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Improving Crop Estimates by Integrating Multiple Data Sources (2017) / Chapter Skim
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Appendix C: Small-Area Modeling in Space and Time with Multiple Data Sources
Pages 125-130

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From page 125...
... County levels in NASS surveys are typical examples of "small areas." The underlying concept of SAE methods is to "borrow strength" by integrating information from multiple data sources, including survey data, or across time and space to improve estimates for small areas. Application of SAE methods at NASS has become increasingly important because of the demand for crop estimates at detailed geographic levels.
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From page 126...
... For example, Bayesian methods are used in geospatial analysis and time domain analysis, as well as with incorporation of multiple types of measurements. When used in a geospatial context, it can be helpful to include explicit spatial effects in order to broaden or borrow spatial support and thus to reduce uncertainty, especially when modeling estimates for small areas.
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From page 127...
... The panel suggests that NASS begin by exploring county-level models using the area-level spatial Fay-Herriot model to describe survey measurements. Each alternative data source could be given its own data model, linked to the larger model in a hierarchical Bayes framework.
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From page 128...
... . • Intuitive isotropic correlation models based on distance lead to dense matrices, i.e., matrices with few zeroes.
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From page 129...
... EXTENSION TO TIME Extension to time is conceptually straightforward, but joint space–time correlation models require care. Including time may not be important for county crop estimates, even though there is substantial correlation from year to year for some variables.
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From page 130...
... combine point and aggregate pollution data, with the latter ­ consisting of outputs from numerical models produced over a gridded surface using MCMC, and evaluate the block kriging integrals on a grid. Berrocal and colleagues (2010)
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Key Terms

  • multiple data
  • data model
  • spatial model
  • sae methods
  • random effects

  • unit level
  • machine learning
  • bayesian approach
  • block kriging
  • process model

  • improved estimates
  • spatial modeling
  • spatial random
  • alternative data
  • bayesian methods

  • correlation models
  • produce reliable
  • models correspond
  • panel suggests
  • domain analysis

  • spatiotemporal modeling
  • basic idea
  • discrete data
  • avoid estimation
  • mortality based

  • considered publishable
  • models produced
  • survey measurements
  • nested laplace
  • integrated nested


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