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In the big data era, rapid visualization of large-scale vector data has become a serious challenge in Geographic Information Science (GIS). To fill the gap, we propose HiIndex, a spatial index that enables real-time and interactive visualization of large-scale vector data. HiIndex improves the state of the art with its low memory requirements, fast construction speed, and high visualization efficiency. In HiIndex, we present a tile-quadtree structure (TQ-tree) which divides the global geographic range based on the quadtree recursion method, and each node in the TQ-tree represents a specific and regular spatial range. In this paper, we propose a quick TQ-tree generation algorithm and an efficient visualization algorithm. Experiments show that the HiIndex is simple in structure, fast in construction, and less in memory occupation, and our approach can support interactive and real-time visualization of billion scale vector data with negligible pre-treatment time. [ABSTRACT FROM AUTHOR]

The visual exploration of geospatial vector data has become an increasingly important part of the management and analysis of geospatial vector big data (GVBD). With the rapid growth of data scale, it is difficult to realize efficient visual exploration of GVBD by current visualization technologies even if parallel distributed computing technology is adopted. To fill the gap, this paper proposes a visual exploration approach of GVBD on the web map. In this approach, we propose the display-driven computing model and combine the traditional data-driven computing method to design an adaptive real-time visualization algorithm. At the same time, we design a pixel-quad-R tree spatial index structure. Finally, we realize the multilevel real-time interactive visual exploration of GVBD in a single machine by constructing the index offline to support the online computation for visualization, and all the visualization results can be calculated in real-time without the external cache occupation. The experimental results show that the approach outperforms current mainstream visualization methods and obtains the visualization results at any zoom level within 0.5 s, which can be well applied to multilevel real-time interactive visual exploration of the billion-scale GVBD. • A Pixel-Quad-R-tree structure is designed to support the efficient visual exploration of geospatial vector big data (GVBD), the index can adapt to the computational requirements in the subsequent tile drawing process and provide efficient data organization support for GVBD visual exploration. • The PQR-tree-based Adaptive Tile Drawing (PATD) algorithm is proposed, PATD adaptively adopts the optimal pixel generation strategy to determine and achieve efficient visualization performance on a single machine. Meanwhile, the visualization results at each zoom level can be generated in real-time without pre-computation for caching. • An open-source visual exploration tool of GVBD is designed, which can browse data in real-time. Users can either zoom out to inspect the overall distribution or zoom in to view the individual details. Moreover, the tool also supports real-time interactive customization of data styles. [ABSTRACT FROM AUTHOR]