Data visualisation, done well, is a sight to see. It’s fascinating to watch, and provides both a look at the entire scope of the data, plus the ability to drill down into it. And the Tube Heartbeat, a presentation of data depicting usage of the London Underground, is a brilliant piece of work.
On every weekday in London just over two million people commuting back and forth on the Tube. That amounts to more than 1.3 billion trips annually. Thankfully, Transport for London not only collects data on these trips, it also makes that data open to developers. And it’s why we have Tube Heartbeat.
The data includes, in fifteen-minute intervals throughout a weekday, the volume of tube passengers moving between every adjacent pair of stations on the entire tube network – 762 links across the 11 lines. It also includes numbers entering, exiting and transferring within each of the 268 tube stations, again at a 15 minute interval from 5am in the morning, right through to 2am.
Just in case you don’t find the link to the key to how the data is represented:
- Line colours are the official colours for that line, as specified by Transport for London.
- Only the higher of the flows in each direction are mapped, with arrows showing which way this is.
- Station borders change colour – red indicate that more people are entering the station than interchanging in it or leaving it, black indicates more interchanges and green indicates more exits.
- When comparing years, orange lines/stations indicate a fall in usage between the years. Blue stations indicate a rise in usage. The station areas and line thicknesses correspond to the size of the rise (or fall).
Want to know more about Tube Heartbeat?
If you’d like to dig into this a little more, Oliver O’Brien’s blog contains two excellent articles, one on how to best use the data visualisation, the second a look into the code used to bring life to the API:
The RODS dataset
Transport for London’s RODS data displayed in the map was taken from from autumn, chosen so as to not be affected by spikes such as summer holidays or Christmas shopping. In the dataset is:
- Boarders and alighters by station, line, and time of day
- Line loading by section, line, and time of day
- Station flows by station and time of day
- Origin-destination matrix by station, zone, and time of day
- Route choice by origin-destination pair
- Journeys involving interchange by zone, and number of interchanges
- National Rail and Docklands Light Railway journeys to and from each London Underground station by zone and time of day
- Total entries and exits by borough and time of day
- Access and egress mode by entry/exit station, zone, and time of day
- Age, gender, and mobility category split by entry/exit station, zone, and time of day
- Average journey time by entry/exit station, zone, and time of day
- Distance travelled by entry/exit station by zone, line, journey purpose, time of day, and ticket type
- Journey frequency by entry/exit station, zone, journey purpose, time of day, and ticket type
- Journey purpose by entry/exit station by zone, time of day, and ticket type
- Ticket type by entry/exit station, zone, and time of day
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