About the Dataset
This project uses the Public School Characteristics 2022-23 collected by the NCES Common Core of Data (CCD), a program funded by the National Center for Education Statistics (NCES) under the Education Demographic and Geographic Estimates (EDGE) initiative. The Public School Characteristics dataset, collected since the 1950s and released annually, provides data on elementary and secondary education in the United States. This project focuses on the 2022-2023 academic year.
This dataset proves instrumental in the analysis by supplying granular information on each public school, including student-teacher ratios, school locations, school types, racial demographics, and economic indicators such as the free or reduced-price lunch program. The data enables fair comparisons across compare urban/city, suburban, and rural areas. In addition, the dataset captures temporal trends in state funding allocation and investment patterns. Most importantly, the classification categories themselves, such as “Regular,” “Alternative,” “Virtual,” and locale codes like “Remote” or “Fringe,” reveal the bureaucratic frameworks that shapes how American public education is organized and valued. These taxonomies make the dataset ideal for interrogating both resource distribution and underlying assumptions embedded in how we measure schools.
What Scholars Tells Us
The literature views educational geography as a system that distributes opportunity unevenly through neighborhood boundaries, demographic sorting, and spatialized resource allocation. We agree that geography functions as an organizing force, and we extend this argument by making these inequalities materially visible through data visualization and spatial analysis.
Scholars consistently show that geography structures access to school resources in patterned and unequal ways. In Sociology of Education, authors Owens (2018) and Logan et al. (2012) demonstrate that spatial segregation produces achievement gaps and unequal teaching conditions, while Jackson (2009) shows that demographic sorting after desegregation intensified geographic disparities in teacher quality. As these authors put it, “separate means unequal” because school location predicts who receives experienced teachers, smaller classes, and well-funded learning environments. Although some argue that variation in teacher quality simply reflects individual labor-market choices, research by Fitchett and Heafner (2017) and Kukla-Acevedo (2009) maintains that teacher preparation and assignment patterns are structurally linked to district demographics and locale rather than personal preference.
Unanswered Questions
Despite existing research, some key questions remain unanswered.
How do teacher qualifications factor into spatial inequalities?
A large majority of datasets prioritize teacher quantity, through student-teacher ratios, and not quality. This leaves unexamined whether areas of high student-teacher ratios also employ teachers of less experience or certification, a correlation potentially compounding educational disadvantages and directly impacting student outcomes.
How do spatial inequalities impact student’s everyday academic experiences?
Several dimensions of daily academic life remain unexplored due to data limitations. There is no dataset that captures the correlation between school commuting times and student motivation, yet travel burden for rural schools likely affects attendance and performance. Similarly, the digital divide presents an emerging area that lacks comprehensive data. The effectiveness of virtual learning through platforms such as Canvas rely heavily on students’ access to stable internet and quiet study spaces. While technology promises equitable learning opportunities, in practice it often widens existing gaps when students lack the necessary infrastructure and environment to participate fully.