Self-exciting point process modelling of crimes on linear networks
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Other documents of the author: D'ANGELO, Nicoletta; Payares García, David Enrique; adelfio, giada; Mateu, Jorge
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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https://doi.org/10.1177/1471082X221094146 |
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Title
Self-exciting point process modelling of crimes on linear networksDate
2022Publisher
SAGE PublicationsISSN
1471-082X; 1477-0342Bibliographic citation
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:10.1177/1471082X221094146Type
info:eu-repo/semantics/articlePublisher version
https://journals.sagepub.com/doi/10.1177/1471082X221094146Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
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. [-]
Is part of
Statistical Modelling, 2024, 24, 2Funder Name
Future Forests Research
Funder ID
FFR
Project code
FFR 2021
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© 2022 Statistical Modeling Society
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