Data, Infrastructure and Public Policy
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Dr Tracey Lauriault from The Programmable City in Maynooth University presents on data, infrastructure and public policy in the Irish domain.
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Data, Infrastructure and Public Policy
- 1. Dublinked Open Data Summit Data Opens Doors Open Data Nation Session Chartered Accountants Ireland, 47 Pearse Street, Dublin City, 7 May 2015 Tracey P. Lauriault Programmable City Project Tracey.Lauriault@NUIM.ie @TraceyLauriault Data, Infrastructures and Public Policy
- 3. The Programmable City • A European Research Council (ERC) and Science Foundation of Ireland (SFI) funding • SH3: Environment and Society • Led by Dr Rob Kitchin, the Primary Investigator • Based at the National Institute for Regional and Spatial Analysis (NIRSA) • At the National University of Ireland Maynooth (NUIM)
- 4. MIT Press 2011 Sage 2014 Aim of the ERC project is to build off and extend a decade of work that culminated in Code/Space book (MIT Press) with a set of detailed empirical studies Aim
- 5. Objectives How is the city translated into software and data? How do software and data reshape the city? Translation: City into Code Transduction: Code Reshapes City THE CITYSOFTWARE Discourses, Practices, Knowledge, Models Mediation, Augmentation, Facilitation, Regulation
- 6. 2. Data
- 7. Government Institutions 1. Agriculture, Food and the Marine 2. Arts, Heritage and the Gaeltacht 3. Children and Youth Affairs 4. Communications, Energy and Natural Resources 5. Defence 6. Education and Skills 7. Environment, Community and Local Government 8. Finance 9. Foreign Affairs and Trade 10.Health 11.Jobs, Enterprise and Innovation 12.Justice and Equality 13.Public Expenditure and Reform 14.Social Protection 15.Taoiseach 16. Transport, Tourism and Sport • 130 Non Commercial State Sponsored Bodies (EPA, Marine Institute, SFI, RIA, Rail, OSI, Universities, Roads, etc.) • 100+ State-sponsored bodies (Utilities, Irish Rail, IDA, Petroleum Corporation) • 31 local authorities (3 are Dublin, 2 are City and Council) • CSO, Archives, etc. • Data Protection Commissioner, Ombunds person, Information Commissioner,
- 8. Data Collection • Meet constitutional commitments & ensure adherence to regulation, treaties, directives • Administer government institutions (budgets, performance indicators, audits, taxes, procurement) • Output of program & service delivery (licences, PPS, registration, fees, ) • Census, maps, surveys, inventories, • Investigation, research, development
- 9. Epistemic groups Research Data GovData GeoData Physical Sciences AdminData Public Sector Data Access to Data Open Data Social Sciences 2005 VGI Crowdsource Citizen Science Scientists, Cultural Institutions E-Government, CTOs
- 14. Within & Between Institutions Institutional Framework Administration Policy Law Skills Finance Technical Standards Data integration Interoperability Preservation Transfer Framework Data Geodetic Base maps Access Network Web services Catalogs Metadata Atlas Infrastructure
- 15. Infrastructural Platform • Comprehensive collection & sharing of authoritative data • Search, discovery, access, & visualization tools built once & reused many times, search once and find everything • Common web-based environment enabling data integration, analysis, & visualization to support informed decision-making • Shared governance & management of geospatial assets and capabilities, through operational standards & policies 2014-… http://www.nrcan.gc.ca/earth-sciences/geomatics/canadas-spatial-data- infrastructure/geospatial-communities/federal
- 16. Infrastructure Principles 1.Open: enables better decision making, the CGDI is based on open, barrier-free data sharing and standards that allow users to exchange data. 2. Accessible: allows users to access data and services seamlessly, despite any complexities of the underlying technology. 3. Evolving: the network of organizations participating in the CGDI will continue to address new requirements and business applications for information and service delivery to their respective users. 4. Timely: the CGDI is based on technologies and services that support timely or real-time access to information. 5. Sustainable: is sustained by the contributions of the participating organizations and broad user community and through the infrastructure’s relevance to these groups. 6.Self-organizing the CGDI enables various organizations to contribute geospatial information, services and applications, and guide the infrastructure’s development. 7. User and community driven emphasizes the nurturing of and service to a broad user community. These users, including Canadians in general, will drive the CGDI’s development based on user requirements. 8. Closest to source maximizes efficiency and quality by encouraging organizations closest to source to provide data and services. Thereby eliminating duplication and overlap. 9. Trustworthy is continually enhanced to protect sensitive and proprietary data. The CGDI offers this protection through policies and mechanisms that enable data to be assessed for quality and trusted by users.Source: : 2012, Canadian Geospatial Data Infrastructure Vision, Mission and Roadmap - The Way Forward
- 17. Attributes Infrastructure Administrative responsibility Organizational viability Financial sustainability Technological and procedural suitability System security Procedural accountability http://www.oclc.org/content/dam/research/activities/trustedrep/repositories.pdf
- 18. Kitchin’s 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. Systemsofthought
- 19. 6. Final Remarks
- 20. Data Infrastructure • Open data is an apéritif, stimulating the e-government community to manage and share its data assets/resources • We are at the amuse-bouche stage, at the level of datasets within the public sector • The conversation is getting good with e-government and administrators mingling with science, geomatics, statistics, becoming multi-sectoral, arguably missing some civil society spice and business pragmatism although innovation is the aim • We are getting ready to have dinner together, but we still need the determine preferences, find and mix ingredients, get the chefs together in the kitchen, need a place to sit and enable the skilled staff to deliver and manage, need a convener or host, and we need to figure out how to pay • Eventually we will share many meals, selected from any number of fine establishments, which will be underpinned by an invisible but robust, sustainable, fair, secure and well functioning global food system.
- 21. Q & A Tracey P. Lauriault Programmable City Project http://www.maynoothuniversity.ie/progcity/ Tracey.Lauriault@NUIM.ie @TraceyLauriault
Hinweis der Redaktion
- The objectives are to provide an interdisciplinary analysis of the two core inter-related aspects of the emerging programmable city: (a) Translation: how cities are translated into code, and (b) Transduction: how code reshapes city life” (Kitchin 2011).
- While this list of institutions is by no means comprehensive, we can say that the people and the territory of the Republic of Ireland are governed by 16 departments, 130 non commercial state sponsored bodies, 100+ state sponsored bodies, 31 local authorities, a number of special offices like the CSO, National Archives, commissioners, ombuds people and so on.
- Governments create, use and maintain massive datasets about the territories they manage, the resources they oversee, and the people they govern. These are publicly funded national assets or resources. These same institutions collect different types of data in order to govern and administer the economy, resources, environment, society, history, knowledge, safety and security, etc.
- Although data are commonly understood in practical terms, understandings differ depending on the actors involved there are different epistemologies and ontologies. Their collection requires specialized knowledge, techniques, sophisticated technologies, and often, significant resources. Data are also owned, regulated, guarded, standardized, and created within a particular community of practice. They are collected according to a particular model of the world based on the author’s worldview, and in turn, become an image or a representation of it. Data can be considered as arrangements of “facts within a specific cultural perspective” (Harley, in Dodge 2011:276). An earth scientist, urban planner, cartographer, electrical engineer or epidemiologist each represents a community of practice or epistemic group, each with their unique outlook on what constitutes data. Definitions, understandings, values and quality parameters also vary according to discipline (e.g., geography, physics, social work, archaeology), sector (e.g., communication, energy, housing, health), level of government and their departments (e.g., city, county, EU), private sector (e.g., Google, Axcion, IBM), non-governmental organization (e.g., CreativeCommons.ca, coastwatch, friends of the earth) or to individual citizens. In addition, data resellers, lawyers, data value-added service providers, and researchers in academia or the private sector value data for different reasons. Finally, the roles people have in relation to data (e.g., data librarian, archivist, network specialist, database manager, GIS specialist, cryptographer, cataloguer, artist, project manager) frame how data are handled.
- Data are also part of and the result of large or complex socio-technological systems, such as the web of submarine cables, or satellite image mosaic which underlies Googlemaps. Data are artifacts in a complex web of technology, people, and social, political and economic structures. Furthermore, data are far from the often conceptualized, neutral arrangements of facts accompanied by descriptive metadata. They are political in their creation, use, arrangement, dissemination, representations, and in their ownership.
- Irrespective of who collects data and for what purpose they are collected, their type, their form, or format, all government data are government records, and all government records should be managed not only by the individuals or teams whose role it is to collect, analyze and disseminate these, but data are the responsibility of the institution under whose remit they were collected in the first place. It is not only a legal requirement to do so, it is simply good practice to responsibly manage ones information assets/resources. Data/record management is synonymous with good governance. Open data, is in essence part of the access, use and reuse part of this particular model, or in other words discover and disseminate.
- Natural Resources Canada, The Federal Geospatial Platform, Presentation for Information 29 October, 2013, Via Anne Martin
- Reference: 2012, Canadian Geospatial Data Infrastructure Vision, Mission and Roadmap - The Way Forward http://ftp2.cits.rncan.gc.ca/pub/geott/ess_pubs/292/292417/cgdi_ip_28e.pdf
- Rob Kitchin, 2014, The Data Revolution: Big Data, Open Data, Data Infrastructures and their Consequences, Sage: London. A data assemblage is conceived as a complex socio-technical system consisting of a number of inter-related elements — systems of thought; forms of knowledge; finance; political economy; governmentalities; materialities and infrastructures; practices; organisations and institutions; subjectivities and communities; places; and marketplaces — that work together to frame how data are produced, managed, analyzed, shared and used. This framework we think has utility in understanding and contextualizing the wider changing data landscape. “Data assemblages form part of a wider data landscape composed of many inter-related and interacting data assemblages and systems. Within the public sector, for example, there are thousands of data systems, each surrounded by a wider assemblage, that interact and work in concert to produce state services and forms of state control at local, regional and national scales. Often, this data landscape extends to the pan-national and the global, through inter-regional and worldwide data sets, data sharing arrangements and infrastructures, and the formulation of protocols, standards and legal frameworks (e.g., Global Spatial Data Infrastructures, INSPIRE). “ Kitchin’s Data Assemblage comprises a mix of disciplines, theories and concepts that direct the researcher toward collecting the ‘things’ related to the subject and objects of study, which then provide the raw materials to discursively analyse them in part and in whole. This provides a useful guide to examine the social, cultural, political, and material parts that characterize data and their related infrastructures.