Comparing footfall between candidate sites fairly

To compare footfall between candidate sites, measure every site the same way (the same circle, the same days, the same window) and then read the figures against each other, not on their own. A site’s footfall becomes useful when you divide it by the rivals around it and set it beside the shape of its day. Mobility Footfall measures the sites; Nearby places counts the rivals; one flow puts them in a single table.

This article is for expansion managers and consultants choosing between a shortlist of sites. It sets out the rules for a fair comparison, a measure that weighs people against competition, a worked example in London, and how trips data adds where visitors come from in Sao Paulo.

Five rules for a fair comparison

1. One circle. A 500 metre circle collects more people-hours than a 200 metre one, so mixing them makes the bigger circle look like the busier site. Pick one, usually 200 metres for a shop front.
2. One window. Every site over the same days. The block reads each site’s own local hours, so sites in different time zones still compare day for day.
3. The days that matter. If you will trade mostly at weekends, compare weekends, with Days set to Weekends only.
4. Check the days behind each figure. footfall_days should be the same for every site. A site with fewer days of data deserves a footnote, not a ranking.
5. Same kind of site. A station forecourt and a quiet residential street will never compare well on footfall alone. Compare like with like, or compare on footfall per rival instead.

Footfall per rival

A busy site with many rivals can be worse than a calmer one with few. A simple way to see this is to divide footfall by the number of businesses of your kind nearby:

• Run Nearby places with Pass on: Each incoming place, with counts, your type of business and a radius you choose, say 500 metres. Each site gets nearby_count.
• Add a Calculated field: footfall_per_rival = footfall_daily / (nearby_count + 1). Adding one keeps a site with no rivals from dividing by zero, and counts your own future unit as one of the places sharing the crowd.
• Sort by footfall_per_rival, highest first.

A worked example in London

A made-up restaurant brand has four candidate units in London. The city’s market is crowded: xMap’s places database lists 3,792 restaurants within 10 km of the centre of London in November 2026. Of the 326 nearest the centre, 50 belong to chains and 276 are independent, with an average rating of 4.4, and 19 of them opened in the last year.

3,792Restaurants listed

326Nearest counted

50Chains

276Independent

4.4Average rating

Made-up footfall and rival counts; footfall per rival = footfall / (rivals + 1).

On footfall alone, site 1 wins. Per rival, it comes last: 25 restaurants already share its crowd. Site 4 has the least footfall but the most per rival, which suits a destination restaurant that people seek out. Site 2 and site 3 sit between, and their peak hours split them: site 3 is a lunch street, site 2 an evening one. A brand built on dinner should look hard at site 2.

Where visitors come from, in Sao Paulo

In Sao Paulo, the block can also read the trips that end near each site. Tick Where visitors come from and each site gets the districts its trips start in, with their share, how they travel, and how far they come on average. Groups of fewer than five trips are never shown.

Say two made-up sites in Sao Paulo have similar footfall. At the first, most trips start in the two neighbouring districts and come on foot or by short ride; at the second, trips come from across the city by car, averaging several kilometres. The first is a neighbourhood site that depends on its streets; the second is a destination that depends on parking and access. That difference is invisible in the daily footfall and decisive for the format you open.

Putting it in one flow

1. Put your shortlist in a table in My data, or use a New rows trigger so new candidates are checked as they are added.
2. Add Mobility Footfall: 200 metres, the last 30 days, Footfall, Busy hours and Busiest days.
3. Add Nearby places with counts for your type of business.
4. Add the Calculated field for footfall per rival, then Sort and limit.
5. Ask xMap LLM to compare the sites in a paragraph each, using only those fields.
6. Send it by Email or as a PDF with Make a report.

For a quick look without a flow, the app’s Compare sites puts two or three spots side by side on people, rivals, roads and risks. Footfall comes from a flow like the one above.