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
comunitat-uji-handle4:
INVESTIGACIONEste recurso está restringido
https://doi.org/10.1007/s00477-023-02420-5 |
Metadatos
Título
A conditional machine learning classification approach for spatio-temporal risk assessment of crime dataFecha de publicación
2023Editor
SpringerISSN
1436-3240; 1436-3259Cita bibliográfica
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/s00477-023-02420-5Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s00477-023-02420-5Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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. [-]
Publicado en
Stochastic Environmental Research and Risk Assessment, 2023, 37Entidad financiadora
Ministerio de Ciencia e Innovación de España
Código del proyecto o subvención
PID2019- 107392RB-I00
Derechos de acceso
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
Aparece en las colecciones
- MAT_Articles [760]