A presentation on Introduction to LIDA by Professor Mark Birkin at HPC and Big Data 2016, Central London

1. Introduction to LIDA
Mark Birkin, Professor of Spatial Analysis
and Policy & Director of LIDA

2. The Value of Big Data

3. The range and
qualitative content of
data is changing quickly
so new research is
needed.
The Data
Landscape

4. Providing civic engagement by providing solutions to real
problems in the city, economy and society.
Delivering world class research in relation to data science, its
applications and form the synergies between them.
Co-producing research and innovation between the academic,
commercial, government and third sector communities.
Acting as a foundation for economic growth and regional
prosperity.
Engaging with a lay audience regarding questions of public
participation, value, ethics and protection.
1.0
2.0
3.0
4.0
5.0
LIDA Vision

5. LIDA Facilities
LIDA has state of the art physical and IT
infrastructures providing the academic
community and our partners in business,
government and the third sector with the
tools to harness the potential of Big Data.

7. Leeds Integrated Research
Campus

8. £7m Medical Research
Council Investment
Our challenge is to link molecular,
phenotypic and health record
datasets, sourced locally and
nationally, for research to deliver
patient benefit.
£11m ESRC Investment
Our aim is to work with
organisations to open up their data
to our trusted researchers so we can
provide solutions that drive
economic growth and improve our
society.
£11m ESRC Big Data Investment which is
led by the University of Leeds and
University College London.
Research Centres

9. Population movements &
behaviours to inform policy and
improve services
Understanding and improving
ethical & sustainable
consumption systems
Providing secure platforms for
data sharing
Training & Capacity Building
activities to address the UK data
analytics skills gap
Facilitating collaborations
between private & public sector
organisations
Aims to open up
consumer data
resources to benefit
researchers in
business,
government and
society at large.
Consumer Data
Research Centre
www.cdrc.ac.uk

10. Big Data and Health
Social Geography of
Consumption
Using spatial analytic data models
to assess food risks.

11. Big Data and Crime Analytics
• Utilising social media
• Better understanding of footfall volumes
in city
• Quantify number of potential crime
victims in area
• Support local authorities in targeting
resource
• Clearer focus for crime reduction
• More strategic incidence response
30
Contains National Statistics data © Crown copyright and database right 2015
Contains Ordnance Survey data © Crown copyright and database right 2015
© OpenStreetMap contributors, CC-BY-SA
Figure 16: GAM Output – Workday Population (2011)
Contains National Statistics data © Crown copyright and database right 2015
Contains Ordnance Survey data © Crown copyright and database right 2015
© OpenStreetMap contributors, CC-BY-SA
Figure 17: GAM Output – Average Hourly Mobile Population, 16:00-19:59 (w/c 13/05/13)
Figure 1. Significant clusters of crime taking
account of the average hourly mobile
population between 16:00-19:59

12. A ‘living’ journey planner
that crowdsources how
the transport system is
operating in real-time to
provide citizens with
frustration-free routing
and cities with granular
data on how people
travel.
Big Data and Transport
Utilising big data from the railways
to understand, simulate and predict
individual movement patterns of
railway passengers.

13. Product assortment planning
(management of waste)
Big Data and Sustainable
Consumption
Enhanced understanding of
customer behaviour
(click and collect)
Consumer attitudes and lifestyles
(format development, pricing and
promotion)

14. Aims to create
research capacity
and infrastructure
to maximise patient
benefits from data
analytics.
Medical Bioinformatics
Centre
Patient outcomes to inform
treatment of the treatment of
future patients
Define more appropriate
smaller populations for
personalised medicine
Linking molecular features,
treatment & outcomes
Introduce a broader range of
information to inform trials and
individual patient meta-analysis
Link medicine, health informatics,
bioinformatics, statistical
epidemiology and computing

15. Hyperpolarised Medical
Resonance Imaging (MRI)
• Promises valuable techniques for early
detection of tumours
• Generates vast swathes of data
• Need for petabyte storage
• Requires computer generated analytics
for filtering & diagnosis
• Reduction in lead time from research to
implementation saves lives