Bringing the Social City to the Smart City

Technological developments, such as the rise in GPS enabled devices and Web 2.0 technologies have created social transformations in how we connect and share information through the mass uptake of smart phones and social media platforms (Croitoru et al, 2014). This new generation of mobile technologies work as individual sensors capturing data on a wide range of human behaviours that have been previously hidden. These include data on individual movement, preferences and opinions. Understanding these behaviours is crucial if we are to create a joined up approach to simulating how cities breath and grow. However, considerable work is required in adapting and developing new technologies from machine learning to extract behaviours which can be embedded into cutting-edge modelling techniques. Creating this bridge between 'big' data representing the 'real' world, and simulations producing alternative versions of reality is of value to both academics and policymakers looking to develop new solutions to many of the challenges that today's cities face. To do this we need to understand how factors within the "Social City" (the impact of individual movements and decisions) play out every day in the "Smart City" (data collected from fixed sensors on for example, traffic counts, air pollution or movements of populations).

However, standard "Smart" City understanding assumes previous flows (e.g. traffic at a specific time of the week, energy requirements or pollution levels over a 24-hour period) will be replicated into the future, lacking both adaptability (how does this alter if a major event in the city is happening?) and predictive power (what is the impact on health if all petrol and diesel vehicles are banned?). This disconnection between the Smart and Social city means policymakers are unable to obtain answers to complex interrelated questions such as: what is the optimal transport infrastructure to promote healthy behaviours and reduce the City's carbon footprint?

Being able to answer these questions is increasingly important as cities are facing significant challenges associated with the pressures from rapidly increasing urban populations. These include improving water and transportation infrastructure, air pollution and waste management as well as provision of adequate housing, energy, health care, education and employment. These pressures on future cities has brought the Smart City agenda to dominate many government initiatives with governments and policymakers looking to new forms of (big) micro data to provide innovative solutions to these challenges. While many models have been developed to forecast future transport, housing or healthcare initiatives, most uses are purely empirical: they lack any consideration of the social processes behind the individual generating the data or the impact of their actions and decisions. This Fellowship will explore how machine learning inspired tools can be used to recognise such emergent patterns and processes within micro-level data sources such as data on how individuals move and use city spaces. Along with the additional methodology of Agent Based Models, which allows smart and social city data to be readily combined, this suite of methods will be explored through looking at the case-studies of (i) the impact of air pollution on individuals and (ii) urban mobility. This work will fundamentally transform our ability to show how the social elements of the Smart City can be recognised and understood, and how to bring 'lived experience' to the analysis of Smart City data.

One of the key elements of the Fellowship is to develop economic and societal impact through engagement with a wide range of stakeholders. The Fellow will be based within the Consumer Data Research Centre (CDRC). The CDRC has established a network of partnerships that the Fellow will thread together over a number of activities. There are a wide range of active partnerships within CDRC including Callcredit, Axciom, Sainsbury's, Zoopla, Whenfresh, Heart Research UK, M&S, Virgin Media, YouGov and Link. Access to data and stakeholders from these and other CDRC partners will be available to the Fellow. Engagement with external partners is facilitated through events including an annual User Forum, which brings together more than 60 commercial partners of CDRC.

How will they benefit?
Research project (RP) 1 and PhD 1: Creating tools to uncover hidden emergent trends and processes in spatial data. Can new tools give greater insight to explain why a phenomenon is prevalent in one area, but not another? Can new data tools reveal new insights into current data, and uncover gaps? Beneficiaries from this work include organisations that need to probe deeper into spatial data to understand the interplay of emergent patterns over time. Examples: Local government planning and transportation departments, public health organisations.
RP 2 and PhD 2: Testing methodologies through ABM: ABM gives the opportunity to bring together Smart and Social city data through simulating individuals. This aspect of the work will create new understanding about the utility of new social media data sets and how they can be readily combined with Smart data. This will create impact for those beneficiaries who are interested in both simulating and understanding individual behaviour and the consequences of individual decisions. Examples: Retail (commercial) and health organisations, local and national government.
RP3: Application to case-studies. This part of the Fellowship has the greatest potential impact as the work can be readily applied to specific case-studies to answer questions about the consequences of new policies or interventions. Examples: Can the transportation infrastructure be optimised to encourage active travel and city spaces to be more readily used? Whilst methodological innovations will have the greatest short-term impact on researchers in academic institutions, the medium to long-term impact of this work will feed through into research orientated public sector organisations, such as those interested in health behaviours, mobility patterns, impact of new infrastructure and new economic initiatives. Examples: Town and Country Planning Association; West Yorkshire Combined Authority.

How will I ensure stakeholders will benefit from the research?
Stakeholder advisory meetings: Creation of a stakeholder group, the purpose of which is to promote interactions and exchanges. Meetings will be scheduled for the beginning of the Fellowship and each subsequent year to allow time to shape research questions and identify additional case-studies.
Creation of new resources including training courses: Open access code, online training materials will be made available to professionals and academics alike. Workshops will be held to train stakeholders, academics and CDT PhD students in new methods and data, thus impacting on research capacity and skills.
Seminars: Turing and CDRC both offer seminar programmes at which research findings from this Fellowship will be disseminated. Webinars will also be used to increase the impact and range of engagement with different stakeholders.
Further funding applications: Engaging with stakeholders and building bridges is of prime importance for future funding opportunities. At month 18, a research incubator event will be held at CDRC. This will involve Turing Fellows as well as invited stakeholders and researchers