Google timeline is fascinating (and slightly terrifying)

Imagine video recording your entire life.  Some of your friends on Facebook may seem dead keen on doing this (and sometimes on behalf of their very young offspring), but few have gone whole-hog and stuck a webcam to their forehead for posterity (or ex-post analysis).  To be honest, I’m not sure I’d want to relive my less sober nights in high definition, but sometimes I regret not getting some recording of the key moments especially ones taken for granted.  So I for one was delighted to discover today that unbeknown to me Google has been stitching together my every move for the last three years (actually I had a deep suspicion this was happening).  Every journey, from the mundane local food shop, going to the cinema, or staggering up Everest Base camp – its all there, along with some attempts to assign transport mode to journeys.

Kindly Google allow you to download all your location data (to be clear your data is entirely private).  This data is exciting for me because it can be used to understand trip frequency, hierarchy and distance.  Trip purpose can be understood, because Google has Geotagged so many locations, and has the algorithms to assign you to the transport network.

People who are not transport geeks may also enjoy the sheer nostalgia of it tieing together past holidays and trips, especially as it hooks up with photos. For example my family recently has a trip to Boston which turned  into a little exploration of the Gulf of Maine.  Google seems to got the routes and modes pretty much spot on.  We used a mixture of the MBTA, taxis, walking and it all displays, along with an occasional job.  Boat trips have slipped through the net, but that’s very easy to fix.

Journeys around Boston. A jog along the Charles River correctly assigned.
Boat trip came out as a car trip.

Trip purpose

Trip purpose is important to understand as different types of transport trip have different spatial patterns in the urban system.  In most cities public transport networks are radial in pattern, yet in many cities especially in the US and UK, large numbers of jobs and services have accumulated on the edge of cities.  Whilst workplace movements are well understood those frequent trips to the supermarket, hospital, DIY store add up and put pressure on the transport networks.  Understanding how frequent these trips are might help develop better spatial planning.  Critically, can we develop better hierarchical design in the spatial location of retail centres.  I touched on this in my comments about the Ipswich Garden Suburb here – its all very well building an urban extension, but if all the store are on the other side of town you will inevitably end up with lots of driving.

A first stab at my own trip hierarchy looks like the bar chart below.  I’ve categorised my trips from the Google data.

My trips by category

Trip distribution

Often in transport studies the biggest problem is usually getting good data on how people move.  Commuting to a main place of work is quite well understood in the UK because of the census, but other journeys are difficult for example trips to the shops, trips to football games, holidays etc.  Traditionally we might try to get a handle on these trips by conducting surveys, but these are expensive and yield limited samples.  One method is to use such samples to validate a more comprehensive model – but sometimes the limited sample can make this task challenging.  Surveys are expensive ultimately because manpower is required.  Other sources include number plate recognition counting – but this doesn’t provide origin and destination details.  Measuring phone signals at cell towers is a fantastically rich source of information but processing and assigning this data to the transport system is expensive.  The google data is at this level of detail, and appears to have been done already.

By anonomising their data over large scales Google could instantly provide excellent origin-destination-mode- route travel data.  With this data at our fingertips we should be able to build much better behavioural models and prioritise investments more efficiently.

Some other thoughts:

So is Google already delivering the panoptic surveillance predicted by many writers on smart cities?  In theory if the police needed to trace your movement, the data is present.  Are Google required to hand over this data for the purpose of investigating crime?

It’s also clear that as well as collecting this data Google clearly are capable of storing massive amounts of information.

Critically, you can opt out of being tracked.  I’m happy to take at face value the idea that opting out means you have opted out.  For now.  But with right wing governments in vogue it is not beyond the realms of possibility that such data could be manipulated.

I’ll come back to this once I’ve had chance to analyse the data.







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