  In this paper, we consider the problem of compressing a trie while supporting
the powerful \emph{locate} queries: to return the pre-order identifiers of all
nodes reached by a path labeled with a given query pattern. Our result builds
on top of the XBWT tree transform of Ferragina et al. [FOCS 2005] and
generalizes the \emph{r-index} locate machinery of Gagie et al. [SODA 2018,
JACM 2020] based on the run-length encoded Burrows-Wheeler transform (BWT). Our
first contribution is to propose a suitable generalization of the run-length
BWT to tries. We show that this natural generalization enjoys several of the
useful properties of its counterpart on strings: in particular, the transform
natively supports counting occurrences of a query pattern on the trie's paths
and its size $r$ captures the trie's repetitiveness and lower-bounds a natural
notion of trie entropy. Our main contribution is a much deeper insight into the
combinatorial structure of this object. In detail, we show that a data
structure of $O(r\log n) + 2n + o(n)$ bits, where $n$ is the number of nodes,
allows locating the $occ$ occurrences of a pattern of length $m$ in
nearly-optimal $O(m\log\sigma + occ)$ time, where $\sigma$ is the alphabet's
size. Our solution consists in sampling $O(r)$ nodes that can be used as
"anchor points" during the locate process. Once obtained the pre-order
identifier of the first pattern occurrence (in co-lexicographic order), we show
that a constant number of constant-time jumps between those anchor points lead
to the identifier of the next pattern occurrence, thus enabling locating in
optimal $O(1)$ time per occurrence.
