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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
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
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
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
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
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
Single systems
Integrated, city & sector wide
Data-driven, networked urbanism
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
www.dublindashboard.ie
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
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.”
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
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
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
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
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
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
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
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
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
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
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
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
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.

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The Real-Time City? Data-driven, networked urbanism and the production of smart cities

  • 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
  • 8. Integrated, city & sector wide
  • 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
  • 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.