Dataset for Optimal School Prioritization Model for Educational Fairness using Heuristic Spatial Analysis

The lack of educational resources through consolidation and closure of small schools has led to a deteriorating educational environment but could also accelerate the collapse of rural communities beyond depriving the communities of educational opportunities. This study explores accessibility changes in the educational environment when primary schools are consolidated or closed in rural areas. We developed a model that supports the decision to prioritize school closures based on fairness using a heuristic p-median algorithm. Considering the number of closed schools and changes in commuting distance, we selected the target area of Chuncheon in South Korea. The results show that the coverage area of school districts and the school-to-home distance are significantly different in urban and rural areas. We confirmed that the overall urban and rural commuting environment would be an effective way to make the closure of schools similar if they are prioritized using the heuristic p-median algorithm.