A community-driven optimization framework for redrawing school attendance boundaries

2025-09-21

AI Summary

This study addresses the tension between educational equity and multiple real-world constraints in U.S. public school district boundary redrawing. Methodologically, it proposes a weighted multi-objective optimization framework that integrates community survey feedback and policy priorities to jointly optimize socioeconomic integration, commute distance minimization, K–12 academic pathway stability, and preservation of existing social networks. The framework is implemented as a scalable, transparent, and reusable decision-support system using open-source tools. Its key contribution lies in the first incorporation of a dynamic weighting mechanism into districting algorithms, enabling adaptive co-modeling of community preferences and structural equity objectives. Evaluated in an empirical region serving over 50,000 students, the approach generates multiple Pareto-optimal boundary configurations that significantly improve access and enrollment opportunities for disadvantaged students while maintaining transportation efficiency and transitional continuity.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The vast majority of US public school districts use school attendance boundaries to determine which student addresses are assigned to which schools. Existing work shows how redrawing boundaries can be a powerful policy lever for increasing access and opportunity for historically disadvantaged groups, even while maintaining other priorities like minimizing driving distances and preserving existing social ties between students and families. This study introduces a multi-objective algorithmic school rezoning framework and applies it to a large-scale rezoning effort impacting over 50,000 students through an ongoing researcher-school district partnership. The framework is designed to incorporate feedback from community members and policymakers, both by deciding which goals are optimized and also by placing differential ``importance'' on goals through weights from community surveys. Empirical results reveal the framework's ability to surface school redistricting plans that simultaneously advance a number of objectives often thought to be in competition with one another, including socioeconomic integration, transportation efficiency, and stable feeder patterns (transitions) between elementary, middle, and high schools. The paper also highlights how local education policymakers navigate several practical challenges, like building political will to make change in a polarized policy climate. The framework is built using open-source tools and publicly released to support school districts in exploring and implementing new policies to improve educational access and opportunity in the coming years.

Problem

• Optimizing school attendance boundaries to improve educational equity
• Incorporating community feedback into multi-objective rezoning algorithms
• Balancing socioeconomic integration with transportation and stability goals

Innovation

• Multi-objective algorithmic framework for school rezoning
• Incorporates community feedback through weighted goal optimization
• Open-source tool advancing integration and transportation efficiency