We propose a dynamic data structure for the distribution-sensitive point location problem. Suppose that there is a fixed query distribution in $mathbb{R}^2$, and we are given an oracle that can return in $O(1)$ time the probability of a query point falling into a polygonal region of constant complexity. We can maintain a convex subdivision $cal S$ with $n$ vertices such that each query is answered in $O(mathrm{OPT})$ expected time, where OPT is the minimum expected time of the best linear decision tree for point location in $cal S$. The space and construction time are $O(nlog^2 n)$. An update of $cal S$ as a mixed sequence of $k$ edge insertions and deletions takes $O(klog^5 n)$ amortized time. As a corollary, the randomized incremental construction of the Voronoi diagram of $n$ sites can be performed in $O(nlog^5 n)$ expected time so that, during the incremental construction, a nearest neighbor query at any time can be answered optimally with respect to the intermediate Voronoi diagram at that time.
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