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Data Analytics for Smart Cities:
Looking Back, Looking Forward
1
Payam Barnaghi
Institute for Communication Systems (ICS)
University of Surrey
Guildford, United Kingdom
AnyPlace AnyTime
AnyThing
Data Volume
Security, Reliability,
Trust and Privacy
Societal Impacts, Economic Values
and Viability
Services and Applications
Networking and
Communication
3
CityPulse: Large-scale data analytics
for smart cities
What type of problems we expect to solve
in
“smart” cities
5
Image courtesy: LA Times, http://documents.latimes.com/la-2013/
Future cities: A view from 1998
6
Image courtesy: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/
Source: wikipedia
Back to the Future: 2013
7
Smart City Data Analysis
− Analysis of thousands of traffic, pollution, weather, congestion, public
transport, waste and event sensory data to provide better transport and city
management.
− Converting smart meter readings to information that can help prediction and
balance of power consumption in a city.
− Monitoring elderly homes, personal and public healthcare applications.
− Event and incident analysis and prediction using (near) real-time data
collected by citizen and device sensors.
− Turning social media data (e.g. Tweets) related to city issues into event and
sentiment analysis.
− Any many more…
8
Smart City Data
− Data is multi-modal and heterogeneous
− Noisy and incomplete
− Time and location dependent
− Dynamic and varies in quality
− Crowed sourced data can be unreliable
− Requires (near-) real-time analysis
− Privacy and security are important issues
− Data can be biased- we need to know our data!
− Data alone may not give a clear picture -we need contextual information,
background knowledge, multi-source information and obviously better data
analytics solutions…
9
Smart Data Collection
− Smart Data Collection
− Intelligent Data
Processing (selective
attention and information-
extraction)
− Region Beta Paradox
10
image source: KRISTEN NICOLE, siliconangle.com
Designing for City Problems
101 Smart City Use-case Scenarios
12
http://www.ict-citypulse.eu/scenarios/
13
Use-case Scenarios
http://www.ict-citypulse.eu/scenarios/
Data Lifecycle
14
Source: The IET Technical Report, Digital Technology Adoption in the Smart Built Environment: Challenges and opportunities of data
driven systems for building, community and city-scale applications,
http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
Big (IoT) Data Analytics
.
.
.
Real World (Live) Data
Smart City Framework
Smart City Scenarios
Data Processing and Information Extraction
Analysis of traffic data in City of Aarhus
University of Surrey Smart Campus data analysis
Twitter data analysis for detecting city events (WSU/UniS)
17
Data/Event Visualisation
Reference Datasets
18http://iot.ee.surrey.ac.uk:8080/datasets.html
Importance of Complementary Data
19
Users in control or losing control?
20
Image source: Julian Walker, Flicker
Data Analytics for Smart Cities
− Great opportunities and many applications;
− Enhanced and (near-) real-time insights;
− Supporting more automated decision making and in-
depth analysis of events and occurrences by combining
various sources of data;
− Providing more and better information to citizens;
− …
21
However…
− We need to know our data and its context (density,
quality, reliability, …)
− Open Data (there needs to be more real-time data)
− Complementary data
− Citizens in control
− Transparency and data management issues (privacy,
security, trust, …)
− Reliability and dependability of the systems
22
The IET Sector Briefing
23
Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
Thank you.
http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/
@pbarnaghi
p.barnaghi@surrey.ac.uk
Acknowledgement: CityPulse Consortium
http://www.ict-citypulse.eu

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Data Analytics for Smart Cities: Looking Back, Looking Forward

  • 1. Data Analytics for Smart Cities: Looking Back, Looking Forward 1 Payam Barnaghi Institute for Communication Systems (ICS) University of Surrey Guildford, United Kingdom
  • 2. AnyPlace AnyTime AnyThing Data Volume Security, Reliability, Trust and Privacy Societal Impacts, Economic Values and Viability Services and Applications Networking and Communication
  • 3. 3 CityPulse: Large-scale data analytics for smart cities
  • 4. What type of problems we expect to solve in “smart” cities
  • 5. 5 Image courtesy: LA Times, http://documents.latimes.com/la-2013/ Future cities: A view from 1998
  • 7. 7
  • 8. Smart City Data Analysis − Analysis of thousands of traffic, pollution, weather, congestion, public transport, waste and event sensory data to provide better transport and city management. − Converting smart meter readings to information that can help prediction and balance of power consumption in a city. − Monitoring elderly homes, personal and public healthcare applications. − Event and incident analysis and prediction using (near) real-time data collected by citizen and device sensors. − Turning social media data (e.g. Tweets) related to city issues into event and sentiment analysis. − Any many more… 8
  • 9. Smart City Data − Data is multi-modal and heterogeneous − Noisy and incomplete − Time and location dependent − Dynamic and varies in quality − Crowed sourced data can be unreliable − Requires (near-) real-time analysis − Privacy and security are important issues − Data can be biased- we need to know our data! − Data alone may not give a clear picture -we need contextual information, background knowledge, multi-source information and obviously better data analytics solutions… 9
  • 10. Smart Data Collection − Smart Data Collection − Intelligent Data Processing (selective attention and information- extraction) − Region Beta Paradox 10 image source: KRISTEN NICOLE, siliconangle.com
  • 11. Designing for City Problems
  • 12. 101 Smart City Use-case Scenarios 12 http://www.ict-citypulse.eu/scenarios/
  • 14. Data Lifecycle 14 Source: The IET Technical Report, Digital Technology Adoption in the Smart Built Environment: Challenges and opportunities of data driven systems for building, community and city-scale applications, http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
  • 15. Big (IoT) Data Analytics . . . Real World (Live) Data Smart City Framework Smart City Scenarios
  • 16. Data Processing and Information Extraction Analysis of traffic data in City of Aarhus University of Surrey Smart Campus data analysis Twitter data analysis for detecting city events (WSU/UniS)
  • 20. Users in control or losing control? 20 Image source: Julian Walker, Flicker
  • 21. Data Analytics for Smart Cities − Great opportunities and many applications; − Enhanced and (near-) real-time insights; − Supporting more automated decision making and in- depth analysis of events and occurrences by combining various sources of data; − Providing more and better information to citizens; − … 21
  • 22. However… − We need to know our data and its context (density, quality, reliability, …) − Open Data (there needs to be more real-time data) − Complementary data − Citizens in control − Transparency and data management issues (privacy, security, trust, …) − Reliability and dependability of the systems 22
  • 23. The IET Sector Briefing 23 Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm