Data Critique
This page explains the datasets used in the LAyers project and critically examines the ways in which they were used.
Before interpreting our data, we adopted a digitally humanist lens by creating critiques of our datasets to evaluate where silences might arise and what perspectives were being left out. Because our project focuses on the U.S. Wealth Gap, the majority of sources we initially found provided information about financial and quantitative data. However, we wanted to take an intersectional approach based on the historical and systemic systems of oppression and incorporated the ways that education, as well as housing, heightened these issues according to certain marginalized groups.
Our philosophy was influenced by Michel-Rolph Trouillot’s “Silencing the Past,” in which he emphasizes the importance of considering data in the context of power, narrative, and history. In addition, we are influenced by Todd Presner and David Shepard’s “Mapping the Geospatial Turn,” which urges readers to view data maps and visualizations as unneutral, allowing them to consider the kinds of narratives they perpetuate about the topics they concern. We attempted to incorporate multiple perspectives and issues in our analysis of the wealth gap’s effect on Los Angeles.
ACS Income and Rent Data
The American Community Survey is a federal survey drawing samples from across the U.S. via internet, mail, telephone and personal visits.
CDE Education Data
The sources of California Dept of Education data are public school districts, county offices of education, and charter schools. The information was collected and reported by school administrators, teachers and staff.
Federal Reserve Data
The Federal Reserve merged financial data from U.S. households collected every three years with national balance sheet data to create this dataset.
Data Sources
[1] “College-Going Rate for HS Completers (16-Month) – Accessing Educational Data (ca Dept of Education).” Ca.gov, 2022, www.cde.ca.gov/ds/ad/filescgr16.asp. Accessed 21 Nov. 2025.
[2] States, United. “Explore Census Data.” Census.gov, 2025, data.census.gov/table/ACSST5Y2023.S1903?q=S1903:+Median+Income+in+the+Past+12+Months+(in+2024+Inflation-Adjusted+Dollars).
[3]“The Fed – Distribution: Distribution of Household Wealth in the U.S. Since 1989.” Federalreserve.gov, 2015, www.federalreserve.gov/releases/z1/dataviz/dfa/distribute/chart/#quarter:143.
Image Sources
[1] “Welcome to Zscaler Directory Authentication.” Cloudinary.com, 2025, res.cloudinary.com/aenetworks/image/upload/c_fill.
[2] Econofact.org, 2025, econofact.org/wp-content/uploads/2022/11/KatzMTO-WEB.png. Accessed 22 Nov. 2025.
[3] Najarro, Ileana. “The Complex Factors Affecting English-Learner Graduation Rates.” Education Week, 8 May 2024, www.edweek.org/teaching-learning/the-complex-factors-affecting-english-learner-graduation-rates/2024/05.
[4] Peters, Adele. “These Beautiful Aerial Photos of L.A. Show What Income Inequality Looks like from Above.” Fast Company, 3 Aug. 2015, www.fastcompany.com/3049263/these-beautiful-aerial-photos-of-la-show-what-income-inequality-looks-like-from-above. Accessed 22 Nov. 2025.