Mapping the Gap – Visualising Urban Inequality at the Micro-Level

When a neighbourhood undergoes any kind of gentrification or ‘urban renaissance’ movement, the usual tell-tale signs are almost always apparent.

New gourmet coffee roasters, organic health food stores and similar markers of upper middle-class affluence quickly spring up and seem to have become the key ingredients (or perhaps the results) of a previously undesirable area that is now showing signals of being ‘on the up’ and increasingly desirable.

A sort of ‘starter kit’ for gentrification – just add water (and money).

Indeed, it’s not surprising to find the usual demographic suspects as those most likely to migrate to these up and coming areas – the young, the creative, and the hip – all who gravitate toward such hoods in search of that perfect slice of geographical benefits with an economically viable price tag and just enough urban-edge to tip the scales from dangerous to desirable.

Of course, there were residents and established communities in such areas long before they became the next big thing and an influx of artists, students and entrepreneurial types doesn’t change this.

Commentators of this phenomenon have long been split into two camps either praising or decrying the outcomes of such changes to the nature of the local urban fabric with those in favour pointing to increased land values and increased diversity in such areas, while critics will often highlight the negative consequences of the very same results.

Indeed, it is those in the latter of these two groups who are perhaps vindicated in their assertions when you begin to look closely at the effects of micro-segregation and instances of the enclave effect which often unfold in such neighbourhood-level changes.

In these instances, those demographic groups which can be split down different socio-economic lines actually don’t tend to frequent the same areas as each other, meaning that far from encouraging residents from different economic and racial backgounds to mix, these groups are instead more likely to act in ways which are contrary to this desired outcome.

However it seems that this kind of economic (and potentially social) inequality doesn’t simply occur at the neighbourhood scale.

The ‘Atlas of Inequality’ is a digital visualisation project by the MIT Media Lab which combines anonymous aggregated data points with interactive cartography to show how the other places we visit in cities (such as restaurants and stores) all tell an equally unequal story that actually stretches far beyond the local level.

 

Putting Urban Inequality on the Map

As part of a larger initiative, the Atlas of Inequality is one component of the MIT team’s research into the ways in which human behaviour and broader, city-scale issues related to housing, segregation, transportation and general inequality correspond to the patterns of individual choices and opportunities of urban stakeholders.

With visualised data mapping currently covering Boston and New York City, the eventual plan of the project is to analyse multiple cities across the US.

Initially, the team behind the map created it through the compilation of aggregated location data originally collected from the digital devices of around 150,000 anonymous users between 2016 and 2017 by the Data for Good Initiative by Cuebiq whereupon each data point collected was assigned to an income bracket for the purposes of classification.

On top of this, a list of places ranging from museums and shops to restaurants and coffee shops was collected in line with the human data in order to gather a resultant determination of visitors per location based upon income group.

The end result of this data integration is the creation of an inequality index which ranks those places which are most unequal (displayed in the system in red), with the most equitable (represented in blue).

In this case, the spectrum from equality to inequality (or blue to red) provides an easy to interpret visualisation of those places where only a single income group type spent time (the more unequal locations) to those where all four income categories spent a similar share of time (and were therefore the most equal).

What this looks like in practice is a fascinating aerial overview of the way in which inequality plays out on the scale of entire swathes of the cityscape, highlighting at a glance, the most glaring examples of inequality against those where balance is more commonplace.

A clear pattern of those places within the city where diversity is present and those where economic homogeneity is the norm begins to emerge when the data is presented in this way, allowing analysts and planners a stratified overview of what is working for who and where.

Many of the ‘clusters’ of inequality are what might be expected between the areas of ‘have’ and those of ‘have less’, however one of the surprising findings of the data when presented in this way, is the fact that there are many instances where locations of a similar type can be virtually next door to each other, yet reflect vastly different economic profiles in terms of those who visit.

If data creates information and information leads to knowledge, it’s just possible that by implementing action from this kind of tool and with the ability to dissect the places most effected by issues of inequality, policy makers, designers and planners can begin to redress the equity balance from the small scale to the large in cities.

With the ability to analyse a cross-section of not just where it’s assumed people frequent, but where they actually do, the options for planning in a way that look to redress inequality rather than inadvertently exacerbating it become greater than they’ve ever been previously.

Indeed, utilising anonymised public data in a way that delivers a kind of ‘passive participation’ has the potential power to include the public in the planning and design process like never before.

The user experience expert Steve Krug once famously condensed the ultimate goal of usability to four words – “Don’t make me think”.

Is it possible that in an era of such data-abundance on at almost every level, that these principles can be transferred to the field of participatory planning?

Could the answer to true involvement and equitable input of every stakeholder (not just the privileged few and the specific interest holders) in shaping the urban realm and reality around them come down to something as simple as not actually asking, but merely observing?

Perhaps it’s too early to answer these questions at this point and with understandable challenges around areas of privacy and consent regarding data, it’s clear that there will be many hurdles which still need to be addressed before the utility of this approach can be fully evaluated and understood.

One thing however is certain.

As more of us interact within an urban, socio-economic and technological landscape that’s evolving faster than at any time in history, the kind of work being carried out by the team at MIT and other institutions around the world are going to increasingly become some of the most powerful tools for helping the designers, planners and makers of our cities to understand the situation on the ground with a level of granularity that would once have been incomprehensible.

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