HiFIVE: High-Fidelity Vector-Tile Reduction for Interactive Map Exploration

TL;DR

HiFIVE is a framework that reduces data size for high-fidelity client-side geospatial visualization, balancing tile size and visual accuracy, enabling efficient exploration of large-scale spatial datasets.

Contribution

It formalizes the visualization-aware tile reduction problem, proves its NP-hardness, and proposes a novel two-stage solution for scalable, high-fidelity geospatial visualization.

Findings

Significant tile-size reduction achieved without compromising visual fidelity.

Maintains interactive performance on terabyte-scale datasets.

Effective data pruning based on information-theoretic and spatial criteria.

Abstract

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.