Manhattan Distance is a term used in mathematics and computer science to measure the distance between two points in a grid-like system, such as a city block. It is named after the grid-like layout of streets in Manhattan, New York City.

The Manhattan Distance between two points is calculated by finding the absolute difference between their x-coordinates (horizontal distance) and the absolute difference between their y-coordinates (vertical distance), and then summing these differences. In simpler terms, it is the total number of blocks you would need to walk to get from one point to another, if you can only move horizontally or vertically.

Manhattan Distance is often used in various applications, such as computer graphics, data mining, and machine learning. For example, it can be used to determine the similarity between two images or to find the nearest neighbor in a dataset.

One advantage of using Manhattan Distance is that it is easier to compute compared to other distance metrics, such as Euclidean distance. This makes it a popular choice in scenarios where efficiency is important.

In conclusion, Manhattan Distance is a measure of distance between two points in a grid-like system. It is calculated by finding the absolute difference between the x-coordinates and y-coordinates of the points, and summing these differences. Its simplicity and efficiency make it a valuable tool in various fields of study and application.