Footfall around transit stations: busy is not the same as useful

A station almost always shows high footfall, but much of it is people moving quickly between a platform and the street. Since footfall counts hours of presence, a station where people wait and change trains scores higher than one they rush through, and the shops that do well beside a station are usually the ones that sell to people with a few minutes to spare: coffee, snacks, newspapers, phone top-ups, a quick pharmacy stop. To judge a station, look at the shape of its day and the size of the circle you measure, not just the total.

This is for retailers and food brands choosing station kiosks and corner units, distributors placing stock near stations, transport authorities planning surveys or new facilities, and property teams valuing sites close to a stop. It covers how a station's figures behave, how to size the circle, what Sao Paulo adds that other cities cannot yet, an example, the usual mistakes, and a flow that ranks stations for you.

How a station's figures behave

Three things make stations different from a high street:

• Sharp commuting shape. Stations used for getting to work often have large morning and evening shares on weekdays and a smaller weekend total, much like an office district but with more evening.
• Interchanges outscore through-stations. Where lines meet, people wait and walk between platforms, so presence builds up. A station on one line with trains every few minutes can see many people and few people-hours.
• Late-night presence is not always homes. Around a large terminus it can be night services, hotels and staff on shift.

Mobility Footfall gives each place its daily people-hours, its busiest hour and day, the four-part split of the day and the week-on-week trend. On its own unit, read what footfall data measures.

Choosing the circle

The circle decides what you are measuring. Up to 200 m, footfall is added up for the small block of the map the station sits in, about 0.1 square kilometres. Wider circles, up to 500 m, take in every block whose middle falls inside.

Use the same circle for every station you compare.

Where visitors come from, in Sao Paulo

In Sao Paulo, and only there so far, the block can also read the trips that end at a place: the districts they come from with their shares, how those trips travel, and how far and long they take on average, shown only for groups of five trips or more. Around a station this separates a stop people reach from far across the city from one used mostly by the neighbourhood. A shop selling everyday goods prefers the second; one selling something people make a trip for can live with the first.

Elsewhere you have footfall and its shape, which is enough to rank stations but not to say where their crowds live.

An example: choosing a breakfast kiosk

A made-up case. A breakfast brand in Mexico City has permission to bid for kiosks at three stations and can afford one. It measures 30 days of weekday footfall within 200 m of each entrance. Suppose the results look like this (invented for illustration):

Invented figures for illustration; not real figures for Mexico City.

Station 1 is the busiest, but its weight is in the evening: people heading home, not looking for breakfast. Station 3 peaks at lunch, which suggests a district around it rather than commuters. Station 2 has the smallest total and the strongest morning, and its busiest hour is eight o'clock. For a breakfast kiosk it is the one to bid on, and the brand might come back to Station 1 later with an evening snack offer.

Mistakes to avoid

• Ranking stations by total alone. The biggest number may sit in the part of the day your product does not sell in.
• Mixing circle sizes. A 500 m figure for one station and 200 m for another is not a comparison.
• Reading people-hours as passengers. Footfall is presence, not entries through the gates.
• Forgetting the timetable of the city. A public holiday or a line closed for works in the window changes the picture; check the dates the figures cover.
• Expecting trips outside Sao Paulo. Where visitors come from is not measured in other cities yet.

Ranking stations with an AI employee

1. Bring in the stations: Nearby places searching train or metro stations in an Area of interest, or My data with the list a landlord gave you.
2. Add Mobility Footfall: weekdays only, Footfall and Busy hours, 200 m, the last 30 days. In Sao Paulo, also tick Where visitors come from.
3. Add a second Nearby places, Each incoming place with counts, for the cafes or convenience shops already within 300 m.
4. Add xMap LLM told what you sell and at what time, asked to score each station and say why.
5. Add Sort and limit on the score and keep the top ten, then Email it.
6. In the inbox: each station with its footfall, busiest hour, shares, the rivals around it, a score and a reason.

Run it once for a bid, or monthly if you supply many station shops and want to know which ones are getting busier.