A conditional machine learning classification approach for spatio-temporal risk assessment of crime data

Alexandre Rodrigues, Jonatan A. González*, Jorge Mateu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Crime data analysis is an essential source of information to aid social and political decisions makers regarding the allocation of public security resources. Computer-aided dispatch systems and technological advances in geographic information systems have made analysing and visualising historical spatial and temporal records of crimes a vital part of police operations and strategy. We look at our motivating crime problem as a spatio-temporal point pattern. Using a conditional approach based on properties of Poisson point processes, we transform the spatio-temporal point process prediction problem into a classification problem. We create spatio-temporal handcrafted features to link future and past events and use machine learning algorithms to learn behavioural patterns from the data. The fitted model is then used to carry out the reverse transformation, i.e. to perform spatio-temporal risk predictions based on the outcomes of the classification problem. Our procedure has theoretical formalism from point process theory and gains flexibility and computational efficiency inherited from the machine learning field. We show its performance under some simulated scenarios and a real application to spatio-temporal prediction and risk assessment of homicides in Bogota, Colombia.

Original languageEnglish (US)
Pages (from-to)2815-2828
Number of pages14
JournalStochastic Environmental Research and Risk Assessment
Issue number7
StatePublished - Jul 2023

Bibliographical note

Funding Information:
This work was partially funded by grant PID2019-107392RB-I00 from the Spanish Ministry of Science and Innovation.

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.


  • Classification
  • Crime prediction
  • Machine learning classifiers
  • Performance criteria
  • Spatio-temporal point processes

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Water Science and Technology
  • Safety, Risk, Reliability and Quality
  • General Environmental Science


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