The Power of Overlay: Making Maps Think for You

Maps are great at showing us where things are, but GIS goes a step further by helping us understand how things relate to each other spatially. One of the most powerful ways GIS does this is through overlay analysis, a technique that works with both vector and raster data to turn layers into insights.

Think of overlay analysis as asking your map to think logically: What overlaps? What’s inside what? Where do multiple conditions occur at the same time?

Overlay analysis combines multiple spatial datasets by stacking them on top of one another and analyzing their relationships. The result is a new layer that contains information from all the inputs — geometry and attributes.

For example, overlay analysis is used to identify areas that meet specific criteria, or to compare environmental, social, or physical factors. It also supports decision-making in planning, conservation, and risk analysis.

Now, the way overlay works depends on whether the data is vector or raster, and each brings its own strengths.

Vector Overlay: Working with Shapes

Vector data represents the world using points, lines, and polygons, making it ideal for precise boundaries like parcels, roads, rivers, and land-use zones.

Vector overlay operations use geometry and topology to create new features. The most common operations include:

• Union: Union combines everything from both layers into one new layer. If you lay two maps on top of each other, nothing gets thrown away. All areas from both layers are kept, and wherever they overlap, the attributes from both layers are included. An application could be combining land-use zones and flood zones so you can see all land types and all flood areas in one map.
• Intersect: Intersect keeps only the areas that overlap in both layers. It answers the question: “Where do these two layers share the same space?” Anything that doesn’t overlap is removed. For example, you can find residential areas that are inside a flood zone.
• Symmetrical difference: Keeps areas that belong to either layer — but not both. It removes the overlapping area and keeps everything else. Think of it as “everything except the overlap.” An example can be showing land areas that are either parks or wetlands, but not both at the same time.
• Update: Replaces parts of one layer with information from another layer. You start with one layer, then use a second layer to overwrite parts of it where they overlap. An example can be updating an old land-use map using newer zoning boundaries.
• Identity: Adds information from one layer to another without changing the original features. It keeps all features from the first layer, but wherever they overlap the second layer, it attaches extra information. One example can be adding soil type information to existing land parcels without changing parcel boundaries.

Vector overlays are especially useful when accuracy matters — such as legal boundaries, infrastructure planning, or environmental protection zones.

Raster Overlay: Thinking in Cells

Raster data represents space as a grid of cells (or pixels), where each cell holds a value. This format is perfect for continuous data like elevation, temperature, rainfall, or satellite imagery.

Raster overlay works differently than vector overlay. Instead of combining shapes, it:

1. Compares cell values across multiple raster layers
2. Uses math or logic (e.g., greater than, less than, equal to)
3. Produces a new raster showing where conditions are met

Some of the tools you can explore are:

• Zonal Statistics: Summarizes values in a raster layer by zones (categories) in another layer. In simple terms, you divide a raster into zones (categories), then calculate statistics inside each zone. The zones usually come from another layer, like vegetation types or administrative boundaries. For each zone, you get values like mean, minimum, maximum, or sum from the raster.
• Combine: Assigns a value to each cell in the output layer based on unique combinations of values from several input layers. When you run combine, each cell in the output raster gets a value based on how multiple raster layers overlap at that location. Cells with the same combination of input values get the same new value. An example can be when you combine land cover, slope, and soil rasters to identify areas with the same environmental conditions.
• Wighted Overlay: This tool adds multiple rasters together after assigning importance (weights) to each one. Think about suitability, vulnerability or risk analysis. When weighting the factors, you decide how important each raster (factor) is, then the tool scores and combines them to produce a suitability, vulnerability or risk map. Higher values usually mean more suitable or more favorable areas.
• Weighted Sum: Overlays several rasters, multiplying each by their given weight and summing them together. It’s like doing math with maps: (Raster × Weight) + (Raster × Weight) + … Each cell’s value reflects the combined influence of all input rasters. A good example can be when combining rainfall, soil moisture, and elevation to model flood risk.

For example, you might overlay slope, elevation, and land cover rasters to find areas suitable for development or areas at risk of erosion. Raster overlay shines when modeling surfaces, analyzing patterns, or working with large-scale environmental data.

When Raster and Vector Work Together

Some of the most powerful GIS analyses happen when raster and vector data team up. Each data type brings something different to the table — vectors give us precise boundaries, while rasters capture continuous surfaces — and together, they unlock deeper insights. For example:

• You can use raster elevation data to calculate slope, then overlay those results with vector parcels to see which properties are on steep terrain.
• You can identify which vector features (like roads, buildings, or neighborhoods) fall inside high-risk raster zones, such as flood probability or wildfire risk surfaces.
• You can even convert vector data to raster (or raster to vector) to support more advanced modeling workflows and analyses.

In this post, I aimed to highlight the core spatial operations that make GIS such a powerful problem-solving tool. By exploring overlay analysis for both vector and raster data, we saw how different tools help answer different types of spatial questions — from precise boundary-based queries to broad surface-based modeling.

When raster analysis is added into the mix, GIS becomes even more flexible and insightful. It allows us to analyze continuous patterns, build suitability models, and weigh multiple factors at once. In the end, it all comes down to turning spatial data into meaningful decisions — one pixel, one polygon, and one overlay at a time.