  $\newcommand{\dist}{\operatorname{dist}}$ In this paper we define the notion
of a probabilistic neighborhood in spatial data: Let a set $P$ of $n$ points in
$\mathbb{R}^d$, a query point $q \in \mathbb{R}^d$, a distance metric $\dist$,
and a monotonically decreasing function $f : \mathbb{R}^+ \rightarrow [0,1]$ be
given. Then a point $p \in P$ belongs to the probabilistic neighborhood $N(q,
f)$ of $q$ with respect to $f$ with probability $f(\dist(p,q))$. We envision
applications in facility location, sensor networks, and other scenarios where a
connection between two entities becomes less likely with increasing distance. A
straightforward query algorithm would determine a probabilistic neighborhood in
$\Theta(n\cdot d)$ time by probing each point in $P$.
  To answer the query in sublinear time for the planar case, we augment a
quadtree suitably and design a corresponding query algorithm. Our theoretical
analysis shows that -- for certain distributions of planar $P$ -- our algorithm
answers a query in $O((|N(q,f)| + \sqrt{n})\log n)$ time with high probability
(whp). This matches up to a logarithmic factor the cost induced by
quadtree-based algorithms for deterministic queries and is asymptotically
faster than the straightforward approach whenever $|N(q,f)| \in o(n / \log n)$.
  As practical proofs of concept we use two applications, one in the Euclidean
and one in the hyperbolic plane. In particular, our results yield the first
generator for random hyperbolic graphs with arbitrary temperatures in
subquadratic time. Moreover, our experimental data show the usefulness of our
algorithm even if the point distribution is unknown or not uniform: The running
time savings over the pairwise probing approach constitute at least one order
of magnitude already for a modest number of points and queries.
