The Manhattan distance, or L1 norm, measures the sum of absolute distance between two vectors. This measure calculates distance in a grid-like path rather than as the crow flies. It is believed that as the dimension of the data increases, the Manhattan Distance is preferred to the Euclidean, as the latter is more prone to suffer from the Curse of Dimensionality.