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Details of Grant 

EPSRC Reference: EP/M00158X/1
Title: Data Analytics for Future Cities
Principal Investigator: Higham, Professor D
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Bloom Agency Capita CountingLab Ltd
ScotRail Railways Limited Siemens
Department: Mathematics and Statistics
Organisation: University of Strathclyde
Scheme: EPSRC Fellowship
Starts: 01 January 2015 Ends: 31 March 2019 Value (£): 643,307
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt Mathematical Aspects of OR
EPSRC Industrial Sector Classifications:
Retail Information Technologies
Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
17 Sep 2014 EPSRC Digital Economy Fellowship Interviews Announced
17 Jul 2014 EPSRC ICT Prioritisation Panel - July 2014 Announced
Summary on Grant Application Form
The "Internet of Things" is a phrase describing the set of technologies, systems and methodologies that underpin the spread of internet-enabled applications. Ultimately, the Internet of Things will involve physical objects seamlessly integrating into the information network for social and economic benefit.

At the heart of the Internet of Things is data---digital records of human, technological and natural interactions. The data streams are large-scale, varied and rapidly changing. In addition to the important, but conceptually simple, tasks of gathering, storing and sharing this data, there is a pressing need to develop powerful and efficient computational algorithms that can extract insights and make useful predictions.

This proposal will add to the "cleverness" that is needed to exploit fully the data deluge. Technology evolves rapidly. The look, feel and functionality of smartphones, tablets, notebooks and desktop PCs have changed dramatically in recent years, and there is a range of new technologies such as wearable devices, smart glasses and implantable sensors. Many of these will add to the technological revolution, before themselves being superseded. However, the challenge of analysing and exploiting the vast realms of data produced by the Internet of Things is universal, and the mathematical concepts and resulting algorithms that underpin these new technologies are fundamental and have very long-term value.

My proposal will develop new analytical concepts that lead to computational algorithms. The results will be aimed directly at the application of Future Cities research---improving the social, environmental and economic aspects of city living. The University of Strathclyde is home to a City Observatory that collects a huge amount of data from the city of Glasgow, including air quality sensors, traffic information, energy usage and measurements of many aspects of human behaviour, such as on-line activity, social media interactions, CCTV data and retail footfall counts. Indeed, Glasgow received £24M of government funding to become the UK's pilot city for this type of digitally-driven enhancement.

The research project therefore focusses on new concepts and algorithms that can help us to understand the very large, fast-moving streams of data describing the interactions between the components that make up the Internet of Things: people, devices and sensors. Just as Google began with a clever mathematical algorithm, PageRank, that was able to bring order to the world wide web, we aim to develop new algorithms that can summarize these vast quantities of data. The work will take place alongside stakeholders in the Future Cities arena: fellow-researchers in social science; SMEs who deliver data analytics solutions to clients in advertising, finance, entertainment, publishing; external partners in hi-tech industry who deliver larger-scale IT solutions; local and national government employees who serve the community. Through a range of knowledge exchange and outreach events, these stakeholders will have the opportunity to critically evaluate and feedback on the results and rapidly deploy the new ideas.

Some illustrative applications that the research will address are:

characterising the social media traits of different user bases, such as drivers/cyclists/pedestrians, to predict the best way to target messages at each group,

stratifying the population of city users according to their portfolio of work/leisure/shopping community memberships in order to maximise the usage of energy/space resources in the city,

predicting crowd levels and crowd behaviour at forthcoming public events,

monitoring the public perception of an ongoing campaign, such as a cycle-to-work-scheme,

monitoring and reacting in real time to the public response in relation to a planned disruption, such as a political march, or an unpredictable event, such as a traffic incident.

Key Findings
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Organisation Website: http://www.strath.ac.uk