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Activity Number:
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413
- Analyses of Environmental Data
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #318906
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Title:
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A Bayesian Change Point Model for Detecting Linear Changes Across Time for Spatio-Temporal Data
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Author(s):
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Candace Berrett* and Brianne Gurney
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Companies:
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Brigham Young University and Brigham Young University
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Keywords:
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Bayesian model selection;
temperature change;
urban heat island;
change point selection
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Abstract:
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Urbanization of an area is known to increase the temperature of the surrounding area (e.g., Oke 1973). 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” and “backwards” 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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Authors who are presenting talks have a * after their name.
