Community Needs Assessment
In developing a decision making framework, our studio prioritized properties located within communities of high need to ensure resulting facilities plans mirror APS’ strategic focus on enhancing equity. To determine community need, we collected and analyzed data across four main categories–housing, economics, transportation and health. We created a rubric, scaled 1 to 5, that helped us identify which census tracts ranked above average in need for each indicator. The census tracts with the most significant need were those that received the highest total score across all categories. When we analyzed the Cooper Street property, some data points came from one tract that included the Cooper Street APS property while other data came from the larger Mechanicsville Statistical area (or a combination of two tracts that was largely coterminous with this boundary). The larger scale was preferable when possible because the subject property, located on the eastern border of one tract, is adjacent to an area experiencing large transformation. (See Resources page for data sources).
Housing
Given the connection between transiency rates and student academic performance, we evaluated housing needs using the following indicators: 1) percentage of the population that is cost-burdened (meaning they spend 30% or more of their income on housing) and 2) the percentage of residents who have been evicted in 2018.
Economics
To evaluate community economic need, we analyzed census-level data on median income and unemployment. We used the Atlanta median value for household income as a baseline to compare against.
Transportation
We wanted to assess site accessibility for each property, so to isolate local transportation needs, we collected data on the percentage of workers without a vehicle. We also used the EPA walkability index to determine which neighborhoods were walkable and which required residents to have alternative transit options.
Health
In the health needs category, we measured the percentage of residents without health insurance and imported data from the Atlanta Regional Commission Health Index, which summarizes exposure to environmental hazards.
Population Projections and School Utilization Assessments
After assessing community need, we evaluated projected population growth and future APS school utilization rates. We understood that as the city population increases, APS may need to use currently vacant properties to accommodate additional students. Recommendations for vacant facilities in higher growth areas must be flexible enough to include academic uses in line with near-term population projections. In contrast, properties in lower growth neighborhoods can be used for more permanent, non-traditional purposes that address the needs of both APS students and the surrounding community. Recognizing this distinction, we categorized properties into groups based on the projected population growth of their local communities. To do so, we referenced APS’s 2024 Projected Population Map included in the May 2018 Board Facilities Retreat presentation to generate census tract-level growth profiles, ranging from very low to very high.
Next, we assessed the current utilization rate of the closest elementary, middle and high schools to each vacant property. For the purposes of our analysis, we considered anything over 80% a high utilization rate. APS provided internal school utilization data for this analysis.
Ultimately, we determined that properties in areas with high projected population growth and schools nearing or at capacity will likely have more immediate academic uses.
Our quantitative analysis of community need and projected population growth and future school utilization rates helped us categorize properties into distinct need-growth profiles.
Based on the results outlined in this graph, we identified 10 properties that were all high need but fell across various population growth and utilization categories and were within different clusters. Ultimately, we recognized that we needed additional information to determine which subset of properties we would use to generate examples of potential recommendations. This led us to our next phase: a qualitative, property-centric analysis.