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Heuristic Search and Information Visualization Methods for School Redistricting

AI Magazine

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.


Apple's developer academy: Inside the elite school where iPhone developers of the future are being trained

The Independent

There are places people leave and places people go. Naples might be Italy's third-biggest city, but it is losing those some of those people all the time. The city was once one of the industrial engines of Italy -- a history still evident from the husks and dirt of factories in parts of its centre -- and its promenades still hold some of the splendid grandeur of a city known for its contribution to the arts, travel, literature, food, football and crime. But in recent years, like many cities built on industry and manufacturing, some of that glory has faded away. And its young people have left, too -- giving Naples that sense of being a place you start, not where you end.


Heuristic Search and Information Visualization Methods for School Redistricting

AI Magazine

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.


Heuristic Search and Information Visualization Methods for School Redistricting

AI Magazine

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.