A conditional machine learning classification approach for spatio-temporal risk assessment of crime data
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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https://doi.org/10.1007/s00477-023-02420-5 |
Metadata
Title
A conditional machine learning classification approach for spatio-temporal risk assessment of crime dataDate
2023Publisher
SpringerISSN
1436-3240; 1436-3259Bibliographic citation
RODRIGUES, Alexandre; GONZÁLEZ, Jonatan A.; MATEU, Jorge. A conditional machine learning classification approach for spatio-temporal risk assessment of crime data. Stochastic Environmental Research and Risk Assessment, 37, 2815–2828 (2023). https://doi.org/10.1007/s00477-023-02420-5Type
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/article/10.1007/s00477-023-02420-5Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
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 ... [+]
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. [-]
Is part of
Stochastic Environmental Research and Risk Assessment, 2023, 37Funder Name
Ministerio de Ciencia e Innovación de España
Project code
PID2019- 107392RB-I00
Rights
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023
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- MAT_Articles [766]