SPATIAL PREDICTION FROM NETWORKS

被引：40

作者：

CRESSIE, N

GOTWAY, CA

GRONDONA, MO

机构：

[1] Department of Statistics, Iowa State University, Ames

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 1990年 / 7卷 / 03期

基金：

美国国家科学基金会;

关键词：

D O I：

10.1016/0169-7439(90)80115-M

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Cressie, N., Gotway, C.A. and Grondona, M.O., 1990. Spatial prediction from networks. Chemometrics and Intelligent Laboratory Systems, 7: 251-271. This article defines a random-field model that can be used for the prediction of pollutants at locations where no data are available, based on data taken from a spatial network of monitoring sites. Acid deposition data collected from the UAPSP network in 1982 and 1983, are analyzed in two stages. Bias-resistant and outlier-resistant techniques are used to determine the spatial dependence; then a spatial model is built that is made up of a quadratic trend surface and the spatially correlated error. Spatial sampling plans and optimal designs for selecting monitoring sites are summarized and discussed. The question of the location of additional sites (and deletion of existing ones) is also addressed. © 1990.

引用

页码：251 / 271

页数：21

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