Cross city learning speeds up school attendance zone planning
Learning to Transfer School Attendance Zoning across Cities
Computers and Society
Summary
Designing school attendance zones that are fair and efficient is hard and usually needs lots of local adjustments and computer work for each city. The authors created a method called COMPASS that learns from previous city zoning plans and predicts good settings for new cities using city features. This prediction helps create school zones quickly without repeating the long search process. Their tests show COMPASS works well for many US cities, making zone planning much faster and usually successful while keeping plans understandable and adjustable.
What this means in practice
- •For city planners: Create fair and balanced school attendance zones faster by using prior city data to guide new zoning plans without extensive recalibration.
- •For educational administrators: Speed up school assignment reviews by obtaining quick, interpretable zoning suggestions adapted from other cities’ successful models.
Authors
Ruiya Zhou, Zhanzhan Zhao
Abstract
Designing multi-school attendance zones requires selecting constraint settings that balance educational equity, accessibility, and school capacity. Repeating this process for each new city can require substantial local calibration and computational search. We introduce COMPASS, a parameter-to-policy framework that transfers knowledge from previous zoning searches to unseen cities through supervised prediction. Using search-derived parameter configurations as offline supervision, COMPASS learns to predict interpretable zoning-control parameters from urban representations and zoning-difficulty signals. A downstream mixed-integer linear program converts these parameters into explicit zoning plans, with conditional parameter repair when the initial solve is unsuccessful. We evaluate parameter prediction, zoning-outcome recovery, and computational efficiency through five-fold cross-validation across 3,000 US cities, complemented by a case study of Atlanta. COMPASS achieves a 99.80% solver-feasible success rate and a 49.14-fold median deployment-time speedup relative to repeated city-specific parameter search. Further analysis shows that transfer performance varies across urban contexts and policy dimensions: structurally difficult cities receive the largest feasibility gains, while enrollment balance remains the hardest outcome to recover. These findings demonstrate how cross-city learning can reduce repeated search while retaining explicit zoning controls and spatial plans for local inspection and adaptation.