scholarly journals Universal kriging of multivariate spatial data under multivariate isotropic power type variogram model

2021 ◽  
Author(s):  
Wayan Somayasa ◽  
Makulau ◽  
Yulius Bara Pasolon ◽  
Desak Ketut Sutiari
Keyword(s):  
Spatial Data ◽  
Universal Kriging ◽  
Power Type ◽  
Multivariate Spatial Data ◽  
Variogram Model

scholarly journals Universal kriging of corn plant data under isotropic power type variogram model

2019 ◽  
Vol 1341 ◽  
pp. 062029
Author(s):  
W Somayasa ◽  
G A Wibawa ◽  
Ruslan ◽  
D K Sutiari
Keyword(s):  
Universal Kriging ◽  
Power Type ◽  
Corn Plant ◽  
Variogram Model ◽  
Plant 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

Spatial Interpolation for Periodic Surfaces in Manufacturing Using a Bessel Additive Variogram Model

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Yuhang Yang ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Interpolation Method ◽  
Spatial Models ◽  
High Resolution Data ◽  
Periodic Surfaces ◽  
Variogram Model ◽  
Geostatistical Interpolation ◽  
Proposed Model

High-resolution spatial data are essential for characterizing and monitoring surface quality in manufacturing. However, the measurement of high-resolution spatial data is generally expensive and time-consuming. Interpolation based on spatial models is a typical approach to cost-effectively acquire high-resolution data. Conventional modeling methods fail to adequately model the spatial correlation induced by periodicity, and thus their interpolation precision is limited. In this paper, we propose using a Bessel additive periodic variogram model to capture such spatial correlation. When combined with kriging, a geostatistical interpolation method, accurate interpolation performance can be achieved for common periodic surfaces. In addition, parameters of the proposed model provide valuable insights for the characterization and monitoring of spatial processes in manufacturing. Both simulated and real-world case studies are presented to demonstrate the effectiveness of the proposed method.

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