Heuristic Search and Information Visualization Methods for School Redistricting

By desJardins, Marie; Bulka, Blazej et al. | AI Magazine, Fall 2007 | Go to article overview

Heuristic Search and Information Visualization Methods for School Redistricting


desJardins, Marie, Bulka, Blazej, Carr, Ryan, Jordan, Eric, Rheingans, Penny, AI Magazine


Motivation and Overview

This research focuses on developing decision support tools for the problem of school redistricting. In this domain, the goal is to assign the students from each geographic region (neighborhood or planning polygon) in a county or school district to a home school at each level (elementary, middle, and high school). We are working with the Howard County, Maryland, school system to develop tools that will aid in generating, evaluating, and comparing alternative school-assignment plans. Related applications include emergency response planning, urban planning and zoning, robot exploration planning, and political redistricting.

The school assignment plan should ideally satisfy a number of different goals, such as meeting school capacities, balancing socioeconomic and test score distributions at the schools, minimizing busing costs, and allowing students in the "walk area" of a school to attend that home school. Since these objectives are often at odds with each other, finding the best plan is a complex multicriteria optimization problem. It is also often desirable to create several alternative plans for consideration; these plans should be qualitatively different--that is, they should represent different tradeoffs among the evaluation criteria. Finally, because of the complexity of the problem, it is difficult for users to fully understand these trade-offs. Therefore, developing effective visualizations is an important challenge.

The contributions of our work are (1) a computational formulation of the school redistricting problem as a multicriteria optimization problem, (2) novel heuristic local search techniques for generating high-quality, diverse (that is, qualitatively different) plans, (3) visualization methods for comparing alternative plans, (1) and (4) empirical results demonstrating the effectiveness of our search methods on actual Howard County school data.

The remainer of this article is organized as follows. We first describe the current redistricting process in Howard County and present some example plan visualizations that we have developed. Next, we describe the search methods and present empirical results comparing manually and automatically generated plans in terms of plan quality and diversity. Finally, we summarize related work and then present our future work and conclusions.

Redistricting Process

The Howard County Public School System (HCPSS) serves a rapidly growing county in suburban Maryland. The pace of development and population growth has necessitated the opening of 25 new schools in the last 14 years, turning the adjustment of school attendance areas into an almost annual event. Under the current process, candidate plans and feasibility studies are generated manually (2) by school system staff. These plans are evaluated and refined by a committee of citizens, then presented at regional meetings for public comment. A small set of candidate plans is forwarded to the superintendent, who presents two or three recommended alternatives to the board of education. The board has final decision-making authority and will typically select one of the recommended plans, sometimes making minor modifications in response to concerns raised by parent groups or staff. Note that this process is specific to Howard County; other school districts may have different processes and models.

Candidate plans are evaluated according to 11 measured criteria: (1) the educational benefits for students, (2) the frequency with which students are redistricted, (3) the number and distance of students bused, (4) the total busing cost, (5) the demographic makeup and academic performance of schools, (6) the number of students redistricted, (7) the maintenance of feeder patterns (that is, the flow of students from elementary to middle to high school), (8) changes in school capacity, (9) the impact on specialized programs, (10) the functional and operational capacity of school infrastructure, and (11) building utilization. …

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