HiFIVE: High-Fidelity Vector-Tile Reduction for Interactive Map Exploration
This addresses the need for scalable, high-fidelity client-side geospatial visualization for users exploring large open-data repositories, representing a novel method for a known bottleneck.
The paper tackles the problem of client-side rendering for large-scale spatial data visualization by introducing HiFIVE, a framework that reduces vector-tile sizes while preserving visual fidelity. Experiments show it achieves substantial tile-size reductions at terabyte scale with maintained interactive performance.
Interactive visualization is a common tool for exploring large open-data repositories, where users quickly explore datasets across diverse domains. When it comes to large-scale spatial data, many existing tools rely on server-side rendering to produce small images that can be viewed at the client-side. However, most users prefer client-side rendering that allows quick styling of the data for better visualization experience. This paper presents HiFIVE, a data-management framework for scalable, high-fidelity client-side geospatial visualization. We formalize the visualization-aware tile reduction problem, which captures the trade-off between tile-size and visualization distortion, and prove its NP-hardness. HiFIVE introduces a two-stage solution combining triage and sparsification to selectively prune records, attributes, and values based on information-theoretic and spatial criteria. Experiments demonstrate substantial tile-size reductions while preserving visual fidelity and interactive performance at terabyte scale.