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Exploring a Relationship Between Aggregate and Individual Levels Spatial Data Through Semivariogram Models
Author(s) -
Pawitan Gandhi,
Steel David G.
Publication year - 2006
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.1538-4632.2006.00688.x
Subject(s) - variogram , aggregate (composite) , covariance , spatial analysis , econometrics , statistics , perspective (graphical) , spatial dependence , aggregate data , computer science , kriging , mathematics , artificial intelligence , materials science , composite material
Analysis of social data is frequently done using aggregate‐level data. There may not be a direct interest in spatial relationships in the data, but the presence of spatial interdependence may still need to be taken into account. This article explores the aggregation effect from a spatial perspective by assuming nonzero covariance for individual data from two different groups. We investigate the bias associated with aggregate‐level data for semivariogram analysis. We show that the bias mainly arises from the average of the semivariogram within the groups. It is also shown how aggregated‐level data may be used to estimate parameters of an individual‐level semivariogram model. A nonlinear regression method is proposed to carry out this estimation procedure and a simulation is done to clarify the results.