Rapid interactive explorations of voluminous spatial temporal datasets

Abstract

Spatial data volumes have grown exponentially alongside the proliferation of sensing equipment and networked observational devices. In this thesis, we describe the framework aQua for performing visualizations and exploration of spatiotemporally evolving phenomena at scale, and Rubiks, which supports effective summarizations and explorations at scale over arbitrary spatiotemporal scopes, which encapsulate the spatial extents, temporal bounds, or combinations thereof over the data space of interest. We validate these ideas in the context of data from the National Hydrology Database (NHD) and the Environmental Protection Agency (EPA) to support longitudinal analysis (53 years of data) for the vast majority of water bodies in the United States. Our methodology addresses issues relating to preserving interactivity, effective analysis, dynamic query generation, and scaling. We extend the concept of data cubes to encompass spatiotemporal datasets with high-dimensionality and where there might be significant gaps in the data because measurements (or observations) of diverse variables are not synchronized and may occur at diverse rates. We consider optimizations and refinements at the server-side, client-side, and how information exchange occurs between the client and server-side. We report both quantitative and qualitative assessments of several aspects of our tool to demonstrate its suitability. Finally, our methodology is broadly applicable to domains where visualization-driven explorations of spatiotemporally evolving phenomena are needed.