This document discusses big data in the context of smart cities. It defines smart cities and big data, describing big data as huge volumes of structured and unstructured data created in real-time from various sources. These sources include directed surveillance, automated data generation from sensors and devices, and volunteered data from social media and crowdsourcing. The document notes that big data in smart cities is growing exponentially and will be critical for monitoring, evaluating and controlling city operations. However, it also warns of risks around data protection and the politics of data collection. Overall, the document argues that while big data can improve city governance when managed responsibly, its political implications and impacts on citizens must be carefully considered.
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ITAS - Big Data in Smart Cities
1. Big Data in Smart Cities
Mario Lelovsky
1st Vicepresident, IT Association Slovakia
Oslo, 4.6. 2018
2. Contents
1. Smart Cities
1.1 Smart city definition
2. Big Data
2.1 Big Data in Smart Cities
2.2 Big Data sources
2.3 Big Data trends and risks
2.4 Big Data politics
2.5 Big Data – Good for Smart Cities ? ConclusionCo
Bibliography
Str. 2
8. 2.1 Big Data in Smart Cities
A key ingredient in the smart city is the production and analysis of „big data‟
– huge in volume, consisting of terabytes or petabytes of data;
– high in velocity, being created in or near real-time;
– diverse in variety, being structured and unstructured in nature, and often
temporally and spatially referenced;
– exhaustive in scope, striving to capture entire populations or systems (n=all);
– Fine-grained in resolution, aiming to be as detailed as possible, and uniquely
indexical in identification;
– flexible, holding the traits of extensionality (can add new fields easily) and
scaleability (can expand in size rapidly).
9. 2.2 Sources of Big Data in Smart Cities
• Directed surveillance
• Automated data generation
– Capture systems
– Digital devices
– Transactional and interactional data
– Clickstream data
– Sensed data
– IoT (Internet of things) and M2M (machine to machine) data
• Volunteered data generation
– Social media
– Crowdsourcing
– Citizen science
17. 2.4 Big urban Data politics
1. Data within smart city initiatives are portrayed as being natural, essential,
neutral and objective measures.
2. Sensors, cameras, algorithms have no politics or agenda; they reflect and
produce truths about the world
3. Such a framing enables smart city projects to present as being politically
benign and commonsensical
4. However, data do not exist independently of the ideas, techniques,
technologies, people and contexts that conceive, produce, process, manage,
analyze and store them; “raw data is an oxymoron”
5. Big data are representations and samples, inflected by social privilege and
social values
6. They are generated within systems designed to enact a particular political and
policy vision
18. 2.5 Big Data – Good for Smart Cities? Conclusion
1. Smart Cities are built around Big Data.
2. Data will grow “forever”, need to be well managed and evaluated and will
allow to monitor, evaluate and control the city life.
3. Real-time big data analysis can be used to improve understanding,
governance, quality of life, efficiency, effectiveness, competitiveness and
productivity.
4. The critical data and “to be protected” data need to be managed with high
priorities.
5. The questions regarding the politics of big urban data need to be answered
7. Benefits should always be focused to the citizen at the end of the day
There is a massive opportunity for data to affect positive change on all of human
society. Not only is data making business more effective, but it is in the process of
transforming every aspect of the individual's life.
20. Sources
1. Rob Kitchin Rob.Kitchin@nuim.ie, National University of Ireland, Smart Cities
and Big Data and their consequences, 2013
2. Data Age 2025: The Evolution of Data to Life-Critical, David Reinsel, John
Gantz and John Rydning, analysts at IDC Corp. on March 2017, sponsored
by Seagate Technology LLC
3. Dr. Antonio J. Jara, jara@ieee.org. HES-SO/Valais, Switzerland
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