People per business: the simplest way to spot an underserved area

People per business is the number of residents in an area divided by the number of businesses of one kind in the same area. If 24,000 people live around a crossroads and there are three pharmacies, that is 8,000 people per pharmacy. Where the figure is much higher than in similar areas nearby, demand may be outrunning supply, and a new business has room. It is the quickest gap measure there is, and also one of the easiest to misread.

This piece is for anyone choosing where to open, invest or sell: a franchise developer, a pharmacy group, a gym operator, a distributor looking for gaps in a city. It covers the arithmetic, a worked case, the traps, and how to have an AI employee keep the figure fresh for you.

How to work it out

You need two numbers for the same piece of ground, measured the same way.

1. Pick the areas to compare: circles of the same size around candidate points, or districts you draw. Same size matters more than it looks.
2. Count the residents in each with Demographics, which gives population for each place or area.
3. Count the businesses of the kind you care about with Nearby places, set to pass on each incoming place with counts. Each area gets nearby_count.
4. Divide. A Calculated field line such as people_per_shop = round(population / max(nearby_count, 1)) does it for every area, and max keeps an area with none from breaking the sum.
5. Rank with Sort and limit, highest first.

A worked case in Toronto

xMap's places database lists 519 pharmacies within 10 km of the centre of Toronto (November 2026). Of the 438 nearest the centre, 119 belong to a chain and 319 are independent. On its own the total says little. It becomes useful once it is split into smaller areas and set against the people who live in each.

519Pharmacies listed

438Nearest counted

119Chains, of those

319Independent, of those

4.3Average rating

Now a made-up illustration of the next step. Suppose a pharmacy group splits that circle into nine equal parts and gets these figures back (they are invented, to show the method):

Invented residents and counts, for illustration only.

The west part stands out: more than four times as many people for each pharmacy as the centre. That is a reason to look closer, not yet a reason to sign a lease.

The traps

The ratio is simple, which is exactly why it misleads when used carelessly.

Water, parks and industry

A circle that takes in a lake, an airport or a port has fewer residents and fewer businesses for reasons that have nothing to do with demand. Toronto's circle runs out over Lake Ontario to the south, and any part of it drawn there will show almost no people and almost no pharmacies, which is not a gap. Draw areas over where people actually live.

Centres that serve visitors

A city centre has few residents and many businesses, so its people per business looks tiny, as if it were oversupplied. Those shops serve commuters and tourists, who are not in a residents count. Compare centres with centres and suburbs with suburbs.

Small numbers

An area with one business and 9,000 people reads 9,000; add a second and it halves. Below a handful of businesses, the ratio jumps around. Look at the raw counts next to it.

The wrong kind of business

A supermarket with a pharmacy counter serves pharmacy needs but may not be listed as a pharmacy. Decide which types count before you divide, and use the same list everywhere. Nearby places lets you pick several types, or a whole group of them.

Distance people will travel

People cross a district boundary for a supermarket but rarely for a coffee. For things people travel to, a wider circle or a drive-time area is fairer (see a radius or a drive time).

Reading the number in Sydney

For comparison, xMap's places database lists 346 pharmacies within 10 km of the centre of Sydney (November 2026), 132 of them in chains. A city with a larger share of chain pharmacies may have fewer, larger stores, which pushes people per pharmacy up without any real gap. That is why the measure works best inside one city, between its own districts, rather than between cities with different shopping habits.

Keeping it fresh with an AI employee

Gaps close. A rival opens, a new apartment block fills. An AI employee can redo the sum every month and tell you when an area crosses a line you set.

• A schedule, then an Area of interest with your districts drawn on the map.
• Demographics with Totals for each area, then Nearby places counting your type in each.
• Calculated field for the ratio, a Filter to keep areas above your line, and Sort and limit.
• An Email with the list and a map. In the inbox: each area, its residents, its businesses and the ratio, highest first.

Templates built this way include Neighbourhoods with many people and few notaries, People per dog park, block by block and People per police station, block by block. Swap the type of place and they work for any trade.