Self-exciting point process modelling of crimes on linear networks
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https://doi.org/10.1177/1471082X221094146 |
Metadatos
Título
Self-exciting point process modelling of crimes on linear networksFecha de publicación
2022Editor
SAGE PublicationsISSN
1471-082X; 1477-0342Cita bibliográfica
D’Angelo N, Payares D, Adelfio G, Mateu J. Self-exciting point process modelling of crimes on linear networks. Statistical Modelling. 2024;24(2):139-168. doi:10.1177/1471082X221094146Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://journals.sagepub.com/doi/10.1177/1471082X221094146Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Although there are recent developments for the analysis of first and second-order characteristics of point processes on networks, there are very few attempts in introducing models for network data. Motivated by the ... [+]
Although there are recent developments for the analysis of first and second-order characteristics of point processes on networks, there are very few attempts in introducing models for network data. Motivated by the analysis of crime data in Bucaramanga (Colombia), we propose a spatiotemporal Hawkes point process model adapted to events living on linear networks. We first consider a non-parametric modelling strategy, for which we follow a non-parametric estimation of both the background and the triggering components. Then we consider a semi-parametric version, including a parametric estimation of the background based on covariates, and a non-parametric one of the triggering effects. Our model can be easily adapted to multi-type processes. Our network model outperforms a planar version, improving the fitting of the self-exciting point process model. [-]
Publicado en
Statistical Modelling, 2024, 24, 2Entidad financiadora
Future Forests Research
Identificador de la entidad financiadora
FFR
Código del proyecto o subvención
FFR 2021
Derechos de acceso
© 2022 Statistical Modeling Society
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