David White
Denison University
Title
An overview of spatiotemporal autocorrelation, with a view towards TGDA
Abstract
I will give an overview of time series data (that is, data sampled at different points in time) and spatially autocorrelated data (e.g., home prices, where the price of the house next to yours contains information about the likely price of your house). I’ll then discuss the most common statistical models for such data, including ARIMA models that factor in what the past knows about the present, spectral models based on Fourier analysis, spatially weighted models, and the general linear mixed model for data with both spatial and temporal autocorrelation. I'll illustrate these models with vignettes from my research, on problems related to policing, protests, and opioid overdose in Ohio. I hope there will be time at the end to brainstorm ways that TGDA could improve on these models.
