A Bayesian change point model for spatio-temporal data

TL;DR

This paper introduces a Bayesian change point model tailored for analyzing long-term temperature data across multiple locations to detect and quantify local changes in temperature trends over time, particularly related to urban heat island effects.

Contribution

The paper presents a novel Bayesian change point methodology for spatio-temporal data, including a model selection process using DIC and application to real temperature data.

Findings

Effective detection of change points in temperature trends

Quantification of long-term temperature behavior changes

Validation on synthetic and real Utah temperature data

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.