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Change of Support Models

Part of the Springer Series in Statistics book series (SSS)

In contrast to the grid-based methods of the previous chapters in this section, the general class of methods often referred to as “change of support” methods evolved mostly to deal with problems of data that are observed at a scale different from the scale at which inference is desired, and the mapping between scales is nonregular. For example, demographic data may be available by postal code, but information about particular city blocks might be of interest. Alternatively, one could be trying to relate such demographic data to voting data, which are reported by voting precinct, and these precincts may not have any relationship to postal codes (so that precincts may overlap multiple postal codes, and postal code regions may contain multiple voting precincts). How is one to use the available data to draw valid conclusions at a different scale? The desire to answer this question is the primary motivation for the change of support problem.

Keywords

Gaussian Process Census Tract Ordinary Kriging Spatial Process Markov Chain Monte Carlo Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2007

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