Date post: | 21-Mar-2017 |
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Dr Weisi Guo
Assistant Professor School of Engineering
Warwick Institute for the Science of Cities (WISC)
University of Warwick, UK
Social Media Data for Planning and
Monitoring Services
Exchange Assistant Professor Centre for Urban Science and Progress
New York University, USA
Visiting Professor SCIE
Shanghai University, China
School of Engineering | Warwick Institute for the Science of Cities
A bit about me
Brief Bio: I graduated with MEng, MA, and PhD degrees in
information engineering and computer science from the
University of Cambridge.
I am currently the joint coordinator in Smart City research theme
at the School of Engineering. I have worked in academia and
industry for over 7 years.
I currently run a research team (3 doctoral and 4 graduate
researchers) working at the inter-section of big data, wireless
networks and smart cities. I want to design solutions that can
integrate big data analytics into traditional ICT systems.
Awards in 2014/15:
IET Innovation Award 2015: Communications Category
Bell Labs Prize Finalist 2014 (only UK recipient)
IEEE Best Paper Award 2014
IEEE Communication Society 2014 Best Project 2nd Prize
Activities at the University of Warwick: WISC &
• Warwick is home to the only UK government funded Doctoral
Training Centre in Smart Cities (training 50-75 PhD students
2014-2023). The students combine research skills in big data,
urban planning, engineering, and social sciences. The centre
is called Warwick Institute for Science of Cities (WISC).
• Warwick is also part of a global 5 university alliance on smart
city research: New York University, Carnegie Mellon, Toronto
University, and IIT-Mumbai. The headquarters is called CUSP
(Centre for Urban Science & Progress), funded by ex-NYC
mayor: Michael Bloomberg.
• CUSP is opening its 1st overseas expansion campus in
London which sees Warwick and KCL join forces to examine
the challenges related to health and big data in cities.
• Warwick is also a core partner in the new big data Alan
Turing Institute.
Why Cities
• Cities are permanent human settlements with a history of almost 10,000 years.
Typical attributes: high population density, specialist economy, public
infrastructure, strong local governance, high import & export volumes.
[Ur City (modern Basra) – 3800 BC]
• Cities occupy 2% of land surface, but account for up to 60-80% of the global
energy consumption.
• In the past decade, first time in history that more than 50% of the world’s
population live in cities. In developed nations, this value is between 70 to 95%.
A third of the most densely populated cities are in the developed world.
[Population density in Paris is comparable to density in Delhi]
• According to the United Nations (2012 Habitat Report), more than 70% of the
world will live in a city by 2050.
• What are the metrics that gauge a city’s performance?
Activities at the University of Warwick: HAT
• The United Nations has published a set of 5
metrics to gauge the performance of cities:
Productivity, Infrastructure, Quality of Life,
Equity (Equality), the Environment.
• Global rankings of cities use metrics such as:
Connectivity, Competitiveness, Power, and
Influence.
• Quality of Living rankings of cities use metrics
such as: Environment, Safety, Public Services
and Stability.
• Such metrics are seen as complex indicators to the
performance of cities in competing for human and
material resources.
Top Global Cities: New York and London
Top Quality of Life Cities: Vienna and Zurich
Scaling Law of Cities
• Cities grow like organisms, and as they grow in
size, they also experience more problems and
convey more benefits.
• The scaling law of problems and benefits is of
interest to us, as many of our cities are growing
in size, whilst some (i.e., Rust Belt of USA) are
shrinking rapidly (20% loss in population in
recent years).
• Research has shown that whilst mammals
experience a sub-linear growth (everything
gets less efficient per kg of weight), cities
experience super-linear growth (everything
gets more per capita).
Challenges Faced by Cities
• Cities face ancient and new challenges, but never
has the scale of the problem been so big, and never
have we been in a better position to use technology
to solve them.
• Examples of universal challenges include: pollution
(air and water), traffic congestion (inter- and intra-
city), crime, energy efficiency, public order, balance
between green space and commerce, acoustic
noise (highest complaint in NYC), and shocks in
temperature (heat is the highest killer in NYC).
• What we have to foster is to allow cities to grow in a
sustainable and prosperous way (i.e., growth of
benefits outweigh problems), otherwise some of our
cities may one day be a historical landmark.
School of Engineering | Warwick Institute for the Science of Cities
How can social media data act as a senor and help us understand cities and services?
• High Resolution: in the past 5 years, the growing penetration of
smartphone usage and social media usage has led to a wealth of
data across a wide range of hardware and application orientated
research. In particular, social media offers high resolution
compared to survey/census approaches:
- Spatial Resolution: wireless assisted GPS (~< 10 metres)
- Time Resolution: seconds
- Scale: Twitter has 316 million users with 500 million
messages/day
• Detailed Context: Not only is the quantifiable data of interest, but
the unstructured text and multimedia data is also of interest.
- Text: what are people saying / feeling and how does
information spread
- Community: how do people connect and follow each other
- Habits: what do people do and what behavioural patterns
emerge
School of Engineering | Warwick Institute for the Science of Cities
Sentiment Mapping of Services
• Sentiment: natural language processing words and
phrases into sentiments
- Real time mapping of emotions on individual and
regional level
- Identify areas of sadness and correlate it to real
challenges in business and services for targeted
prioritised intervention
• Case Study of London: converted 600,000 tweets into
geo-tagged sentiments
- Blue = Sadness
- Red = Happiness
Unhappiest Wards: Barking, Newham
Happiest Wards: Westminster, Hillingdon, Camden
School of Engineering | Warwick Institute for the Science of Cities
Creating Networks from Data
• Relationship between Stakeholders: it is important to
analyse the relationship between stakeholders, rather than
treat them in isolation.
• Complex Network: As an example, we model short-ranged
trade network across Europe to reveal the following attributes:
- Areas of redundancy (benefits: robustness against
failure, cons: inefficiency)
- Areas of strategic importance / influence or areas of
vulnerability
- How the network can improve or adapt subject to a
constraint or a perceived threat
This analysis can be adapted to small-scale networks (within a
company) or large-scale multi-level systems (i.e., transport
network within a city or country).
(b) No. of Connecting Links
A
C
B
(g) Modularity Class
1
2
3
(c) Average Link Distance
D E
F
(d) Cluster Coefficient
F
G
H
I
J
(a) No. of Critical Links
A B
School of Engineering | Warwick Institute for the Science of Cities
Analysing a Real Trade Network
Critical
Nodes
Importance
A Critical & Important Links
Connections
Influential
B Critical & Important Paths
Connections
C Important Paths
Connections
Influential
F Central
Cluster
(e) Influence
A
C
(f) Page Rank
A
C
B
Dr Weisi Guo
[email protected] School of Engineering
Warwick Institute for the Science of Cities (WISC)
University of Warwick, UK
Thank you for Listening