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Direct Data Manipulation for Local Decision Analysis as Applied to the Problem of Arsenic in Drinking Water from Tube Wells in Bangladesh
Author(s) -
Gelman Andrew,
Trevisani Matilde,
Lu Hao,
Van Geen Alexander
Publication year - 2004
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.0272-4332.2004.00553.x
Subject(s) - nonparametric statistics , remedial education , parametric statistics , cluster analysis , arsenic , data mining , arsenic contamination of groundwater , computer science , engineering , environmental engineering , statistics , mathematics , machine learning , chemistry , mathematics education , organic chemistry
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.