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.

The visualization of geographic vector data is an important premise for spatial analysis and spatial cognition. Traditional geographic vector data visualization methods are data-driven, and their computational costs have increased rapidly with the growth of the scale of data used. Even if the distributed parallel strategy is used, it is still difficult to achieve a real-time response when dealing with big geographic vector data (BGVD). To solve this problem, this paper proposes a viewport generalization model and a visualization method for the online interactive visualization of BGVD. The method takes the viewport display pixel as the analysis unit and synthesizes the existence or quantity results of geographic vector data in the corresponding spatial range of each viewport display pixel into the display value of this display pixel; thus, it converts traditional computational complexity, dependent on the data scale, into computational complexity dependent on the number of pixels in the viewport. When the number of pixels in the viewport is much smaller than that of the geographic vector data, the visualization efficiency is greatly improved. In order to realize the above conversion, the pixel quadtree index (VPQ) structure and the real-time visualization algorithm of geographic vector data based on VPQ are proposed. Experiments show that the proposed method can achieve the near-real-time interactive visualization of BGVD, and provides more than a tenfold performance improvement over the best existing methods.

A distance range join query (DRJQ) is a fundamental and critical operation in spatial database queries. It identifies geographic elements within specified distance ranges. This technique has a wide range of applications in multiple domains, including Geographic Information Systems (GISs), urban planning, and environmental monitoring. However, performing a DRJQ on large-scale spatial data remains a challenging problem, as the computational complexity of existing techniques escalates rapidly with increasing volumes of data. We propose PixelQuery, an efficient DRJQ method specifically optimized for visualization analysis. PixelQuery integrates spatial indexing with visualization-oriented strategies. It directly computes the display values of query results within the viewport, substantially lowering computational costs. Experiments conducted on datasets of varying scales demonstrate that this method can handle visualization queries involving tens of millions of elements on a standard laptop, with a maximum processing time of only 7.64 s. This technology provides a robust solution for rapid DRJQ processing and the visualization of large-scale vector data, offering promising potential for a diverse range of applications.