Resources

What went into our project?

Sources

Dataset Sources

All of our datasets are collected from a range of sources that aggregate information on topics likely affected by one’s socioeconomic status. We focused on datasets that looked at education in terms of college-going rates by schooling opportunity, census income and rent rates, transportation geographical routes, and the overall wealth distributions across the US. All of this information is used to create visualizations to help showcase the connection between different socioeconomic factors that result in wealth disparities within the Los Angeles geographical area.

Educational Sources

Our educational sources are from the UCLA Library and Google Scholar sites, based on wealth distribution specifically in Los Angeles. The sources we gathered examine the underlying cause of the wealth gap, examining how factors such as race, systemic structures, household dynamics, and geographic location influence individuals’ ability to generate income and build wealth. This research supplements the findings we made in our data visualizations, which highlight and amplify the connections between these issues, including how students of color experience lower college going rates and lower income households need to pay larger proportions of their wealth, creating difficulties in upward economic mobility. 

Bibliography

Aladangady, Aditya, and Akila Forde. “Wealth Inequality and the Racial Wealth Gap.” The Fed – Wealth Inequality and the Racial Wealth Gap, www.federalreserve.gov/econres/notes/feds-notes/wealth-inequality-and-the-racial-wealth-gap-20211022.html?utm_medium=Partnership. Accessed 7 Nov. 2025. 

Cruz-Viesca, M. D. L., Ong, P. M., Comandon, A., Darity, W. A., & Hamilton, D. (2018). Fifty Years After the Kerner Commission Report: Place, Housing, and Racial Wealth Inequality in Los Angeles. RSF : Russell Sage Foundation Journal of the Social Sciences, 4(6), 160–184. https://doi.org/10.7758/rsf.2018.4.6.08

Davis, Mike, and Robert Morrow. City of Quartz : Excavating the Future in Los Angeles  / Mike Davis ; Photographs by Robert Morrow. 1st Vintage Books ed., Vintage Books, 1992, http://catdir.loc.gov/catdir/description/random048/91050492.html.

De La Cruz-Viesca, Melany, et al. “The color of wealth in Los Angeles.” Durham, NC/New York/Los Angeles: Duke University/The New School/University of California, Los Angeles (2016).

Pfeffer, Fabian T. . 1 June 2018 “Growing Wealth Gaps in Education”. Demography; 55 (3): 1033–1068. doi: https://doi.org/10.1007/s13524-018-0666-7

Ryan, M. E. (2020). The Enduring Legacy: Structured Inequality in America’s Public Schools (1st ed.). University of Michigan Press. https://doi.org/10.3998/mpub.11645040

Wolff, Edward N. (2021) : Wealth inequality in the United States, NBER Reporter, ISSN 0276-119X, National Bureau of Economic Research (NBER), Cambridge, MA, Iss. 2, pp. 14-17

Zewde, N., Rodriguez, S. R., & Glied, S. A. (2024). High-Deductible Health Insurance May Exacerbate Racial And Ethnic Wealth Disparities. Health Affairs (Millwood, Va.), 43(10), 1455–1463. https://doi.org/10.1377/hlthaff.2023.01199

Looney, Adam and Kevin B. Moore (2015). “Changes in the Distribution of After-Tax Wealth: Has Income Tax Policy Increased Wealth Inequality?,” Finance and Economics Discussion Series 2015-058. Washington: Board of Governors of the Federal Reserve System, http://dx.doi.org/10.17016/FEDS.2015.058.

Adames, Alexander, & Bryer, Ellen. “The Development of Racial Wealth Gaps in Early Adulthood.” Social Science Research, vol. 120, May 2024, 103010. https://doi.org/10.1016/j.ssresearch.2024.103010

McConnell, Eileen Diaz. (2012). House Poor in Los Angeles: Examining Patterns of Housing-Induced Poverty by Race, Nativity, and Legal Status. Housing Policy Debate, 22(4), 605–631. https://doi.org/10.1080/10511482.2012.697908

Processing

Distributional Financial Accounts Federal Reserve

The Federal Reserve has multiple datasets of wealth by wealth percentile, education, and more. The tables, which are formatted in CSVs, were mostly used directly and needed little to no processing. For things like the wealth percentile, we were able to parse the pct formatted string range to visualize the data on charts using python.

California Department of Education

The California Department of Education has multiple datasets over multiple years for public and private schools across California on different factors such as the college going rate, the  We decided to focus on the College-Going Rate for HS Completers (16-month) to correlate the relationship of what school you go to and your chances of being high income based on whether you end up going to college. We narrowed down our data to only include schools in the county of LA. Further, we joined this data with the general public schools database to get geographic information about each school to plot on a map. We cleaned this data using python scripts using pandas.

Census Income/Rent Data (ACS)

The Census Bureau runs the American Community Survey and has datasets for different time periods. We used the most recent version, 2023, over a 5 year period and were able to extract census data over tracts localized to Los Angeles. Combined with SHAPE files also provided on the Census website we are able to per-tract and geographic data to plot on a map. This data was also cleaned using python scripts and exported to readable CSVs.

Presentation

We built and designed our website using WordPress, an open-source platform recognized for its accessibility, flexibility, and creative freedom. With its wide range of free themes, templates, and plug-ins, WordPress allowed us to bring our ideas to life without requiring advanced coding skills. Its collaborative editing features also made it easy for our team to design, revise, and build together in real time. Because WordPress is web-hosted, our project will remain publicly available even after the Digital Humanities 101 course concludes. We chose the Inspiro theme for its clean layout and clear contrasts in color, font, and organization that make the site intuitive and engaging for visitors.