While it is given that the vestiges of 1930’s redlining still haunt social and economic capital, it is challenging to accurately and systematically assess how urban areas are affected by class disparity. To develop a better understanding of this issue, Myeong Lee, who leads the Community Informatics Lab within the College of Engineering and Computing at George Mason University, saw an opportunity to dive deeper on this subject using the 4-VA collaborative. Lee wanted to construct a multidimensional model of social capital using two datasets: Chetty et al.’s (2022a, 2022b) Social Capital Atlas (Facebook) data on social connectedness, and Meetup event and group data which Lee himself had collected on almost 800 U.S. metro counties (2017–2021). Together with his co-PI from Virginia Commonwealth University, Victor Chen, Lee wanted to look at how ethnic heterogeneity — the presence of multiple, distinct ethnic or cultural groups within a specific population or geographic area — impacts social capital, including social cohesion, public goods provision, economic outcomes, and crime.


Approved for funding by 4-VA, Lee and his team got to work. George Mason University graduate students,Latifah Abubakr and Gie Myung Lee focused on data processing and statistical analysis; and undergraduate student Tugce Gundogdu assisted with geospatial analysis and data collection.
They examined the spatial relationships between various social capital — measured at the county and zip-code level; and structural racial and class-based inequalities — measured through current neighborhood ethnic heterogeneity and historical legacies of redlining and urban renewal projects.
Over the research period, faculty and students at George Mason and VCU held bi-weekly meetings via Zoom. Their data-processing stage required intensive computation, including large-scale geospatial data processing, data aggregation, and computational modeling of key variables.
The resulting variables were used in statistical models, which show that although some core relationships reported in prior literature remain consistent, as in lower socioeconomic status is associated with lower social capital; they noted that the dynamics varied depending on specific dimensions of “prosociality” — voluntary behaviors intended to benefit others, including sharing, comforting, helping, and cooperating.
Preliminary findings were presented at several conferences, including the American Sociological Association Annual Meeting, the Eastern Sociological Society Conference, and Society for the Study of Social Problems. The final results are currently being edited for submission to a high-impact journal.
Perhaps even more importantly, this initial research has opened another interesting avenue for further investigation:
- Incorporating “urban change” as a key moderator and assessment tool in shaping prosociality in American cities. To do this, the team is developing computational techniques to quantify the degree of change in urban areas by leveraging Home Owners’ Loan Corporation redlining maps. The computational component of this work was presented and published for the Association for Information Science and Technology Annual Meeting. They plan to submit a proposal to the Social Science Research Council as well as the National Science Foundation Sociology Program to further build out this tool.
Lee is pleased with the results of the 4-VA-enabled research, “Thanks to this seed funding, we were able to engage researchers and students from multiple institutions. Also, it allowed us to purchase key resources, such as Meetup subscriptions and OpenAI, which are core to the project. We now have the groundwork to enlarge our examination of this important issue.”




























