Google knows you took the bus: on the creepy accuracy of Google Maps Timeline

You are here. And here. And here. And... Image: Google Maps.

Knowledge is power, they used to say. Nowadays, they say “data is power”, and they’re not wrong. Unlike many of the modern, high-value tradable goods in our society like oil or gold, data is a limitless resource that we’re constantly creating more of day after day.

What the actors who own this data choose to do with it can often be a point of vast contention: should I be happy for Google to reliably know where I am, where I’ve been, and most frighteningly, where I’m going? It’s not up for dispute that the scope of these tools can be immense – but how much of that scope should we take for granted?

Google Maps is a tool full of wonderful surprises. It can plan a journey for you, tell you what deals to get at the supermarket, and give you updates at the bus stop. Some of the things Maps can do, it does without us even asking; Google knows when we pop to the shops, or when we stand by a bus stop.

This concept is called “geofencing”: cross-referencing geolocation data with the services at that location, and issuing notifications to a device on that basis. Google knows I’m in the supermarket because my location matches up with the area the supermarket is known to occupy, and through a complex series of phone masts and wifi access points, it knows I’m between the vegetable aisles. Okay, maybe things aren’t quite that specific, but the detail is stellar – and often, slightly concerning.

A simple flick through the timeline feature of Google Maps reveals that Google can plot day by day where you were, when you went home, and, maddeningly, how you took that journey – or at least, it can make an educated guess. By applying geofencing programming, Google can calculate when we are near a bus stop, and cross-reference that data with bus routes and other bus stops to determine with a reasonable degree of certainty when its users are taking the bus. Google doesn’t go as far as to try and guess which bus, but it could make an educated guess.

The same is true of train stations; pause in one, follow the expected route of the railway line, and travel through additional train stations, and Google will have no trouble in informing you after the fact that you have travelled by train. A reminder that you don’t need to have planned a journey on Maps for Google to surmise this: it is all calculated based on shifting geolocation data, and nothing more.

Walking, cycling and driving are harder for Google to calculate, because there are no geofenced points of entry for these modes of transport. It is therefore likely that, once bus, train and metro have been eliminated from the mix, Google simply inspects the time taken between harvested geolocation data to calculate the transport mode used. But without geofencing, it’s harder to determine the exact route taken by a user: because they’re not following a prescribed route, and because geolocation data is much easier to take while stationary, routes on timeline taken independent of public transport can end up looking… messy.

Google fails to surmise that some of this journey was taken by train and presumes I took an unorthodox drive through Kent in the early hours. Image: Google Maps/CityMetric.

The system isn’t perfect. For one, it can’t account for anomalies. I took a rail replacement bus service recently, and Timeline was dumbstruck by how I’d managed to get home. But the ever-increasing availability of data surrounding transport timetables means that the assumptions Google can make about our transport choices are only bound to get more accurate. That’s important, because its information that few organisations beyond Google are likely to have real access to.

If we take London as an example, we know that Transport for London (TfL) can use data on traffic flows, ticket barriers, and incomes for bus routes to determine how people use a service. In fact, TfL has even used its own wifi services to calculate route maps on the Tube. However, without undergoing intricate surveys, they will struggle to plot exactly how journeys are taken beyond the Tube Map, especially with regards to buses, disparately owned NR services, and so on.


Google has exactly the information to remedy this – and it’s integrated into Timeline, simply because people consented to having their location data collected. If your local borough council asked to do the same, and the only provision it could grant was that you might get a better bus service, many people would probably opt-out. Part of the reason why we accommodate the location-harvesting of Google is because we consider Maps such a vital service, and its domain – at least in terms of its rights to record our geolocation – is hardly contested. Even those of us who use Citimapper regularly tend to have Maps downloaded on our phone.

Google Maps is in a unique position to mark the differences between journeys that are entirely spontaneous and journeys that are pre-planned, because it is measuring both. That information could be highly useful in designing timetables and shaping user-friendly services.

Moreover, as geolocation data grows more precise, it will be able to help us pin down the flows of pedestrians and cyclists in our cities. While it’s possible to gather this data in the public domain without geolocation, it’s economically prohibitive to do so in less densely populated areas. This data would help prioritise cycle-friendly and pedestrian friendly developments on the understanding of where demand is greatest.

This sort of data inevitably carries such a high risk factor, however – not only as far as personal privacy is concerned, but also surrounding efficacy. We presume that if we know every individual's travel patterns, we can design perfect travel services – but patterns change all the time. An algorithm can never incorporate the latest change before it is registered by the system. While data like that collected by Google Timeline could be put to better use by transport authorities, it shouldn't be abused, nor serve as a panacea for good design.

Worst of all, it’s hardly clear that this data is up for public consumption. The furore over data protection means it would be considered deeply unethical for Google to hand this location data over to anyone, let alone a local government body like TfL. It may be moot point; Google itself claims that Timeline is for our own amusement and little more.

But maybe we’d get better services if it wasn’t; after all, geolocation isn’t slowing down anytime soon.

 
 
 
 

Older people need better homes – but then, so does everybody else

Colne, Lancashire. Image: Getty.

Towards the end of last year, I started as an associate director at the Centre for Ageing Better, working particularly on our goal around safe and accessible homes. Before I arrived, Ageing Better had established some ambitious goals for this work: by 2030, we want the number of homes classed as decent to increase by a million, and by the same date to ensure that at least half of all new homes are built to be fully accessible.

We’ve all heard the statistics about the huge growth in the number of households headed by someone over 65, and the exponential growth in the number of households of people over 85. Frustratingly, this is often presented as a problem to be solved rather than a major success story of post war social and health policy. Older people, like everyone else, have ambitions for the future, opportunities to make a full contribution to their communities and to continue to work in fulfilling jobs.

It is also essential that older people, again like everyone else, should live in decent and accessible homes. In the last 50 years we have made real progress in improving the quality of our homes, but we still have a lot to do. Our new research shows that over 4 million homes across England fail to meet the government’s basic standards of decency. And a higher proportion of older people live in these homes than the population more generally, with over a million people over the age of 55 living in conditions that pose a risk to their health or safety.

It shouldn’t be too difficult to ensure all our homes meet a decent standard. A small number of homes require major and expensive remedial work, but the overwhelming majority need less than £3,000 to hit the mark. We know how to do it. We now need the political will to make it a priority. Apart from the benefits to the people living in the homes, investment of this kind is great for the economy, especially when so many of our skilled tradespeople are older. Imagine if they were part of training young people to learn these skills.


At a recent staff away day, we explored where we would ideally want to live in our later lives. This was not a stretch for me, although for some of our younger colleagues it is a long way into the future.

The point at which the conversation really took off for me was when we moved away from government definitions of decency and accessibility and began to explore the principles of what great homes for older people would be like. We agreed they needed light and space (by which we meant real space – our national obsession with number of bedrooms as opposed to space has led to us building the smallest new homes in Europe).

We agreed, too, that they needed to be as flexible as possible so that the space could be used differently as our needs change. We thought access to safe outdoor space was essential and that the homes should be digitally connected and in places that maximise the potential for social connection.

Of course, it took us just a few seconds to realise that this is true for virtually everyone. As a nation we have been dismal at moving away from three-bed boxes to thinking differently about what our homes should look like. In a world of technology and factory building, and as we build the new generation of homes we desperately need, we have a real chance to be bold.

Great, flexible homes with light and space, in the places where people want to live. Surely it’s not too much to ask?

David Orr is associate director – homes at the Centre for Ageing Better.