A Data Science Approach to Understanding Residential Water Contamination in Flint
| Autor: | Eric M. Schwartz, Daniel Zhang, Arya Farahi, Jacob Abernethy, Jared Webb, Guangsha Shi, Alex Chojnacki, Chengyu Dai |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Water supply Public policy Machine Learning (stat.ML) Sample (statistics) 02 engineering and technology 010501 environmental sciences Statistics - Applications 01 natural sciences Machine Learning (cs.LG) Lead (geology) Statistics - Machine Learning 020204 information systems 0202 electrical engineering electronic engineering information engineering Applications (stat.AP) Environmental planning 0105 earth and related environmental sciences Government business.industry 6. Clean water Computer Science - Learning Geography Local government Water quality business Risk assessment |
| Zdroj: | KDD NASA Astrophysics Data System |
| DOI: | 10.1145/3097983.3098078 |
| Popis: | When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamination event in Flint. This is the nation's largest dataset on lead in a municipal water system. In this paper, we predict the lead contamination for each household's water supply, and we study several related aspects of Flint's water troubles, many of which generalize well beyond this one city. For example, we show that elevated lead risks can be (weakly) predicted from observable home attributes. Then we explore the factors associated with elevated lead. These risk assessments were developed in part via a crowd sourced prediction challenge at the University of Michigan. To inform Flint residents of these assessments, they have been incorporated into a web and mobile application funded by \texttt{Google.org}. We also explore questions of self-selection in the residential testing program, examining which factors are linked to when and how frequently residents voluntarily sample their water. Comment: Applied Data Science track paper at KDD 2017. For associated promotional video, see https://www.youtube.com/watch?v=0g66ImaV8Ag |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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