Alderman (2022) explains that the 1964 Freedom Schools are an often-overlooked part of geography education because they used a critical regional teaching approach to help Black students stand up against White supremacy. The authors look at lessons from the Freedom School curriculum, including “The South as an Underdeveloped Country” and “Comparison of Students’ Realities with Others,” using materials collected by Emery et al. (2004). The article is important because it encourages today’s geography teachers to make their lessons more inclusive, focus on Black experiences, and actively teach against racism. This source supports our thesis by showing how race and geography are connected in shaping who gets access to quality education. It also gives examples of how students learned to recognize unfairness in their surroundings by studying Census data, comparing the conditions of Black and White schools, and mapping the systems of power that kept segregation in place. Ultimately, Alderman et al. show that when geography is taught critically, it can become a tool of liberation that helps students see and challenge the systems of inequality around them.

Becnel contends that the United States is built upon a “values infrastructure” rooted in racism, which is maintained through “public pedagogy” such as legal codes, social norms, and institutional habits. The author builds this argument by tracing a historical timeline from the 1661 Slave Codes through Jim Crow laws to modern gang injunctions, demonstrating a continuity of legal exclusion. This work is important because it frames current educational policies not as neutral administrative decisions, but as extensions of a long historical lineage of racial control and spatial containment. Specifically, this supports my thesis that the disproportionate number of “Alternative” school clusters in California is not an accident of geography, but part of a state “values infrastructure” designed to segregate and manage specific “at-risk” populations.

Bynoe reports that Massachusetts continues to prioritize equity through the Student Opportunity Act, utilizing a “foundation program” to level funding across divergent districts to meet student needs. The evidence for this report comes from Fiscal Year 2023 state budget data and legislative policy documents regarding the Chapter 70 aid formula. This resource is important as it provides a concrete, contemporary policy example of how a state can use fiscal mechanisms to combat the negative effects of economic clustering. For my project, this source serves as the comparative foil to Texas and Georgia, proving that the “Specialization” model of school types is possible if a state prioritizes financial redistribution over raw “Standardization.”

In “Documenting Geographic Isolation of Schools and Examining the Implications for Education Policy,” Curran and Kitchin argue that geographic isolation affects access to education and resources. This article uses data from the 2013- 2014 NCES Common Core of Data and Private School Universe Survey, which includes each school’s geographic location and income-based characteristics (ex. Free reduced lunch). This resource shows what students might lack due to their geographic locations. For our thesis specifically, this gives us quantitative data that illustrates inequalities in access to educational resources. 

This book outlines a framework for using intersectional feminist thought to challenge power structures embedded in data science. For this project, the authors’ principle of “Considering Context” (Chapter 6) was instrumental in justifying the integration of qualitative research such as news archives and historical accounts alongside the quantitative NCES dataset. Their work also guided the project’s ethical approach to data cleaning (“Examine Power,” Chapter 1), framing the exclusion of certain data points not just as a technical necessity but as an editorial decision that shapes the final narrative.

The article argues that while overall differences between rural and nonrural student achievement are modest, there is significant variation within rural America based on geographic region, relative isolation, local economy, and student demographics. The authors analyze data from the Stanford Education Data Archive which contains 430 million standardized test scores from over 6,500 rural school districts across America, examining third-grade achievement and learning rates between third and eighth grades. This study is important as it provides unprecedented granularity in understanding educational opportunity across rural America, challenging simplified narratives and revealing nuanced patterns of achievement that vary by region, demographics, and economic context. This research directly informs the project by providing evidence that socioeconomic status operates differently in rural contexts versus nonrurals, and how bureaucratic classifications (rural fringe, distant, remote) reflect real inequity.

Fahmy argues that Georgia’s policy of denying in-state tuition to undocumented students violates Kantian ethics by treating these students as means to a political end rather than ends in themselves. The article applies Kant’s “Formula of Humanity” to the specific text of Georgia’s Board of Regents policies and federal immigration laws to construct this ethical critique. This is an important resource because it provides the ethical framework for criticizing exclusionary clustering in the South, defining exactly who is left out of the educational map. I use this source to define the concept of “Shadow Students,” linking the lack of vocational schools and the high number of “Alternative” schools to a broader state priority of systematically excluding vulnerable populations from the economic mainstream.

This study argues that both student demographics and teacher characteristics significantly influence elementary students’ history knowledge outcomes. It uses data from the National Assessment of Educational Progress (NAEP) and statistical modeling to analyze links between student performance, teacher preparation, and classroom demographics. The resource is important because it highlights how inequities in teacher distribution and preparation affect learning across demographic groups. It provides empirical evidence that connects teacher qualifications and classroom demographics to student achievement disparities, supporting my focus on how educator diversity and background influence learning outcomes in public schools.

The authors contend that improving computer science education requires more than just curriculum changes; it requires systemic policy intervention to reach rural and minority students who lack organic access to technology. They base this argument on evaluation data from a six-year, NSF-funded statewide intervention program involving thousands of students and teachers across Georgia. This study is important because it empirically proves that “Regular” schools in rural or poor areas cannot organically generate technical opportunities without outside help or specialized structures. This reinforces my thesis’s critique of the “Standardization” model, showing that without specific clusters—like distinct Tech schools—rural students are left behind in the digital economy.

Hardin asserts that the steep decline of Vocational ESL (VESL) programs is a direct result of 1990s welfare reforms that prioritized rapid “work-first” placement over long-term educational development. To demonstrate this, the author conducts a corpus study and policy analysis of adult education legislation and academic literature over several decades. This is crucial for explaining the absence of data in my project—specifically why states like Texas and Georgia might show zero “Career Schools” in the dataset. This source allows me to interpret the “0” in the bar chart not as a mistake, but as a deliberate historical shift toward a low-wage, “work-first” economic priority in specific states.

Jackson argues that the end of formal school desegregation led to increased sorting of teachers by student demographics, resulting in unequal access to high-quality teachers. He uses quasi-experimental econometric analysis of historical data on teacher assignment and student demographics from desegregated U.S. school districts. This resource is important because it empirically links policy shifts to systemic inequities in teacher quality distribution.It demonstrates how demographic shifts and institutional sorting mechanisms perpetuate inequality, reinforcing my argument that demographic imbalances in teaching staff are structural, not incidental.

The collective authors argue for a “new key” in educational philosophy that emphasizes civic reasoning and the need to address the colonial and racial legacies embedded in schooling. The article relies on a survey of contemporary philosophical scholarship and snapshots of current educational challenges in the U.S. and Canada. This resource is important because it connects the specific data of school types to the broader democratic purpose of education, moving the conversation beyond just economic statistics. It frames the conclusion of my project, suggesting that the current system of “bureaucratic sorting” undermines the civic reasoning necessary for a healthy democracy.

Kukla-Acevedo argues that specific teacher preparation factors—such as certification type, graduate education, and years of experience—significantly influence student achievement outcomes. She uses longitudinal data from U.S. elementary and middle schools, applying regression analysis to isolate the effects of teacher preparation variables on student test scores. The resource is important because it identifies concrete teacher characteristics that improve student learning outcomes. It provides a quantitative foundation for analyzing how teacher training and certification impact classroom equity, helping me connect demographic and professional factors in assessing educational quality.

This study argues that if there is going to be a continuous school segregation of race and ethnic backgrounds, children and the schools that they attend will be unequal in performance. This study uses relevant data on all public schools, elementary schools, and school district populations, provided from national resources such as the National Center for Education Statistics. This study is important because it expands and reinforces our research findings in identifying the effect of the number of students from different ethnic backgrounds in our data and how that might contribute to variables such as the number of teachers and enrollment at those schools. In addition, if we see a high number of minority students at certain schools, we can actually learn a lot about the teachers since the study helps us to know that “black students attend schools with less experienced teaching staff than do white students.”

Turabian’s manual serves as the structural guide for the project’s narrative and argument. Specifically, Chapters 5 and 6 (“Planning Your Argument”) provided the blueprint for the website’s “social contract” with the user—stating the main claim early to establish clear expectations. This resource was essential for ensuring that the digital project adhered to rigorous academic standards of argumentation, transforming the website from a simple data display into a cohesive, evidence-based research paper in digital form.

This dataset from the National Center for Education Statistics provides comprehensive information on approximately 98,000 public schools across the United States for the 2022-23 academic year, including location data, enrollment figures, grade levels served, and school type classifications. The dataset serves as a critical primary source for analyzing spatial patterns in educational infrastructure and understanding the distribution of educational resources across different states and regions, making it essential for examining structural inequalities in American public education.

This study by Ann Owens argues that there is a huge gap in achievement between high- and low-income students and between black and white students. Ann Owens uses the national data to find evidence of the achievement gap due to income in segregated metropolitan areas. The resource is important because our project’s research questions focused greatly on inequalities and geographic disparities. From the study by Ann Owens, the indication of high-quality teachers can connect to our dataset’s variable of the number of teachers at different schools, thus helping us to determine the educational quality being offered at school in certain states and areas, helping us to argue/find inequalities. Since the study is about achievement, we can even further predict the students’ performance from different district areas.

This chapter in the book consists of research done by Taylor and Cantwell argue that the unequal funding, which is done through whether students pay tuition or not, creates a significant difference in the opportunities offered to students. The authors use institutional data, policy analysis, and examples to show the disparities between students who are wealthy versus those who are less wealthy. The resource is important because it helps us to go beyond just understanding geography, but also to look into how other students who come from wealthy backgrounds have more advantages than those who don’t, because schools with more access to funding can offer a lot and better opportunities to students. It helps us determine why the number of students in certain grades or ethnicities is small in certain school districts, because it could then potentially show that wealthy families are enrolling their children in private schools.

This report provides a comprehensive snapshot of state performance, arguing implicitly through its data that high-poverty schools (Title I) face distinct resource challenges compared to non-Title I schools. The evidence is derived from aggregated state-reported data required by the No Child Left Behind Act for the 2003-04 school year. This document is important because it provides the baseline definitions for “Regular” versus “other” schools and establishes the systemic link between poverty and school classification. For my thesis, this source validates the use of NCES data categories and supports the argument that “Alternative” or “Charter” labels are often proxies for managing economic disparities.

Yau’s text focuses on the communicative and exploratory aspects of data visualization. Chapter 4 (“Exploring Data Visually”) influenced the project’s “exploratory” phase, encouraging the team to use R and Python to investigate the data structure before finalizing designs. Additionally, Yau’s insights on “Designing for an Audience” (Chapter 6) informed the decision to use interactive tools like Tableau and Palladio, as well as the deliberate choice of a geographic color palette (green and blue) to help users visually and intuitively connect the abstract statistics to real-world locations.