Big data is a big issue right now - and we are perhaps about to realise just how much information Google and Apple have on us. Data is hugely important in understanding how we travel, but while we've been very good at measuring car traffic, how we measure cycling and walking is far more primitive.

David McArthur, at Glasgow University's Urban Big Data Centre, is trying to change that. Using Strava Metro data, and 'spare' CCTV camera capacity he was busy trying to work out who cycles and walks where - until the COVID crisis hit. Now his work is being turned to measure some of the changes we are facing around how we move around, and the new importance active travel is playing in the new normal.

Most methods of measuring active travel only give us part of a picture, however - and while the granular data on our lives is held by tech companies like Apple and Google, we might be glad that data isn't more widely available.

No one method of can capture everyone, though. Is there a way of making sure we are all visible in the right ways, in this new big data world? Is a national data centre for active travel the answer? And where on earth does government cycling data come from?

You can find out more about the Urban Big Data Centre, and David McArthur's work, here: https://www.ubdc.ac.uk/

Transcript

David McArthur interview FINAL MP3.mp3

Laura Laker [00:00:00 ] Hi and welcome to the active travel podcast. A brand new podcast brought to you by the Active Travel Academy, which is an academic think tank on all things cycling, walking and micro mobility. It's part of the University of Westminster in London and works in collaboration with folks from inside and outside the university. That's people like me. I'm Laura Laker an active travel journalist working with the Active Travel Academy on this podcast. Amongst other projects, and this is the first of a two part on data in active travel.

Laura Laker [00:00:28 ] The Active Travel Podcast is joined by David McArthur, who is a senior lecturer in urban studies at the University of Glasgow. David is with us today to talk about two pieces of research. The first is using crowdsourced data from Strava Metro to establish cycling patterns. And the second is using spare CCTV capacity to identify pedestrian volumes and movement, which is not as 'Big Brother' as it seems. David assures me so. Welcome, David. Nice to have you with us.

David McArthur [00:00:56 ] Thank you very much. Pleasure to be here.

Laura Laker [00:00:58 ] So you specialise in big data around transport and urban analytics. Can you just tell us a bit about how that works?

David McArthur [00:01:05 ] I'm based at the Urban Big Data Centre. So this was a center funded a few years ago with the idea that the UK wasn't making the most of the big data revolution. Our job was to try to establish ways in which new forms of data could be used to address substantial social science questions. So my stream of work was in transport. We've tried to look at what datasets are out there, what can they tell us about our transport network and how to improve our cities and what make the limitations of this sort of data. People were quite it was the hype curve where people were very excited it was going to change the world. We were trying to be a bit critical to those ideas.

Laura Laker [00:01:44 ] And people get very excited about new tech developments as shiny new toys kind of. But it's not always as wonderful as you might think. So can you tell us where transport data is right now and where it's going and presumably focusing on active travel?

David McArthur [00:01:59 ] It's quite interesting. There is amazing data out there. It's not always accessible, though. One thing we tried to do in the center was to price it out of the hands of data owners. But that's not always so successful. Sometimes there's legal regulatory licensing issues with the data. So if some local authority has used the commercial product or ordinance survey data, they can't necessarily share that data with a third party afterwards. There's also issues of perhaps it's commercially sensitive. So with a deregulated bus network, for instance, the data may be helped by the operator of a bus service. So it might not be available easily to outside researchers, which is a shame because it would be nice to have better data on who takes the bus and where do they go, but it's commercially sensitive information. So there's lots of great data, but the governance issues tend to pose far more challenges than the technical issues of analysing it.

Laura Laker [00:02:55 ] Obviously, there's going to be privacy issues around people's data, and especially if it contains demographic data or even personal data. So you've got to be very careful about who gets that, haven't you?

David McArthur [00:03:05 ] Absolutely. We would definitely want the data owners to protect the data subjects. And it's a legal requirement after GDPR especially. Well we always had data protection legislation but I think GDPR sharpened people's focus on this idea. But some of the data, I don't think needs to be shielded quite as much. So cycle counter data of how many people go past that particular point in time, I'm not sure it's so sensitive, but certain people are not happy to share it or they're worried that something might be done with it that they don't like.

Laura Laker [00:03:38 ] Really? cycle counter data - numbers?

David McArthur [00:03:42 ] Yes, I've had some arguments with local authorities because they don't want to release it, even though it's six people past this point in an hour. So I think it's as far removed from personal data as you might be able to get.

Laura Laker [00:03:55 ] That's interesting. I remember writing an article last year, I think it was, collecting cycle counter data from around the UK. And I got maybe a handful, and those are just the visible ones with the totem poles. But it was quite hard to get hold of, which was quite a surprise. And I think I was working on it for a few months, actually, partly because there were a number of issues. Some of the cycle counters broke down and some of the London ones have broken down. So I was kind of waiting on them. But also, like you say, it's quite hard to get information from people, and that's just the ones with the totem poles and the numbers on that are visible. And I guess there must be a lot more embedded in pavements that you just never see.

David McArthur [00:04:33 ] Yes, there are, there’s some hidden. So the council will have data on them, but maybe you get it, maybe not. But it's a shame not to have that data available for people to use.

Laura Laker [00:04:44 ] So you're working on both these projects, the pedestrian project and the cycling project, and that was pre-lock down, and obviously life changed for everyone. Since then, people stopped moving around as much. And I'm just wondering obviously the scope of the project is changing as the transport environment changes. And you wrote a couple of blogs about this, didn't you? The phenomenon of COVID and the changes that are happening. And I'm just wondering how much you've changed what you're doing since then.

David McArthur [00:05:10 ] It's been a really interesting time for transport data because we've often had this fragmented ownership of the datasets, trouble having access to them. Suddenly, though, everyone needed data on who was where and who was moving where and what modes of transport they were using. It's been interesting to see that the tech giants, Apple and Google have been the ones stepping in to provide consistent data across the UK. But a bit of a black box in terms of how does it go from raw data into these aggregates that they're publishing. But this has been used to formulate policy now, so we might be a bit concerned that if we had our own data and we had a national data service for transport data and it had all been there [LNE1] in a consistent way, we could easily have pulled up the information that we needed. But at the moment, as you said, it's a big job to try and gather all of it and that other people have stepped in to provide other versions of it. So it's interesting.

Laura Laker [00:06:07 ] And where is this Apple and Google data coming from?

David McArthur [00:06:10 ] I believe Google's using their location service, which sense for people are through combination of G.P.S. and Wi-Fi, looking at what Wi-Fi networks are nearby. I believe Apple is using where people are searching for directions about. So from that, they can infer something about the purpose of the travel was and where it is. And then they've published these mobility reports that you may have seen getting some media coverage, about how activity at different locations has changed over time. So it's very valuable information at the moment, but it's unfortunate we don't necessarily know all the details about how robust is it and is it excluding certain types of people from the analysis.

Laura Laker [00:06:52 ] People without mobile phones?

David McArthur [00:06:54 ] Yes. It's one of the key challenges for big data. So it could be people without mobile phones or the privacy conscious people who've opted out of sharing this sort of information. Apple data, it's a parti