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How many are missing: gap analysis, explained simply

Two areas with no grocery store are not the same: one has 3 000 residents, the other has 50. Gap analysis (leakage) tells them apart by counting how many are missing at the London rate. Here is how it works and how to read it.

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ByTinkal Gogoi(LinkedIn profile, opens in a new tab)September 16, 20268 min read

Two areas with no grocery store are not the same

Colour a map by “areas with no grocery store” and every one of them ties. An area of 3 000 residents with none and an area of 50 residents with none look identical - and only one of them is an opportunity.

How many are missing tells them apart. It works out how many grocery stores each area would have if London's overall rate applied to its residents, then subtracts what is really there. The 3 000-resident area comes out several stores short; the 50-resident area comes out short by a fraction of one. The field calls this gap analysis, or leakage - demand that has to leak out of the area to be served elsewhere.

How many are missingthe field's term: Gap / leakage

Grocery stores within a 10-minute walk, missing at the London rate. Blue is a shortfall - people without the stores their numbers would predict. Red has more than expected. Grey is roughly as expected.

The idea, in one sentence

What should be here, minus what is here. The “should” comes from the city itself: London's rate of grocery stores per resident, applied to this area's residents. It is the Compared to the average idea rearranged into a count you can act on - not “0.4× the average” but “2.8 stores short”.

Fewer than expected

A positive gap. The residents here would support more stores than exist. The bigger the shortfall, the deeper the blue.

As expected

Supply roughly matches what the residents would predict. Most of any city sits here.

More than expected

A negative gap. More stores than the residents alone explain - a high street, a retail park, a district that serves its neighbours.

Switching it on

Missing is a row in the Map type picker, beside Fair share under Compared with - the two maps that answer with a middle meaning “neither”.

Where to click

  1. Map type
  2. Missing
  3. Grocery store
  4. Count as: Per 1 000 residents
  5. Look at: Within a 10-minute walk
The panel is titled Missing, and the badge underneath reads 'How many are missing · Gap / leakage'. The scope row changes its question too: not 'Average of' but 'Expected at the rate of'.

This map insists on a division, and picks one for you

A gap needs something to count against: how many grocery stores for what. So “Total” is struck out of the Count as row on this map entirely, and if you arrive from a map that was counting totals, Missing quietly picks a division that works rather than opening a map it would have to refuse.
Count as on the Missing map, already set to a division. Open it and there is no 'Total' in the list - a gap measured against nothing is the one answer this map is guaranteed to refuse.
The rate can be London's, or the rate of an area you drew - useful when one borough is the only fair comparison.

We read the measure within a 10-minute walk here, and it is worth saying why. A single area is small - about 0.1 km² - and rarely holds more than one grocery store, so most single-area gaps are fractions. Over an area and its neighbours the expected counts become whole stores, and the shortfalls become districts rather than dots. The walk post covers that choice on its own.

Reading the map

Unlike the comparison map, the legend's steps are counts, not multiples: “2.8 grocery stores short” means exactly that. The class edges are cut once over the whole city, so panning never recolours an area that did not change.

Zero is pinned in the middle. Blue counts stores short, red counts stores over, and the outer steps are set by how spread out London's gaps are.
Hovering a red neighbourhood: the London rate predicts 7.4 grocery stores within this walk, and there are ten more than that - a district that serves far beyond its own residents.

The maths, honestly

Two steps and a subtraction. The figures are illustrative.

Grocery stores missing in one London neighbourhood
London's rateall grocery stores ÷ all residents × 1 000
0.3 per 1 000
Residents within the walk
18 000
Expected at London's rate
0.3 × 18 = 5.4 stores
Actually within the walk
2 stores
How many are missing
5.4 − 2 = 3.4 short

The same sum for a 50-resident area gives 0.015 expected, so its gap is a rounding error - which is the whole point. It no longer ties with the neighbourhood above.

'Show the maths' states the subtraction in one line: what the London rate predicts for this area, minus what is actually here. Positive means a shortage.

A shortfall is a lead, not a verdict

The gap uses residents as the demand. It does not see commuters, tourists or the shop just over the boundary, and it cannot tell a shortage from a park. Treat a deep-blue district as the shortlist to go and look at - and check the population coverage line in the panel, because an area with no census data has no gap either.

It works inside a score, too

The Missing map draws the gap on its own. In the Score map, any factor counted per 1 000 residents can also be shown as “How many are missing” - the Score map presets nothing on its factors, so each one keeps its own “Show” control. That is how you make a score reward genuinely under-served places rather than simply empty ones.

Open this map of London

The whole idea, in four lines

  • Expected = the city's rate × this area's residents. That is how many there 'should' be.
  • How many are missing = expected − actual. Positive is a shortfall; negative is more than expected.
  • Areas with zero no longer tie: the one with more residents is short by more, and ranks higher.
  • Blue is short, red is over, grey is as expected - and the steps are counts, cut once over the whole city.
  • Hover for the verdict and the expected count; use 'Show the maths' for the subtraction itself.

Quick questions

What does 'How many are missing' calculate?

It works out how many of something an area would have at the city-wide rate (the city rate times the area's residents), then subtracts what is actually there. A positive number is a shortfall; a negative one means more than expected.

Why not just look for areas with zero shops?

Because every area with zero ties. An area of 3 000 residents with no grocery store and an area of 50 residents with none look identical on a plain count. The gap ranks the first one far higher, because far more are missing there.

What does the map show?

Grey is about as expected. Blue means fewer than expected (a shortfall, deepening with its size). Red means more than expected. The class edges are counts, not multiples, so they are set once over the whole city and do not change when you pan.

Can I use it in a score, not just on its own?

Yes. In the Score view, set a factor's Show to 'How many are missing' and it contributes its shortfall instead of its raw count, so areas that are genuinely under-served rank higher.

Is a shortfall a guarantee that a shop would work there?

No. It is decision support built from open census and map data: it finds the places where demand is not yet matched by supply. Confirm the final call with local knowledge and footfall on the ground.

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.

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About the author

Tinkal Gogoi(LinkedIn profile, opens in a new tab)Founder & Location Data Engineer

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.