Spatio-Temporal Poverty Analysis with INLA in Hierarchical Bayes Ecological Regression

Abstract Spatio-temporal analysis widely used to describe geo-referenced data that contain information about space and time, with many important response variables and predictors. The models are usually presented as maps to represent the spatial dependence and temporal correlation from time to time. Spatio-temporal models presented in this paper are designed with hierarchical fashion and estimated with INLA (Integrated Nested Laplace Approximation) as the current estimation method for Bayesian analysis. INLA based on latent Gaussian posterior distribution which provides great computational benefit and solve the convergence issue in MCMC (Markov Chain Monte Carlo) algorithm. We model the poverty data set using classical, dynamic and space-time interaction of spatio-temporal models, and investigate the poverty relationship with socio-economics predictors. Using R-INLA package and deviance information criteria for models best fit selection, we conclude dynamical non-parametric is the most proper model on its ecological regressions.

[1]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[2]  M. Yunus,et al.  Spatial and population drivers of persistent cholera transmission in rural Bangladesh: Implications for vaccine and intervention targeting. , 2018, Spatial and spatio-temporal epidemiology.

[3]  H. Rue,et al.  Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .

[4]  D. Clayton,et al.  Bayesian analysis of space-time variation in disease risk. , 1995, Statistics in medicine.

[5]  Gianluca Baio,et al.  Spatial and spatio-temporal models with R-INLA. , 2013, Spatial and spatio-temporal epidemiology.

[6]  M. Dolores Ugarte,et al.  Statistical Methods for Spatio-temporal Systems , 2006 .

[7]  Yang Liu,et al.  Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004–2013 , 2015, Environmental health perspectives.

[8]  Boris Worm,et al.  Applying Bayesian spatiotemporal models to fisheries bycatch in the Canadian Arctic , 2015 .

[9]  J. R. P. D. Carvalho,et al.  Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones , 2016 .

[10]  Andrea Riebler,et al.  Stomach cancer incidence in Southern Portugal 1998–2006: A spatio‐temporal analysis , 2014, Biometrical journal. Biometrische Zeitschrift.

[11]  H. Rue,et al.  Spatio-temporal modeling of particulate matter concentration through the SPDE approach , 2012, AStA Advances in Statistical Analysis.

[12]  Ulf Dieckmann,et al.  Spatio-Temporal Processes in Ecological Communities , 1999 .

[13]  R. N. Rachmawati,et al.  Spatio-temporal Models Using R-INLA with Generalized Extreme Value Distribution in Hierarchical Bayes Regression , 2018 .

[14]  L Knorr-Held,et al.  Bayesian modelling of inseparable space-time variation in disease risk. , 2000, Statistics in medicine.

[15]  J. Gulliver,et al.  Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas , 2013, Journal of Exposure Science and Environmental Epidemiology.

[16]  Duanping Liao,et al.  Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors , 2014, Environmental Health.