Modeling noisy time-series data of crime with stochastic differential equations
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Título
Modeling noisy time-series data of crime with stochastic differential equationsFecha de publicación
2022-11-01Editor
SpringerISSN
1436-3240; 1436-3259Cita bibliográfica
CALATAYUD, Julia; JORNET, Marc; MATEU, Jorge. Modeling noisy time-series data of crime with stochastic differential equations. Stochastic Environmental Research and Risk Assessment, 2022, p. 1-14Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s00477-022-02334-8Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
We develop and calibrate stochastic continuous models that capture crime dynamics in the city of Valencia, Spain. From the emergency phone, data corresponding to three crime
events, aggressions, stealing and women ... [+]
We develop and calibrate stochastic continuous models that capture crime dynamics in the city of Valencia, Spain. From the emergency phone, data corresponding to three crime
events, aggressions, stealing and women alarms, are available from the year 2010 until 2020. As
the resulting time series, with monthly counts, are highly noisy, we decompose them into trend
and seasonality parts. The former is modeled by geometric Brownian motions, both uncorrelated and correlated, and the latter is accommodated by randomly perturbed sine-cosine waves.
Albeit simple, the models exhibit high ability to simulate the real data and show promising for
crimes-interaction identification and short-term predictive policing [-]
Publicado en
Stochastic Environmental Research and Risk Assessment, 2022, p. 1-14Entidad financiadora
Universitat Jaume I | Generalitat Valenciana | Ministerio de Ciencia e Innovación
Código del proyecto o subvención
POSDOC/2021/02 | PID2019-107392RB-I00 | AICO/2019/198
Derechos de acceso
"This is a post-peer-review, pre-copyedit version of an article published in Stochastic Environmental Research and Risk Assessment .
The final authenticated version is available online at: https://doi.org/10.1007/s00477-022-02334-8".
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
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