Visual Inquiry of Spatio-Temporal Multivariate Patterns

While many large, multivariate datasets carry geographic and temporal references, our ability to analyze these datasets lags behind our ability to collect them because of the challenges posed by complexity and scalability issues. This research aims to develop a visual analytics approach that integrates visual, computational and cartographic methods and couples them with human knowledge and judgment to support the exploratory analysis of spatio-temporal, high dimensional, large datasets. By combining both human and machine strengths, the proposed research has a better chance to discover novel, relevant and potentially useful information than can be achieved by applying any of the methods in isolation. The effectiveness of the proposed approach will be tested via case analysis on a variety of systematically selected sample datasets.