Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/14400
Title: Geostatistical prediction and simulation of point values from areal data
Authors: Kyriakidis, Phaedon 
Yoo, Eun-Hye
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Population distribution;Census;Areal interpolation
Issue Date: 1-Apr-2005
Source: Geographical Analysis, 2005, vol. 37, no .2, pp. 124-151
Volume: 37
Issue: 2
Start page: 124
End page: 151
Journal: Geographical Analysis 
Abstract: The spatial prediction and simulation of point values from areal data are addressed within the general geostatistical framework of change of support (the term support referring to the domain informed by each measurement or unknown value). It is shown that the geostatistical framework (i) can explicitly and consistently account for the support differences between the available areal data and the sought-after point predictions, (ii) yields coherent (mass-preserving or pycnophylactic) predictions, and (iii) provides a measure of reliability (standard error) associated with each prediction. In the case of stochastic simulation, alternative point-support simulated realizations of a spatial attribute reproduce (i) a point-support histogram (Gaussian in this work), (ii) a point-support semivariogram model (possibly including anisotropic nested structures), and (iii) when upscaled, the available areal data. Such point-support-simulated realizations can be used in a Monte Carlo framework to assess the uncertainty in spatially distributed model outputs operating at a fine spatial resolution because of uncertain input parameters inferred from coarser spatial resolution data. Alternatively, such simulated realizations can be used in a model-based hypothesis-testing context to approximate the sampling distribution of, say, the correlation coefficient between two spatial data sets, when one is available at a point support and the other at an areal support. A case study using synthetic data illustrates the application of the proposed methodology in a remote sensing context, whereby areal data are available on a regular pixel support. It is demonstrated that point-support (sub-pixel scale) predictions and simulated realizations can be readily obtained, and that such predictions and realizations are consistent with the available information at the coarser (pixel-level) spatial resolution. © 2005 The Ohio State University.
URI: https://hdl.handle.net/20.500.14279/14400
ISSN: 167363
DOI: 10.1111/j.1538-4632.2005.00633.x
Rights: © John Wiley & Sons
Type: Article
Affiliation : University of California 
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