ScholarGate
Get started
MCDMDecision-makingDistanceMath steps

Manhattan Distance — L1 norm (city-block distance) between two vectors

OriginatorDezert, J., Tchamova, A., Han, D., Bhotto, M. Z. A.Year2020Sources1Related methods1

DIST-MANHATTAN (Manhattan Distance — L1 norm (city-block distance) between two vectors) is a distance multi-criteria decision-making (MCDM) method introduced by Dezert, J., Tchamova, A., Han, D., Bhotto, M. Z. A. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Key highlights

  • Follows a transparent, reproducible computational procedure that can be audited step by step.
  • Handles multiple criteria of differing scales and units within a single decision matrix.

Intuition

This section is available to Pro members. Upgrade to Pro

How it works

This section is available to Pro members. Upgrade to Pro

When to use it

d ≥ 0; d=0 iff a=b. Manhattan Distance is symmetric.

Strengths & limitations

Strengths
  • Follows a transparent, reproducible computational procedure that can be audited step by step.
  • Handles multiple criteria of differing scales and units within a single decision matrix.
Limitations
  • Results depend on the chosen normalisation, weights, and parameter settings.

Common pitfalls

This section is available to Pro members. Upgrade to Pro

Sources

  1. 1.
    Dezert, J., Tchamova, A., Han, D., Bhotto, M. Z. A. (2020). Manhattan Distance. IEEE Transactions on Cybernetics
    https://scholar.google.com/scholar?q=Manhattan+Distance+Dezert

You have read it. What now?

Ask about this methodSimilar methods

Cite this page

ScholarGate. (2026, June 2). DIST-MANHATTAN. ScholarGate. https://scholargate.app/decision-making/dist-manhattan