Footfall vs the people who live nearby: two different questions

Population and footfall answer different questions. The number of residents around a site tells you how many people sleep there, the base a pharmacy, a nursery or a gym can hope to win as regulars. Footfall tells you how busy the streets are and at what time of day, which includes those residents but also office workers, shoppers, students and people passing through. A site can score high on one and low on the other, and the gap between them is often the most useful thing you learn.

This article is for anyone weighing a location who has both figures in front of them, or could have: retail and clinic planners, landlords, franchise teams. It explains what each figure counts, four typical combinations and what they suggest, a worked example, and how to put both next to every site with one AI employee.

What each figure counts

Residents come from the Demographics block: the number of people living within a distance you choose, with men and women and nine age groups. It is a count of homes' occupants, read from a grid of small hexagons. It does not change between Tuesday morning and Saturday night, and it says nothing about who works in the area.

Footfall comes from Mobility Footfall: people-hours of presence around the place, with the busiest hour, the busiest day and how the day splits. The unit is explained at length in what footfall data measures; the short version is that time spent counts, not heads.

Four combinations and what they suggest

Patterns to test, not rules. Your own customers decide which matters.

The second row needs care. Because residents at home are present, a residential street can show a lot of late-night footfall. That is not a crowd of potential customers walking past; it is people asleep. Look at footfall_by_part before treating a high daily total as busy streets.

A worked example in Lyon

A made-up case to show how the two read together. A pharmacy group is choosing between two premises in Lyon, a city where population figures are available. It asks for the residents within 500 m of each, and 30 days of footfall within 200 m. Suppose the answers are these (invented for illustration):

Invented figures for illustration; not real figures for Lyon.

On footfall alone Premises B wins. But a third of A's footfall is late at night, which fits a residential neighbourhood, and A has three times the residents and a larger share over 60: the people who fill repeat prescriptions every month. B's crowd is mostly a weekday daytime crowd, likely workers who buy a painkiller at lunch and go home somewhere else. For a pharmacy that lives on regulars, A is the stronger site. For a shop selling sandwiches, B would be.

The point is not which figure is better. It is that each one corrects the other's blind spot.

Turning the pair into one measure

Some teams like a single number to sort by. Two simple ones, worked out with Calculated field:

• Daytime share: the morning plus midday shares of footfall. High means a working or shopping area; low means homes.
• Footfall per resident: footfall_daily divided by population. Well above the others in your list means people come in from elsewhere; well below means the area mostly empties by day.
• Keep the raw figures beside any ratio. A ratio on a tiny population swings wildly.

Compare these only between places measured the same way: the same footfall circle, the same window, the same Demographics distance. A 500 m population figure and a 200 m footfall figure are fine together as long as every site uses the same pair.

Mistakes to avoid

• Adding the two together. Residents are people; footfall is hours. They are not the same unit.
• Assuming residents walk past. A dense estate behind a wall or a ring road may never use the shopfront on the main street. A Travel area on foot can be fairer than a circle; see a radius or a drive time.
• Reading footfall as office headcount. A busy midday is a strong hint of workers, not a count of them.
• Forgetting the lag. Footfall covers days that ended about ten days ago; population is a slower-moving figure. Neither describes today's street.
• Using a country without population data. In London or New York you will get footfall but no residents; the comparison cannot be made there.

Both figures for every site, with an AI employee

1. Bring in the sites: My data with your shortlist, or Nearby places to find premises of a kind.
2. Add Demographics, Each incoming place, within 500 m or 1 km.
3. Add Mobility Footfall with Footfall, Busy hours and Busiest days, 200 m, the last 30 days.
4. Add Calculated field for the daytime share and footfall per resident.
5. Add xMap LLM told what your business lives on (regulars, lunch trade, evening visits) and asked to say which pattern each site fits.
6. Send it by Email. In the inbox: each site with its residents, age shares, daily footfall, day split, busiest day, the two ratios and a sentence on why it fits or not.

Run it once for a shortlist, or monthly over your own stores to see which ones serve neighbours and which serve passers-by. That split often explains why two stores with the same sales need different ranges and opening hours.