Jonathan Corley

Quantifying Urban Revitalization

MIT News, 10/24/2016

Researchers have developed a system that estimates how safe visitors will perceive an area to be based on photographs of the area. The researchers began with a crowdsourcing effort to build an initial database of images and safety ratings of the area in the image. Over 1.4 million responses have then been used to train a machine-learning algorithm to identify these aspects automatically.

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

The Conversation, 09/08/2016

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.

IoT Early Warning System Helps Save People From Mudslides

Network World, 08/24/2016

This article describes an early warning system for mudslides and flood in rural El Salvador that uses a mesh network of simple devices, within local villagers’ needs and cultural structures. The article can be used to highlight differences between the U.S., where a vast array of technological advances can aid in mitigating disasters, and remote areas of the world where many of the basic technologies involved, such as cell-phone networks, do not exist or are not reliable.