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School redistricting: embedding GIS tools with integer programming

Author

Listed:
  • F Caro

    (Universidad de Chile)

  • T Shirabe

    (Technical University of Vienna)

  • M Guignard

    (University of Pennsylvania)

  • A Weintraub

    (Universidad de Chile)

Abstract

The paper deals with a school redistricting problem in which blocks of a city must be assigned to schools according to diverse criteria. Previous approaches are reviewed and some desired properties of a good school districting plan are established. An optimization model together with a geographic information system environment are then proposed for finding a solution that satisfies these properties. A prototype of the system is described, some implementation issues are discussed, and two real-life examples from the city of Philadelphia are studied, one corresponding to a relatively easy to solve problem, and the other to a much harder one. The trade-offs in the solutions are analysed and feasibility questions are discussed. The results of the study strongly suggest that ill-defined spatial problems, such as school redistricting, can be addressed effectively by an interaction between objective analysis and subjective judgement.

Suggested Citation

  • F Caro & T Shirabe & M Guignard & A Weintraub, 2004. "School redistricting: embedding GIS tools with integer programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(8), pages 836-849, August.
    • Handle: RePEc:pal:jorsoc:v:55:y:2004:i:8:d:10.1057_palgrave.jors.2601729
    DOI: 10.1057/palgrave.jors.2601729
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    13. Christian Haas & Lee Hachadoorian & Steven O Kimbrough & Peter Miller & Frederic Murphy, 2020. "Seed-Fill-Shift-Repair: A redistricting heuristic for civic deliberation," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-34, September.
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    15. Wei, Ran & Feng, Xin & Rey, Sergio & Knaap, Elijah, 2022. "Reducing racial segregation of public school districts," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    16. Sven Müller & Knut Haase & Sascha Kless, 2009. "A Multiperiod School Location Planning Approach with Free School Choice," Environment and Planning A, , vol. 41(12), pages 2929-2945, December.
    17. Amy Cohn & Michael Magazine & George Polak, 2009. "Rank‐Cluster‐and‐Prune: An algorithm for generating clusters in complex set partitioning problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(3), pages 215-225, April.
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    19. Guillermo Durán & Rafael Epstein & Cristian Martinez & Gonzalo Andres Zamorano, 2011. "Quantitative Methods for a New Configuration of Territorial Units in a Chilean Government Agency Tender Process," Interfaces, INFORMS, vol. 41(3), pages 263-277, June.
    20. Yin Xia & Dianfeng Liu & Yaolin Liu & Jianhua He & Xiaofeng Hong, 2014. "Alternative Zoning Scenarios for Regional Sustainable Land Use Controls in China: A Knowledge-Based Multiobjective Optimisation Model," IJERPH, MDPI, vol. 11(9), pages 1-28, August.
    21. Kathryn Grace & Ran Wei & Alan T. Murray, 2017. "A spatial analytic framework for assessing and improving food aid distribution in developing countries," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(4), pages 867-880, August.
    22. Teixeira, Joao C. & Antunes, Antonio P., 2008. "A hierarchical location model for public facility planning," European Journal of Operational Research, Elsevier, vol. 185(1), pages 92-104, February.
    23. Bouzarth, Elizabeth L. & Forrester, Richard & Hutson, Kevin R. & Reddoch, Lattie, 2018. "Assigning students to schools to minimize both transportation costs and socioeconomic variation between schools," Socio-Economic Planning Sciences, Elsevier, vol. 64(C), pages 1-8.
    24. Liao, Cong & Scheuer, Bronte & Dai, Teqi & Tian, Yuan, 2020. "Optimizing the spatial assignment of schools to reduce both inequality of educational opportunity and potential opposition rate through introducing random mechanism into proximity-based system," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).

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