Keynote talk presented at IGU Urban conference in Dublin, August 9th. The paper discusses the transition from data-informed to data-driven, smart cities and the impact of such a transition on city governance and wider society.

1. Prof. Rob Kitchin
NIRSA , Maynooth University
Rob.Kitchin@nuim.ie @robkitchin
The Real-Time City?
Data-driven, networked urbanism
and the production of smart cities
IGU Urban Meeting, 9 August 2015

2. Data and the city
• Rich history of data being generated about cities
• Produced in many ways: audits, cartographic surveying, interviews,
questionnaires, observations, photography, remote sensing, etc
• Stored in ledgers, notebooks, albums, files, databases, other media
• These data provide a wealth of facts, figures, snapshots and opinions
• converted into various forms of derived data
• transposed into visualisations
• analyzed statistically or discursively
• interpreted and turned into information and knowledge
• Urban data thus form a key input for understanding city life, solving
urban problems, formulating policy and plans, guiding operational
governance, modelling possible futures, and tackling a diverse set of
other issues
• For as long as data have been generated about cities then, various kinds
of data-informed urbanism has been occurring
• Data-informed urbanism is increasingly being complemented and
replaced by data-driven, networked urbanism

3. Data and the city
• Since the start of computing urban data have been increasingly
digital in nature, either digitized from analogue sources or born
digital
• Processed and analyzed using various software systems, including
GIS
• From the 1980s onwards, public administration records, official
statistics, and other forms of urban data were released
predominately in digital formats
• These data were (and continue to be) generated and published
periodically and often several months after generation
• Very few datasets exhaustive and data released aggregated,
often with poor spatial resolution
• Post-Millennium, the urban data landscape is being transformed
moving from small to big data

4. Small data / big data
Characteristic Small data Big data
Volume Limited to large Very large
Exhaustivity Samples Entire populations
Resolution and
indexicality
Coarse & weak to tight
& strong
Tight & strong
Relationality Weak to strong Strong
Velocity Slow, freeze-framed Fast
Variety Limited to wide Wide
Flexible and scalable Low to middling High

5. Urban big data
• Directed
o Surveillance: CCTV,
drones/satellite
o Scaled public admin records
• Automated
o Automated surveillance
o Digital devices
o Sensors, actuators, transponders,
meters
o Interactions and transactions
o IoT (Internet of things) and M2M
(machine to machine)
• Volunteered
o Social media
o Sousveillance/wearables
o Crowdsourcing
o Citizen science

6. Urban big data
• Diverse range of public and private generation of fine-scale data
about cities and their residents in real-time:
• utilities (use of energy, water, lighting)
• transport providers (location, routes, traffic flow)
• environmental agencies (pollution, weather, env risk)
• mobile phone operators (location, app use)
• travel and accommodation websites (reviews)
• social media sites (opinions, photos, personal info, location)
• financial institutions and retail chains (consumption)
• private surveillance and security firms (location, behaviour)
• emergency services (policing, response)
• Increasingly being sold/leased data brokers or made available
through APIs

7. Single systems

8. Integrated, city & sector wide

9. Data-driven, networked urbanism

10. Urban informatics and science
• In order to make sense of big data a suite of new data
analytics that rely on machine learning (artificial
intelligence) techniques have emerged consisting of:
• data mining and pattern recognition;
• data visualization and visual analytics;
• statistical analysis;
• prediction, simulation, and optimization modelling
• These are enabling the development of:
• urban informatics, an informational and human-computer
interaction approach to examining and communicating urban
processes
• urban science, a computational modelling approach to
understanding and explaining city processes, blending
geocomputation, data science and social physics

11. www.dublindashboard.ie

12. Data-driven, networked urbanism
• Cities are becoming ever more instrumented and
networked, their systems interlinked and integrated
• Consequently, cities are becoming knowable and
controllable in new dynamic ways
• Urban operational governance and city services are
becoming highly responsive to a form of networked
urbanism in which big data systems are:
• prefiguring and setting the urban agenda
• producing a deluge of contextual and actionable data
• influencing and controlling how city systems respond and
perform in real-time

13. City governance
• Urban big data is being used not only to guide operational practices but
to underpin forms of new managerialism
• Dashboards provide a set of data levers to monitor urban systems,
discipline under-performance, reward those meeting and exceeding
targets, and to guide new strategies, policy, and budgeting
• Understands cities as a set of knowable and manageable systems that
act in largely rational, mechanical, linear and hierarchical ways and can
be steered and controlled
• Technocratic, proscriptive and mechanistic
• Baltimore’s Citistat; Atlanta Dashboard
• “The Atlanta Dashboard ... uses weekly meetings of the mayor’s
cabinet to review performance reports ... with programmatic changes
formulated as necessary to address shortfalls.”

14. Smart cities...
• Data-driven, networked urbanism is the key mode of
production for what have widely been termed smart cities
• Smart economy
• entrepreneurship, innovation, productivity, competiveness
• Smart government
• e-gov, open data, transparency, accountability, evidence-informed
decision making, better service delivery
• Smart mobility
• intelligent transport systems, multi-modal inter-op, efficiency
• Smart environment
• green energy, sustainability, resilience
• Smart living
• quality of life, safety, security, manage risk
• Smart people
• more informed, creativity, inclusivity, empowerment, participation

15. Smart city
• The smart city promises to solve a fundamental conundrum
of cities:
• how to reduce costs and create economic growth and
resilience at the same time as producing sustainability and
improving services, participation and quality of life
• And to do so by utilising a fast-flowing torrent of
commonsensical, pragmatic, neutral and apolitical urban
data and data analytics, algorithmic governance, and
responsive, networked urban infrastructure

16. Eight critiques of smart cities
• City as a knowable, rational, steerable machine
• Ahistorical, aspatial and homogenizing
• The politics of urban data
• Technocratic governance and solutionism
• Corporatisation of governance
• Serve certain interests and reinforce inequalities
• Buggy, brittle, hackable urban systems
• Social, political, ethical effects

17. Eight critiques of smart cities
• City as a knowable, rational, steerable machine
• Ahistorical, aspatial and homogenizing
• The politics of urban data
• Technocratic governance and solutionism
• Corporatisation of governance
• Serve certain interests and reinforce inequalities
• Buggy, brittle, hackable urban systems
• Social, political, ethical effects

18. The politics of urban data
• Urban operating systems and dashboards are powered by a realist
epistemology that privileges a particular ontological framing (city as
numbers) and modes of analysis (data science)
• They claim to show cities as they really are through well defined
measures that are:
• objective, value-free, and independent of external influence;
• systematic and continuous in operation and coverage
• verifiable and replicable;
• timely and traceable over time;
• easy, quick and cost-effective to collect, process and update
• easy to present, interpret, and to compare across locales through
interactive graphs/maps and stats
• Makes claims with respect to the truth about urban systems and city life
and has utility by facilitating action in relation to that knowledge
• However, data do not exist independently of the ideas, instruments,
practices, contexts, knowledges and systems used to generate, process and
analyze them

19. The politics of urban data
Material Platform
(infrastructure – hardware)
Code Platform
(operating system)
Code/algorithms
(software)
Data(base)
Interface
Reception/Operation
(user/usage)
Systems of thought
Forms of knowledge
Finance
Political economies
Governmentalities & legalities
Organisations and institutions
Subjectivities and communities
Marketplace
System/process
performs a task
Context
frames the system/task
Data assemblage

20. The politics of urban data
• Urban OS/dashboards/control rooms seek to translate the
messiness and complexities of cities into rational, detailed,
systematic, ordered forms of knowledge
• Do not simply process and present data, they actively
produce meaning
• They shape what questions can be asked of the underlying
data and what answers can be obtained
• They do not act as mirrors, but as engines
• They actively frame and do work in the world
• Data-driven, networked urbanism is thus thoroughly
political seeking to produce a certain kind of city

21. Data concerns
• Corporatisation of governance
• Data access, data ownership, data control
• Buggy, brittle, hackable urban systems
• Data security, data integrity
• Social, political, ethical effects
• Data protection and privacy
• Dataveillance/surveillance
• Data uses/data determinism: Social sorting,
predictive profiling, anticipatory governance,
control creep, dynamic pricing, official statistic

22. Technical data concerns
• Data coverage and access
(openness)
• Data integration and
interoperability (data standards)
• Data quality and provenance:
veracity (accuracy, fidelity),
uncertainty, error, bias,
reliability, calibration, lineage
• Quality, veracity and
transparency of data analytics
• Ecological fallacy and
interpretation issues
• Skills and organisational
capabilities and capacities

23. An alternative epistemology?
• Given issues outlined should we opposing the use of Urban OS/dashboards
to guide urban policy and governance?
• Rather than advocating such projects be abandoned ― since they do have
utility and value ― they are better re-imagined
• One solution is to try and reframe such initiatives so that their
epistemology openly recognizes and acknowledges:
• the multiple, complex, interdependent nature of cities means they
cannot be unproblematically disassembled into data, nor be easily fine-
tuned and steered through a limited set of data levers
• they are not toolkits but complex socio-technical systems infused with
politics
• their lineage, data provenance, metadata, and levels of error and
uncertainty
• they have all kinds of social, political and economic effects
• there are a multitude of other useful ways to see and understand the
city and many forms of active urbanism
• It is to approach such initiatives as one might critical GIS or radical
statistics; to be healthily sceptical of the claims of data-driven, networked
urbanism and to use the data/tools to forward alternative city visions

24. Conclusion
• We are entering an era of embedded and mobile computation
• Devices and infrastructures are producing vast quantities of data
in real-time, and are responsive to these data, enabling new
kinds of monitoring, regulation and control
• Cities are becoming data-driven and are enacting new forms of
algorithmic governance
• However, the data and algorithms underpinning them are far
from objective and neutral
• The smart cities that data-driven, networked urbanism purports
to create are then smart in a qualified sense
• Their production and operation is based on much more data and
derived information than previous generations of urbanism, but it
is a form of urbanism that is nonetheless still selective, crafted,
flawed, normative and politically-inflected

25. Conclusion
• As such, whilst data-driven, networked urbanism
undoubtedly provides a set of solutions for urban
problems, we also have to recognize that it has a number
of shortcomings and a number of potential perils
• The challenge facing urban managers and citizens in the
age of smart cities is to realise the benefits of planning
and delivering city services using urban data and real-
time responsive systems whilst minimizing pernicious
effects
• To do that we have to be as smart about urban data, data
analytics and urban theory as we would like to be about
cities
• That requires us to thoroughly understand the praxes and
politics of data-driven, networked urbanism

26. Rob.Kitchin@nuim.ie
@robkitchin
http://www.nuim.ie/progcity
@progcity
Kitchin, R., Lauriault, T. and McArdle, G. (2015) Knowing and governing cities through urban
indicators, city benchmarking and real-time dashboards. Regional Studies, Regional Science
2: 1-28
Kitchin, R. (2014) The real-time city? Big data and smart urbanism. GeoJournal 79(1): 1-14.