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Capturing Clustering Behavior in Spatio-Temporal Data (307944)*Laura Lindley Tupper, Williams College
Keywords: spatio-temporal data, spatial analysis, time series analysis, clustering, climate data
Measurements of wind speed, precipitation, and other climate-related data often show spatial, temporal, or spatio-temporal clusters or repeated patterns, some of which may be evident only in specific representations of the data. To obtain realistic simulations and forecasts, models for these processes should reflect similar patterns of behavior. Some clustering behavior can be recreated simply from the local dependence structure defined in a model, but specific methods are necessary to capture and reproduce other types of clusters. We discuss modifications to standard models that ensure the preservation of clustering behavior, either by matching the overall level of "clusteredness" in the data or by recreating specific cluster patterns found in the original data.