Abstract
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.
Highlights
Geographic vector data plays an important role in urban planning, land use, environmental factor analysis, and many other fields
In HiVision, a current visualization method based on display-driven computing model (DisDC), the vector data are organized based on the R-tree index structure, which leads to long data pre-processing time and large index occupation while dealing with large-scale geographic vector data
We propose HiIndex, a data organization method for DisDC, to realize the rapid organization of large-scale vector data and provide a rapid visualization algorithm for DisDC
Summary
Geographic vector data plays an important role in urban planning, land use, environmental factor analysis, and many other fields. Based on the core idea of DisDC, the visualization of geographic vector data can be converted into the key process for determining whether the spatial location of a pixel is within a certain pixel width of the boundary of vector objects. A TQ-treebased visualization algorithm (TQTBV) is designed, which is designed only to make an existential judgment of nodes in the constructed TQ-tree This eliminates the need for time-consuming spatial retrieval operations and further improves the rate of pixel value generation. Analyzes the demand for pixel generation, points out the limitations of applying current spatial indexing methods, and designs a TQ-tree index structure that can quickly determine pixel values without storing the original data, based on the characteristics of DisDC, the TQ-tree index is designed to enable rapid visualization of vector data through a simple structure.
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