The crux of this study is to support browser-based visualizations of spatiotemporally evolving phenomena. Such phenomena arise in myriad domains spanning terrestrial, oceanic, and atmospheric processes. The data are voluminous, have diverse representational formats and projection systems, and are multivariate. We rely on a novel mix of tiling, caching, compression, perceptual limits, speculative prefetching, and dynamic generation of tiles. Our refinements at the client and server-side work in concert with each other to leverage client-side resources, minimize duplicate processing, and effective prefetching to ensure interactive explorations at scale. Our benchmarks profiled several aspects of our methodology and demonstrate the suitability of our refinements.

Exponential growth in spatial data volumes have occurred alongside increases in the dimensionality of datasets and the rates at which observations are generated. Rapid summarization and explorations of such datasets are a precursor to several downstream operations including data wrangling, preprocessing, hypothesis formulation, and model construction among others. However, researchers are stymied both by the dimensionality and data volumes that often entail extensive data movements, computation overheads, and I/O. Here, we describe our methodology to support 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. Summarizations can be performed over all variables representing the dataspace or subsets specified by the user. 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 couple our data summarization features with a rapid Choropleth visualizer that allows users to explore spatial variations of diverse measures of interest. We validate these concepts in the context of an Environmental Protection Agency dataset which tracks over 4000 chemical pollutants, presenting in natural water sources across the United States from 1970 onwards.

Spatia1 data volumes have grown exponentially alongside the proliferation of sensing equipment and networked observational devices. In this study, we describe our framework aQua for performing visualizations and exploration of spatiotemporally evolving phenomena at scale. We validate our 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, GPU accelerated visualizations, dynamic query generation, and scaling. 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.