Retrieval Scheme Sample: Eagle Mountain, UT

This is the second article about demographic retrieval schemes.  The first article, which described the methodologies behind the most popular retrieval schemes in use today, can be found here.  This article will take the methodologies outlined in the previous article and will apply them to a real world location in Eagle Mountain, UT.

The Study Area

We will be analyzing a 1 mile, 1.5 mile, and a 3 mile radius around the intersection of Cedar Fort Rd & Ranches Pkwy in Eagle Mountain, UT (click here to see on a map).  According to this website, Eagle Mountain was incorporated in December 1996 and has grown from 250 residents to more than 20,000 today.  In addition, the population is expected to grow at 10% over the next several years.  Given its explosive growth, this will be an excellent area to compare/contrast the various retrieval methodologies.  The aerials and the location of the housing subdivisions indicate the majority of Eagle Mountain’s population is located within the 3 mile trade area.

Summary of Findings

The following table outlines the total population that would be shown on a demographic report for the selected intersection.  The only difference between each of these numbers is the demographic retrieval scheme that was selected, the base block group population counts are exactly the same.

Table 1: Summary Results

Retrieval Scheme	1.0 Mile	1.5 Miles	3.0 Miles
Proportional Area	682	1,459	6,425
Block Group Centroid	0	1,817	9,729
Block Centroid	1,353	2,417	13,717
Postal/Building	2,733	8,132	19,294
Street	1,485	3,064	10,262

 

Finding Detail

Proportional Area:

Why are the population numbers generated using proportional area so much lower than the 20,000 known to be in the area?  The discrepancy results from the greatest flaw in the proportional area methodology, the assumption of uniform distribution of population.  As discussed in the previous blog entry, proportional area retrieval assumes that population is uniformly distributed throughout the block group.  Therefore, if 10% of the area of the block group is within your trade area, you get 10% of the population.  Figure 1 shows the location of the 3 trade areas we are analyzing along with the block groups that overlap them.

Figure 1: Proportional Area Detail

As you can see there are several block groups that have a very slight area overlap with the trade areas.  For example, look at block group 490490101024.  The proportional overlap between this block group and the trade area is very small, thus this methodology will proportion a very small percentage of the overall population within this block group to the trade areas.  By the way, this block group has the greatest population of any of the block groups in the area and its population is concentrated in some communities located within the 3 mile trade area.

Block Group Centroid:

The block group centroid methodology includes/excludes a block group based on the location of the block group’s centroid in relation to the trade area.  The fact that this methodology returned more accurate numbers at 3 miles than proportional area retrieval is more dumb luck than anything else (it returned 0 people within 1 mile which is completely wrong).  You often see very large changes in population when using this methodology as you increase the size of the trade area a small amount.  This is a result of the wholly included/excluded nature of the methodology.  It is suggested that this methodology should NOT be used for small scale trade area analysis.

Block Centroid:

If this is the most common form of demographic retrieval, why are the numbers so low?  The answer is do to the fact that the census 2000 block weights were used for this analysis (which is the most common form of block centroid retrieval).  This is assuming that the population distribution as it existed in April of 2000 is the same as it exists today.  This area has changed dramatically since April 2000 and has seen substantial housing development within 3 miles of the site.  If you are going to use this retrieval methodology, make sure you use an updated block centroid file that has updated weights.  A few demographic data providers provide up-to-date block centroid files.

Postal/Building:

The postal methodology performed very well here because of its ability to work with updated USPS zip+4 data.  Thus, as the new housing communities where built and started receiving mail they became available within the base data used for retrieval.  Figure 2 shows the location of the USPS zip+4s in the area.

Figure 2: Postal Methodology Detail

Each of the red dots in Figure 2 shows the geographic location of a USPS zip+4 centroid.  As you can see, the location of the zip+4s are concentrated around the location of the planned communities.  This sample also highlights one of the weaknesses of the postal methodology, the houses need to be receiving mail directly.  There are a lack of zip+4s in areas of large farms and other very sparsely populated areas that are more than likely on rural delivery routes.  However, for the purposes of most types of retail trade area analysis, these areas are not overly important and constitute a small percentage of the overall population.  However, if you are working in extremely rural areas that do not receive mail than you should use an alternative retrieval methodology.

Street:

In theory, the street retrieval methodology has merit.  So what happened to it in this example?  The discrepancy results from the rural areas of the block groups having a large total length of streets, however they are just very sparsely populated.  Figure 3 shows the streets as light grey lines.

Figure 3: Street Methodology Detail

By looking at the aerials in this area, several of the streets are connecting large farms.  Thus you could have a 5 mile long road with just a few households on it.  In contrast, the planned communities in this area are relatively dense which means there is a very high population located in an area with relatively low street length.  This methodology would work in areas that have similar size housing lots or areas that are relatively uniform in population density.  However, as this sample shows, it should be avoided in non-homogeneous areas.