Bayesian Mixture Poisson Regression for Modeling Spatial Point Pattern of Primary Health Centers in Surabaya

Tri Murniati, Nur Iriawan, Dedy Dwi Prastyo


Primary Health Centres (PHC) are the first referral health facilities for Indonesian people to seek treatment. The varying distribution of PHC location in central Surabaya, therefore, causes its process to follow the Non-homogeneous Poisson Point Process (NHPP). We use Bayesian analysis coupled with Markov Chain Monte Carlo (MCMC) to model the mixture Poisson regression on NHPP intensity estimation. The result shows that two mixture components are significantly involved in the model along with four variables; i.e., the total population, the number of clean households, Accessibility Index, and the length of road; that produce the smallest Deviance Information Criteria (DIC).


Keywords: Bayesian Analysis; Mixture Poisson Regression; NHPP Intensity; Primary Health Centres

Full Text:




  • There are currently no refbacks.

UTM Logo

Copyright © 2016 Penerbit UTM Press, Universiti Teknologi Malaysia

Disclaimer: This website has been updated to the best of our knowledge to be accurate. However, Universiti Teknologi Malaysia shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.

Best viewed: Mozilla Firefox 4.0 & Google Chrome at 1024 × 768 resolution.