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Data-driven urbanism
Prof. Rob Kitchin
Maynooth University
Data and the city
• Rich history of data being generated about cities
• Long had data-informed urbanism
• Being complemented and replaced by data-driven
urbanism
• Part of smart city agenda
Urban big data
• Directed
o Surveillance: CCTV,
drones/satellite
o Public admin records
• Automated
o Automated surveillance
o Digital devices
o Sensors, actuators,
transponders, meters (IoT)
o Interactions and transactions
• 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 (uniquely
indexical) data about citizens and places in
real-time:
• utilities
• transport providers, logistics systems
• environmental agencies
• mobile phone operators
• app developers
• social media sites
• travel and accommodation websites
• home appliances and entertainment
systems
• financial institutions and retail chains
• private surveillance and security firms
• remote sensing, aerial surveying
• emergency services
• Producing a data deluge that can be
combined, analyzed, acted upon
IoT and data-driven cities
• Networks of sensors, meters, actuators,
cameras
• Real-time monitoring and management of
city systems
• Traffic control room
• Sound sensing and modelling
800
380
1000
10000’s
Traffic System Data
Data-driven urbanism (Amsterdam, Jan 2017)
Data-driven urbanism (Amsterdam, Jan 2017)
Data-driven urbanism (Amsterdam, Jan 2017)
Data-driven urbanism (Amsterdam, Jan 2017)
Algorithmic governance
• Both systems are highly automated; differ with
respect to their adaptiveness and action
• Traffic control room
• automated management — makes and enacts
automated, automatic and automated decisions
• However, human-on-the-loop
• Sound sensing and modelling
• fixed and non-adaptive
• engenders no immediate material action
• human in the loop with regards to interventions
Single domains
Integrated, city & sector wide
Data-driven cities
www.dublindashboard.ie
Smart city apps
Data-driven urbanism
• Cities are becoming:
• ever more instrumented and networked, their systems
interlinked and integrated
• knowable and controllable in new dynamic ways
• Urban operational governance and city services are
becoming highly responsive to a form of data-driven
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
• transforming practices of city governance
Smart
Cities
Smart government
e-gov, open data,
transparency, accountability,
evidence-informed decision
making, better service
delivery
Smart living
quality of life,
safety, security,
manage risk
Smart mobility
intelligent transport
systems, multi-modal inter-
op, efficiency
Smart
environment
green energy,
sustainability,
resilience
Smart people
more informed, creativity, inclusivity,
empowerment, participation
Smart economy
entrepreneurship,
innovation,
productivity,
competiveness
Promise of data-driven/smart cities
Critiques of data-driven urbanism
and smart cities
Critiques of data-driven urbanism
and smart cities
• Operational/practical concerns
• Data quality and provenance
• City as a knowable, rational, steerable machine
• Objective, neutral, non-ideological approach
• Technocratic governance and solutionism
• Neoliberal political economy & corporatisation of
governance
• Ahistorical, aspatial, homogenizing and bounded
• Scalar and stakeholder issues
• Reinforce power geometries & inequalities
• Ethics
• Hacking the city
• Normative framing
Operational/practical concerns
• Data and systems governance
• Internal vs external facing
• Access to data
• Data ontology and interoperability;
standards
• Resourcing, capacity & sustainability
issues (staff, finance, infrastructure)
• Obstacles (institutional culture,
legacy systems, resistance, politics)
• Data literacy and analytical
competency (staff & public)
• Making data actionable – from data
to tools; from browsing to answers
• Analytics are dependent on
veracity and provenance of data
• Urban data often published with
metadata concerning
measurement, sampling frame,
handling, veracity (accuracy,
fidelity), uncertainty, error, bias,
reliability, calibration, lineage
• Rarely are the algorithmic black-
boxes exposed so that calculations
are open to scrutiny
• There are issues such as MAUP and
other ecological fallacies that
shape interpretation
Data quality and
provenance
Scalar and stakeholder issues
• Fractured landscape
• With respect to geography
• Back-to-back services and planning
across municipalities
• Scalar organisation – local, county,
regional, state, federal
• Mismatch of functional territories
and administrative geographies
• With respect to stakeholders
• Within municipalities, across
municipalities, with public sector
agencies, industry, universities,
NGOs, community organisations
• Different goals, resources,
practices, institutional structures,
funding models, etc.
• Variations in data ontologies within and
between scales/stakeholders
• Lack of joined up smart city systems
• Sub-optimal planning
City as a knowable, rational,
steerable machine
• Cities are understood to consist of a set
of knowable and manageable systems
that act in largely rational, mechanical,
linear and hierarchical ways and can be
steered and controlled
• Operational governance performed using
a set of mechanistic data levers
underpinned by an instrumental
rationality in the form of KPIs and
analytics
• Includes forms of automated
management (automatic, autonomous,
automated)
• Driving new forms of new managerialism
• Cities are fluid, open, complex, multi-
level, contingent and relational systems
Technocratic governance and
solutionism
• All aspects of a city can be treated as
technical problems and solved through
technical approaches
• Practices ‘solutionism’: complex open
systems can be disassembled into neatly
defined problems that can be fixed or
optimized through computation
• All that is required is sufficient data and
suitable algorithms
• Often sticking plaster solutions and do not
address deep-routed structural issues
• Undermines/replaces other forms of knowing
cities, plus phronesis (knowledge derived
from practice and deliberation) and metis
(knowledge based on experience)
• Marginalizes other forms of governance and
solutions
Normative framing
• Normative questions
• For whom and what purpose are
smart cities being developed?
• Are smart cities primarily about – or
should be about:
• creating new markets and profit?
• facilitating state control and
regulation?
• improving the quality of life of
citizens?
• What kind of cities do we want to
create and live in?
• Not simply from an instrumental
perspective, but with respect to
issues such as fairness, equity,
justice, citizenship, democracy,
governance and political economy
Conclusion
• Entering an era of embedded and mobile computation
• Vast quantities of real-time data, cities are responsive to
these data, and enable new kinds of monitoring, regulation
and control
• Cities are becoming data-driven and are enacting new
forms of algorithmic governance
• Whilst data-driven, networked urbanism undoubtedly
provides a set of solutions for urban problems it also raises
a number of ethical and normative questions
• The challenge is to realise the benefits whilst minimizing
pernicious effects
Background
http://www.maynoothuniversity.ie/progcity
@progcity
Rob.Kitchin@nuim.ie
@robkitchin
https://www.maynoothuniversity.ie/people/rob-kitchin

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Data-driven urbanism (Amsterdam, Jan 2017)

  • 1. Data-driven urbanism Prof. Rob Kitchin Maynooth University
  • 2. Data and the city • Rich history of data being generated about cities • Long had data-informed urbanism • Being complemented and replaced by data-driven urbanism • Part of smart city agenda
  • 3. Urban big data • Directed o Surveillance: CCTV, drones/satellite o Public admin records • Automated o Automated surveillance o Digital devices o Sensors, actuators, transponders, meters (IoT) o Interactions and transactions • Volunteered o Social media o Sousveillance/wearables o Crowdsourcing o Citizen science
  • 4. Urban big data • Diverse range of public and private generation of fine-scale (uniquely indexical) data about citizens and places in real-time: • utilities • transport providers, logistics systems • environmental agencies • mobile phone operators • app developers • social media sites • travel and accommodation websites • home appliances and entertainment systems • financial institutions and retail chains • private surveillance and security firms • remote sensing, aerial surveying • emergency services • Producing a data deluge that can be combined, analyzed, acted upon
  • 5. IoT and data-driven cities • Networks of sensors, meters, actuators, cameras • Real-time monitoring and management of city systems • Traffic control room • Sound sensing and modelling
  • 11. Algorithmic governance • Both systems are highly automated; differ with respect to their adaptiveness and action • Traffic control room • automated management — makes and enacts automated, automatic and automated decisions • However, human-on-the-loop • Sound sensing and modelling • fixed and non-adaptive • engenders no immediate material action • human in the loop with regards to interventions
  • 13. Integrated, city & sector wide
  • 17. Data-driven urbanism • Cities are becoming: • ever more instrumented and networked, their systems interlinked and integrated • knowable and controllable in new dynamic ways • Urban operational governance and city services are becoming highly responsive to a form of data-driven 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 • transforming practices of city governance
  • 18. Smart Cities Smart government e-gov, open data, transparency, accountability, evidence-informed decision making, better service delivery Smart living quality of life, safety, security, manage risk Smart mobility intelligent transport systems, multi-modal inter- op, efficiency Smart environment green energy, sustainability, resilience Smart people more informed, creativity, inclusivity, empowerment, participation Smart economy entrepreneurship, innovation, productivity, competiveness Promise of data-driven/smart cities
  • 19. Critiques of data-driven urbanism and smart cities
  • 20. Critiques of data-driven urbanism and smart cities • Operational/practical concerns • Data quality and provenance • City as a knowable, rational, steerable machine • Objective, neutral, non-ideological approach • Technocratic governance and solutionism • Neoliberal political economy & corporatisation of governance • Ahistorical, aspatial, homogenizing and bounded • Scalar and stakeholder issues • Reinforce power geometries & inequalities • Ethics • Hacking the city • Normative framing
  • 21. Operational/practical concerns • Data and systems governance • Internal vs external facing • Access to data • Data ontology and interoperability; standards • Resourcing, capacity & sustainability issues (staff, finance, infrastructure) • Obstacles (institutional culture, legacy systems, resistance, politics) • Data literacy and analytical competency (staff & public) • Making data actionable – from data to tools; from browsing to answers
  • 22. • Analytics are dependent on veracity and provenance of data • Urban data often published with metadata concerning measurement, sampling frame, handling, veracity (accuracy, fidelity), uncertainty, error, bias, reliability, calibration, lineage • Rarely are the algorithmic black- boxes exposed so that calculations are open to scrutiny • There are issues such as MAUP and other ecological fallacies that shape interpretation Data quality and provenance
  • 23. Scalar and stakeholder issues • Fractured landscape • With respect to geography • Back-to-back services and planning across municipalities • Scalar organisation – local, county, regional, state, federal • Mismatch of functional territories and administrative geographies • With respect to stakeholders • Within municipalities, across municipalities, with public sector agencies, industry, universities, NGOs, community organisations • Different goals, resources, practices, institutional structures, funding models, etc. • Variations in data ontologies within and between scales/stakeholders • Lack of joined up smart city systems • Sub-optimal planning
  • 24. City as a knowable, rational, steerable machine • Cities are understood to consist of a set of knowable and manageable systems that act in largely rational, mechanical, linear and hierarchical ways and can be steered and controlled • Operational governance performed using a set of mechanistic data levers underpinned by an instrumental rationality in the form of KPIs and analytics • Includes forms of automated management (automatic, autonomous, automated) • Driving new forms of new managerialism • Cities are fluid, open, complex, multi- level, contingent and relational systems
  • 25. Technocratic governance and solutionism • All aspects of a city can be treated as technical problems and solved through technical approaches • Practices ‘solutionism’: complex open systems can be disassembled into neatly defined problems that can be fixed or optimized through computation • All that is required is sufficient data and suitable algorithms • Often sticking plaster solutions and do not address deep-routed structural issues • Undermines/replaces other forms of knowing cities, plus phronesis (knowledge derived from practice and deliberation) and metis (knowledge based on experience) • Marginalizes other forms of governance and solutions
  • 26. Normative framing • Normative questions • For whom and what purpose are smart cities being developed? • Are smart cities primarily about – or should be about: • creating new markets and profit? • facilitating state control and regulation? • improving the quality of life of citizens? • What kind of cities do we want to create and live in? • Not simply from an instrumental perspective, but with respect to issues such as fairness, equity, justice, citizenship, democracy, governance and political economy
  • 27. Conclusion • Entering an era of embedded and mobile computation • Vast quantities of real-time data, cities are responsive to these data, and enable new kinds of monitoring, regulation and control • Cities are becoming data-driven and are enacting new forms of algorithmic governance • Whilst data-driven, networked urbanism undoubtedly provides a set of solutions for urban problems it also raises a number of ethical and normative questions • The challenge is to realise the benefits whilst minimizing pernicious effects