Quantifying Urban Revitalization

Quantifying Urban Revitalization

Published By: MIT News, 10/24/2016

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Summary

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.

Extended Discussion Questions

  • The article discusses how the system can automatically assess images to rate areas on perceived safety. How could this system be leveraged by city planners to improve areas?
  • The article mentions that the volunteer responses were used to train the machine-learning algorithm. All machine learning algorithms must be trained on some set of data, which may have unintentional biases (e.g., training a traffic routing system based on data obtained only from cyclists). What are some problems that could occur if this dataset has some undetected bias?

Relating This Story to the CSP Curriculum Framework

Global Impact Learning Objectives:

  • LO 7.2.1 Explain how computing has impacted innovations in other fields.
  • LO 7.3.1 Analyze the beneficial and harmful effects of computing.
  • LO 7.4.1 Explain the connections between computing and real-world contexts, including economic, social, and cultural contexts.

Global Impact Essential Knowledge:

  • EK 7.1.2E Some online services use the contributions of many people to benefit both individuals and society.
  • 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 13”, 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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