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Urban indicators, city benchmarking, and real time dashboards: Knowing and governing cities through open and big data


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Talk presented at the Conference of the Association of American Geographers, Tampa, April 8-12. First attempt at presenting a paper presently being written for publication.

Urban indicators, city benchmarking, and real time dashboards: Knowing and governing cities through open and big data

  1. 1. Urban indicators, city benchmarking, and real- time dashboards: Knowing and governing cities through open and big data Rob Kitchin, Tracey P. Lauriault, and Gavin McArdle NIRSA, NUIM
  2. 2. Introduction • Over past 25 years there has been a proliferation of urban indicator and city benchmarking projects • More recently such projects are becoming open, real-time, and visualised through (interactive) dashboards • This paper considers what these projects mean for how cities are known and governed; how they are enrolled in the production of smart cities • We argue that indicator and benchmarking projects promote a narrowly conceived but powerful realist epistemology – the city as visualised numbers - that is reshaping city governance
  3. 3. Framing • Our discussion is framed by: • a critical understanding of data that recognizes that they do not exist independently of the ideas, instruments, practices, contexts, knowledges and systems used to generate, process and analyze them - there is a politics to data assemblages, such as indicator/benchmarking initiatives.
  4. 4. Data Assemblage Attributes Elements Systems of thought Modes of thinking, philosophies, theories, models, ideologies, rationalities, etc. Forms of knowledge Research texts, manuals, magazines, websites, experience, word of mouth, chat forums, etc. Finance Business models, investment, venture capital, grants, philanthropy, profit, etc. Political economy Policy, tax regimes, public and political opinion, ethical considerations, etc. Govern-mentalities / Legalities Data standards, file formats, system requirements, protocols, regulations, laws, licensing, intellectual property regimes, etc. Materialities & infrastructures Paper/pens, computers, digital devices, sensors, scanners, databases, networks, servers, etc. Practices Techniques, ways of doing, learned behaviours, scientific conventions, etc. Organisations & institutions Archives, corporations, consultants, manufacturers, retailers, government agencies, universities, conferences, clubs and societies, committees and boards, communities of practice, etc. Subjectivities & communities Of data producers, curators, managers, analysts, scientists, politicians, users, citizens, etc. Places Labs, offices, field sites, data centres, server farms, business parks, etc, and their agglomerations Marketplace For data, its derivatives (e.g., text, tables, graphs, maps), analysts, analytic software, interpretations, etc.
  5. 5. Framing • Our discussion is framed by: • a critical understanding of data that recognizes that they do not exist independently of the ideas, instruments, practices, contexts, knowledges and systems used to generate, process and analyze them - there is a politics to data assemblages, such as indicator/benchmarking initiatives. • our own practices of creating/managing indicator projects since 2005 through AIRO (All-Island Research Observatory) and a new dashboard for Dublin City Council – Dublin Dashboard (not yet launched)
  6. 6. • How’s Dublin Doing • Dublin Indicators and Benchmarking tools • Dublin Real-Time • Real-time data from sensors across Dublin • Dublin Mapped • Detailed Census maps for 2006 & 2011 Census • Dublin Planning • Zoning and Planning Permission • Dublin Near To Me • Community and service accessibility maps • Dublin Housing • Various housing modules, commuter maps • Dublin Reporting • FixMyStreet/CityWatch • Dublin Social • Live map of activity in Dublin based on social network interactions • Dublin Data Stores • Access to data used in the dashboard • Dublin Modelled • Proposed modelling and Scenario tools • Dublin Apps • Directory of apps relevant to Dublin • Have Your Say • Feedback from Users
  7. 7. Indicators, benchmarking and real-time dashboards
  8. 8. Indicators • Different types of indicators, generated for varying purposes • Single direct and indirect indicators • Composite indicators • Descriptive/contextual indicators • Diagnostic, performance and target indicators • Predictive and conditional indicators
  9. 9. City benchmarking • Standardized indicators for comparison within and across cities. • Enables performance to be benchmarked with other places and against best practice; to identify relative strengths and weaknesses • Can produce league tables and ranks of relative performance and to set targets. • Used to formulate policy and undertake place promotion • JLL detail over 150 city benchmarking initiatives. • is a joint project of The World Bank, UN-Habitat, the World Economic Forum, OECD, the Government of Canada that are also working on an ISO standard for city benchmarking indicators.
  10. 10. Real-time dashboards • Mainly real-time operational data • Generally feeding control rooms, sometimes open • Big urban data • Centro De Operacoes Prefeitura Do Rio • 30 real-time systems + public administration + crowdsourced data • Surveillance + dataveillance
  11. 11. Epistemology, politics of data, governance
  12. 12. Realist epistemology • Indicators, benchmarking and dashboards promote a realist epistemology by privileging a particular ontological framing (city as numbers) and modes of analysis (data viz) with respect to cities and their citizens • Supposedly provide well defined measures that are: • objective, neutral, value-free, and independent of external influence; • Systematic and continuous in operation and coverage (rather than one off and constrained by time, geography and limit sampling) • 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 • Makes claims with respect to the truth about urban systems and life and has utility by facilitating action in relation to that knowledge
  13. 13. Critique • Not simply technical tools: they are framed socially, political, ethically, philosophically in terms of their form, selection, analysis and deployment • Indicators express a normative notion about what should be measured, for what reasons, and what they should tell us - full of values and judgements shaped by a range of views and contexts • And they have normative effect - being used to influence decision-making, modify institutional behaviour, condition workers, etc. ... but also enact Campbell’s Law
  14. 14. Critique • Indicator projects promote an instrumental rationality based on a narrowly framed episteme and techne that: • is reductionist – atomizing complex, contingent relationships into simplified, one-dimensional measures that do not give full picture; decontextualizes the city from history, political economy and wider set of social, economic and environmental relations • undermines and replaces other scientific forms of urban knowing that are less systematic and continuous – policy analysis, interviews, focus groups, surveys, etc; as well phronesis (knowledge derived from practice and deliberation) and metis (knowledge based on experience). • enables longitudinal analysis, but this often ignores the temporal register of urban processes (that different processes and policies work at different speeds) demanding quick change and response • Full of absences and silences – phenomena that are difficult to quantify or are politically contentious.
  15. 15. Critique - indicators • Quality of indicators is dependent on veracity and provenance of data • rarely are indicators published with metadata concerning measurement, sampling frame, handling, veracity (accuracy, fidelity), uncertainty, error, bias, reliability, calibration, lineage. • Composite indicators can be opaque in method (aggregation, normalization, weightings) and sources, quality and commensurability of data; and can have issues of multicollinearity and be highly sensitive to adjustment (e.g. of weightings) • There are spatial boundary issues (where is the city?) and leakage (cities are open, porous systems) • Somewhere in the translation from data indicators gain confidence and stature and shed constraints and parameters.
  16. 16. Critique - benchmarking • Difficulty of standardizing measures across jurisdictions • Selection of indicators, parameters, weightings inherently tend to favour some locales over others • Is set up as a zero-sum game – ranked 1 to n • Benchmarking assumes there is a normative standard by which places should be judged, some ideal state they are all seeking to achieve • Glosses over fact that phenomenon/places differ from one another often for good reason, and that different places should have varying goals/policy • Places have different histories and trajectories, varying political economies and varieties of capitalism, different forms of state apparatus and governance structures • Promotes imitation and copying rather contextualised policy • Despite criticism indicator/benchmarking projects are being widely rolled and translated into policy, planning and decision-making; this reinforces the rationale for their use
  17. 17. Indicator initiatives and city governance
  18. 18. Governance • How cities view indicators, the kinds of indicators that are chosen and deployed, and how cities employ them varies markedly • Some municipalities use indicators to underpin forms of new managerialism, wherein they are used to guide operational practices with respect to specified targets and to provide evidence of the success or failure of schemes, policies, units and personnel • Metrics are used to discipline under-performance, reward those meeting and exceeding targets, and to guide new strategies, policy, and budgeting. • Technocratic, proscriptive and mechanistic • Underpinned by/promotes neoliberalism • Baltimore’s Citistat; Atlanta dashaboard • “The Atlanta Dashboard ... uses weekly meetings of the mayor’s cabinet to review performance reports. Each week the performance of selected departments is reviewed against the departmental plan, with programmatic changes formulated as necessary to address shortfalls.”
  19. 19. Governance • In other cases, indicators are used in a more descriptive way to provide robust and clear city intelligence, which complements a variety of other information, to help inform policy making and implementation • Here indicators are more contextual rather than performance/target orientated. • E.g., Dublin and Belgium • Formulation of initiatives can be expert-led, consultancy-led, stakeholder-led, community-participatory-led; business-led • Many benchmarking initiatives are created by businesses and supra-national bodies • In all cases indicators form a key element in the move towards data-driven, evidence-based policy formulation and operations management • Open systems promotes transparency, accountability and participation; also reinforces the value of the realist epistemology by promoting the value of indicator data as the means through which the citizen can make sense of the city and engage the state
  20. 20. Conclusion • Indicator, benchmarking, dashboard projects have proliferated in recent years; trend is for such projects to become more open and real-time • Form a central pillar in the conception and roll-out of ‘smart cities’ • Promote a realist epistemology for knowing cities • Translated into how cities are managed and governed • However, how cities develop and utilise indicators projects varies markedly • And within city administrations there are complex and paradoxical processes at work: open vs closed, regulation/control vs transparency/participation, etc. • Smart cities coming into being in different ways – need a variegated and more nuanced narrative • Need some in-depth comparative work to explore the ways in which indicator projects are deployed in different cities; to tease apart their data assemblage
  21. 21. @robkitchin @progcity MIT Press, 2011 Sage, Aug 2014 Kitchin, R. (2014) The real-time city? Big data and smart urbanism. GeoJournal 79(1): 1-14