A Bayesian change point modeling approach to identify local temperature changes related to urbanization

Changes to the environment surrounding a temperature measuring station can cause local changes to the recorded temperature that deviate from regional temperature behavior. This phenomenon—often caused by construction or urbanization—occurs at a local level. If these local changes are assumed to represent regional or global processes it can have significant impacts on historical data analyses. These changes or deviations are generally gradual, but can be abrupt, and arise as construction or other environmental changes occur near a recording station. We propose a methodology to examine if changes in temperature behavior at a point in time exist at a local level at various locations in a region assuming that regional or global processes are correlated among nearby stations. 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 “forward” selection process using deviance information criterion. We then fit the selected version of the model and examine the linear slopes across time to quantify the local changes in long-term temperature behavior. We show the utility of this model and method using both synthetic data and observed temperature measurements from eight stations in Utah consisting of daily temperature data for 60 years.