Skip to main content
padlock icon - secure page this page is secure

A spatially adaptive decomposition approach for parallel vector data visualization of polylines and polygons

Buy Article:

$60.00 + tax (Refund Policy)

With the wide adoption of big spatial data and the emergence of CyberGIS, the nontrivial computational intensity introduced by massive amount of data poses great challenges to the performance of vector map visualization. The parallel computing technologies provide promising solutions to such problems. Evenly decomposing the visualization task into multiple subtasks is one of the key issues in parallel visualization of vector data. This study focuses on the decomposition of polyline and polygon data for parallel visualization. Two key factors impacting the computational intensity were identified: the number of features and the number of vertices of each feature. The computational intensity transform functions (CITFs) were constructed based on the linear relationships between the factors and the computing time. The computational intensity grid (CIG) can then be constructed using the CITFs to represent the spatial distribution of computational intensity. A noninterlaced continuous space-filling curve is used to group the lattices of CIG into multiple sub-domains such that each sub-domain entails the same amount of computational intensity as others. The experiments demonstrated that the approach proposed in this paper was able to effectively estimate and spatially represent the computational intensity of visualizing polylines and polygons. Compared with the regular domain decomposition methods, the new approach generated much more balanced decomposition of computational intensity for parallel visualization and achieved near-linear speedups, especially when the data is greatly heterogeneously distributed in space.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: computational intensity; decomposition; load-balance; vector map; visualization

Document Type: Research Article

Affiliations: 1: Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan, Hubei, China 2: GIS Software and Application Research Center, Ministry of Education of China, China University of Geosciences (Wuhan), Wuhan, Hubei, China

Publication date: August 3, 2015

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more