A semiparametric spatiotemporal Hawkes-type point process model with periodic background for crime data
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https://doi.org/10.1111/rssa.12429 |
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Título
A semiparametric spatiotemporal Hawkes-type point process model with periodic background for crime dataFecha de publicación
2018-12Editor
Wiley; Royal Statistical SocietyCita bibliográfica
ZHUANG, Jiancang; MATEU, Jorge. A semiparametric spatiotemporal Hawkes‐type point process model with periodic background for crime data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 2018.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12429Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Past studies have shown that crime events are often clustered. This study proposes
a spatiotemporal Hawkes-type point process model, which includes a background component
with daily and weekly periodization, and a ... [+]
Past studies have shown that crime events are often clustered. This study proposes
a spatiotemporal Hawkes-type point process model, which includes a background component
with daily and weekly periodization, and a clustering component that is triggered by previous
events. We generalize the non-parametric stochastic reconstruction method so that we can
estimate each component in the background rate and the triggering response that appears in
the model conditional intensity: the background rate includes a daily and a weekly periodicity, a
separable spatial component and a long-term background trend. Two relaxation coefficients are
introduced to stabilize and secure the estimation process. This model is used to describe the
occurrences of violence or robbery cases in Castell ́
on, Spain, during 2 years. The results show
that robbery crime is highly influenced by daily life rhythms, revealed by its daily and weekly
periodicity, and that about 3% of such crimes can be explained by clustering. Further diagnostic
analysis shows that the model could be improved by considering the following ingredients: the
daily occurrence patterns are different between weekends and working days; in the city centre,
robbery activity shows different temporal patterns, in both weekly periodicity and long-term
trend, from other suburb areas. [-]
Derechos de acceso
© 2018 Royal Statistical Society
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