Paper Info
Title
A Spatial Conditioned Latin Hypercube Sampling Method for Mapping Using Ancillary Data.
Abstract
For obtaining maps of good precision by the spatial inference method, the distribution of sampling sites in geographical and feature space is very important. For a regional variable with trends, the predicting error comes from trend estimation, variogram estimation and spatial interpolation. Based on the cLHS (conditioned Latin hypercube Sampling) method, a sampling method called scLHS (spatial cLHS) considering all these three aspects with the help of ancillary data is proposed in this article. Its advantage lies in simultaneously improving trend estimation, variogram estimation and spatial interpolation. MODIS data and simulated data were used as sampling fields to draw sample sets using scLHS, cLHS, cLHS with x and y coordinates as covariates, simple random and spatial even sampling methods, and the distribution and prediction errors of sample sets from different methods were evaluated. The results showed that scLHS performed well in balancing spreading in geographic and feature space, and can generate points pairs with small distances, and the sample sets drawn by scLHS produced smaller mapping error, especially when there were trends in the target variable.
Year
DOI
Venue
2016
10.1111/tgis.12176
TRANSACTIONS IN GIS
Field
DocType
Volume
Data mining,Variogram,Covariate,Feature vector,Ancillary data,Multivariate interpolation,Simple random sample,Computer science,Sampling (statistics),Latin hypercube sampling
Journal
20.0
Issue
ISSN
Citations 
5.0
1361-1682
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Bingbo Gao100.34
Yuchun Pan2108.82
Zi-Yue Chen3113.42
Fang Wu400.68
Xuhong Ren500.68
Mao-Gui Hu6387.73