Spring 2018 – 497-304: Inspiring Public Safety Innovation and Using Big Data Visualization to Improve Responsiveness to Natural Disasters

Meeting Days/Time
Thursdays from 10:00 am to 12:40 pm
Motorola Solutions Foundation
Daniel Chichester (ID) (dchichester@id.iit.edu), Matt Robison (Undergraduate Education) and Bo Rodda (Undergraduate Education)
Appropriate Majors
All interested students are welcome
Social Innovation

A study published in Science magazine shows powerful hurricanes have become more common over the past 35 years. Storms are more likely to develop into dangerous Category 4 or 5 hurricanes. Scientists attribute this to a global trend of warmer ocean waters. (Source: “Study: Severe Hurricanes Increasingly Common, Richard Harris, NPR, September 16, 2005.)

 “There were three times as many natural disasters from 2000 through 2009 as there were from 1980 through 1989. … the growth is mainly in climate-related events, accounting for nearly 80% of the increase, whereas trends in geophysical events have remained stable.”. (Source: New England Journal of  Med; 369:1836-1842, Jennifer Leaning, M.D., and Debarati Guha-Sapir, Ph.D., November 7, 2013)

The U.S. has sustained 218 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion… The total cost of these 218 events exceeds $1.2 trillion. This total does not yet include the costs for Hurricanes Harvey, Irma and Maria… (nor does it include the monetary and human costs of nearly half million acers burned from active fires in October 2017 alone). The 2017 events (through Oct. 6) include two floods, a freeze, seven severe storms, three tropical cyclones, a drought and wildfire – collectively causing 282 fatalities. (Source: “Billion-Dollar Weather and Climate Disasters: Overview”, The National Centers for Environmental Information (NCEI) October 7, 2017)

Natural disaster management and prediction has never been more critical to the safety of its citizens, their property, and the nation’s economy, than it is today. The purpose of this IPRO project is to apply “Big Data” techniques to improve public safety relative to natural disasters. Our initial focus will be on learning the “Big Data” analytical research process – Data Gathering, Pre-Processing, Modeling and Visualizing the data. We will then explore our findings for logical implications for the future, and Predictive Analytics. If possible, near the end of the semester, we would like to invite local area high school students to share in our processes and provide them with rewarding experience in which they can participate in helping us create powerful, data driven, expressions, possibly including artistic expressions to reach a public audience.

The individual student teams will decide on what output they would like to create to enhance public safety. Mechanical and Civil engineers may be interested in the protection of cities, Architects and City Planners may be interested in structures and public safety systems. Humanities students may want to emphasize human impacts, and Computer and Data scientists will find many applications for their skills. Teams with mixed skill sets may have a wide variety options to produce enhancements for public safety.

Given the above purpose, there are three overarching goals for the IPRO team to pursue:

  1. Work in teams to select and explore a specific research question in a socially relevant topic. This goal will be accomplished by serious minded students sharing what is important to them and using their passions to select a topic area and a specific research question to address public safety in the face of natural disasters.
  2. Learn each step of the Big Data analytical research process. This goal is addressed by organizing IPRO team members that meet weekly to collectively work on a large data set and answer their research question in a compelling way and show their results.
  3. As an aspirational goal:  Create and deliver a “big data experience” for high school students that exposes them to big data and how to visualize information effectively. This goal is addressed by inviting high school scholars from two or three high schools to learn about big data sets and develop creative ways to express the patterns of information and insights they represent. We plan to invite students from the following high schools: Von Steuben High School (Exelon-IIT Partnership), Lake View High School (Microsoft Partnership) and IIT/Perspectives Math & Science Academy

This IPRO section is inspired by the conviction that data science is the foundation field on which to introduce a progressive, developmental approach to building “data consciousness” into the fabric of learning. This includes learning how to make decisions based on the best available data, and to discern and understand the meaning of the data they may encounter. Issues of ethics, privacy and security also come into play in a variety of organization settings. The goal is to advance all learners toward higher levels of data competency: data literacy (understanding data and statistical concepts) begins in the early years, data science expertise (designing data science analyses) typically starts in the undergraduate college years, and data science mastery (building data science systems and performing complex analyses) is developed at the graduate school and working professional levels. This then extends to the responsibility professionals have as citizens and as leaders in a range of roles in society, the economy and government.

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