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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. [ABSTRACT FROM AUTHOR]

Global positioning systems (GPSs} and geographical information systems (GISs) have been widely used to collect and synthesize spatial data from a variety of sources. New advances in satellite imagery and remote sensing now permit scientists to access spatial data at several different resolutions. The Internet facilitates last and easy data acquisition. In any one study, several different types of data may be collected at differing scales and resolutions at different spatial locations and in different dimensions. Many statistical issues are associated with combining such data for modeling and inference. This article gives an overview of these issues and the approaches for integrating such disparate data, drawing on work from geography, ecology, agriculture, geology, and statistics. Emphasis is on state-of- the-art statistical solutions to this complex and important problem. [ABSTRACT FROM AUTHOR]

The article presents abstracts on topics related to public policy including an analysis of errors in responses to surveys, statistical validity of the federal equality control procedure used for the Food Stamp and Aid to Families with Dependent Children programs, and racial profiling in traffic stops.

Compares several methods of testing factor effects in a two-way cross-classification analysis in a Monte Carlo simulation study, using over-dispersed count data generated from the family of negative binomial distributions. Description and results of the simulation study; General linear model.

Presents a comparative study on the estimation of extreme quantiles based on sensitivity tests. Robbins and Monro method; Review of sequential designs; Sample distributions of extreme quantile estimates based on five sequential designs; Simulation study; Root mean squared error of estimates.