Extensions to spatial factor methods with an illustration in geochemistry

被引:17
作者
Bailey, TC [1 ]
Krzanowski, WJ [1 ]
机构
[1] Univ Exeter, Sch Math Sci, Exeter EX4 4QE, Devon, England
来源
MATHEMATICAL GEOLOGY | 2000年 / 32卷 / 06期
关键词
multivariate spatial analysis; spatial factors; spatial principal components; coregionalization analysis; multivariate spatial prediction;
D O I
10.1023/A:1007589505425
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Many applications involving spatial data require several layers of information to be simultaneously analyzed in relation to underlying geography and topographic detail. This in turn generates a need for forms of multivariate analysis particularly oriented to spatial problems and designed to handle spatial structure and dependency both within and between spatially indexed multivariate responses. In this paper we focus on one group of such methods sometimes referred to as "spatial factor analysis." Use of these techniques has so far been mostly restricted to applications in the geosciences and in some forms of image processing, but the methods have potential for wider use outside these fields. They are concerned with identifying components of a multivariate data set with a spatial covariance structure that predominantly acts over a particular spatial range or zone of influence. We review the various forms of spatial factor analysis that have been proposed and emphasize links between them and with the linear model of coregionalization employed in geostatistics. We then introduce extensions to such methods that may prove useful in exploratory spatial analysis, both generally and more specifically in the context of multivariate spatial prediction. Application of our proposed exploratory techniques is demonstrated on a small but illustrative geochemical data set involving multielement measurements from stream sediments.
引用
收藏
页码:657 / 682
页数:26
相关论文
共 35 条
[1]  
ARMSTRONG M, 1988, GEOSTATISTICS
[2]   MULTIVARIABLE VARIOGRAM AND ITS APPLICATION TO THE LINEAR-MODEL OF COREGIONALIZATION [J].
BOURGAULT, G ;
MARCOTTE, D .
MATHEMATICAL GEOLOGY, 1991, 23 (07) :899-928
[3]   MULTIVARIATE SPATIAL INTERPOLATION AND EXPOSURE TO AIR-POLLUTANTS [J].
BROWN, PJ ;
LE, ND ;
ZIDEK, JV .
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 1994, 22 (04) :489-509
[4]   THE GEOMETRY OF CANONICAL VARIATE ANALYSIS [J].
CAMPBELL, NA ;
ATCHLEY, WR .
SYSTEMATIC ZOOLOGY, 1981, 30 (03) :268-280
[5]  
Cleveland W.S., 1992, STAT MODELS, DOI DOI 10.1201/9780203738535-8
[6]  
Cressie NA, 1991, STAT SPATIAL DATA
[7]  
Deutsch C. V., 1992, GSLIB GEOSTATISTICAL
[8]  
Flury B., 1988, Common Principal Components and Related Multivariate Models
[9]   FACTORIAL KRIGING ANALYSIS - A USEFUL TOOL FOR EXPLORING THE STRUCTURE OF MULTIVARIATE SPATIAL SOIL INFORMATION [J].
GOOVAERTS, P .
JOURNAL OF SOIL SCIENCE, 1992, 43 (04) :597-619
[10]   SPATIAL ORTHOGONALITY OF THE PRINCIPAL COMPONENTS COMPUTED FROM COREGIONALIZED VARIABLES [J].
GOOVAERTS, P .
MATHEMATICAL GEOLOGY, 1993, 25 (03) :281-302