- Start date: 1 July 2018
- End date: 30 June 2021
- Funder: ESRC
- Value: £232,908
- Primary investigator: Professor Alison Heppenstall
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.