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Data Science for Smart Cities: Gentrification Analysis Group

Posted by on Tuesday, June 5, 2018 in Data Science Methods for Smart City Applications, News.

This spring, the University Course Data Sciences Methods for Smart City Applications was offered to Vanderbilt University undergraduate and graduate students for the first time. The course was designed to examine and address issues facing cities and metropolitan areas by bringing together concepts and methodologies from systems engineering, data sciences and machine learning, modeling and simulation, optimization and social sciences. Students reviewed these topics and focused on machine learning, Python programming and script generation, qualitative methods for data collection, and ethics issues related to data collection and privacy.

A significant portion of the semester was devoted to team projects with students working together. The projects covered four separate themes: transportation, energy, gentrification and transportation, and emergency response. Each team worked with a faculty mentor and a graduate student advisor to research their projects, implement qualitative and quantitative methods for data collection, and analyze the data to answer research questions. The students presented their findings at the end of the semester and submitted reports.

This is the fourth in a series of four student-written blogs describing the projects.

Gentrification Analysis Group
Written by David Knorr, Cole Richardson, Jamal Pace, Aron Aziz

Nashville is experiencing rapid growth and high traffic congestion. To mitigate this and enhance the growing population’s ability to get around Nashville, the city proposed a public transit expansion plan to extends existing bus infrastructure and add new light rail, rapid bus routes and transit centers throughout the metro area. There has been anecdotal evidence of gentrification, but little rigorous analysis to identify the phenomenon in Nashville.

In light of the changing Nashville landscape, we investigated the potential impact of the proposed transit plan through the lens of neighborhood change and gentrification. We collect qualitative data from residents, transit users and policymakers, coupled with a Twitter sentiment analysis to gauge public opinion of the proposed plan. We then identified neighborhood change typologies from 2000 – 2016 and categorized census tracts into discrete socio-economic typologies using unsupervised machine learning techniques.  Finally, we analyzed the demographics served by the existing and proposed bus and light rail routes, presenting the results in an interactive web application ( These analyses attempt to provide data-driven insights to identify vulnerable communities and neighborhoods in flux with respect to the transit plan that was proposed. The qualitative results from our interviews show that there is shared concern about public safety and neighborhood transit access, particularly with regard to low-income communities.

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