Cities are now home to the majority of the world’s population, and are drivers of economic growth, wealth creation, social interaction and well-being. They also present huge inequalities in health, affluence, education and lifestyle with persistent challenges for management, administration and policy. For example, alongside an existing global market of £400 billion for smart city technology, the UN estimates that $350 trillion or five times global GDP needs to be spent on urban infrastructure to address urgent needs.
The urban analytics programme at the Turing is focused on the process, structure, interactions and evolution of agents, technology and infrastructure within and between cities across spatial and temporal scales. Data science and AI will be developed and exploited alongside spatial analysis, geostatistics and a wide variety of disciplinary perspectives.
Urban analytics draws from data which are captured by governments, businesses and other intermediaries. Conventional mechanisms such as censuses and surveys are complemented by devices which are increasingly ubiquitous. The programme supports assessments, projections and interventions which determine the economic and social welfare of people, businesses, governments and third sector agencies.
In addition to government and public planning, the programme is relevant to organisations across sectors including retail, financial services, mobility, health, policing, and utilities. The diversity in content and approaches gives rise to productive overlaps with many other Turing programmes and challenges.
Programme challenges
Urban populations
Data science is supporting advances in the personalisation of individual circumstances, with crucial impacts for medical diagnosis, business and service delivery, traffic management and energy consumption. Microsimulation and mathematical models of citizen behaviour, mobilities, decision-making and choices will help to support healthier lifestyles, active travel, safety and cleaner cities.
The Turing is fostering ground-breaking innovations, for example in agent-based modelling, probabilistic programming, reinforcement learning and data assimilation to create more powerful representations, projections and scenario forecasts for city futures. Advanced statistical approaches are complemented by rigorous theory, for example using methods such as causal inference to create robust and scalable insights into population dynamics and behaviour.
Urban infrastructure
Urban infrastructure is becoming transformed by advances in AI and robotics. Current research will lead to roads, pipes and mains which can repair themselves, vehicles which can talk to one another, and self-aware bridges which know when to rest or regenerate. Learning organisations of the future will accelerate business performance through enhanced understanding of their customers and employees.
Cities are often the geographic nexus of interdependent critical infrastructures. Nevertheless, simulations are advancing to become ‘digital twins’ of the real world in which the impact of both natural events and human interventions can be anticipated with increasing confidence.
We advocate the recognition of data itself as infrastructure, seeking new ways to encourage the sharing of data between organisations for the public good.
Urban environments
The programme richly informs the study of future urban environments, underpinned by ubiquitous networks which can sense the status and rhythms of the city, from air quality to housing, urban design and business performance. The urban analytics programme will exploit enhanced monitoring of these environments – and the development of new methods for the integration of data into consistent models – to gauge the equity, sustainability and liveability of cities. Long-term projections of urban dynamics will be complemented by nowcasting the condition of the city at or close to real-time.
The programme aims to take a leading role in education and promotion of the benefits of responsible data research, but also supports critiques which challenge the boundaries of ethical practice, and controls on digital surveillance which balance the public good with individual privacy, consent and personal preference.
Urban policy
The deployment of data science technologies in the urban context allows businesses to become more efficient and productive, for example through better understanding of their customers, or more efficient distribution networks.
The urban analytics programme will embed models and simulations within scenario designs to enable more effective appraisal of interventions through high level government policy (e.g. sugar tax), local planning contexts (e.g. land-use zoning), specific investment decisions (e.g. construction of a shopping centre or suburban railway station) and allocation of resources (to hospitals, schools, libraries or emergency services).
Data science and AI can support living laboratories in which intelligence can be extracted through the natural variation between places, and by longitudinal assessment of incremental changes e.g. dynamic pricing of congestion zones. We seek to provide planners of the future with the skills and the systems to support effective interventions to the benefit of economy and society.
