The days of getting lost on unfamiliar city streets are fast becoming a quaint relic of the past. According to research conducted by the Ordnance Survey, 60 per cent of millennials say that they “rely” on their mobile map when going anywhere new and in day-to-day life, and more than a quarter are “very reliant” on digital maps. This percentage is, as one would expect, increasing among younger generations.
The databases that power our now-ubiquitous virtual maps, analysing and visualising geographic data, are called GIS, or Geographic Information Systems. They can be incredibly useful, providing users with the quickest suggested route for travel. Tools have been introduced that can allow users to filter their suggestions, further tailoring a journey. For instance, learner drivers can exclude motorways from car routes, disabled people can prioritise routes with step-free access on public transport, and it’s also possible to filter by travel fare
More recently, companies using GIS have explored the possibility of introducing routes that are “healthy”, for example, in terms of noise pollution, air quality or green spaces. Heidelberg University in Germany did research on this, using data from the public domain OpenStreetMap to suggest routes through green or quiet spaces. The aim was to produce stress-reducing journeys, with mental well-being prioritised. Users could also filter by environmental and design factors which may make a route feel safer, such as the presence of streetlighting or CCTV cameras. According to code revealed by the online community XDA developers, Google Maps has used streetlight data to try to provide “safer” routes. The possibilities of bespoke data tailoring are clearly endless.
But there are disadvantages to GIS, too. Filtering a route based on “appeal” could exacerbate spatial inequality. Areas rich with parks, uncongested roads and streetlights will attract visitors and tourists – and these are the parts of a city that tend to be wealthier already. In fact, the Office for National Statistics has found that being close to a public green space raises the price of houses in urban areas by an average of £2,500. Likewise, when the estate agent Benham and Reeves investigated the average price of green space in London boroughs, it found that there is a “green space premium”. The value of that in Kensington and Chelsea was £753 for instance.
As became increasingly visible during the pandemic in UK inner city areas, wealthier populations have better access to green spaces. The influence of GIS could therefore have profound socio-economic consequences. It could accentuate the divisions already stark in many UK cities by channelling visitors and, consequently, capital through well-to-do areas.
It could also contribute to the stigmatisation of certain areas. For instance, “Ghetto-Tracker”, later named “Good Part of Town”, was an app used in the US in the early 2010s. It combined user-contributed personal reviews with census data such as crime, demographic and socio-economic data to suggest routes for users to avoid. These “dangerous” neighbourhoods were, of course, poor communities with large populations of ethnic minorities, and the app was widely criticised for entrenching racism and classism. Such an approach echoes the practice of redlining – where loans, insurance, mortgages or other services are discriminatorily restricted to certain people in a community, normally based on race.
Clancy Wilmott, an assistant professor at the University of California, Berkeley specialising in GIS, says that these routes are based on “equivalences”. Equivalences are assumptions that a=b. For instance, “there is an assumption that lower socioeconomics always equals more crime, but this assumption is not necessarily stable”, Wilmott tells Spotlight. The coding of our cities, she says, is founded on prejudiced conceptions surrounding race, class and culture that are not necessarily based on fact. This not only contributes to the stigma around particular areas, it also worsens inequality in urban centres.
Pre-assigned journeys also homogenise what is considered “healthy” and “safe”. But can a map ever offer a universally safe route? Do all users feel safer with the presence of police officers on their journey? Wilmott highlights how “‘safe routes’ make a promise they can’t uphold”. Perceived safety based on pre-assigned factors and live location data does not equate to invincibility.
Of course, there are instances when filtering a route is wise – and when this is will depend on the user. But while the chance to filter our routes has countless benefits, we should be aware of the potential consequences. “Safe” does not mean the same thing for everyone – in fact, it can mean opposite things.
Plus, exploring places without the aid of a map can be eye-opening, bringing us to places we never would have found. So, if you feel comfortable and have time to spare, why not try exploring without the aid of your phone for a change – you might be pleasantly surprised.