Manhattan Distance — L1 norm (city-block distance) between two vectors
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.
Read the full method
Sign in with a free account to read this section.
Method map
The neighbourhood of related methods — select a node to explore.
When to use it
d ≥ 0; d=0 iff a=b. Manhattan Distance is symmetric.
Strengths & limitations
- 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.
- Results depend on the chosen normalisation, weights, and parameter settings.
Sources
- Dezert, J., Tchamova, A., Han, D., Bhotto, M. Z. A. (2020). Manhattan Distance. IEEE Transactions on Cybernetics link ↗
How to cite this page
ScholarGate. (2026, June 2). Manhattan Distance — L1 norm (city-block distance) between two vectors. ScholarGate. https://scholargate.app/en/decision-making/dist-manhattan
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →