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Defining Equitable Geographic Districts in Road Networks via Stable Matching

TLDR
The problem of defining geographic districts in road networks that are equitable, in that every district has the same number of vertices and the assignment is stable in terms of geographic distance, can be solved in O(n √ n log n) time.
Abstract
We introduce a novel method for defining geographic districts in road networks using stable matching. In this approach, each geographic district is defined in terms of a center, which identifies a location of interest, such as a post office or polling place, and all other network vertices must be labeled with the center to which they are associated. We focus on defining geographic districts that are equitable, in that every district has the same number of vertices and the assignment is stable in terms of geographic distance. That is, there is no unassigned vertex-center pair such that both would prefer each other over their current assignments. We solve this problem using a version of the classic stable matching problem, called symmetric stable matching, in which the preferences of the elements in both sets obey a certain symmetry. In our case, we study a graph-based version of stable matching in which nodes are stably matched to a subset of nodes denoted as centers, prioritized by their shortest-path distances, so that each center is apportioned a certain number of nodes. We show that, for a planar graph or road network with $n$ nodes and $k$ centers, the problem can be solved in $O(n\sqrt{n}\log n)$ time, which improves upon the $O(nk)$ runtime of using the classic Gale-Shapley stable matching algorithm when $k$ is large. Finally, we provide experimental results on road networks for these algorithms and a heuristic algorithm that performs better than the Gale-Shapley algorithm for any range of values of $k$.

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Citations
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Proceedings ArticleDOI

Balanced centroidal power diagrams for redistricting

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TL;DR: A method for redistricting, decomposing a geographical area into subareas, called districts, so that the populations of the districts are as close as possible and the Districts are compact and contiguous is proposed.
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On the computational tractability of a geographic clustering problem arising in redistricting

TL;DR: If the diameter of the graph is moderately small and the number of districts is very small, the algorithm is useable, and it is shown that, under a complexity-theoretic assumption, no algorithms with running time of the form $O(c^wn^{k+1})$ exist.
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Euclidean TSP, Motorcycle Graphs, and Other New Applications of Nearest-Neighbor Chains.

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References
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