Incremental Voronoi Diagrams

We study the amortized number of combinatorial changes (edge insertions and removals) needed to update the graph structure of the Voronoi diagram (and several variants thereof) of a set S of n sites in the plane as sites are added to the set. To that effect, we define a general update operation for planar graphs that can be used to model the incremental construction of several variants of Voronoi diagrams as well as the incremental construction of an intersection of halfspaces in \(\mathbb {R}^3\). We show that the amortized number of edge insertions and removals needed to add a new site to the Voronoi diagram is \(O(\sqrt{n})\). A matching \(\Omega (\sqrt{n})\) combinatorial lower bound is shown, even in the case where the graph representing the Voronoi diagram is a tree. This contrasts with the \(O(\log {n})\) upper bound of Aronov et al. (LATIN 2006: Theoretical Informatics. Lecture Notes in Computer Science, Springer, Berlin, 2006) for farthest-point Voronoi diagrams in the special case where the points are inserted in clockwise order along their convex hull. We then present a semi-dynamic data structure that maintains the Voronoi diagram of a set S of n sites in convex position. This data structure supports the insertion of a new site p (and hence the addition of its Voronoi cell) and finds the asymptotically minimal number K of edge insertions and removals needed to obtain the diagram of \(S \cup \{p\}\) from the diagram of S, in time \(O(K\,\mathrm {polylog}\ n)\) worst case, which is \(O(\sqrt{n}\;\mathrm {polylog}\ n)\) amortized by the aforementioned combinatorial result. The most distinctive feature of this data structure is that the graph of the Voronoi diagram is maintained explicitly at all times and can be retrieved and traversed in the natural way; this contrasts with other known data structures supporting nearest neighbor queries. Our data structure supports general search operations on the current Voronoi diagram, which can, for example, be used to perform point location queries in the cells of the current Voronoi diagram in \(O(\log n)\) time, or to determine whether two given sites are neighbors in the Delaunay triangulation.

1. While the introduction used n for the number of sites in S, the combinatorial part of this article uses n for the number of vertices in the Voronoi diagram. By Euler’s formula, those two values are asymptotically equivalent, up to a constant factor.

2. Although the last two authors are honored by the flattering renaming of the flarb operation in the literature [15], this paper uses original terminology.

3. We caution the reader that while this construction is algorithmic in nature, it is used purely to provide an upper-bound and does not reflect the behavior of the algorithm presented in Sect. 5 that gives our desired runtime.

The first named author is supported by NSF Grants CCF-0747250, CCF-1116594, and the Graduate Research Fellowship Program under Grant No. DGE-1252522. Luis Barba is supported by the ETH Postdoctoral Fellowship.

Authors and Affiliations

1. Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, 15213-3891, USA

   Sarah R. Allen

2. Department of Computer Science, ETH Zürich, 8092, Zürich, Switzerland

   Luis Barba

3. Department of Computer Science and Engineering, Tandon School of Engineering, New York University, New York, NY, 11201, USA

   John Iacono

4. Départment d’Informatique, Université Libre de Bruxelles, 1050, Bruxelles, Belgium

   Stefan Langerman

Corresponding author

Correspondence to Luis Barba.

Editor in Charge: Kenneth Clarkson

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