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FUSING POINT AND AREAL LEVEL SPACE-TIME DATA WITH APPLICATION TO WET DEPOSITION
Citation:
Sahu, S., A. Gelfand, AND D. M. HOLLAND. FUSING POINT AND AREAL LEVEL SPACE-TIME DATA WITH APPLICATION TO WET DEPOSITION. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. American Statistical Association, Alexandria, VA, 59(1):77-103, (2010).
Impact/Purpose:
Healthy Communities and Ecosystems - by providing new approaches to characterize landscape features, conditions, and change.
Description:
Motivated by the problem of predicting annual wet chemical deposition in the eastern United States, this paper develops a framework for joint modeling of point and grid referenced spatio-temporal data. The proposed hierarchical model is able to provide accurate spatial interpolation and temporal aggregation by combining information from observed point referenced data and gridded output from a numerical simulation model known as the Community Multi-scale Air Quality (CMAQ) model. The technique avoids the change of support problem which arises in other hierarchical models for data fusion settings to combine point and grid referenced spatial and spatio-temporal data. The hierarchical space-time model is fitted to weekly wet chemical deposition data both for the sulfate and nitrate compounds covering the eastern United States. The model is validated with set-aside data from a number of monitoring sites. Predictive Bayesian methods are developed and illustrated for inference on aggregated summaries such as quarterly and annual deposition maps.