Maryland Sea Grant seeks to hire a Legal Fellow and a Graduate Assistant. More details.
On Information Criteria for Dynamic Spatio-temporal Clustering
Schaeffer, ED; Testa, JM; Gel, YR; Lyubchich, V
Modern climate data sets, including paleoreconstructions, long-term weather monitoring records, and remote sensing data, contain a wealth of space-timeinformation that leads to a variety of challenges related to data storage, management, and analysis. This has sparked an interest in dynamic space-time clustering algorithms that are particularly suitable for the analysis of large data streams. The trend-based clustering algorithm TRUST allows segmentation of space-time processes in real time, but requires the user to set multiple tuning parameters, and this step is usually performed in a subjective manner. Here we propose a data-driven automatic approach to simultaneously select the tuning parameters based on a penalized loss function. We focus on the two most important parameters of the TRUST algorithm, which define short-term closeness of observations across locations and long-term persistence of such closeness within an analyzed time window. We demonstrate the performance of the enhanced clustering procedure using simulated time series, and illustrate its applicability using long-term records of water temperature in Chesapeake Bay.
'Related Research Project(s)' link to details about research projects funded by Maryland Sea Grant that led to this publication. These details may include other impacts and accomplishments resulting from the research.
'Maryland Sea Grant Topic(s)' links to related pages on the Maryland Sea Grant website.