Add least-cost corridor analysis (#965)#968
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Computes corridors of low cumulative cost between source locations by summing two cost-distance surfaces and normalizing. Supports absolute and relative thresholding, pre-computed cost-distance inputs, and multi-source pairwise corridors.
Covers symmetry, optimal path minimum, absolute and relative thresholding, precomputed surfaces, multi-source pairwise, barriers, unreachable sources, single-cell input, and input validation. All parametrized across numpy and dask+numpy backends.
Adds API reference entry under Proximity, a user guide notebook (20_Corridor_Analysis) covering all major features, and a row in the README feature matrix.
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Summary
least_cost_corridor()-- sums two cost-distance surfaces, normalizes by the minimum, and optionally masks by a thresholdcost_distancewith no new kernels, so all four backends (NumPy, CuPy, Dask+NumPy, Dask+CuPy) work out of the boxTest plan
pytest xrspatial/tests/test_corridor.py-- 20 tests: symmetry, optimal-path minimum, absolute/relative thresholds, precomputed surfaces, pairwise mode, barriers, unreachable sources, single-cell input, input validation20_Corridor_Analysis.ipynbend-to-endCloses #965