HiVecMap: A parallel tool for real-time geovisualization of massive geographic vector data

Chen, Luo; Liu, Zebang; Ma, Mengyu

doi:10.1016/j.softx.2022.101144

Cited by 1 publication

(1 citation statement)

References 19 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of geographic time series data visualization, some experts have also proposed using data aggregation and generalization strategies to compress information and reduce the number of variables that need to be displayed [5]. Based on the analysis of the above research ideas, in our previous work [22][23][24], we designed a series of methods including the computing framework, data index, and visualization tool for BGVD visualization, which achieved a greater performance improvement compared with traditional mainstream methods. Furthermore, in this paper, we carry out theoretical abstraction and modeling analysis on the research ideas and processing flow, and obtain the viewport generalization visualization model, which provides a stronger theoretical support for the method.…”

Section: Related Workmentioning

confidence: 99%

Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model

Chen

Liu

2022

Applied Sciences

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%