A geostatistical approach to linking geographically aggregated data from different sources

作者：Gotway Carol A^*; Young Linda J

来源：Journal of Computational and Graphical Statistics, 2007, 16(1): 115-135.

DOI：10.1198/106186007X179257

• change-of-support
• disaggregation
• ecological inference
• generalized estimating equations
• modifiable areal unit problem

The widespread availability of digital spatial data and the capabilities of Geographic Information Systems (GIS) make it possible to easily synthesize spatial data from a variety of sources. More often than not, data have been collected at different geographic scales, and each of the scales may be different from the one of interest. Geographic information systems effortlessly handle these types of problems through raster and geoprocessing operations based on proportional allocation and centroid smoothing techniques. However, these techniques do not provide a measure of uncertainty in the estimates and lack the ability to incorporate important covariate information that may be used to improve the estimates. They also often ignore the different spatial supports (e.g., shape and orientation) of the data. On the other hand, statistical solutions to change-of-support problems are rather specific and difficult to implement. In this article, we present a general geostatistical framework for linking geographic data from different sources. This framework incorporates aggregation and disaggregation of spatial data, as well as prediction problems involving overlapping geographic units. It explicitly incorporates the supports of the data, can adjust for covariate values measured on different spatial units at different scales, provides a measure of uncertainty for the resulting predictions, and is computationally feasible within a GIS. The new framework we develop also includes a new approach for simultaneous estimation of mean and covariance functions from aggregated data using generalized estimating equations.

• 出版日期2007-3