Jeremy L. Mennis

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This research demonstrates the application of association rule mining to spatio-temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A → B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio-temporal association rule occurs when there(More)
The advancement of GIS data models to allow the eŒective utilization of very large heterogeneous geographic databases requires a new approach that incorporates models of human cognition. The ultimate goal is to provide a cooperative human-computer environment for spatial analysis. We describe the pyramid framework as an example of this new approach within(More)
The purpose of this research is to develop a new kind of semantic GIS data model that is better able to represent users’ conceptual models of geographic domains than the conventional vector and raster data models. To this end, I look to the principles of cognition, how humans represent geographic information in their minds, to inform the development of this(More)
Due to the increasing volume of spatio-temporal data generated from remote sensing, sensor networks and computational simulation, there is a need for a generic, domain-independent framework for spatio-temporal data analysis. This research presents a generic set of data processing and manipulation tools for spatio-temporal raster data called multidimensional(More)
The advancement of GIS data models to allow the effective utilization of very large heterogeneous geographic databases requires a new approach that incorporates models of human cognition. The ultimate goal is to provide a cooperative human-computer environment for spatial analysis. We describe the Pyramid framework as an example of this new approach within(More)
Exploration of complex spatio-temporal environmental data demands creative methods of analysis. We present a suite of interactive visualization techniques to explore a three-dimensional Fourier transformation of a geographic time series. Such a transformation decomposes geographic time series data into coherent spatial and temporal periodicities that may be(More)