Manhattan Distance
Manhattan Distance, also known as L1 distance or taxicab geometry, is a metric for measuring the distance between two points in a grid-based space by summing the absolute differences of their coordinates. It represents the shortest path between points when movement is restricted to horizontal and vertical directions, akin to navigating city blocks in Manhattan. This concept is widely used in computer science, mathematics, and fields like robotics and data analysis for spatial calculations.
Developers should learn Manhattan Distance for applications involving grid-based algorithms, such as pathfinding in games (e.g., A* search), clustering in machine learning (e.g., k-medians), and image processing (e.g., pixel comparisons). It is particularly useful when movement is constrained to orthogonal directions, making it more efficient than Euclidean distance in scenarios like urban navigation simulations or discrete optimization problems.