  Motivated by challenges in pangenomic read alignment, we propose a
generalization of Wheeler graphs that we call Wheeler maps. A Wheeler map
stores a text $T[1..n]$ and an assignment of tags to the characters of $T$ such
that we can preprocess a pattern $P[1..m]$ and then, given $i$ and $j$, quickly
return all the distinct tags labeling the first characters of the occurrences
of $P[i..j]$ in $T$. For the applications that most interest us, characters
with long common contexts are likely to have the same tag, so we consider the
number $t$ of runs in the list of tags sorted by their characters' positions in
the Burrows-Wheeler Transform (BWT) of $T$. We show how, given a straight-line
program with $g$ rules for $T$, we can build an $O(g + r + t)$-space Wheeler
map, where $r$ is the number of runs in the BWT of $T$, with which we can
preprocess a pattern $P[1..m]$ in $O(m \log n)$ time and then return the $k$
distinct tags for $P[i..j]$ in optimal $O(k)$ time for any given $i$ and $j$.
We show various further results related to prioritizing the most frequent tags.
