Manhattan Distance vs Chebyshev Distance

Developers should learn Manhattan Distance for applications involving grid-based algorithms, such as pathfinding in games (e meets developers should learn chebyshev distance when working on problems involving grid-based pathfinding, such as in game development for chess or king movements, or in image processing for pixel comparisons. Here's our take.

Manhattan Distance

Nice Pick

Developers should learn Manhattan Distance for applications involving grid-based algorithms, such as pathfinding in games (e

Pros

• g
• Related to: euclidean-distance, chebyshev-distance

Cons

• Specific tradeoffs depend on your use case

Chebyshev Distance

Developers should learn Chebyshev distance when working on problems involving grid-based pathfinding, such as in game development for chess or king movements, or in image processing for pixel comparisons

Pros

• It is also valuable in machine learning for clustering algorithms like k-nearest neighbors when data has uniform scaling across dimensions, and in computational geometry for defining neighborhoods in multi-dimensional spaces
• Related to: euclidean-distance, manhattan-distance

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Manhattan Distance if: You want g and can live with specific tradeoffs depend on your use case.

Use Chebyshev Distance if: You prioritize it is also valuable in machine learning for clustering algorithms like k-nearest neighbors when data has uniform scaling across dimensions, and in computational geometry for defining neighborhoods in multi-dimensional spaces over what Manhattan Distance offers.

The Bottom Line

Manhattan Distance wins

Developers should learn Manhattan Distance for applications involving grid-based algorithms, such as pathfinding in games (e