Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women

Vicente, Gonzalo; Goicoa, T.; Ugarte, M. D.

doi:10.1093/biostatistics/kxab042

Cited by 11 publications

(5 citation statements)

References 44 publications

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“…This smooth surface for each t can be written as

, where

is a B-spline k -dimensional basis of dimension

arising from the Kronecker product per row of the marginal B-spline bases for

, and

. Note that

and

are the number of columns of the marginal bases

and

, respectively, and depend on the number of nodes and degree of polynomials used to generate these bases (see [ 34 ]). The generalized cross-validation (GCV) criterion is used to estimate the smoothing parameters, which provide the degree of smoothness.…”

Section: Methodsmentioning

confidence: 99%

A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of Crimes

Escudero

Angulo

Mateu

2022

Entropy

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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.

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“…This smooth surface for each t can be written as

, where

is a B-spline k -dimensional basis of dimension

arising from the Kronecker product per row of the marginal B-spline bases for

, and

. Note that

and

are the number of columns of the marginal bases

and

Section: Methodsmentioning

confidence: 99%

A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of Crimes

Escudero

Angulo

Mateu

2022

Entropy

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show abstract

“…. Se debe tener en cuenta que k 1 y k 2 son los números de columnas de las bases marginales B 1 y B 2 , respectivamente, y dependen del número de nodos y grado de polinomios utilizados para generar estas bases (Vicente et al, 2021). Aquí, se utiliza el criterio de validación cruzada generalizado (GCV) para estimar los parámetros que proporcionan el grado de suavizado.…”

Section: Estimación De La Función De Factores Exógenosunclassified

Estructuras estocásticas notables en procesos puntuales espacio-temporales y medidas de riesgo bivariantes

Villa¹

2023

Preprint

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La modelización estadística constituye un conjunto de formulaciones matemáticas que proporcionan un mejor entendimiento de los fenómenos, especialmente en los que subyacen relaciones entre variables que a su vez poseen influencia aleatoria. En este trabajo de tesis se realiza la modelización estadística del fenómeno delictivo, mediante el desarrollo de estructuras estocásticas en procesos puntuales espacio-temporales, con el propósito de reproducir eventos de la forma más exacta posible a la realidad en función de su evolución en el espacio, tiempo y espacio-tiempo.

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“…Boqué et al (2022) highlighted that one of the INLA advantages is that it avoids long computing time when compared with other MCMC approaches. Vicente Fuenzalida et al (2021) proposed Multivariate Bayesian spatio-temporal P-splines models fitted using INLA to study various types of violence against women in India. The model was effective in showing spatial patterns of different kinds of violence against women and in identifying high risk crime in specific areas (Vicente Fuenzalida et al 2021).…”

Section: Inlamentioning

confidence: 99%

Exploring the usefulness of the INLA model in predicting levels of crimes in the City of Johannesburg, South Africa

Coleman,

Mokilane,

Holloway

et al. 2023

Preprint

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Crime prediction serves as a valuable tool for deriving insightful information that can inform policy decisions at both operational and strategic tiers. This information can be used to optimize resource allocation and personnel management for crime prevention. Traditionally, the Poisson model has been the widely used model for crime prediction. However, recent statistical advancements introduce Integrated Nested Laplace Approximations (INLA) as a promising alternative for spatial and temporal data analysis. This study focusses on crime prediction using the INLA model. Specifically, the first-order autoregressive model under the INLA modelling framework is employed on longitudinal data for crime predictions in different regions of the City of Johannesburg, South Africa. The model parameters and hyperparameters considering space and time are estimated through the INLA model. In this work, the suitability and performance of the INLA model for crime prediction is assessed, which effectively captures spatial and temporal patterns. This study contributes to research by first introducing a novel approach for South African crime prediction, secondly developing a model using no demographic information other than clustering attributes as an exogenous variable, thirdly quantifying prediction uncertainty, and finally addressing data scarcity through demonstrating how INLA can provide reliable crime predictions, where conventional methods are limited. Based on our findings, the INLA model accurately ranked areas by crime levels, achieving a 29% Mean Absolute Percentage Error (MAPE) and 0.8 R-Squared value for crime predictions.

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Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women

Cited by 11 publications

References 44 publications

A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of Crimes

A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of Crimes

Estructuras estocásticas notables en procesos puntuales espacio-temporales y medidas de riesgo bivariantes

Exploring the usefulness of the INLA model in predicting levels of crimes in the City of Johannesburg, South Africa

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