Skip to main content

Self-exciting Point Processes with Image Features as Covariates for Robbery Modeling

  • 1894 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 283)


State-of-the-art crime prediction models exploit the spatio-temporal clustering patterns and the self-exciting nature of criminality to predict vulnerable crime areas. However, omitting spatial covariates correlated with the occurrence of crimes potentially bias the estimated parameters. This research combines self-exciting point processes, generalized additive models and environmental attributes extracted through convolutional neural networks from street-level images to predict robbery hotspots across the locality of Chapinero in Bogota, Colombia. Our model using image features as covariates outperforms a standard self-exciting point process and shed light on the association between crime occurrence and the socioeconomic and environmental conditions of the city.


  • Self-exciting point process
  • Crime modeling
  • Street-level images
  • Environmental attributes

Results of the project “Diseño y validación de modelos de analítica predictiva de fenómenos de seguridad y convivencia para la toma de decisiones en Bogotá” funded by Colciencias with resources from the Sistema General de Regalías, BPIN 2016000100036. The opinions expressed are solely those of the authors.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-80119-9_58
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-80119-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.


  1. 1.

    We consider a robbery as the act of taking something from a person and using force, or the threat of force to do it. Hence, robbery is considered a theft with the use of violence.


  1. Acosta, S.A., Camargo, J.E.: Predicting city cafety perception based on visual image content. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 177–185 (2019)

    Google Scholar 

  2. Barreras, F., Díaz, C., Riascos, A.J., Ribero, M.: Comparación de diferentes modelos para la predicción del crimen en bogotá. Economía y seguridad en el posconflicto, p. 209 (2018)

    Google Scholar 

  3. Dulce, M., Ramírez-Amaya, S., Riascos, A.: Efficient allocation of law enforcement resources using predictive police patrolling. arXiv preprint arXiv:1811.12880 (2018)

  4. Eck, J., Chainey, S., Cameron, J., Wilson, R.: Mapping crime: Understanding hotspots (2005)

    Google Scholar 

  5. Ensign, D., Friedler, S.A., Neville, S., Scheidegger, C., Venkatasubramanian, S.: Runaway feedback loops in predictive policing. arXiv preprint arXiv:1706.09847 (2017)

  6. Goldsmith, V., McGuire, P.G., Mollenkopf, J.B., Ross, T.A.: Analyzing crime patterns: Frontiers of practice. Sage Publications (1999)

    Google Scholar 

  7. Greengard, S.: Policing the future. Commun. ACM 3, 19–21 (2012)

    Google Scholar 

  8. Hyeon-Woo, K., Hang-Bong, K.: Prediction of crime occurrence from multi-modal data using deep learning. PloS One 12(4), e0176244 (2017)

    Google Scholar 

  9. Lum, K., Isaac, W.: To predict and serve? Significance 13(5), 14–19 (2016)

    CrossRef  Google Scholar 

  10. Martin, B., Britt, D.: The Multiple Impacts of Mapping It Out: Police, Geographic Information Systems (GIS) and Community Mobilization During Devil’s Night in Detroit, Michigan (1998)

    Google Scholar 

  11. Mohler, G.: Marked point process hotspot maps for homicide and gun crime prediction in chicago. Int. J. Forecast. 30(3), 491–497 (2014)

    CrossRef  Google Scholar 

  12. Mohler, G.O., Short, M.B., Brantingham, P.J., Schoenberg, F.P., Tita, G.E.: Self-exciting point process modeling of crime. J. Am. Stat. Assoc. 106(493), 100–108 (2011)

    Google Scholar 

  13. Mohler, G.O., et al.: Randomized controlled field trials of predictive policing. J. Am. Stat. Assoc. 110(512), 1399–1411 (2015)

    Google Scholar 

  14. Reinhart, A., Greenhouse, J.: Self-exciting point processes with spatial covariates: modeling the dynamics of crime. arXiv preprint arXiv:1708.03579v2 (2019)

  15. Rosser, G., Cheng, T.: Improving the robustness and accuracy of crime prediction with the self-exciting point process through isotropic triggering. Applied Spatial Analysis and Policy, vol. 12, no. 07 (2016)

    Google Scholar 

  16. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  17. Wang, X., Brown, D.E.: The spatio-temporal modeling for criminal incidents. Secur. Inform. 1(1), 2 (2012)

    Google Scholar 

  18. Weisburd, D., Bushway, S., Lum, C., Yang, S.M.: Trajectories of crime at places: a longitudinal study of street segments in the city of seattle. Criminology 42, 283–322 (2004)

    Google Scholar 

  19. Weisburd, D., Groff, E., Yang, S.M.: The criminology of place: street segments and our understanding of the crime problem, pp. 1–288 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Mateo Dulce Rubio .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

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

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Dulce Rubio, M., Rodríguez Díaz, P., Moreno Pabón, J.S., Riascos, Á.J., Camargo, J.E. (2022). Self-exciting Point Processes with Image Features as Covariates for Robbery Modeling. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham.

Download citation