Data Analytics for Smart Cities: Looking Back, Looking Forward

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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 andCommunication

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CityPulse: Large-scale data analytics for smart cities

What type of problems we expect to solve in

“smart” cities

5Image courtesy: LA Times, http://documents.latimes.com/la-2013/

Future cities: A view from 1998

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Image courtesy: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/

Source: wikipedia

Back to the Future: 2013

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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…

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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…

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Smart Data Collection

− Smart Data Collection

− Intelligent Data Processing (selective attention and information-extraction)

− Region Beta Paradox

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image source: KRISTEN NICOLE, siliconangle.com

Designing for City Problems

101 Smart City Use-case Scenarios

12http://www.ict-citypulse.eu/scenarios/

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Use-case Scenarios

http://www.ict-citypulse.eu/scenarios/

Data Lifecycle

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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

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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)

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Data/Event Visualisation

Reference Datasets

18http://iot.ee.surrey.ac.uk:8080/datasets.html

Importance of Complementary Data

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Users in control or losing control?

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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;

− …

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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

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The IET Sector Briefing

23Available 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