Quantcast

Defining Equitable Geographic Districts in Road Networks via Stable Matching

Research paper by David Eppstein, Michael Goodrich, Doruk Korkmaz, Nil Mamano

Indexed on: 29 Jun '17Published on: 29 Jun '17Published in: arXiv - Computer Science - Data Structures and Algorithms



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. In order to achieve this running time, we present a novel dynamic nearest-neighbor data structure for road networks, which may be of independent interest. This data structure maintains a subset of nodes of the network and allows nearest-neighbor queries (for other nodes in the graph) in $O(\sqrt{n})$ time and updates in $O(\sqrt{n}\log n)$ time. 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 $k$.