We describe an application of geospatial visualization and AI search 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. Additionally, school assignments need to be made for three different levels (elementary, middle, and high school) in a way which

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... lows children to move from one school to the next with a cohort of sufficient size. 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 visualization techniques which can be used to visualize the quality of spatial partititioning plans, compare the alternatives presented by different plans, and understand the interrelationships of plans at different educational levels. We demonstrate these techniques on partitions created through both manual construction and intelligient search processes for the population data of the school district of Howard County, Maryland. 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 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.