Use of a tree-structured hierarchical model for estimation of location and uncertainty in multivariate spatial data

2014 ◽  
Vol 28 (6) ◽  
pp. 523-538 ◽  
Author(s):  
Liyuan Chen ◽  
Steven D. Brown
Keyword(s):  
Hierarchical Model ◽  
Spatial Data ◽  
Multivariate Spatial Data

Decomposition of multivariate spatial data into latent factors

2021 ◽  
Vol 153 ◽  
pp. 104773
Author(s):  
Felipe Cabral Pinto ◽  
Johnathan G. Manchuk ◽  
Clayton V. Deutsch
Keyword(s):  
Spatial Data ◽  
Latent Factors ◽  
Multivariate Spatial Data

scholarly journals Multivariate spatial data visualization: a survey

2019 ◽  
Vol 22 (5) ◽  
pp. 897-912 ◽  
Author(s):  
Xiangyang He ◽  
Yubo Tao ◽  
Qirui Wang ◽  
Hai Lin
Keyword(s):  
Data Visualization ◽  
Spatial Data ◽  
Multivariate Spatial Data

Surface Glyphs for Efficient Visualization of Spatial Multivariate Data

2006 ◽  
Vol 5 (2) ◽  
pp. 112-124 ◽  
Author(s):  
Camilla Forsell ◽  
Stefan Seipel ◽  
Mats Lind
Keyword(s):  
Spatial Data ◽  
Vision Research ◽  
Structural Information ◽  
Multivariate Data ◽  
3D Structure ◽  
Qualitative Properties ◽  
Multivariate Spatial Data ◽  
Local Sign ◽  
New Type ◽  
Visualization Experiments

We present a first effort to evaluate the possible utility of a new type of surface glyphs intended for visualizations of multivariate spatial data. The glyphs are based on results from vision research suggesting that our perception of metric 3D structure is distorted and imprecise relative to the actual scene before us; only a class of qualitative properties of the scene is perceived with accuracy. These properties are best characterized as being invariant over affine but not Euclidean transformations. A large number of possible 3D glyphs for the visualization of spatial data can be constructed using such properties. One group of such glyphs is based on the local sign of surface curvature. We investigated this group in two visualization experiments. The results show that available sources of 3D structural information were sufficient for our subjects to make fast and accurate judgments. Some implications for visualization are also discussed.

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