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

• Directedo Surveillance: CCTV,

drones/satelliteo Scaled public admin records

• Automatedo Automated surveillanceo Digital deviceso Sensors, actuators, transponders,

meterso Interactions and transactionso IoT (Internet of things) and M2M

(machine to machine)

• Volunteered o Social mediao Sousveillance/wearableso Crowdsourcingo 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

Contextframes 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.