Gaussian process (GP) is a staple in the toolkit of a spatial statistician. Well‐documented
computing roadblocks in the analysis of large geospatial datasets using GPs have now …

Fitting and modeling spatial-temporal processes are essential research topics in
transportation studies. Recently, due to the analytically tractable formulation and good fitting …

Matching and pickup processes are core features of ride-sourcing services. Previous studies
have adopted abundant analytical models to depict the two processes and obtain …

This paper introduces techniques in Gaussian process regression model for spatio-temporal
data collected from complex systems. This study focuses on extracting local structures and …

Remote sensing data products often include quality flags that inform users whether the
associated observations are of good, acceptable or unreliable qualities. However, such …

Environmental monitoring is a task that requires to surrogate system‐wide information with
limited sensor readings. Under the proximity principle, an environmental monitoring system …

The Matérn covariance function is a popular choice for prediction in spatial statistics and
uncertainty quantification literature. A key benefit of the Matérn class is that it is possible to …

Abstract The US Drought Monitor is the leading drought monitoring tool in the United States.
Updated weekly and freely distributed, it records the drought conditions as geo-referenced …

Gaussian Processes (GP) is a staple in the toolkit of a spatial statistician. Well-documented
computing roadblocks in the analysis of large geospatial datasets using Gaussian …

Remote sensing data products often include quality flags that inform users whether the
associated observations are of good, acceptable or unreliable qualities. However, such …