Download - Data-driven urbanism (Amsterdam, Jan 2017)
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
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
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, participationSmart economy
entrepreneurship,
innovation,
productivity,
competiveness
Promise of data-driven/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
@robkitchin
https://www.maynoothuniversity.ie/people/rob-kitchin