A Location Quotient (LQ) is a population density metric. LQs answer two density questions: 1) What is the local concentration in a specific region? 2) How does this compare to the national concentration? LQ is simply a ratio comparing two separate ratios: Local Concentration (LC) divided by National Concentration (NC). That is it.
Here is a super simple example with super fake data:
LC = Poodles in Peoria = 2500 ÷ Dogs in Peoria = 100,000
——————————————————————
NC = Poodles in the US = 150,000 ÷ Dogs in the US = 5,000,000
(2500/100,000) .025
LQ = ————————- = ————— = .83
(150,000/5,500,000) .03
From this example we can conclude that the local concentration of poodles in Peoria is lower when compared with the national concentration of poodles in the US. Using terms non-nerds can understand – Peoria has fewer poodles on average.
Here is a real life sports example using the same math:
LC = Steph Curry 3 pt Shooting Percentage = 41%
——————————————————————
NC = Average NBA 3 pt Shooting Percentage = 36.7%
41%
LQ = ————— = 1.12
36.7%
From this example we can conclude the local concentration of three pointers Steph Curry makes is higher when compared with the national concentration of three pointers across all NBA players. Translation – Steph makes more three pointers than most the league.
Location Quotients are not really used in sports statistics because players are not places (i.e. locations) and you don’t need the extra calculation step as a reader to create understanding. You intuitively know that 41% is greater than 36.7%. Let’s rewrite the poodle example above as a percentage. Is it easier to understand?
Peoria Poodle % = 2.5%
National Poodle % = 3%
It is pretty clear that Peoria has fewer poodles on average just by comparing the percentages. That’s really all an LQ represents – a percentage of the comparison. An LQ of .83 means Peoria has 83% of the national standard concentration of poodles. Steph’s LQ of 1.12 is 112% of the national standard concentration of 3 point baskets in the NBA. Therefore, when you see an LQ just turn it into a percentage. Let’s use one more real life example from the Bureau of Labor Statistics data with an actual location – Wichita.
Referred to as the Air Capital of the World, Wichita is famous for engineering airplanes. In fact, 35% of United States airplanes come out of this area. As one might expect, a high number of airplane manufacturers means Wichita will be home to many Aerospace Engineers. To the numbers:
LC = Aerospace Engineers In Wichita = 1980 ÷ Population of Wichita = 390,000 ——————————————————————————————————————-
NC = Aerospace Engineers in the US = 108,240 ÷ Population of the US = 328,000,000
(1980/390,000) .00507
LQ = ————————- = ————— = 15.36
(108,240/328,000,000) .00033
Wichita has a LQ of 15.36, meaning 1536% more aerospace engineers live in Wichita than would be expected for a town of equal population.
Why LQ’s matter for ecosystems
Over the years this high concentration of engineers has attracted businesses and talent in the aerospace field to Wichita and has created additional startups that spin out of established aerospace companies. The Wichita community has embraced this identity, resulting in the creation of programs such as the National Institute for Aviation Research; this nurturing of its talent ensures that Wichita is the best community to be an aerospace engineer. This is an example of building upon the strengths of an established industry to benefit the community. LQ’s are a great tool for identifying the potential strengths and weaknesses in a given community or ecosystem and measuring ecosystem development overtime.
If you have any questions about Location Quotients or you would like to see an article on your ecosystem, then send us an email at hello@mug.news.
- In this example, we use total population data for simplicity purposes. The calculation of OES location quotients typically uses Total population in the workforce rather than Total population.