👉 Manhattan distance, or L1 distance, is a measure of similarity between two points in multi-dimensional space. It calculates the sum of the absolute differences of their coordinates, effectively summing up how far apart the points are along each dimension. For example, given two points (x1, y1) and (x2, y2), the Manhattan distance is |x1 - x2| + |y1 - y2|. This metric is particularly useful in scenarios where movement is restricted to a grid-like path, such as city streets or game board movements, and it provides a straightforward, intuitive way to quantify distance without considering the magnitude of differences in each dimension.
