Fanglan Chen, Subhodip Biswas, Zhiqian Chen, Shuo Lei, Naren Ramakrishnan and Chang-Tien Lu presented work at AAAI 2023 on exploring the feasibility of automatically generating school redistricting plans. In this interview, Fanglan tells us more about this work, the difficult balancing act when drafting such plans, their methodology, and results of a case study that they carried out. Our paper explores the feasibility of automatically generating school redistricting plans in an efficient manner and addressing tradeoffs in balancing different criteria in the process. The goal of this research is to assist school board members and urban planners in drafting qualitatively different redistricting plans that represent a variety of considerations in decision making and facilitating better utilization of educational resources. To accommodate the changes to current student enrolment numbers or projections, the school attendance zones need to be assessed and redrawn each year.

We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives, such as school capacity, busing costs, and socioeconomic distribution, must be considered. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different tradeoffs in the decision space. We present heuristic search methods that can be used to find a set of qualitatively different plans, and give empirical results of these search methods on population data from the school district of Howard County, Maryland.

We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different trade-offs in the decision space. We show the resulting plans using novel visualization methods that we have developed for summarizing and comparing alternative plans.

We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives, such as school capacity, busing costs, and socioeconomic distribution, must be considered. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different trade-offs in the decision space. We present heuristic search methods that can be used to find a set of qualitatively different plans, and give empirical results of these search methods on population data from the school district of Howard County, Maryland. We show the resulting plans using novel visualization methods that we have developed for summarizing and comparing alternative plans.