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Geostatistical Smoothing of Areal Data: Mapping Employment Density with Factorial Kriging. 面状数据的地统计滤波：采用因子克里格方法制作就业密度图
Author(s) -
Nagle Nicholas N.
Publication year - 2010
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/j.1538-4632.2009.00784.x
Subject(s) - kriging , smoothing , smoothness , covariance , factorial , mathematics , geostatistics , covariance function , variogram , statistics , factorial experiment , analysis of covariance , autocovariance , kernel smoother , function (biology) , kernel method , computer science , spatial variability , mathematical analysis , fourier transform , artificial intelligence , evolutionary biology , radial basis function kernel , support vector machine , biology
This article summarizes area‐to‐point (ATP) factorial kriging that allows the smoothing of aggregate, areal data into a continuous spatial surface. Unlike some other smoothing methods, ATP factorial kriging does not suppose that all of the data within an area are located at a centroid or other arbitrary point. Also, unlike some other smoothing methods, factorial kriging allows the user to utilize an autocovariance function to control the smoothness of the output. This is beneficial because the covariance function is a physically meaningful statement of spatial relationship, which is not the case when other spatial kernel functions are used for smoothing. Given a known covariance function, factorial kriging gives the smooth surface that is best in terms of minimizing the expected mean squared prediction error. I present an application of the factorial kriging methodology for visualizing the structure of employment density in the Denver metropolitan area.