mcda-suitability-analysis
Always invoke for spatial suitability, site selection, AHP, criteria weights, or weighted-overlay work, including audits of inconsistent pairwise judgments and requests for only a final map. Covers consistency, standardization, constraints, ranked surfaces, shortlists, and sensitivity. Route travel-time placement and location-allocation to network-accessibility-analysis.
Install
With your agent
Install OMGSkills skill "muend/geoai-skills:skills/mcda-suitability-analysis" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/muend/geoai-skills /tmp/geoai-skills && ln -s /tmp/geoai-skills/skills/mcda-suitability-analysis ~/.claude/skills/mcda-suitability-analysisFrom README
MCDA & Suitability Analysis Purpose: produce suitability maps whose weights, scales, and assumptions are explicit, consistent, and stress-tested. A suitability map without a sensitivity analysis is an opinion with a legend. Workflow Structure: goal → criteria (factors) → constraints. Constraints are binary masks (legal exclusions, water bodies, slope > threshold) applied at the END by multiplication; factors are continuous and weighted. Keep them apart — encoding a constraint as a heavily-weighted factor is a classic error that lets forbidden areas score "acceptable". Criteria layers: each factor as a raster on a COMMON grid (same CRS, extent, cell size, snap). Resample categorical layers with nearest, continuous with bilinear; document each. Standardization to a common suitability scale (0-1 or 0-255): Linear min-max for monotonic "more is better/worse". Fuzzy membership (sigmoid/linear with control points) when suitability saturates — justify control points from domain knowledge.
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