In this blog we look at different ways to define small area geographies. How can we define a trade area or location using radius sampling (defining a geography around a particular point at different size ranges)? It sounds simple enough. You pick a point and draw a circle around the location. Done, right? Not exactly. The issue is how you identify geography within the circle. How precisely can we get at those addresses?
You can look at census blocks or block groups, or you can look at zip codes. It all depends on how large the radius is. Typically, we use census blocks for 5, 8, and 10 mile diameters, and because the blocks tend to fit well around the edges, we only need to do some minor carving.
With smaller radii, it becomes more of a challenge, though. Census blocks or block groups tend to fall way out of that range, or we get under coverage because not enough of the blocks or block groups fit inside the circle.
In episode 7 of our Coffee Quip video series, subject matter experts David Malarek (Senior Vice President, Sampling & Database Services) and Dennis Dalbey (Manager, Geodemographic Services) demonstrate the two basic ways to define radius geography using census blocks within a 10-mile radius—area inclusion using polygons and block centroids.
For example, we can start with a 10-mile radius around a point. Then we overlay census blocks (or block groups). On the overlay all block groups that intersect or have some relationship to the 10-mile radius can be seen. Where the color overlay falls outside the radius, we can decide which block groups to keep in or out of the sample frame.
The Polygonal Approach
By using polygon geometry, we can apply area inclusion. We can determine or make a cut on the percentage of the polygon area within the radii. For instance, any block group where the geographic area is at least 50% or more within the radius can be selected. When this is mapped, you will find holes along the edges of the circle where the geography once existed but was cut out because the inclusion was under 50%. Note that no matter how you carve the fringe, there will always be some overstatement of the sample frame or some under coverage depending on how well the block group geography fits the radius.
The Block Centroid Approach
Another way to do this is to use the center point of a polygon to assign geography to a radius. It’s a different type of geometry being put to use. Instead of using polygons where we’ll make an inclusion, we are actually using what’s considered the center of the polygon—the census block centroid. This method will allow us to select blocks that are theoretically 50 percent or more within the radii without having to apply an inclusion like we did with the polygon earlier. It is a more efficient way of doing it, but keep in mind that we are making a 50 percent cut across the board. You will encounter tradeoffs such as introducing under coverage or over coverage here, too.
One of the benefits of using the block centroid is that we can vary the distance. Let’s say we are not really sure whether the 10-mile radius is going to meet your quota. We may want to overstate the geography at 20 miles. With block centroid we can apply the distance and go from 10 to 15 to 20 miles until we meet the population or household quota. Note that this can be applied to polygons as well, but it is much easier to do with the block centroid.
The Address Level Approach
Yet another way to do this is to plot all the known addresses that fall within the blocks or block groups touching the circle and exclude any address that falls outside the circle. This is the most accurate way of getting accurate household counts and eliminates under and over coverage. It’s a two-step process and a more involved methodology but it is also the most accurate. One downside to this approach is demographic data is only available at some larger aggregation of geography such as block group and not by individual addresses.
Dave and Dennis compare the polygon and block centroid methods, and why they sometimes yield different results. Sometimes we actually need to plot ABS address locations in the blocks within the radius and remove the ones outside the radius, so we get a better fit for the overall target population. This is really the best methodology in terms of making sure that households are completely within a radius without having to worry so much about the regular geography, where part of it is in and part out.
Find more details and visualizations of these methods, in our Coffee Quips #7 video. It is the first in a series of geodemographic coffee quips. For additional information on our geodemographic services click here.