When would one use Manhattan distance as opposed to Euclidean distance?
According to this interesting paper, Manhattan distance (L1 norm) may be preferable to Euclidean distance (L2 norm) for the case of high dimensional data.
The authors of the paper even go a step further and suggest to use Lk norm distances, with a fractional value of k, for very high dimensional data in order to improve the results of distance-based algorithms, like clustering.