A wide variety of tools are available, both parametric and nonparametric, for analyzing spatial data. However, it is not always clear how to translate statistical inferences into decision recommendations. This article explores the possibilities of estimating the effects of decision options using very direct manipulation of data, bypassing formal statistical analysis. We illustrate with the application that motivated this research, a study of arsenic in drinking water in nearly 5,000 wells in a small area in rural Bangladesh. We estimate the potential benefits of two possible remedial actions: (1) recommendations that people switch to nearby wells with lower arsenic levels; and (2) drilling new community wells. We use simple nonparametric clustering methods and estimate uncertainties using cross-validation.
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Document Type: Research Article
Department of Statistics and Department of Political Science, Columbia University, NY.
Department of Economic and Statistical Sciences, University of Trieste, Italy.
Thales Corp., NY.
Lamont-Doherty Earth Observatory, Palisades, New York.
Publication date: 2004-12-01