| Full text | |
| Author(s): |
Ludwig, Guilherme
;
Wang, Yuan
;
Chu, Tingjin
;
Wang, Haonan
;
Zhu, Jun
Total Authors: 5
|
| Document type: | Journal article |
| Source: | SPATIAL STATISTICS; v. 67, p. 17-pg., 2025-02-28. |
| Abstract | |
Statistical analysis has been successfully applied to crime data for identification of crime hot spots and prediction of future crimes. In this paper, our main objective is to identify key factors for gun violence in Rio de Janeiro and study the relationship between these key factors and the number of reported events. We use a Bayesian hierarchical stochastic Poisson regression model for spatial counts, which enables us to address the over-dispersed count data and to handle the spatial correlation. Moreover, we propose a variable selection method for key factor identification based on the spike-and-slab prior distribution for the regression coefficients. A new Gibbs sampler is developed for sampling from the posterior distributions with the help of augmentation of P & oacute;lya-Gamma auxiliary variables. Simulation studies are used to demonstrate the performance of our proposed approach. Our analysis of the gun violence data in Rio de Janeiro reveals the relationship between violence events and socio-demographic covariates as well as an interpretable spatial random effect that accounts for unmeasured covariate information. (AU) | |
| FAPESP's process: | 23/02538-0 - Time series, wavelets, high dimensional data and applications |
| Grantee: | Aluísio de Souza Pinheiro |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/04654-9 - Time series, wavelets and high dimensional data |
| Grantee: | Pedro Alberto Morettin |
| Support Opportunities: | Research Projects - Thematic Grants |