  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.
