Spatio-temporal stochastic differential equations for crime incidence modeling
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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Título
Spatio-temporal stochastic differential equations for crime incidence modelingFecha de publicación
2023Editor
SpringerCita bibliográfica
Calatayud, J., Jornet, M. & Mateu, J. Spatio-temporal stochastic differential equations for crime incidence modeling. Stoch Environ Res Risk Assess (2023). https://doi.org/10.1007/s00477-022-02369-xTipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
We propose a methodology for the quantitative fitting and forecasting of real spatio-temporal crime data, based on
stochastic differential equations. The analysis is focused on the city of Valencia, Spain, for which ... [+]
We propose a methodology for the quantitative fitting and forecasting of real spatio-temporal crime data, based on
stochastic differential equations. The analysis is focused on the city of Valencia, Spain, for which 90247 robberies and
thefts with their latitude-longitude positions are available for a span of eleven years (2010–2020) from records of the
112-emergency phone. The incidents are placed in the 26 zip codes of the city (46001–46026), and monthly time series of
crime are built for each of the zip codes. Their annual-trend components are modeled by Itoˆ diffusion, with jointly
correlated noises to account for district-level relations. In practice, this study may help simulate spatio-temporal situations
and identify risky areas and periods from present and past data. [-]
Publicado en
Stochastic Environmental Research and Risk Assessment (2023).Entidad financiadora
Ministerio de Ciencia e Innovación | Generalitat Valenciana
Código del proyecto o subvención
PID2019-107392RB-I00 | AICO/2019/198
Derechos de acceso
info:eu-repo/semantics/openAccess
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