Now that we’ve rolled out thematic mapping by state, county, and zip code in SpatialKey, you can produce some fantastic thematic maps with only a few mouse clicks. But it’s important to understand how these thematic maps represent your data, and when it might be appropriate to use thematic maps versus density maps. Both are useful, and SpatialKey makes switching between the two methods easier than it has ever been before.
We’ll compare a zip-code thematic map with a heatmap. Both maps show average home sale price by geographic area (either zip codes or clusters of points). The image below shows the two map types side by side.
Now we’ll step through an analysis of these different map types to see why they produce different views of the same data.
Thematic map by zip code
First, let’s take a look at mapping home sales in Sacramento by zip code. The map below shows thematic zip codes colored by the average sale price. You can see the highest range is $400,000 and up and includes 3 zip codes in the image below. I want to focus on comparing the two labeled zip codes, 95818 and 95822. You can see that the 95822 zip code area has a much lower average sale price than 95818, which is immediately north of it.
Density heatmap with zip-code boundaries
However, if we switch to a density heatmap we see a different picture. Switching from thematic zip codes to a density map takes literally 3 clicks in SpatialKey. The map below shows average sale price as a density map, with the boundaries of the zip codes overlaid in red. This is the exact same data showing the exact same attribute (home sales showing average sale price). But if you compare this image with the thematic map above you’ll notice that the hotspots tell a different story. A fluid area that overlaps both the zip codes we looked at above is actually the area with the high average prices. That area doesn’t cleanly fall into a single zip code.
This isn’t too shocking, since it intuitively makes sense that fairly arbitrary boundaries like zip codes wouldn’t directly map to more or less expensive areas of town. But it illustrates the difficulty of rendering your data thematically by certain shapes, like zip codes or counties.
Density heatmap with neighborhood boundaries
To further analyze the dataset I decided to load in the boundaries of the neighborhoods in Sacramento (the file was downloaded here). Now we see boundaries that come much closer to matching the home prices. Intuitively this also makes sense; if you think about home prices in your city you’ll likely think of expensive and cheap neighborhoods, not zip codes.
Everything has its place
Both thematic maps and density maps are useful when exploring geographic data. Both show you important aspects of your data, but it’s important to keep in mind the inherent limitations of the different methods. With SpatialKey, we provide you with the tools to easily switch back and forth between these rendering methods in seconds.
Try it out for yourself
You can start uploading your own data and making thematic maps right away by signing up for the 30-day trial of SpatialKey.