  We consider spatial voting where candidates are located in the Euclidean
$d$-dimensional space and each voter ranks candidates based on their distance
from the voter's ideal point. We explore the case where information about the
location of voters' ideal points is incomplete: for each dimension, we are
given an interval of possible values. We study the computational complexity of
computing the possible and necessary winners for positional scoring rules. Our
results show that we retain tractable cases of the classic model where voters
have partial-order preferences. Moreover, we show that there are positional
scoring rules under which the possible-winner problem is intractable for
partial orders, but tractable in the one-dimension spatial setting (while
intractable in higher fixed number of dimensions).
