Big data in the big city

Catherine Lido on using novel technology to explore inclusion in Learning Cities.

It’s time for psychologists to rethink their engagement with novel streams of data. It can help us to explore concepts such as belonging, social inclusion, quality of life and participation (in learning and in cultural and political activity). Psychologists are beginning to realise that existing data (be it administrative, third sector, private or public) may capture our everyday lived experiences. We begin in large cities, where the ‘dataverse’ is ubiquitous, and our movements, purchases and health metrics are regularly recorded.

Have you heard the term ‘Urban Big Data’? Perhaps you have dismissed it as irrelevant to you as a psychologist. I must admit that when I was offered a post researching inclusion in learning with ‘big data’, I thought ‘Oh, that’s for computing scientists, urban planners and transportation nerds’. As the only psychologist in the building, I often wondered where I fit in. But in this article, I’ll explain the emerging concepts of big data and novel technologies, and how I’ve come to realise they may be the key to illuminating our behaviour in a naturalistic and reliable way.

Data can be ‘big’ in various ways, but most people agree it’s about more than numbers and size. We can think in terms of the ‘Various Vs’, talking in terms of its Volume, Velocity, Variety (Kitchin, 2014); the less frequently mentioned Veracity and Value (Demchenko et al., 2014); as well as Variability and Visualisation (see Li et al., 2016, for overview). So big data, for example from social media or body sensors, may be collected continuously in ‘real-time’, or it may be ‘big’ due to the complexity of the data, and the ‘big’ aspect can refer to the need for novel methods to capture, analyse, interpret and/or visualise it (Osborne & Lido, 2015)

Such datasets are becoming bigger, more interrelated and more open. Psychologists can use such data, tapping into existing resources to improve knowledge of our cities (or where data exists, rural regions), and to ensure equal and equitable participation in all aspects of life. Ethical and methodological challenges to the use of big data must be acknowledged: we are intensely aware of ethical, privacy/ surveillance concerns of such big and open datasets, and we write about these elsewhere. We must understand that we live in a society where our ‘data’ is regularly mined by private enterprise – our purchases, footfall, use of public transport, health, and more. Here we offer directions for the collection of novel and open datasets for social good, including citizens in the use of their own data…researching ‘with’ and not ‘on’ people.

Harnessing the power
Since its launch in 2014 I have had the privilege of working in, and with, the Urban Big Data Centre (UBDC) at Glasgow University. UBDC was funded by the Economic and Social Research Council as part of the investment in big data to ‘harness the power’ of existing data to improve the social, economic, and environmental wellbeing, specifically to address urban challenges (following on from the creation of the UK’s Administrative Data Research Service).

UBDC’s fundamental mission is to improve urban citizens’ lives. Its remit is interdisciplinary, but focuses mainly on transport, housing, technology, civic (and cultural) participation and educational inclusion. It attempts this partially by making data more open to the public, offering skills training and creating international networks for knowledge exchange around data usage, linkage and implications. But how does this all relate to psychology? Centres such as UBDC (and related big data investments such as Consumer Data Research Centre and Business and Local Government Data Research Centre) offer researchers opportunities to work with real-time, naturalistic data, without the cost and effort of having to collect it. They also offer opportunities for training and networking with non-psychologists, offering complimentary perspectives, alternative academic languages, and diverse statistical and visual-spatial modelling methodologies. Through such interdisciplinary ‘knowledge exchange’ I have personally rediscovered my love of maps, and have been able to engage with training on geospatial analyses, such as using multilevel approaches to explore neighbourhood, regional and national ethnic segregation (with Richard Harris, Professor of Quantitative Social Geography at Bristol University).

In my own research, part of my role at UBDC consisted of exploring educational disadvantage and how it is situated within ‘place’ in the Greater Glasgow area. My main achievement to date has been in contributing to UBDC’s first open dataset – the integrated Multimedia City Data (iMCD) project. The iMCD consists of a large-scale, representative survey of 1500 households within the Glasgow area (including a 24-hour travel diary), combined with a sub-sample of sensor data; namely, GPS trails for one week’s worth of travel, and ‘Lifelogging’ camera images from 48 hours of travels (with an additional 24-hour travel diary matched with the GPS trails and Lifelogger images). Set over the same 12-month period of data collection, we also have archived a large-scale social media capture (of photos and text), some of which are geolocated in Glasgow, and some of which includes topical hashtags (covering the Commonwealth Games, Scottish independence referendum and educational events/ organisations within the city). The iMCD is data is housed alongside administrative data for the city (in domains that include education and a range of urban indicator data), as well as cycling app data (STRAVA), mobile phone data, satellite and LiDAR data.  

Exploring only a single strand of the iMDC data – the ‘Understanding Glasgow’ representative household survey (of every eligible adult in the household) – we can see that it holds a host of usable data of interest to psychologists. This includes measuring attitudes, behaviours and literacies (operationalised as knowledge) in the domains of education, transport, sustainability, technology, and cultural and civic engagement, as well as a travel diary assessing adults’ patterns of travel activity and daily tasks.

Developing a survey for ‘open data use’ by academics and non-academic stakeholders alike, was a major challenge. We had to collect data without specific research questions and without specific researchers in mind (for a diverse variety of potential users from academics to citizen hacktivists). Following widespread scoping activities with potential users, we developed the survey iteratively, with a review of (largely UK) national survey questions in the domains of interest, and content validity was assessed by a team of eight subject matter experts (SMEs) from interdisciplinary backgrounds. The draft survey content was compared against the 42 UNESCO (2013) features of Learning Cities to ensure we could explore key concepts for social inclusion and learning participation. The survey collected a rich variety of demographic information, including age, ethnicity, nationality and religion, as well as household demographic information, such as children, housing and employment (full income and benefits information). The survey also collected extensive data on educational qualifications and engagement in any learning (formal, non-formal and informal learning).

One interesting idea for use of the iMCD survey concerns UNESCO’s Learning Cities agenda. Learning Cities, unlike Smart or Future cities, acknowledge that knowledge lies at the heart of the economic, social and civic success of any urban context (or wider region). The concept of ‘Learning City or Region’ has been well documented within education since the 1970s (Longworth & Osborne, 2010), and historically perhaps for centuries (Osborne et al., 2018). Equitable participation in lifelong education has remained at the forefront of political and social interests worldwide over several decades, and is now acknowledged as key to UNESCO’s Sustainable Development Goal vision for 2030 (Goal 4; United Nations 2015). UNESCO have consistently advocated for wider participation in further and higher education, and have placed lifelong learning at the heart of their Learning Cities agenda, operationalising the key features for success (2013). Thanks to education researchers, such as Professor Mike Osborne (Professor of Adult and Lifelong Learning), iMCD data was developed to offer benchmarks or comparisons to the hundreds of cities in the UNESCO Learning Cities Network worldwide. Triangulating data from surveys, sensors, GPS trails, images and social media allows global and holistic comparisons, for instance surrounding educational inclusion.

Let me give an example. In our 2016 study on learning engagement in the modern city, we illustrated that older adult engagement in physical, social and learning activities was lower than their counterparts and national averages. However, there was a subset of ‘actively ageing’, socially and technologically engaged older-adult ‘learner-citizens’, participating in educational, physical, cultural, civic and online activities, as well as working and care-taking. These older adults reported better health overall, and their GPS trails show more city activity (than matched counterparts). We can even zoom in to see that the engaged ‘Silver Citizen Surfers’ are engaging in more walking around their communities, even stopping (presumably talking or shopping) in their local area, as well as taking public transportation across Scotland. By then linking the survey to the Scottish Indices of Multiple Deprivation, and other place-based variables (data zone, local authority), such as feeling safe and belonging to the area, we could see how such factors moderated participation in learning activities. This provides interesting avenues for how to keep older citizens mentally, physically and socially active (particularly in areas of deprivation).

Through my recent research with Kate Reid (fellow psychologist in our School of Education), we have incorporated public engagement into our research project, supported by our ESRC Impact Acceleration Grant. We have been committed in involving the wider public with more opportunities to consider and discuss the use of ‘urban big data’, and specifically the iMCD survey and GPS findings surrounding ‘Lifewide Literacies’ (specifically, health, environmental, financial and digital literacies). We have created, in collaboration with digital artists’ collectives (Maklab and Creative Stirling), interactive 3-D laser cut objects on plywood. We have been keen to use these interactive objects to promote discussion and debate around big data research as it relates to our own specific project and have met with a range of public audiences at the flagship Glasgow Science Festival and the ESRC Festival of Social Science hosted by Glasgow Ikea in November 2017. The objects created include a moving map of Glasgow, a large-scale jigsaw representing the local authorities where the ‘big data’ was collected, and a child-friendly ‘literacy’ person badge, which could be personalised with desired literacy tokens, such as reading/writing, maths, science, artistic/creative, cultural, digital, foreign language and geo-literacy. At our first event (Glasgow Science Sunday) over 150 badges were created, coloured and taken away, and our second event at IKEA we produced over 300 more (as part of the ESRC Festival of Social Sciences), with an estimated footfall of 8000 people through the stall area on the day. Our UBDC blog explains the design-led strand of our project and how ‘good design took big data to IKEA’ (see

Symphonic social science
The recent push for large-scale confirmatory research, to ensure replicability and best practice in traditional psychological research, is great to witness. However, it feels to me that the future of psychology is mixed and multi-methodology, triangulating findings from as many disciplines as possible, often by accessing more naturalistic real-time data. Our work with the Digital Preservation Coalition illustrates that there is clear resistance, particularly by some funding bodies, to considering diverse strands of data – particularly alongside each other, and when the research is conducted by early-career researchers. I was recently introduced to the concept of Symphonic Social Science (Halford & Savage, 2017) – a call for re-examining social science approaches to big data, moving beyond inductive and deductive approaches. We should go beyond the quantitative–qualitative divide, to the notion of using theory to guide our exploration of big data, looking for meaningful shapes and patterns (and patterns within patterns) using ‘abductive’ reasoning and iterative approaches. Halford and Savage conclude: ‘Overall, the mobilisation of big data with a symphonic approach calls for the self-conscious and iterative assemblage of data, method and theory addressing major social questions and informed by sociological [and psychological?] theory’ (p.1143).  

My UBDC colleague Jon Minton refers to demographic and population data as ‘old big data’. More modern developments, or using novel visualisations, are in line with the aims of the Farr Institute in advancing health through big/novel data methods ( We are presently working on an interdisciplinary collaboration on ‘Data Cakes’, led by Jon, whereby we use 3-D printed visualisations to produce moulds, baking ‘data so good you can eat it’, using archival data from humanities, epidemiology, demography and education, to go from graphs and stats into edible treats (e.g. a lexis cube of fertility rates in Scotland 1945–2011: Pattaro et al., 2018).

It’s out there
I hope to have illustrated some applied and impactful big data research. It doesn’t need to be ‘faceless’ and scary… big data should be inclusive, engaging academics and the public alike in dialogue. There are interdisciplinary networks out there, data holders, and sources of secondary data looking to work with psychologists just like you and me. In times of dwindling funding and increasing demands for data that is large and valid, why not look outside our discipline to engage in social research? If the data is there, why not use it?

- Catherine Lido is Senior Lecturer in Psychology and Adult Learning, School of Education, University of Glasgow
[email protected]

Image: Derived from the work of Lido et al. (2016), on older adults’ mobility in Glasgow in iMCD sub-set. The map illustrates six older adult travel patterns around Glasgow over one week, via different modes of transport. The three learning engaged older adults indicated in green. 

Key sources
This article is based on work soon to be published in a special issue of the Oxford Review of Education on Learning Cities:
Lido, C., Reid, K. & Osborne, M. (2019). Lifewide Learning in the City: Novel big data approaches to exploring learning with large-scale surveys, GPS, Lifelogging images & social media.

Demchenko, Y., De Laat, C., & Membrey, P. (2014, May). Defining architecture components of the Big Data Ecosystem. Paper presented at the International Conference on Collaboration Technologies and Systems (CTS).
Halford, S., & Savage, M. (2017). Speaking sociologically with big data: Symphonic Social Science and the future for big data research. Sociology, 51(6), 1132–1148.
Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12.
Li, S., Dragicevic, S., Castro, F. A. et al. (2016). Geospatial big data handling theory and methods. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 119–133.
Lido, C., Osborne, M., Livingston, M. et al. (2016). Older learning engagement in the modern city. International Journal of Lifelong Education, 35(5), 490-508.
Longworth, N. & Osborne, M. (2010). Six ages towards a learning region: a retrospective. European Journal of Education, 45(3), 368–401.
Osborne, M., Houston, M., & Lido, C. (2018). The role of big data in elucidating learning cities ancient, present and future. In J.R. Stenger (Ed.) Learning cities in late antiquity (pp. 24-46). Abingdon: Routledge.
Osborne, M. & Lido, C. (2015) Lifelong learning and big data. In U. Gartenschlaeger & E. Hirsch (Eds.) Adult education in an interconnected world (pp.116–125).Bonn: DVV International.
Osborne, M. (2013) Access and retention. In T.G.K. Bryce, W.M. Humes, D. Gillies & A. Kennedy (Eds.) Scottish education (pp.316–326). Edinburgh: Edinburgh University Press.
Pattaro, S., Vanderbloemen, L. & Minton, J. (2018, 29 September). Exploring age-specific and cumulative cohort rates using composite fertility lattice plots. doi:10.31219/
UN (2015). Transforming our world: The 2030 agenda for sustainable development. New York: Author.
UNESCO (2013) Key features of Learning Cities: Introductory note. Hamburg: UIL.

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