S., Kumbhar, R., Chavan, S., & Patil, M. R. (2018). Crime rate prediction using data clustering algorithms. International Research Journal of Engineering and Technology (IRJET). e-ISSN, 2395–0056.

    Google Scholar 

  4. Pande, V., Samant, V., & Nair, S. (2016). Crime detection using data mining. International Journal of Engineering Research & Technology (IJERT), 5(01), 2.

    Google Scholar 

  5. Gupta, M., Chandra, B., & Gupta, M. P. (2008). Crime data mining for Indian police information system. Computer Society of India, 40(1), 388–397.

    Google Scholar 

  6. Lavanyaa, S., & Akila, D. (2019). Crime against women (CAW) analysis and prediction in Tamilnadu police using data mining techniques. International Journal of Recent Technology and Engineering (IJRTE), 7(5C), 261.

    Google Scholar 

  7. Sangani, A., Sampat, C., & Pinjarkar, V. (2019 April). Crime prediction and analysis. In 2nd International Conference on Advances in Science & Technology (ICAST). SSRN Electronic Journal.

    Google Scholar 

  8. Gandhi, P., & Sharma, S. (2018 March). Approach of predictive modeling on crime against women problem. International Journal of Recent Research Aspects, 5(1), 284–288. ISSN: 2349–7688.

    Google Scholar 

  9. Palmer, P. B., & O’Connell, D. G. (2009). Regression analysis for prediction: Understanding the process. Cardiopulmonary Physical Therapy Journal, 20(3), 23.

    Article  Google Scholar 

  10. Goel, P., & Yadav, V. (2013). Crime against Indian women–women crime susceptibility indexes (WCSI): A principal component analysis. OIDA International Journal of Sustainable Development, 6(7), 93–102.

    Google Scholar 

  11. Anitha, A. (2020). Prediction of crime rate using data clustering technique. In Soft computing for problem solving (pp. 443–454). Singapore: Springer.

    Chapter  Google Scholar 

  12. Yamuna, S., & Bhuvaneswari, N. S. (2012). Datamining techniques to analyze and predict crimes. The International Journal of Engineering and Science (IJES), 1(2), 243–247.

    Google Scholar 

  13. Abbott, D. (2014). Applied predictive analytics: Principles and techniques for the professional data analyst. John Wiley & Sons.

    Google Scholar 

  14. Pepper, J. V. (2008 December). Forecasting crime: A city-level analysis. In Understanding crime trends: Workshop report (pp. 177–210). Washington, DC: National Research Council (The National Academies Press.

    Google Scholar 

  15. Han, J., & Kambel, M. (2012). Data mining: Concepts and techniques. Morang.

    Google Scholar 

  16. Kiani, R., Mahdavi, S., & Keshavarzi, A. (2015). Analysis and prediction of crimes by clustering and classification. International Journal of Advanced Research in Artificial Intelligence, 4(8), 11–17.

    Article  Google Scholar 

  17. Rahmatika, Y., Sediyono, E., & Widodo, C. E. (2020). Implementation of K-means clustering and weighted products in determining crime-prone locations. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(3), 3.

    Google Scholar 

  18. Sonaqwanev, T., Shaikh, S., Shaikh, S., Shinde, R., & Sayyad, A. (2015). Crime pattern analysis visualization and prediction using data mining. International Journal of Advance Research and Innovative Ideas in Education, 1, 681–686.

    Google Scholar 

  19. Agarwal, J., Nagpal, R., & Sehgal, R. (2013). Crime analysis using k-means clustering. International Journal of Computer Applications, 83(4), 1.

    Article  Google Scholar 

  20. Malathi, A., & Baboo, S. S. (2011). An enhanced algorithm to predict a future crime using data mining. International Journal of Computer Applications, 21, 1.

    Google Scholar 

  21. Kaur, B., Ahuja, L., & Kumar, V. (2018). Factors affecting crime against women using regression and K-means clustering techniques. In Industry interactive innovations in science, engineering and technology (pp. 149–162). Singapore: Springer.

    Chapter  Google Scholar 

  22. Bhattacharya, D. (2018 February). Crime detection technique using data mining and K-means. International Journal of Engineering Research & Technology (IJERT), 7(2). ISSN: 2278–0181.

    Google Scholar 

  23. Hamdy, E., Adl, A., Hassanien, A. E., Hegazy, O., & Kim, T. H. (2015 July). Criminal act detection and identification model. In 2015 seventh international conference on advanced communication and networking (ACN) (pp. 79–83). IEEE.

    Chapter  Google Scholar 

  24. Vijayarani, S., Suganya, E., & Navya, C. (2020 Jan-Feb). A comprehensive analysis of crime analysis using data mining techniques. International Journal of Computer Science Engineering (IJCSE), 9(1), ISSN: 2319–7323.

    Google Scholar 

  25. Garg, R., Malik, A., & Raj, G. (2018 January). A comprehensive analysis for crime prediction in Smart City using R programming. In 2018 8th international conference on cloud computing, Data Science & Engineering (confluence) (pp. 14–15). IEEE.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. VIT Vellore, Vellore, Tamil Nadu, India

    Preeti Rachel Jasper & G. Chemmalar Selvi

Authors
  1. Preeti Rachel Jasper
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Chemmalar Selvi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Preeti Rachel Jasper .

Editor information

Editors and Affiliations

  1. Professor (Associate) and Research Head, Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, TN, India

    Anandakumar Haldorai

  2. Professor, Department of Computer Science and Engineering, Presidency University, Bengaluru, KA, India

    Arulmurugan Ramu

  3. Principal, Sri Eshwar College of Engineering, Coimbatore, TN, India

    Sudha Mohanram

  4. Professor, Department of Computer Science, School of Computing and Engineering, Centre for Planning, Autonomy and Representation of Knowledge, University of Huddersfield, Huddersfield, UK

    Joan Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jasper, P.R., Chemmalar Selvi, G. (2022). Predictive Crime Analytics Using Data Science (India and the USA). In: Haldorai, A., Ramu, A., Mohanram, S., Lu, J. (eds) 3rd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-78750-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78750-9_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78749-3

  • Online ISBN: 978-3-030-78750-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation