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Multiobjective spanning tree based optimization model to political redistricting

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Abstract

A successful political redistricting plan should be neutral by satisfying a number of requirements, such as equal population, and contiguity as well as compactness. Modeling approaches to solving this problem becomes either computationally intensive when the contiguity requirement is explicitly addressed, and requirements are used as more than one objective In this paper, a multiobjective mixed integer optimization model was developed based on developments in solving land acquisition problems. Using the approach, geographical criteria such as contiguity and compactness can be explicitly addressed. Tradeoff between compactness and equal population can be evaluated. The use of the approach based on synthetic data was demonstrated using a set of experiments.

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Notes

  1. www.ncsl.org.

  2. In computational complexity theory, the P problems are the problems solved in a polynomial time. The NP problems (non-deterministic polynomial time) are problems, which have no information whether or not problem is solved in polynomial time. NP-complete problem are the most difficult problems in NP problem and every problem in NP is reducible to it. NP-hard problems are the problems which are NP-complete and do not solved in a polynomial time.

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Authors and Affiliations

  1. Policy Research Division, GBSA (Gyeonggido Business & Science Accelerator), Suwon, Gyeonggi, Republic Of Korea

    Myung Jin Kim

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  1. Myung Jin Kim
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Correspondence to Myung Jin Kim.

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Kim, M.J. Multiobjective spanning tree based optimization model to political redistricting. Spat. Inf. Res. 26, 317–325 (2018). https://doi.org/10.1007/s41324-018-0171-5

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  • DOI: https://doi.org/10.1007/s41324-018-0171-5

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