This workshop introduces Bayesian spatial and spatiotemporal modeling using R. It will be developed for graduate students across the whole geography spectrum, who would like to apply Bayesian statistics in their own research.
July 11, 2022, 11:00am Eastern Time – September 6, 2022, 2:30pm Eastern TimeWebinar Ended
Dr. Hui Luan is an Assistant Professor in the Department of Geography, University of Oregon. He has extensive experience in using spatiotemporal statistics, implemented with the Bayesian paradigm, to explore inequities of urban environmental exposures and their associations with human behaviors and health. His work uses both Markov chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) to implement Bayesian models that address common statistical issues in geographical datasets, including but not limited to spatial/temporal autocorrelation, zero-inflation, and data suppression.
We will select up to 20 graduate students to participate in this workshop. Selection will be based on your AAG membership status, your research needs, and time of registration. If you are selected, we will notify you ahead of the workshop and provide you all the workshop details and session links. If you are selected, the expectation is that you will participate in all sessions of the workshop.
The selected participants should be familiar with R programming for spatial statistics and mapping, understand concepts including probability distributions (e.g., Normal, Binomial, and Poisson), spatial weight matrix, and spatial/temporal autocorrelation.
This workshop will meet at the following times (Eastern Time):
Throughout the week, expect to also spend a few hours working independently on readings or short assignments for the workshop.