Neighborhood Dynamics and the Inequality of Place: Using Large-Scale Mobility Behavior Data to Understand Socio-Spatial Disparities in Community Connectedness
INSTITUTION
New York University, NY
PRINCIPAL INVESTIGATOR
Lance M Freeman
FUNDING
$400K
YEAR
2024
MOONBASE SCORE
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Abstract
This research examines the interconnectedness of residents from varied neighborhoods, using smartphone-derived mobility data, including mobility networks of 45 million devices that cover 220,000 census blocks. The research explores how residents from different neighborhoods engage with other communities, how these interactions change over time and space and vary with neighborhood socioeconomic and demographic characteristics, and how neighborhood mobility networks and the nature of connections between neighborhoods are associated with measures of community well-being. Broader impacts of the research include data transparency and accessibility, robust training opportunities for junior researchers, diverse educational and experiential learning pathways for students, and the development of tools and indicators for urban planning, policy, and community-led support. Using large-scale mobility data and a range of ancillary land use, socioeconomic, and demographic data, together with machine learning and network analysis methods, this project explores how neighborhood mobility networks and the nature of connections between neighborhoods are associated with measures of community well-being, and whether time-varying network structures can predict neighborhood change. Specific objectives of the research project are to: (1) identify the nature and extent of representativeness bias in mobility data, and develop methods to correct for observed biases; (2) model neighborhood connectedness networks using mobility data and analyze changing network structures over time and in response to various exogenous shocks; (3) identify and define community and neighborhood boundaries as a function of connectedness observed through mobility behaviors; and (4) utilize community networks to predict neighborhood change and evaluate neighborhood integration and segregation as a function of mobility behavior between communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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