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

800380

1000

10000’s

Traffic System Data

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, participationSmart 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