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Data Analytics for Smart Cities: Looking Back, Looking Forward


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Cities Convention, 22 April, 2015, Altitude London, Millbank Tower, London

Published in: Education

Data Analytics for Smart Cities: Looking Back, Looking Forward

  1. 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. 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. 3 CityPulse: Large-scale data analytics for smart cities
  4. 4. What type of problems we expect to solve in “smart” cities
  5. 5. 5 Image courtesy: LA Times, Future cities: A view from 1998
  6. 6. 6 Image courtesy:[default]/0/ Source: wikipedia Back to the Future: 2013
  7. 7. 7
  8. 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. 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. 10. Smart Data Collection − Smart Data Collection − Intelligent Data Processing (selective attention and information- extraction) − Region Beta Paradox 10 image source: KRISTEN NICOLE,
  11. 11. Designing for City Problems
  12. 12. 101 Smart City Use-case Scenarios 12
  13. 13. 13 Use-case Scenarios
  14. 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,
  15. 15. Big (IoT) Data Analytics . . . Real World (Live) Data Smart City Framework Smart City Scenarios
  16. 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)
  17. 17. 17 Data/Event Visualisation
  18. 18. Reference Datasets 18
  19. 19. Importance of Complementary Data 19
  20. 20. Users in control or losing control? 20 Image source: Julian Walker, Flicker
  21. 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. 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. 23. The IET Sector Briefing 23 Available at:
  24. 24. Thank you. @pbarnaghi Acknowledgement: CityPulse Consortium