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arXiv · 2105.10637

A Bayesian change point model for spatio-temporal data

Candace Berrett·Brianne Gurney·David Arthur·Todd Moon·Gus P. Williams
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Abstract

Urbanization of an area is known to increase the temperature of the surrounding area. This phenomenon -- a so-called urban heat island (UHI) -- occurs at a local level over a period of time and has lasting impacts for historical data analysis. We propose a methodology to examine if long-term changes in temperature increases and decreases across time exist (and to what extent) at the local level for a given set of temperature readings at various locations. Specifically, we propose a Bayesian change point model for spatio-temporally dependent data where we select the number of change points at each location using a "forwards" selection process using deviance information criteria (DIC). We then fit the selected model and examine the linear slopes across time to quantify changes in long-term temperature behavior. We show the utility of this model and method using a synthetic data set and temperature measurements from eight stations in Utah consisting of daily temperature data for 60 years.

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Candace Berrett, Brianne Gurney, David Arthur, Todd Moon, Gus P. Williams. 2021-07-29. A Bayesian change point model for spatio-temporal data. https://arxiv.org/abs/2105.10637

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