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Transport accessibility in North England.

On Wednesday 17 July 2019, Stef Garasto of NESTA gave a talk in Cambridge at The Productivity Insights Network special sessions . It shaped a lot of the work that has led to this blog post.

Lots of other discussions, tests, ideas, and pieces of feedback helped too. Dozens of organisations have contributed to the work I describe here. But it was that talk and our discussion after it that helped me understand what we needed to do at ODILeeds.

Stef's work was part of a wider effort at NESTA to understand skills, involving Jyldyz Djumalieva, Cath Sleeman, and others. Their work on creating a skills taxonomy for the UK is essential to understanding the mismatch between the skills UK adults have and the skills that UK employers need. I've been a fan for a long time. But this blog post isn't about skills.

I asked, I think, I forget the details with time, why their work was only using travel times by car between employment sites and potential workers. It is mostly low skilled and younger workers who struggle with skills mismatch, and they often don't have access to a car. The answer was simple, accessibility by car was all the data that they had. Simple and honest answers are the best answers.

I could fix that, I said. I could provide travel time data by public transport.

Two years later, that work is done, for North England. The biggest thanks goes to Transport for the North who got the project over the line. Sheffield City Region, Network Rail, Greater Manchester Combined Authority, The University of Leeds (especially ITS), ARUP, and many of our sponsors have helped us with this for years.

Today we're releasing two datasets for North England. They are both available via a live demo site on and on the ODILeeds GitHub page .

15, 30, 45, and 60 minute accessibility isochrones by foot, bicycle, car, and public transport for every MSOA in North England.

First, isochrones of 15, 30, 45, and 60 minute accessibility for every MSOA in North England.

The isochrones show you how far away you could start and arrive at the road closest to the centre of every MSOA in North England for 08:30am on 10 September 2019.

The isochrones extend into the English Midlands, Wales, and Scotland, our neighbours and we thank Transport for Wales, Transport Scotland, and Transport for the West Midlands for their assistance. Thanks also to Alasdair Rae and Onward for the road-snapped MSOA centroids.

15, 30, 45, and 60 minute isochrones by public transport for central Bradford.
30 minute isochrones by foot, bicycle and public transport for central Batley. In all cases bicycle gives the best access of the three methods.
30 minute isochrones to access Manchester Piccadilly by car and public transport.
The isochrones look great. Together with testing individually planned routes they are essential for debugging our data and our methods. But they are not what researchers want.

MSOA centroid to MSOA centroid journey times (up to 60 minutes) by foot, bicycle, car, and public transport for all pairs of MSOAs in North England.

Researchers want big tables ready to load into R, STATA, or Excel to help them with their analysis. So alongside calculating our isochrones, we've calculated those tables.

Most researchers have told us that they don't want tools to generate isochrones, or the isochrones themselves, or tools to generate connectivity matrices. They want connectivity matrices. So that's what we've published.

In the previous blog post I wrote on this project I showed how we verified the quality of our data by comparing it to journey times calculated by Google Maps. This was part of how we've been testing our methods and our data. Research at The University of Manchester for the Nuffield Foundation — Moving on from GCSE 'failure': Why the English education system must do better at post-16 transitions — used data from both methods to estimate the educational choices of learners in Greater Manchester and North East England.

We hope that our dataset can help improve more such research on North England, and potentially beyond.