Heuristic search and information visualization methods for school redistricting

Authors:
Marie desJardins;Blazej Bulka;Ryan Carr;Andrew Hunt;Priyang Rathod;Penny Rheingans
Affiliations:
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD
Venue:
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
Year:
2006

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Abstract

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 must be considered, such as school capacity, busing costs, and socioeconomic distribution. 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 show the resulting plans using novel visualization methods that we have developed for summarizing and comparing alternative plans.