Multilevel real-time visualization technology for large-scale geographic vector linestring data

被引：0
|
作者：
Liu Z. [1 ]
Chen L. [1 ]
Ma M. [1 ]
Yang A. [1 ]
Zhong Z. [1 ]
Jing N. [1 ]
机构：
[1] College of Electronic Science and Technology, National University of Defense Technology, Changsha
来源：
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2023年 / 45卷 / 05期
关键词：
big data; geographic vector linestring data; multilevel; real-time visualization; spatial index;
D O I：
10.11887/j.cn.202305020
中图分类号：
学科分类号：
摘要：
Aiming at the difficulty of mainstream methods to support the multilevel real-time visualization of large-scale geographic vector linestring data, a multilevel real-time visualization technique for large-scale geographic vector linestring data was proposed. An adaptive visualization model for multilevel tile rendering was established, and a PQR (pixel-quad-R) tree spatial index and an adaptive visualization algorithm based on PQR-tree were designed to support the data organization and visualization of the model, respectively. Experiments on billion-scale datasets show that the technique can calculate visualization results at any zoom level within 0. 57 s. Meanwhile, its visualization time is significantly less than mainstream methods. When the data scale increases sharply, the technology still has good visualization performance at each zoom level, and the lowest visualization rate exceeds 100 tiles/s, which is much better than mainstream methods. The technique can support multilevel real-time visualization of large-scale geographic vector linestring data in the single machine, and has a good application prospect in the field of exploration and analysis of spatial big data. © 2023 National University of Defense Technology. All rights reserved.
引用
下载
收藏
页码：173 / 183
页数：10
相关论文
共 38 条
  • [1] ZHOU C H., Prospects on pan-spatial information system[J], Progress in Geography, 34, 2, pp. 129-131, (2015)
  • [2] 2021 transport industry development statistical bulletin [EB/OL]
  • [3] OpenStreetMap data extracts
  • [4] ALI S M, GUPTA N, NAYAK G K, Et al., Big data visualization: tools and challenges [C], Proceedings of 2nd International Conference on Contemporary Computing and Informatics, (2016)
  • [5] KARIM A, SIDDIQA A, SAFDAR Z, Et al., Big data management in participatory sensing
  • [6] issues, trends and future directions, Future Generation Computer Systems, 107, pp. 942-955, (2020)
  • [7] COLTEKIN A, GRIFFIN A L, SLINGSBY A, Et al., Geospatial information visualization and extended reality displays, Manual of Digital Earth, pp. 229-277, (2019)
  • [8] ZHU Q, FU X., The review of visual analysis methods of multi-modal spatio-temporal big data [J], Acta Geodaetica et Cartographica Sinica, 46, 10, pp. 1672-1677, (2017)
  • [9] GUAN X F, XIE C, HAN L X, Et al., MAP-vis: a distributed spatio-temporal big data visualization framework based on a multi-dimensional aggregation pyramid model, Applied Sciences, 10, 2, (2020)
  • [10] KILSEDAR C E, BROVELLI M A., Multidimensional visualization and processing of big open urban geospatial data on the web, ISPRS International Journal of Geo-Information, 9, 7, (2020)
1234