Inference, COmputation and Numerics for Insights into Cities (ICONIC)

Lead Research Organisation: University of Cambridge
Department Name: Engineering

Abstract

There are many interesting open questions at the interface between applied mathematics, scientific computing and applied statistics.
Mathematics is the language of science, we use it to describe the laws of motion that govern natural and technological
systems. We use statistics to make sense of data. We develop and test computer algorithms that make these ideas concrete. By bringing these concepts together in a systematic way we can validate and sharpen our hypothesis about the underlying science, and make predictions about future behaviour. This general field of Uncertainty Quantification is a very active area of research, with many challenges; from intellectual questions about how to define and measure uncertainty to very practical issues concerning the need to perform intensive computational experiments as efficiently as possible.

ICONIC brings together a team of high profile researchers with the appropriate combination of skills in modeling, numerical analysis, statistics and high performance computing. To give a concrete target for impact, the ICONIC project will focus initially on Uncertainty Quantification for mathematical models relating to crime, security and resilience in urban environments. Then, acknowledging that urban analytics is a very fast-moving field where new technologies and data sources emerge rapidly, and exploiting the flexibility built into an EPSRC programme grant, we will apply the new tools to related city topics concerning human mobility, transport and infrastructure. In this way, the project will enhance the UK's research capabilities in the fast-moving and globally significant Future Cities field.

The project will exploit the team's strong existing contacts with Future Cities laboratories around the world, and with nonacademic stakeholders who are keen to exploit the outcomes of the research. As new technologies emerge, and as more people around the world choose to live and work in urban environments, the Future Cities field is generating vast quantities of potentially valuable data. ICONIC will build on the UK's strength in basic mathematical sciences--the cleverness needed to add value to these data sources--in order to produce new algorithms and computational tools. The research will be conducted alongside stakeholders--including law enforcement agencies, technical IT and infrastructure providers, utility companies and policy-makers. These external partners will provide feedback and challenges, and will be ready to extract value from the tools that we develop. We also have an international Advisory Board of committed partners with relevant expertise in academic research, policymaking, law enforcement, business engagement and public outreach. With these structures in place, the research will have a direct impact on the UK economy, as the nation competes for business in the global Future Cities marketplace. Further, by focusing on crime, security and resilience we will directly improve the lives of individual citizens.

Planned Impact

The advances in fundamental research arising from the programme will impact academic researchers in applied/computational
mathematics, statistics and computer science; in particular those working in numerical analysis, statistical inference,
uncertainty quantification, machine learning, data science, network science, stochastic simulation, mathematical modelling, data assimilation and high performance computing. Moreover, the application-oriented results will impact colleagues in geography, urban design, architecture, transport, crime studies, politics and policy making.
Outside academia, because the project is embedded within a Future Cities framework, with emphasis on security, crime and resilience, immediate beneficiaries will include

- SMEs who develop bespoke data analytics applications for clients in local and national government, who will gain access to more insightful data analytics tools that can be customized to their needs,

- larger hi-tech companies developing complex IT solutions across multiple platforms,

- clients of these companies, including those in transport, sensors, satellite imaging, construction, energy and other
utilities, charities and governments, who will benefit from the extra efficincies and insights through improved products and services,

- companies working in tourism, event planning, sports and retail who will benefit from quantified city-level information,

- policy makers and law enforcement officers, who will be better placed to make informed decisions,

- user-communities living, working or spending leisure time in a city environment, who will experience better and safer
services.

A key aspect of the impact delivery plan is to exploit our existing, strong links with important players at the
academic/external interface. In particular, we have in-house connections with Imperial's Digital City Exchange, the Oxford Martin School's programme in Cities, in Future Technology, and in Science and Society, and Strathclyde's Institute for Future Cities. We also have very close working relationships with the UK's Future Cities Catapult, Dublin's Trinity Centre for Smart and Sustainable Cities, New York's Center for Urban Science and Progress (CUSP) and Chicago's Urban Centre for Computation and Data. These partners give an excellent mechanism for outreach to relevant stakeholders and for public engagement, with a level and scope that would not be possible from a single, isolated programme.

Girolami and D. Higham have been drivers in the ATI continuing development of an Urban Analytics research theme. ICONIC will be completely distinct from this and fully complementary, it is driven by the urgent need for fundamental research in mathematical sciences, driven by mathematical and statistical modelling challenges that form an holistic UQ pipeline. ATI is viewed as a UK organisation that can exploit and translate the novel tools emerging from ICONIC and co-engage in additional outreach and impact generation. Similarly, D. Higham's Digital Economy Fellowship, which employs the PDRA Dr Francesca Arrigo as a Data Scientist and has a focus on dynamic networks of digital interactions, has external partners such as Bloom Agency, Capita, CountingLab & Siemens, who can make rapid use of ICONIC's research outputs.

Relevant new links to external partners have also been established: see letters of support from representatives of UK Government Home Office, Metropolitan Police Service, New York Police Department, Police Scotland & West Midlands Police. In addition to shaping and stress-testing the research, these partners will also provide routes to rapid and high-value deployment.

Further, by training PDRAs, the project will deliver skilled, outward facing, future-leaders with experience of interacting with external partners. These individuals will be well placed to act as agents-for-change in shaping the UK demand for Future City Analytics in way that strengthens UK competitiveness.

Publications

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Cockayne J (2019) Bayesian Probabilistic Numerical Methods in SIAM Review

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Povala J (2020) Burglary in London: Insights from Statistical Heterogeneous Spatial Point Processes in Journal of the Royal Statistical Society Series C: Applied Statistics

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Duffin C (2021) Statistical finite elements for misspecified models. in Proceedings of the National Academy of Sciences of the United States of America

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Boys B (2021) PeriPy - A high performance OpenCL peridynamics package in Computer Methods in Applied Mechanics and Engineering

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Girolami M (2021) The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions in Computer Methods in Applied Mechanics and Engineering

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Virtanen S (2021) Spatio-Temporal Mixed Membership Models for Criminal Activity in Journal of the Royal Statistical Society Series A: Statistics in Society

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Niederer S (2021) Scaling digital twins from the artisanal to the industrial in Nature Computational Science

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Duffin C (2022) Low-rank statistical finite elements for scalable model-data synthesis in Journal of Computational Physics

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Fang W (2022) Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information. in Medical decision making : an international journal of the Society for Medical Decision Making

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Povala J (2022) Variational Bayesian approximation of inverse problems using sparse precision matrices in Computer Methods in Applied Mechanics and Engineering

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Vargas F (2022) Bayesian learning via neural Schrödinger-Föllmer flows in Statistics and Computing

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Kuok S (2022) Broad learning robust semi-active structural control: A nonparametric approach in Mechanical Systems and Signal Processing

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Akyildiz Ö (2022) Statistical Finite Elements via Langevin Dynamics in SIAM/ASA Journal on Uncertainty Quantification

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Povala J (2022) Variational Bayesian approximation of inverse problems using sparse precision matrices in Computer Methods in Applied Mechanics and Engineering

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Gaskin T (2023) Neural parameter calibration for large-scale multiagent models. in Proceedings of the National Academy of Sciences of the United States of America

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Vadeboncoeur A (2023) Fully probabilistic deep models for forward and inverse problems in parametric PDEs in Journal of Computational Physics