KFC in CT: a Geodemographic View

1994: KFC Cambridge, NZ

When KFC came to my small town in the mid 1990s it was a big deal. It was our first fast food restaurant, and naturally took pride of place on the main street just down from the town's earlier place of worship from a less secular era, St Andrew's Church. It was a prime location, with both large local and transient markets. 

KFC expanded rapidly in New Zealand during this time and became a ubiquitous feature of the landscape. Your town or neighbourhood had 'made it' if a KFC opened. Consequently it was a source of shame, at least to a teenage mind, if KFC chose to bypass yours for a neighbouring town. In later years, road trip routes were planned according to KFC locations.

2014: KFC New Haven, CT

A Geographic View

Fast forward 20 years to New Haven, Connecticut, my current location. In the birth country of KFC I had expected the Colonel's face smiling from prime locations to be common. But it didn't appear that way. Perhaps I wasn't getting out enough? Or perhaps the mature fast food market in the US dictates KFC be more selective in its location decisions? 

I am analytical. So I decided to do some exploratory analysis. I also like maps. So the analysis was a visualisation: a quick map to see where KFC is located in Connecticut. There are 44, according to the data I collected. Interestingly this is roughly half the number in New Zealand, and is a ratio of one KFC per 80,000 people (or one per 113 square miles), versus one per 50,000 (1,130 square miles) in NZ.

This didn't tell me much, without context. I added a thematic grid layer, produced from US Census data, to show relative population density. This produced the following map:

KFC Locations in Connecticut. Background grid thematic shows population density (2010)

At this relatively high zoom level it appears that KFC generally follows the population density, as would be expected. However, zooming to a lower level, the intra-city variation becomes more apparent:

KFC locations in New Haven, CT. One mile radii are placed around each location. The grid layer again shows population density.

The area I have spent most of my time in is the area between the words 'Haven' and 'Whitneyville' on the map. I wasn't simply unobservant: KFC is notably absent from this area. 

A Demographic View

Having advised on site location decisions, I knew that a location-dependent organisation like KFC would use at least some science when augmenting its network. One powerful factor in site location modeling is the demographic composition of a catchment: the income, age and ethnic (etc) profile. Could I infer KFC's neighbourhood preferences from its existing network in CT?

I started by importing the census data - at block group level - into the existing map. I was then able to run a spatial query and aggregate the block groups that fell within 1 mile of a KFC. The aggregated block groups were then compared to the overall state (urban) profile, to produce the following demographic views:

The income profile shows that KFC is more likely to be found in areas with lower household income.

The ethnic profile shows a strong skew toward African American neighbourhoods.

The age profile doesn't exhibit any strong skews.

The demographic views show the strength of each variable, indexed. An index of 100 represents the average; an index over (under) 100 is above (below) average. 


EDIT: What About McDonald's?

A useful way to understand an enterprise's network strategy is to compare it to a competitor. Let's use McDonald's. McDonald's has a much stronger presence in CT, with over 250 stores. This pervasiveness is likely to reduce some of the demographic skews we see with KFC. The comparison:

Like KFC, McDonald's also skews toward lower-income neighbourhoods - though not as markedly.

McDonald's has a slight bias toward African American neighbourhoods, though it much less pronounced than KFC.

The McDonald's age profile, like the KFC age profile, doesn't exhibit particularly strong skews.

Summary

From this rough analysis, across a small sample, it appears KFC does indeed favour certain geodemographic profiles. Whether this is by top-down design or bottom-up demand is hard to say without further investigation. 

Reflecting back on the map, I also realise that I need to get out (of my neighbourhood) more.

What software did I use? Python to collect the store coordinates and concatenate the census files; MapInfo to produce the maps; Excel to produce the index profiles.