Date of Award
Open Access Thesis
Epidemiology and Biostatistics
The Norman J. Arnold School of Public Health
The models with constant coefficients of the covariates across space and time are commonly used in spatio-temporal analyses. However, the associations between risk factors and the outcome could have locally differential temporal trends in many cases. In this study, a Bayesian latent cluster modeling strategy is employed to identify potential spatial clusters in which locally specific sets of temporally varying coefficients of covariates are allowed. A state-level panel data of police officers occupational fatal victimization for the years 1979-2010 is used. To accommodate overdisperson and excess zeros, a negative binomial model and zero-inflated Poisson/negative binomial models are also utilized. A series of alternative models are also applied to this data. The model comparison shows that the proposed latent clusters Zero-Inflated Poisson model is superior to the other models. The analysis using the proposed model illustrates the heterogeneity in the associations between police fatal victimization outcome and specific risk factors across the latent spatial clusters.
Xing, X.(2016). Spatio-Temporal Analysis Of The Occupational Fatal Victimization Of Law Enforcement Officers In The US. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/3846