The More Means Better blog has an interesting new post on universities that have moved home, in some cases to entirely different parts of the country. Some I knew about, others were new. It’s a helpful reminder that even universities, seen as one of the most dependable ‘anchor institutions’, can up and leave when they deem it necessary (although it is pretty rare).
The college of the future
One institution embedded in more communities and neighbourhoods than universities is the further education college – often overlooked in conversations about anchors. I joined a seminar last week by the Independent Commission on the College of the Future asking what we want and need from our colleges from 2030 onwards. Most discussions returned to the essential local role of colleges (and a lack of public appreciation for the wide-ranging responsibilities they take on), their ability to act as community and business hubs (especially for the long tail of less-productive small businesses), and as a conduit to higher education, careers and apprenticeships.
This commission seems to have the ear of government, is chaired by the highly regarded Sir Ian Diamond (who has an extensive background in higher education, and will soon be the UK’s National Statistician), and is well-timed given the widely acknowledged need for support for the further education sector. The final report will be published in Spring 2020 and I expect it to be influential.
Better local data
I first wrote about the POLAR dataset back in 2016. It is helpful to measure the extent to which young people participate in higher education at a local level because where you are from is closely linked to your future education prospects, your health and your economic prosperity.
The Office for Students has introduced an experimental but more sophisticated successor to POLAR called TUNDRA. Wonkhe have published a helpful summary with some of the limitations and context.
More detailed data at a more local level is definitely a good thing – as with previous work by HEFCE on ‘cold spot’ areas that are missing a university. However, as data becomes more complex it often needs heavy caveating, as we have seen with the LEO data on graduate earnings – which is beginning to include a place dimension.
Back in 2017 when I was presenting my work on internationalisation for the British Council at conferences I would ask the audience to picture in their minds a big map of a city they knew and to shade in red the areas where there was most international activity.
For most cities, the deeper shades of red would be in the centre of the city: the central business district, the tourist hotspots, the shopping streets and, often, a university (especially if it bears any resemblance to Hogwarts). There could also be ‘pockets’ of internationalisation in more marginalised areas where universities set up a summer school, ran public events, built student residences or held community engagement activities.
The thinking was that universities could help bring the benefits of internationalisation to these ‘cold spots’. I’ve been thinking about the concept of a heatmap of university-city interaction in more detail and sketch out some initial thoughts below.
What is hot?
Beyond international activity, there are many other interesting dimensions a heatmap could capture. A basic map may capture any initiative between universities and city hall, between universities and businesses, or between universities and communities or community organisations. Darker shading may represent scale of activity or depth of engagement or a longer history of working together.
Less tangibly, it could represent informal collaboration, or any activity where the university reinforces the goals of city hall or supports communities, or vice versa. Activity that undermines other actors might emit a chilling shade of blue; a warming red means partners working towards similar goals.
Heat could represent individuals participating in higher education or people otherwise engaging with a university – from attending a public lecture to using sports facilities. It could capture the flow of these people to and from their home or workplace and the university, showing how their engagement is shaping transport use and public spaces. Movement patterns will differ from university to university and each tell a unique story (Toronto’s universities are jointly studying the travel behaviour of 600,000 students).
Instead of mobility, the flow of money or investment in and out of universities could be measured. In doing so we would veer into the territory of university impact studies and input-output analysis. Given the limitations of such studies, a heatmap approach with added contextual data may offer a more complete picture of regional impact. A broader impact heatmap may look at perception data or a combination of economic, social and cultural measures.
A map could show ownership. Most obviously this could be the land and buildings owned by the universities (perhaps a more granular version of this data from the UK showing the dominance of the Oxford and Cambridge estates). In cities that have a degree of ‘ownership’ of institutions (through regulatory controls or funding mechanisms) the degree of autonomy could be mapped.
Or we could (try to) map where collaboration or engagement is less or more than expected. This mirrors nuanced higher education participation data produced in the UK (my post on that here) which maps the proportion of young people participating in higher education compared to that expected given GCSE-level attainment and ethnic profile. How we measure or define what is expected given the different make up of cities and universities is an interesting question, and leads us nicely to…
Heatmaps offer a nice visual representation of the heterogeneity and complexity of both universities and cities. ‘The city’ is made up of countless constituent parts, and it is similarly difficult to generalise ‘the university’ as a single actor. Even the most outward-looking university will have departments and teams with strong engagement with people outside the institution and others which remain mostly insulated from outside.
We can apply heatmapping to universities. Here’s the organogram for Hungary’s University of Physical Education (ranked highly in Google Image Search), with a completely fictitious heatmap applied that could apply to international or community or business engagement. You can get even more detailed: within each unit you could shade each individual. And university structures change over time, and in turn so does the heatmap shading. You could do a similar exercise across a map of the campus.
A unique heatmap signature
Every city and every university will have a unique heatmap ‘signature’. This is partly affected by the structure of the city itself: a heatmap for Paris would look very different to London or Dublin or Baltimore or Toronto. A long history of city planning, the decisions of millions of individuals and thousands of businesses and organisations, political and cultural and social and economic forces lends urban areas a unique fingerprint. In Paris social housing is concentrated in the banlieues or suburbs that form a ring around the centre of the city, whereas in London social housing is woven into the fabric of the city. The result can be intense spots next to each other, or softer scattered blobs.
Universities are actors that make decisions but simultaneously are themselves shaped by wider forces. Dublin City University is in a historically poorer part of the city whereas Trinity College Dublin is right in the centre, forging their own unique heatmap signatures. In Toronto the four main universities have very different footprints and very different heatmap signatures. In London, three universities that may be seen by some as institutionally similar are engaging in vast campus expansions in new areas of the city. The heat signatures for UCL, Imperial and Kings College London will show a new, emerging concentration of heat in their new campuses, a second centre of gravity which – depending on what you are measuring and the success of their developments – may over time have implications for their existing sites, the surrounding areas and all the bits in-between. London South Bank University is focusing on working with local partners such as further education colleges in the borough of Southwark; again, the signature for LSBU would look quite different to UCL. Precisely where you are located matters.
Heatmaps may also be a good way of visualising activity on the ‘periphery’ – a focus of recent academic inquiry from higher education to smart cities.
Is there a dark side to universities?
Not all university impact and engagement is positive. Complaints may be relatively trivial – from students taking over too many houses to making too much noise or not paying enough local taxes. But they can also be more serious criticisms: universities that exacerbate ‘existing cleavages of class and race’ in the race to redevelop and expand their campus, or otherwise reproduce wider inequalities in society. Such conversations often emerge when universities embark on urban regeneration projects – a prime candidate for heat mapping – and the debate often intersects with wider discussions of gentrification and community identity.
The Guardian explored some of these issues earlier this year in coverage of Johns Hopkins University’s ambitious development plans in east Baltimore. The piece quoted several locals:
“This is gentrification, a big institution pushing out a vulnerable community for its benefit,” says Lawrence Brown, a critical urbanist who teaches in the school of community health and policy at Morgan State, Baltimore’s historically black university… Marisela Gomez, a physician and activist in the fight for fair treatment of displaced residents, is blunter. “Every community that’s black and brown and low-income in Baltimore is at risk.”
There’s also an acceptance that the city needs the university. “We need Hopkins to succeed, because that’s the anchor institution in east Baltimore” says the leader of the ‘Baltimoreans United in Leadership Development’ group. And the university recognises the interdependence of the university and the city: “It is inconceivable that Hopkins would remain a pre-eminent institution in a city that continues to suffer decline”.
Needless to say, mapping such interactions needs to be supported by broad contextualisation. And ideally mapping would reflect some other, significant, changes taking place, such as a blurring of the edges around the campus:
With fences, skywalks and forbidding facades broken by loading docks, the medical campus sent hostile signals to its surroundings, and got hostility in return. Assault and theft were common; beggars set up at traffic lights. “Fundamentally it was a hunker-down strategy,” [Ron] Daniels [president of Johns Hopkins University] says. “The traditional thinking was that the best way to protect the university was to ensure that its perimeters were effectively controlled, and that you were creating safe zones within them.” … By contrast, the new office and lab buildings in the EBDI [East Baltimore Development Initiative] feel like they welcome – and want to generate – foot traffic. It is nothing fancy: ground floor retail, some steps and patios, small setbacks creating spaces to meet and gather.
There are other limitations. Maps can be stubbornly one-dimensional: they often show a fixed point in time, whereas patterns will change from day to night and times of the year. Unless they can show effectiveness or durability or inclusivity there is a risk of giving the illusion of successful engagement; some projects could create bold heat maps despite having largely negative effects.
With the development of ‘smart cities’ you can, in real time, transpose data onto the map. Although sensor information may show supposed engagement, the data is technical and the metrics unlikely to accurately reflect social realities. Maps need to capture phenomena such as ‘splintering urbanism’, whereby urban infrastructure can drive social and spatial inequality.
Lastly, consideration should be given to how to represent regional, national and international dimensions. To pick just one facet of international links, universities that are close to global flight hubs perform better in league tables, and cheap flights mean more research partnerships; similarly places with a direct flight to Silicon Valley raise more venture capital. But these links won’t benefit all people in the city or parts of the university and a heatmap could help us consider how benefits can be spread further.
September 2019 update: The Office for Students has introduced an experimental but more sophisticated successor to POLAR called TUNDRA. Wonkhe have published a helpful summary with some of the limitations and context. The links below may no longer work following the closure of HEFCE, but POLAR can be found on the same page as TUNDRA.
I’ve been working with some of the Higher Education Funding Council for England’s (HEFCE) datasets on young people’s participation in higher education, and was thinking how useful and interesting this information would be on a global scale.
There are two key maps. The first is Participation of Local Areas (POLAR); see an example for Brighton, UK, above. The POLAR classification looks at how likely young people are to participate in higher education across the UK and shows how this varies by area. Areas in red are those that have the lowest participation rates while areas in dark blue are those that have the highest participation rates. Yellow is in the middle.
The second is more nuanced and perhaps more useful. It shows gaps in participation – the proportion of young people participating in higher education compared to that expected given GCSE-level attainment and ethnic profile. In the example above, also for Brighton, in those areas shaded red young participation is much lower than expected. In those areas shaded blue, participation is much higher than expected. In those areas shaded white, young participation rates are as expected.
Whilst higher education participation data is probably available at country or broader sub-country level for most countries, the real value is in the hyper-local detail – in this case at the level of electoral wards, of which there are 7,669 in England alone. The potential insights are obvious from the discrepancies in colour between neighbouring wards. I’d be fascinated to see other countries divided into 7,500 sections and each section scored for higher education participation – would countries with similar inequality levels or income levels look similar? How do urban and rural areas compare across countries? How about those areas in close proximity to a cluster of universities or a major transport network? Or those close to a border of a wealthier neighbour?