What is suitability analysis?

Suitability analysis is a GIS method that scores every location in a study area by how well it meets a set of criteria, then combines those scores into one map that ranks the best places for a use. It answers questions like "Where should the new solar farm go?", "Which parcels can take new housing?" or "Which land should we protect first?"

It is also called site suitability analysis, weighted overlay, or multi-criteria decision analysis (MCDA). The idea goes back to the landscape architect Ian McHarg, who in Design with Nature (1969) stacked transparent hand-drawn maps of soils, slopes and habitats on a light table to see where development would do the least harm. GIS does the same thing digitally, for every cell or parcel, with numbers you can check.

When to use it

Suitability analysis fits any decision where location matters and several factors pull in different directions:

• Siting housing, warehouses, schools, clinics or parks
• Finding land for solar or wind farms
• Choosing retail or service locations
• Prioritising land for conservation or tree planting
• Screening parcels before buying land or starting due diligence

Factors and constraints

Criteria come in two kinds, and keeping them apart is the most important design choice:

• Constraints rule land out completely: wetlands, floodways, protected land, existing buildings, or slopes too steep to build on. A location either passes or it doesn't.
• Factors make land better or worse by degree: gentler slope, closer to roads, cheaper land, more sunshine. These get scores and weights.

Treating a real constraint as a low-weighted factor lets a high score elsewhere "buy back" land that should never be built on. Treating a factor as a constraint throws away good options too early.

How suitability analysis works, step by step

1. Define the question. Suitable for what, and for whom? "Best land for a 50 MW solar farm in this county" leads to very different criteria than "best land for affordable housing near transit".
2. Choose the criteria. List the constraints and factors with the people who will use the result. Fewer, well-justified criteria beat a long list.
3. Gather the data. Elevation for slope, land cover from satellite imagery, roads, parcels, zoning, flood zones, utilities, demographics. Note each layer's source, date and resolution.
4. Put factors on a common scale. Slope is in percent, distance in metres, land cover in categories. Each factor is reclassified to the same scale, often 1 (poor) to 5 (best), or 0 to 1.
5. Weight the factors. Weights say how much each factor matters and add up to 100%. They can come from stakeholder workshops, policy, or a structured method such as the Analytic Hierarchy Process (AHP), which builds weights from pairwise comparisons ("Is slope more important than road access? By how much?").
6. Combine. Multiply each factor score by its weight, add them up, and remove the land excluded by constraints.
7. Test and check. Change the weights a little and see whether the top sites change (sensitivity analysis). Check the results against places people know.
8. Map and explain. Share the suitability map, the shortlist, and the method, so the decision can be traced and repeated.

The maths, with a small example

For each location, the suitability score is a weighted sum of its factor scores, multiplied by its constraints (1 if allowed, 0 if excluded):

Suitability = (w₁ × score₁ + w₂ × score₂ + … ) × constraints

The diagram below runs this on a small six-by-six study area with three factors scored 1 to 5: slope (weight 0.40), road access (0.35) and land cover (0.25). Wetland and protected cells are excluded.

Notice how the weights shape the answer. Raise the weight on road access, and the best cells shift east toward the roads. That's why the weights should be agreed and written down, not picked to fit an answer.

A real-world example: siting a solar farm

A typical first pass for utility-scale solar might start with weights like these. They're illustrative; every project and region sets its own.

Constraints would remove wetlands, flood zones, protected and conserved land, existing buildings, and slopes over about 15%. The result is a map of the remaining land, ranked, plus a shortlist of parcels large enough for the project. Our slope maps often feed straight into this step.

Common mistakes

• Double counting. Using "distance to roads" and "distance to highways" together quietly doubles the weight on access.
• Weights without reasons. If no one can explain why a factor gets 30%, the map can't be defended at a public meeting.
• Mixing scales and dates. A 1 m lidar slope layer combined with 1 km climate data and ten-year-old land cover gives false precision.
• Skipping constraints. Letting a great score elsewhere outweigh a wetland.
• Treating the map as the decision. Suitability analysis produces a shortlist. Site visits, owners, cost and community input still decide.

Tools for suitability analysis

ArcGIS Pro has a dedicated Suitability Modeler and a Weighted Overlay tool. In QGIS the same work is done with reclassification and the Raster Calculator. For repeatable or large analyses, Python with libraries such as rasterio, NumPy and GeoPandas makes every step scripted and easy to rerun when data or weights change.

Frequently asked questions