In GIS distance analysis, which distance is defined as the shortest distance or path between two points or pixels?

• A. Manhattan distance
• B. Chebyshev distance
• C. Euclidean distance
• D. Haversine distance

Answer: (C)

For two points on a flat plane, the shortest path between them is a straight line, and that distance is the Euclidean distance. It is computed as the square root of the sum of squared coordinate differences: sqrt((Δx)^2 + (Δy)^2). In a raster GIS context, this corresponds to the straight-line distance between the centers of two pixels when the grid spacing is uniform, representing the minimal possible separation if you could travel in any direction.

Other measures reflect different movement rules. Manhattan distance sums horizontal and vertical steps and is longer when diagonal shortcuts are possible. Chebyshev distance uses only the largest coordinate difference, effectively modeling movement in eight directions but not following the true straight line. Haversine distance, on the other hand, calculates the great-circle distance on the Earth’s surface, appropriate for geographic coordinates on a sphere rather than a flat plane.

Thus, the Euclidean distance best captures the shortest path between two points on a plane.