Published By: UANews, 10/12/2016

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Summary

The University of Arizona is using big data to help resolve public transportation issues in Fortaleza, Brazil. The algorithms they have created help identify problem areas that need more buses, bus lanes, or bus stops.

Extended Discussion Questions

• The article talks about how leveraging big data to improve transportation helps create “smart cities”.
  • What is a smart city?
  • What are other areas where big data could be used to improve everyday life and contribute to making a smart city?
• The objects that transmit data help to build an Internet of Things (IoT), where common objects have network connectivity. What are some personal interactions you have witnessed or had with the IoT? Prompt: For example, do your pets have microchips in case they get lost?
• This article gives an example of how data collection and the Internet of things can be used to improve society. What are some potential dangers or risks involved with providing network connectivity to everyday objects?

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 7.2.1 Explain how computing has impacted innovations in other fields.

Global Impact Essential Knowledge:

• EK 7.1.1I Global Positioning System (GPS) and related technologies have changed how humans travel, navigate, and find information related to geolocation.
• EK 7.1.1K Smart grids, smart buildings, and smart transportation are changing and facilitating human capabilities.
• EK 7.1.2D Human capabilities are enhanced by digitally enabled collaboration.
•  EK 7.2.1A Machine learning and data mining have enabled innovation in medicine, business, and science.
• EK 7.2.1B Scientific computing has enabled innovation in science and business.

Other CSP Big Ideas:

• Idea 3 Data and Information
• Idea 4 Algorithms

Banner Image: “Network Visualization – Violet – Crop 8”, 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