Spatiotemporal Crime Hotspots Analysis and Crime Occurrence Prediction

  • Niyonzima IbrahimEmail author
  • Shuliang Wang
  • Boxiang Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)


Advancement of technology in every aspect of our daily life has shaped an expanded analytical approach to crime. Crime is a foremost problem where the top priority has been concerned by the individual, the community and government. Increasing possibilities to track crime events give public organizations and police departments the opportunity to collect and store detailed data, including spatial and temporal information. Thus, exploratory analysis and data mining become an important part of the current methodology for the detection and forecasting of crime development. Spatiotemporal crime hotspots analysis is an approach to analyze and identify different crime patterns, relations, and trends in crime with identification of highly concentrated crime areas. In this paper spatiotemporal crime hotspots analysis using the dataset of the city of Chicago was done. First, we explored the spatiotemporal characteristics of crime in the city, secondary we explored the time series trend of top five crime types, Thirdly, the seasonal autoregressive integrated moving average model (SARIMA) based crime prediction model is presented and its result is compared to the one of the recently developed models based on deep learning algorithms for forecasting time series data, Long Short-Term Memory (LSTM). The results show that LSTM outperforms SARIMA model.


Crime prediction Time series SARIMA LSTM 



This work was supported by National Key Research and Development Program of China (2016YFC0803000), Beijing Municipal Science and Technology Projects under Grant (No. Z171100005117002).


  1. 1.
    Dubey, N., et al.: Int. J. Eng. Res. Appl. 4(3), 396–400 (2014). ISSN: 2248-9622 (Version 1)Google Scholar
  2. 2.
    Sathyadevan, S., Devan, M.S., Surya, G.S.: Crime analysis and prediction using data mining. In: International Conference on Networks & Soft Computing, pp. 406–412 (2014)Google Scholar
  3. 3.
    Thongtae, P., Srisuk, S.: An analysis of data mining applications in crime domain. In: International Conference on Computer and Information Technology Workshops, pp. 122–126 (2008)Google Scholar
  4. 4.
    Grover, V., Adderley, R., Bramer, M.: Review of current crime prediction techniques. In: Ellis, R., Allen, T., Tuson, A. (eds.) Applications and Innovations in Intelligent Systems. SGAI 2006, vol. 14, pp. 233–237. Springer, London (2007). Scholar
  5. 5.
    Liu, H., Brown, D.E.: Criminal incident prediction using a point-pattern- based density model. Int. J. Forecast. 19, 603–622 (2003)CrossRefGoogle Scholar
  6. 6.
    Vold, G.B.: Prediction methods applied to problems of classification within institutions. J. Crim. Law Criminol. 26, 202–209 (1951)CrossRefGoogle Scholar
  7. 7.
    Babakura, A., Sulaiman, M.N., Yusuf, M.A.: Improved method of classification algorithms for crime prediction. In: International Symposium on Biometrics and Security Technologies (ISBAST), pp. 250–255 (2014)Google Scholar
  8. 8.
  9. 9.
    Brantingham, P.L., Brantingham, P.J., Vajihollahi, M., Wuschke, K.: Crime analysis at multiple scales of aggregation: a topological approach. In: Weisburd, D., Bernasco, W., Bruinsma, G.J. (eds.) Putting Crime in its Place, pp. 87–107. Springer, New York (2009). Scholar
  10. 10.
    Eck, J., Chainey, S., Cameron, J., Wilson, R.: Mapping crime: Understanding hotspots. National Institute of Justice, Washington DC (2005)Google Scholar
  11. 11.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  12. 12.
    Egenhofer, M.J., Franzosa, R.D.: Point-set topological spatial relations. Int. J. Geogr. Inf. Syst. 5(2), 161–174 (1991)CrossRefGoogle Scholar
  13. 13.
    Tseng, F.-M., Tzeng, G.-H.: A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets Syst. 126, 367–376 (2002)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Niyonzima Ibrahim
    • 1
    Email author
  • Shuliang Wang
    • 1
  • Boxiang Zhao
    • 1
  1. 1.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingPeople’s Republic of China

Personalised recommendations