the distance between two points in a grid

Manhattan distance is the distance between two points in a grid (like the grid-like street geography of the New York borough of Manhattan) calculated by only taking a vertical and/or horizontal path.

a rectilinear distance

"Manhattan distance" is a rectilinear distance, named after the number of blocks north, south, east, or west a taxicab must travel on to reach a taxicab's destination on the grid of streets in parts of New York City.

the sum of absolute differences

Euclidean distances are root sum-of-squares of differences, and manhattan distances are the sum of absolute differences.

the mathematical distance between two points, which is the sum of the absolute difference of their Cartesian coordinates

Manhattan Distance is the mathematical distance between two points, which is the sum of the absolute difference of their Cartesian coordinates.

in an N-dimensional space

Manhattan distance is a distance metric to find the distance between two points in an N-dimensional space.

the sum of absolute differences between points across all the dimensions

Manhattan Distance is the sum of absolute differences between points across all the dimensions.

the sum of the x and y distances

This is really interesting, and in fact the sum of the x and y distances is called the Manhattan distance.

the rectilinear distance between two points: d = |x1 -

The Manhattan distance is the rectilinear distance between two points: d = |x1 -

the distance between a pair of vectors

The Manhattan distance (also known as L1 norm and Taxicab Distance) - calculates the distance between a pair of vectors, as if simulating a route for a Manhattan taxi driver driving from point A to point B - who is navigating the streets of Manhattan with the grid layout and one-way streets.

the distance between two points

The Manhattan distance is the distance between two points if you had to follow a regular grid — such as the city blocks of Manhattan.

the first root of the sum of differences raised to the first power, and

The Manhattan distance, or 1-norm, is the first root of the sum of differences raised to the first power, and the Euclidean distance, or 2-norm, is the square root of the sum of differences raised to the second power.

the distance between two points measured along right-angled axes

Euclidean distance is the linear and shortest distance, while Manhattan distance is the distance between two points measured along right-angled axes.

the first root of the sum of differences , and the Euclidean distance, or raised to the first power

The Manhattan distance, or 1-norm, is the first root of the sum of differences raised to the first power, and the Euclidean distance, or 2-norm, is the square root of the sum of differences raised to the second power.

the simple sum of the horizontal and vertical components

The Manhattan distance is the simple sum of the horizontal and vertical components, whereas the diagonal distance might be computed by applying the Pythagorean theorem.

the distance

The Manhattan Distance is taking the distance from going all the way on the X axis and adding that to the distance all the way on the Y axis to go from point A to point B.