Computer Science ›› 2020, Vol. 47 ›› Issue (9): 117-122.doi: 10.11896/jsjkx.190800121

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Display-oriented Data Visualization Technique for Large-scale Geographic Vector Data

MA Meng-yu, WU Ye, CHEN Luo, WU Jiang-jiang, LI Jun, JING Ning   

  1. College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China
  • Received:2019-08-23 Published:2020-09-10
  • About author:MA Meng-yu,born in 1992,Ph.D.His main research interests include GIS,geo-computation methods,high performance parallel computing,spatial data analysis and visua-lization.
    JING Ning,born in 1962,Ph.D,professor,Ph.D supervisor,is a senior fellow of China Computer Federation.His main research interests include geographical information systems,database systems,spatial data analysis,and visua-lization.
  • Supported by:
    National Natural Science Foundation of China (41471321,41871284).

Abstract: Rapid visualization of large-scale geographic vector data remains a challenging problem in geographic information scien-ce.In existing visualization methods,the computational scales expand rapidly with data volumes,leading to the result that it is difficult to provide real-time visualization for large-scale geographic vector data,though parallel acceleration technologies are adop-ted.This paper presents a display-oriented data visualization method for large-scale geographic vector data.Different from traditional methods,the core task of the display-oriented method is to determine the pixel range according to the screen display and then calculate the value of each pixel in the range.As the number of pixels for display is stable,the display-oriented data visualization method is less sensitive to data volumes and can be used to provide real-time data visualization for large-scale geographic vector data.Experiments show that our approach is capable of handling 100-million-scale geographic vector data.

Key words: Data visualization, Display-oriented computing, Parallel computing, Real-time, Spatial big data

CLC Number: 

  • TP391
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