NeighborDrive: Community-driven Neighborhood Well-being Improvement through Active Vehicular Crowdsensing - NSF-SCC project

About: There has been an increase in the number of people experiencing homelessness in cities, especially since the beginning of the COVID-19 pandemic. Providing timely and early intervention can improve the well-being of those experiencing homelessness; however, the dynamic nature of unhoused populations makes it difficult to determine the magnitude and location of service needs. We are using granular vehicle and mobile crowdsensing data collected in San Jose to uncover complex mechanisms that link neighborhood environments to well-being and optimize intervention strategies to improve overall wellness. Our goal is to understand how natural and visible neighborhood environments, such as litter, building facade and maintenance, air pollution, and greenery, affect individual and community well-being. While studies have shown that neighborhood environments are correlated with well-being metrics, the specific mechanisms linking them together and with neighborhood inequities in well-being are not well understood.

Research Tasks: There is a diverse list of tasks the student might help with, including but not limited to (1) conducting literature reviews into affordable housing, sustainability, and public policy, (2) collecting, organizing, and analyzing well-being survey data and eye-tracking study data using programming languages such as R, (3) researching frameworks for well-being interventions given granular spatiotemporal data, and (4) learning to give technical presentations and provide brief technical write-ups of their work.