Unit-Disk Range Searching and Applications

TL;DR

This paper develops efficient data structures for unit-disk range searching in the plane, enabling faster queries and improvements in related geometric problems, with applications to batched range counting and intersection problems.

Contribution

It adapts simplex range searching techniques to unit-disk queries, achieving optimal space and query time, and improves algorithms for several classical geometric problems.

Findings

Built a data structure with O(n) space and O(√n) query time.

Reduced batched range searching time from O(n^{4/3} log n) to O(n^{4/3}).

Enhanced solutions for intersecting pairs of unit circles and other geometric problems.

Abstract

Given a set $P$ of $n$ points in the plane, we consider the problem of computing the number of points of $P$ in a query unit disk (i.e., all query disks have the same radius). We show that the main techniques for simplex range searching in the plane can be adapted to this problem. For example, by adapting Matou\v{s}ek's results, we can build a data structure of $O(n)$ space so that each query can be answered in $O(\sqrt{n})$ time. Our techniques lead to improvements for several other classical problems, such as batched range searching, counting/reporting intersecting pairs of unit circles, distance selection, discrete 2-center, etc. For example, given a set of $n$ unit disks and a set of $n$ points in the plane, the batched range searching problem is to compute for each disk the number of points in it. Previous work [Katz and Sharir, 1997] solved the problem in $O(n^{4/3}\log n)$ time while our new algorithm runs in $O(n^{4/3})$ time.