The Manhattan Norm is a statistical concept used to measure distances in a grid-like pattern, similar to how streets are laid out in Manhattan, New York City. It calculates the distance between two points by summing the absolute differences of their coordinates, rather than using the straight-line distance. This method is particularly useful in urban planning and navigation. In mathematical terms, if you have two points, A(x1, y1) and B(x2, y2), the Manhattan distance is calculated as |x2 - x1| + |y2 - y1|. This approach is often applied in various fields, including computer science, data analysis, and machine learning, where grid-based movement is common.