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  2. Taxicab geometry - Wikipedia

    en.wikipedia.org/wiki/Taxicab_geometry

    Taxicab geometry or Manhattan geometry is geometry where the familiar Euclidean distance is ignored, and the distance between two points is instead defined to be the sum of the absolute differences of their respective Cartesian coordinates, a distance function (or metric) called the taxicab distance, Manhattan distance, or city block distance.

  3. Minkowski distance - Wikipedia

    en.wikipedia.org/wiki/Minkowski_distance

    The Minkowski distance or Minkowski metric is a metric in a normed vector space which can be considered as a generalization of both the Euclidean distance and the Manhattan distance. It is named after the Polish mathematician Hermann Minkowski .

  4. Similarity measure - Wikipedia

    en.wikipedia.org/wiki/Similarity_measure

    Manhattan distance is commonly used in GPS applications, as it can be used to find the shortest route between two addresses. [ citation needed ] When you generalize the Euclidean distance formula and Manhattan distance formula you are left with the Minkowski distance formulas, which can be used in a wide variety of applications.

  5. Chebyshev distance - Wikipedia

    en.wikipedia.org/wiki/Chebyshev_distance

    A sphere formed using the Chebyshev distance as a metric is a cube with each face perpendicular to one of the coordinate axes, but a sphere formed using Manhattan distance is an octahedron: these are dual polyhedra, but among cubes, only the square (and 1-dimensional line segment) are self-dual polytopes.

  6. Canberra distance - Wikipedia

    en.wikipedia.org/wiki/Canberra_distance

    The Canberra distance is a numerical measure of the distance between pairs of points in a vector space, introduced in 1966 [1] and refined in 1967 [2] by Godfrey N. Lance and William T. Williams. It is a weighted version of L ₁ (Manhattan) distance . [ 3 ]

  7. Von Neumann neighborhood - Wikipedia

    en.wikipedia.org/wiki/Von_Neumann_neighborhood

    Manhattan distance r = 1 Manhattan distance r = 2. In cellular automata, the von Neumann neighborhood (or 4-neighborhood) is classically defined on a two-dimensional square lattice and is composed of a central cell and its four adjacent cells. [1]

  8. Distance matrix - Wikipedia

    en.wikipedia.org/wiki/Distance_matrix

    In general, a distance matrix is a weighted adjacency matrix of some graph. In a network, a directed graph with weights assigned to the arcs, the distance between two nodes of the network can be defined as the minimum of the sums of the weights on the shortest paths joining the two nodes (where the number of steps in the path is bounded). [2]

  9. Manhattan distance - Wikipedia

    en.wikipedia.org/?title=Manhattan_distance&...

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