Fair share: the Location Quotient, explained simply
A number on its own tells you little. 2.7× the London average tells you a lot. Here is the formula behind the Fair share map (the Location Quotient), worked through with cafés in London.
Take one hex in London - one hexagon on the map, about 350 m across (roughly 0.1 km²). It has 1.5 cafés per 1 000 residents. Is that a lot? You genuinely cannot tell. It depends entirely on what is normal for London - and that is the one thing a plain number never tells you.
The Fair share map fixes that with one division. It takes the hex's rate, divides it by the city's rate, and hands you a multiple: 2.7× the London average. Now you know. That multiple has a name in the field - the Location Quotient - and it is probably the most useful formula on the map that almost nobody outside economic geography has heard of.
Fair sharethe field's term: Location Quotient
Cafés per 1 000 residents, compared to the London average. Red hexes have more than their fair share of cafés; blue hexes have fewer. The center is a solid café neighborhood.
The idea, in one sentence
Does this hex have more than its fair share? If cafés were spread across London exactly in proportion to where people live, every hex would sit at 1×. Anything above 1× has more cafés than its residents alone would explain; anything below has fewer.
0.4× - a gap
Only 40% of its fair share. Plenty of people, not many cafés. Depending on what you are planning, that is either a warning or an opportunity.
1× - the fair share
Exactly the London average. Not a café neighborhood, and not short of cafés either. Most of any city sits somewhere near here.
2.7× - a café neighborhood
Nearly three times the normal amount. Cafés gather here for reasons beyond the residents - offices, a high street, people passing through.
Switching it on
Every map MapBees can draw is a row in the Map type picker, at the top of the map: nine map types in five groups. Each row names the formula twice - plain words, then the field's own term - and says which question it answers. Fair share is the one you want here.
The Map type picker, with Fair share highlighted. Map types are grouped by the kind of legend they draw, so everything under 'Compared with' reads the same way: a diverging scale with a middle that means 'neither'.
Pick Fair share, choose a measure, and decide how to count it. You never set the comparison separately - choosing the map is choosing it.
Where to click
Map type
Fair share
Café
Count as: Per 1 000 residents
The panel is titled with the map you are on - Fair share - and its question. The badge reads 'Fair share · Location Quotient', the sentence spells out what is being drawn, and the coverage line says how many hexes have an answer.
The badge names the formula in both registers. 'Guide' opens this guide; 'Show the math' opens the arithmetic and the method card.One dropdown is the half the map type does not decide: which average. The whole city, your selected neighborhood or area, or the country's cities.
Counting per 1 000 residents matters. Comparing raw counts with the average would only tell you where the most people live; comparing rates tells you where cafés are unusually common for the people who live there.
Reading the map
The legend is fixed around 1×, and that is deliberate. On most maps the middle color is whatever the median happens to be; here the middle is always the fair share, so gray means the same thing in every city and for every measure.
The steps are multiples, the same in both directions: 1.1×, 1.5×, 2× and 3× the average on the red side, and the mirror of those (0.9×, 0.67×, 0.5×, 0.33×) on the blue side. Under them, advice on reading this map, with one-tap alternatives.
Hover any hex and the card leads with the multiple, then gives the rate behind it. The rate is the fact; the multiple is what the fact means.
One hex south of the river: 1.5 cafés per 1 000 residents, which is 2.7 times what London as a whole manages.
Two more tools work on this map. Standouts outline the groups of nearby hexes that differ most from the fair share, and Only where … mutes the hexes that miss a condition you set - without changing the average they are compared with.
The math, honestly
Two rates and one division. The numbers below are illustrative; the shape of the sum is exactly what the app runs.
Cafés in one London hex, compared to the average
London's rateall cafés ÷ all residents × 1 000
0.55 per 1 000
This hex's rateits cafés ÷ its residents × 1 000
1.5 per 1 000
Fair share
1.5 ÷ 0.55 = 2.7×
Read it as: this hex has 2.7 times its fair share of cafés. A value of 1.0 would be exactly the London average.
'Show the math' names the method, states the division, admits the small-numbers adjustment and cites the data release. The method card below it holds the same in a form you can copy: the method version, a map id, the formulas and the sources.
One thing to keep in mind
A multiple is only as honest as the rate under it. A hex with three residents and one café would show a wild rate, so MapBees pulls tiny bases toward the city's value before comparing. The legend says how often that happened - in 43 of every 100 London hexes for cafés - and offers to read the map within a 10-minute walk, where far fewer hexes need it. The per 1 000 residents post explains that adjustment.
Compared with the country's cities
London's average is not the only yardstick. Set Average of to The country's cities and every hex is compared with the average of the 12 UK cities MapBees covers instead. Choosing it sets Count as to Per 1 000 residents: only a count per 1 000 residents can be compared across cities of different sizes.
Where to click
Fair share
Café
Average of: The country's cities
The same map, now compared with the country's cities. The badge sentence changes with it.The legend names the yardstick: the 12 UK cities MapBees covers. The steps are unchanged.
Against the 12 UK cities, London looks almost the same: its own café rate is close to theirs, so the fair share barely moves.A hex with 1.5 cafés per 1 000 residents is 2.7× the London average but 2.9× the average of the 12 UK cities - London has slightly more cafés per resident than they do.
Because a rank throws away size. “Top 5% for cafés” sounds impressive, but it is true of a hex with 41 cafés and a hex with 40, and it tells you nothing about how far either is from normal. 2.7× the average is a size. It is something you can put in a sentence to a landlord, compare between two cities, and act on.
It also needs no ranking step at all: the number comes out already meaning something, which is why it is the comparison the Missing map is built from as well.
The whole idea, in six lines
Fair share = this hex's rate ÷ the city's rate. That multiple is the Location Quotient.
1× is the fair share. Above it, more than its share; below it, fewer. The legend is pinned there on purpose.
Count per 1 000 residents first, so you are comparing how well people are served, not how many people live there.
Gray is average, red is more, blue is fewer - and the steps are the same multiples in both directions.
Average of picks the yardstick: the whole city, your selected neighborhood or area, or the country's cities.
Hover for the multiple and the rate behind it; open 'Show the math' for the method card and the source.
Quick questions
What is a Location Quotient?
It is a local rate divided by the city-wide rate. 1.0 means a hex has exactly its fair share of something; 2.6 means it has 2.6 times the city average; 0.4 means 40% of its fair share. In MapBees it is the Fair share map.
How do I make a location quotient map without GIS software?
Open the Map type picker in MapBees, choose 'Fair share', pick a measure and count it per 1 000 residents. The map colors every hex by how many times the city average it is, with 1× pinned in the middle.
What do the blue and red hexes mean?
Gray is about average (the fair share). Red is more than its fair share, deepening at 1.1×, 1.5×, 2× and 3× the average. Blue is fewer than its fair share, at the same steps in the other direction.
Why is a location quotient better than a ranking?
A rank says a hex is in the top 5%; a location quotient says it has 2.6 times the normal amount. The second is a size you can act on, and it needs no ranking step at all.
Can I compare with something other than the whole city?
Yes. Set 'Average of' to 'Your selected neighborhood or area' to compare with a neighborhood or an area you drew, or to 'The country's cities' to compare with the average of the 12 UK cities MapBees covers; choosing that counts per 1 000 residents.
See it on a real map
The best way to understand the score is to play with it. The full app is on a Free Tier, with no sales call.
Builds MapBees's location-intelligence pipeline end to end — census and Overture Maps ingestion, H3 hex scoring, and the map interface — and writes the methodology behind every number on the site.
