Interagency Agreement to Support The National Imagery and Mapping Agency University Research Initiatives - NURI Activities

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This action provides funds to be transferred from the National Science Foundation (NSF) to the National Imagery and Mapping Agency (NIMA). These funds will be used by NIMA to help support a project on Spatio-Temportal Panning and Zooming that has been selected through a NIMA-administered competitive evaluation process as part of the NIMA University Research Initiative (NURI) program. The project will be conducted by Kathleen Hornsby and colleagues at the University of Maine.Spatio-temporal knowledge representation often requires changing from one level of detail to another so that users can carry out a desired task. Although abstraction, or the opposite task of shifting to more detailed views, are routinely and intuitively carried out by humans, formalizing these shifts for integration into information system query languages offers many challenges. Current geographic information systems (GIS), for example, allow for zooms based on geometric properties only. Similarly, GIS users can change only their perspective of data by panning. This project goes beyond graphic zooming or panning by considering another important perspective - time. This project will develop a formal model to support changes in levels of temporal detail. Using the identity-based approach, a set of temporal pan and zoom operators will be introduced. These operators will be embedded into a spatial query language and ultimately integrated with the spatial pan and zoom operators to become a set of spatio-temporal operations. The project will also examine the utility and mathematical properties of using lattices as an underlying structure for organizing views of objects at different levels of temporal detail. Temporal panning and zooming result in either simpler views that are easier to understand or more detailed views that uncover information that is otherwise unknown. The research will advance the theory on temporal abstractions, which is important for users of very large geospatial databases. The findings will be of particular relevance to information management of massive databases, and will be applicable to a variety of phenomena that involves time such as meteorological occurrences and population movements.