Abstract

This research we propose the spatial nonhomogeneous Poisson process(SNHPP)
on geostatistical data by adding time component that called a space-time geostatistical
data, to implement in the study case of air pollution that is measured by the concentration
of NO2 and SO2 in 12 fixed observed locations in Indonesia
in 2012 to 2015 that measured twice in a year. The frequency of air pollution
gases exceed the thresholds over the years, so air pollution cases can be used in the
implementation of SNHPP model. By utilizing prior information, we use a Bayesian
approach to inference the proposed model using Markov Chain Monte Carlo(MCMC)
method. To generate samples of the conditional density of posterior distribution, we
wield Gibbs Sampling algorithm with Metropolis-Hastings step, and we obtained that
it has good convergence in this case. Deviance Information Criterion(DIC) is used to
get a fit SNHPP model, which is widely used in Bayesian modeling. In this case,
obtaining information about the districts in Indonesia a with each
concentration which most often exceeding each threshold. In addition, in the implementation
of the SNHPP model shows that space-time geostatistical data provides a
good enough performance.


Keywords: Spatial Nonhomogeneous Poisson Process(SNHPP),Space-time
Geostatistical Data, MCMC, Gibbs Sampling Algorithm with Metropolis-Hasting
Steps, Air Pollution

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