Direct data manipulation for local decision analysis as applied to the problem of arsenic in drinking water from tube wells in Bangladesh.
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.
Gelman
Andrew
A
Department of Statistics and Department of Political Science, Columbia University, NY, USA. gelman@stat.columbia.edu
Trevisani
Matilde
M
Lu
Hao
H
van Geen
Alexander
A
D000465:Algorithms; D001151:Arsenic / Q000032:analysis*; D020261:Arsenic Poisoning; D001459:Bangladesh; D016000:Cluster Analysis; D003661:Decision Support Techniques; D004784:Environmental Monitoring; D006801:Humans; D015233:Models, Statistical; D014874:Water Pollutants, Chemical; D014877:Water Pollution, Chemical; D018508:Water Purification / Q000379:methods*; D014881:Water Supply
D014874:Water Pollutants, Chemical; D001151:Arsenic