Smart cities, big data & their consequences


Published on

Presentation at the 3rd National Smart Cities Summit, Croke Park, Dublin, Nov 20th 2013

Published in: Technology, Education

Smart cities, big data & their consequences

  1. 1. Smart Cities, Big Data and Their Consequences Rob Kitchin National University of Ireland, Maynooth Dublin, 20 November 2013
  2. 2. Smart urbanism • Two schools of ideas: – Instrumentation and regulation • Cities composed of „everyware‟: ICT, infrastructure, devices, sensors, software, big data • Cities become knowable and controllable in new, dynamic, reactive ways – Policy, development and governance • Cities as competitive, entrepreneurial, knowledge-driven systems • human capital, creativity, innovation, education, sustainability, governance undergirded and driven by advances in ICT • Both underpinned by ICT • Going to focus on first view
  3. 3. Instrumented view • The city is composed of “constellations of instruments across many scales that are connected through multiple networks which provide continuous data regarding the movements of people and materials” (Batty et al., 2012: 482) • “The modern city exists as a haze of software instructions. Nearly every urban practice is becoming mediated by code.” (Thrift and Amin 2002)
  4. 4. Instrumented view + + + + + = Extensive ICT networks everyware: ubiquitous/pervasive/mobile computing; computation available everywhere, built into everything social and locative media distributed and forever storage sophisticated data analytics software-enabled automated management (automatic, automated, autonomous modes of regulation) Smart city
  5. 5. Big data • A key ingredient in this smart city vision 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), or at least much larger sample sizes than would be employed in traditional, small data studies; – Fine-grained in resolution, aiming to be as detailed as possible, and uniquely indexical in identification; – relational in nature, containing common fields that enable the conjoining of different data sets; – flexible, holding the traits of extensionality (can add new fields easily) and scaleability (can expand in size rapidly).
  6. 6. Sources of big urban data • 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
  7. 7. The data revolution • • • • • • Global data stored – 2000: 800,000 petabytes (250 bytes) – 2011: 1.8 zettabytes (270 bytes) 2012 Facebook processing 2.5 billion pieces of content (links, stores, photos, news, etc) and 500+ terabytes of data, 2.7 billion „Like‟ actions and 300 million photo uploads per day 2012: Wal-Mart generating >2.5 PB of data relating to more than 1 million customer transactions every hour (“equivalent to 167 times the information contained in all the books in the Library of Congress”) 2013 EU commissioner for Digital Agenda, Neelie Kroes, stated that 1.7 million billion bytes of data per minute were being generated globally (1.7 terrabytes - 240 bytes) Hal Varian, Chief Economist at Google, estimates that more data are being produced every two days than in all of history prior to 2003 2020: 35 zettabytes stored?
  8. 8. Real-time city analytics • Instrumented city, plus volunteered data offers the promise of real-time analytics underpinned by massive, dynamic, varied, detailed, inter-related, flexible, low cost data that can be interlinked to provide highly detailed views • The city can be known and managed in real-time and can be sentient to some degree
  9. 9. Single systems (oligopticons)
  10. 10. Integrating systems • Centro De Operacoes Prefeitura Do Rio • 30 real-time systems + crowdsourced data + public administration data • Surveillance + dataveillance
  11. 11. The promise of big data for cities • Real-time information and services for citizens • More transparency and accountability of government and services • Enhanced participation in city life • Better models and simulations for future development; enhanced understanding of cities • More efficient, competitive and productive service delivery; better run cities • Able to tackle particular issues more effectively; enhanced quality of life • Stimulate creativity, innovation and economic growth
  12. 12. Possible perils of smart urbanism
  13. 13. The politics of big urban data • Data within smart city initiatives are portrayed as being natural, essential, neutral and objective measures. • Sensors, cameras, algorithms have no politics or agenda; they reflect and produce truths about the world • Such a framing enables smart city projects to present as being politically benign and commonsensical • 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” • Big data are representations and samples, inflected by social privilege and social values • They are generated within systems designed to enact a particular political and policy vision
  14. 14. Technocratic governance • Promotes an evidence-based, algorithmic approach to city governance • Presumes that all aspects of a city can be monitored, measured and treated as technical problems which can be addressed through technical solutions (instrumental rationality or „solutionalism‟) • Highly narrow in scope and reductionist and functionalist in approach • Fails to take account of the wider effects of culture, politics, policy, governance and capital that shape city life and how it unfolds • Technological solutions are not going to solve deep rooted structural problems of cities; they tackle the manifestations of a problem not its causes
  15. 15. Corporatisation of governance • Governance is being overly shaped and captured by large corporate interests • The concern around such a move is three-fold • First, that it actively promotes a neoliberal political economy and the marketisation of public services • Second, that it creates a technological lock-in that beholden cities to particular technological platforms/corporate solutions • Third, that it leads to „one size fits all smart city in a box‟ solutions that takes little account of local diversity and uniqueness and encourages the city to adapt to the software and not vice versa
  16. 16. Buggy, brittle and hackable cities • Creating city services and spaces that are dependent on software to function – code/spaces • Yet software is an unusual product because it is inherently partial, provisional, porous and open to failure • Leaves city systems vulnerable to viruses, glitches, crashes, and security hacks -- risks of critical failures, „normal accidents‟, criminality and security breaches • And as systems become ever more complex, interconnected and dependent on software, the challenge of producing stable, robust and secure devices and infrastructures magnifies • Whilst smart cities solve some problems, to what extent to they create others?
  17. 17. The panoptic city? • Big urban data radically increases surveillance and dataveillance • Now possible to track and trace individuals and their actions, interactions and transactions in minute detail across a number of domains • Integration binds data streams together to move from oligopticons to panopticon • Enables fine-grained profiling, social sorting, and anticipatory governance • Significant threat to privacy, confidentiality, freedom of expression
  18. 18. Conclusion • Instrumentation and big data are key components of smart city developments • Offers the promises highly granular, real-time analysis of city life that can be used to enhance understanding, governance, quality of life, efficiency, effectiveness, competitiveness, productivity, etc. • They also raise questions concerning the politics of big urban data, the roll out of technocratic and corporatized governance, vulnerabilities and the creation of buggy, brittle and hackable cities, and widespread surveillance/dataveillance • As well as creating technological solutions underpinned by rather simple pro-discourses, we need to focus critical attention on the nature and implications of the smart cities we are envisaging and building to ensure we make the kinds of cities citizens want rather than those that best suit states or corporations
  19. 19. Full written version of paper: @robkitchin