A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of Crimes
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of CrimesFecha de publicación
2022-06-29Editor
MDPICita bibliográfica
Escudero, I.; Angulo, J.M.; Mateu, J. A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of Crimes. Entropy 2022, 24, 892. https://doi.org/10.3390/e24070892Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Crime is a negative phenomenon that affects the daily life of the population and its development. When modeling crime data, assumptions on either the spatial or the temporal relationship between observations are ... [+]
Crime is a negative phenomenon that affects the daily life of the population and its development. When modeling crime data, assumptions on either the spatial or the temporal relationship between observations are necessary if any statistical analysis is to be performed. In this paper, we structure space–time dependency for count data by considering a stochastic difference equation for the intensity of the space–time process rather than placing structure on a latent space–time process, as Cox processes would do. We introduce a class of spatially correlated self-exciting spatio-temporal models for count data that capture both dependence due to self-excitation, as well as dependence in an underlying spatial process. We follow the principles in Clark and Dixon (2021) but considering a generalized additive structure on spatio-temporal varying covariates. A Bayesian framework is proposed for inference of model parameters. We analyze three distinct crime datasets in the city of Riobamba (Ecuador). Our model fits the data well and provides better predictions than other alternatives. [-]
Publicado en
Entropy, 2022, 24(7)Entidad financiadora
MCIU/AEI/ERDF | European Union | ERDF Operational Programme 2014–2020 | Economy and Knowledge Council of the Regional Government of Andalusia, Spain | Ministerio de Ciencia, Innovación y Universidades (Spain) | Universitat Jaume I
Código del proyecto o subvención
PGC2018-098860- B-I00 | A-FQM-345-UGR18 | CEX2020-001105-M MCIN/AEI/10.13039/501100011033 | PID2019-107392RB-I00/AEI/10.13039/501100011033 | UJI-B2018-04
Derechos de acceso
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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