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BARI and BIP Award Three Research Seed Grants

The Boston Area Research Initiative and the Boston Indicators Project are proud to announce the recipients of research seed grants for the Spring 2017 semester. We are pleased to note that the recipients hail from three different disciplines at three universities and feature distinct methodologies, including an innovative experiment, the design of a societally-minded game, and the analysis of an extensive database from a social media app.

 

Housing Affordability and NIMBYism in Boston

Michael Hankinson, a PhD candidate at Harvard studying Government and Social Policy, is examining the role of political restrictions in contributing to the housing affordability crisis facing Boston. Using a novel GIS interface, Hankinson seeks to explain NIMBYism (i.e., “Not In My BackYard”) by capturing how citizen preferences for the placement of new housing reflect racial, economic, and spatial biases. The project’s goal is not only to spark conversation around the bias in the political voice reaching elected officials regarding urban development, but also to translate the research interface into an interactive tool that city governments can use to gauge public opinion on housing and development plans.

 

A Game for Health and Climate Adaptation Planning

Ella Kim, a PhD candidate in MIT’s Department of Urban Studies and Planning, is developing two new tools—a face-to-face role-play simulation and an online game—that focus on the health impacts of climate change, enhance awareness of climate risks, and increase support for climate adaptation policies. The project looks at ways in which we can enhance understanding and decision-making capacity among various sub-populations, enabling cities to increase local engagement and inform climate adaptation planning.

 

Detection and Optimization of Traffic Problems from Waze data

Jing Zhang, a PhD candidate in BU’s Department of Systems Engineering, is using Waze data for the city of Boston to develop ways to relieve traffic congestion as it is occurring. The project will uncover “non-typical” events in order to identify “actionable” traffic jams that might be solved by altering traffic light timing, dispatching emergency services, or by instituting a new policy. Zhang seeks to develop an algorithm that can predict congestion before it becomes severe, enabling cities to maximize the efficiency and safety of their roadways in real time.

 

Click here for more information on the research seed grant program.

Published On: January 30, 2017 |
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