Estimating spatial attribute means in a GIS environment  

作　　者：CHRISTAKOS George 

机构地区：[1]Department of Geography,San Diego State University,San Diego CA 92182-4493,USA

出　　处：《Science China Earth Sciences》2010年第2期181-188,共8页中国科学（地球科学英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos.40471111,70571076,40601077/D0120);National High Technology R & D Program of China (Grant Nos.2006AA12Z215,2007AA12Z233);Knowledge Innovation Project of the Chinese Academy of Sciences (Grant No.KZCX2-YW-308);the California Air Resources Board,USA (Grant No.55245A)

摘　　要：The estimation of geographical attributes is a crucial matter for many real-world problems,and the issue of accuracy stands out when the estimation is used for between-regions comparison.In this work,our concern is area attribute estimation in a GIS environment.We estimate the area attribute value with a mean Kriging technique,and the probability distribution of the estimate is derived.This is the best linear unbiased observed spatial population mean estimate and can be used in more relaxed situations than the block Kriging technique.Both theoretical analysis and empirical study show that the mean Kriging technique outperforms the ordinary Kriging,spatial random sampling,and simple random sampling techniques in estimating the observable spatial population mean across space.The estimation of geographical attributes is a crucial matter for many real-world problems,and the issue of accuracy stands out when the estimation is used for between-regions comparison.In this work,our concern is area attribute estimation in a GIS environment.We estimate the area attribute value with a mean Kriging technique,and the probability distribution of the estimate is derived.This is the best linear unbiased observed spatial population mean estimate and can be used in more relaxed situations than the block Kriging technique.Both theoretical analysis and empirical study show that the mean Kriging technique outperforms the ordinary Kriging,spatial random sampling,and simple random sampling techniques in estimating the observable spatial population mean across space.

关 键 词：SPATIAL mean mean KRIGING SPATIAL DEPENDENCE GIS 

分 类 号：P208[天文地球—地图制图学与地理信息工程]