How Big Data and Algorithms Are Slashing the Cost of Fixing Flint’s Water Crisis

How Big Data and Algorithms Are Slashing the Cost of Fixing Flint’s Water Crisis

Published By: The Conversation, 09/08/2016

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Summary

Beyond tech companies such as Amazon and Google, big data has a significant effect on science, engineering, and even plumbing. As the government in Flint, Michigan has worked to correct the dangerous levels of lead contamination reported earlier this year,  researchers at the University of Michigan have aggregated and analyzed data for homes in the area. Their analysis is providing new insights into how the government can best direct their efforts, to reduce costs and increase impact.

Extended Discussion Questions

  • The article points out that the statistical analysis can indicate which homes are at high risk, but that it does not guarantee identification of which homes do or do not have a contamination issue. So, how can these non-guaranteed results be applied to improve the government’s efforts? Can the results be used to determine when no further action is needed?
  • The article notes that some data is missing or incorrect (under “Flint’s Lead Pipe Records Are Spotty….”). How does this incomplete and incorrect data impact the results of the analysis? Are the results valid? Can you think of some ways new data could be used to improve the predictions as the correction efforts test each home?
  • The article discusses a number of factors that are used to help predict which homes are at risk. Similar processes are used in other areas, such as insurance. Thinking about health insurance, what types of information could be collected to help predict which individuals are at high risk for health issues?

Relating This Story to the CSP Curriculum Framework

Global Impact Learning Objectives:

  • LO 7.1.1 Explain how computing innovations affect communication, interaction, and cognition.
  • LO 7.4.1 Explain the connections between computing and real-world contexts, including economic, social, and cultural contexts.

Global Impact Essential Knowledge:

  • EK 7.1.1F Public data provides widespread access and enables solutions to identified problems.
  • EK 7.2.1A Machine learning and data mining have enabled innovation in medicine, business, and science.

Other CSP Big Ideas:

  • Idea 3 Data and Information

Banner Image:Network Visualization – Violet – Crop 9“, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0

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