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Expansion analysis

Choosing the next city with open data

A data model that compares 46 Spanish cities to decide where Easy Locker opens next.

Client
Easy Locker, our own business
Sector
Travel services
Services
Data and decision support
Scope
46 cities in Spain
Spain's rail network rebuilt from open timetable data: thicker line, more trains a day; bigger circle, more passengers

The challenge

Easy Locker needed a second city. Choosing the wrong one means paying rent for years on a spot that travellers don't walk past.

What we did

  • Joined open data for 46 cities in Python: train timetables (GTFS) for 996 stations and 1,147 line segments, passengers per station, buses and hotel stays.
  • Rebuilt Spain's rail network as a map: the thicker the line, the more trains a day.
  • Matched 202 August bookings with every train arrival. Travellers pay 10–19 minutes after their train gets in (p = 0.001 against 5,000 random shuffles).
  • Wrote a daily script that finds premises for rent near each candidate spot, and a payback model that weighs revenue against real rents.

The result

The search narrowed from whole cities to the walk from the station, where travellers actually go. The daily search for premises keeps running.

Our bookings against train arrivals: travellers pay 10–19 minutes after their train gets in.
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