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The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences
Abstract
Chapter 1: Conceptualising Data What are data? Kinds of data Data, information, knowledge, wisdom Framing data Thinking critically about databases and data infrastructures Data assemblages and the data revolution Chapter 2: Small Data, Data Infrastructures and Data Brokers Data holdings, data archives and data infrastructures Rationale for research data infrastructures The challenges of building data infrastructures The challenges of building data infrastructuresData brokers and markets Chapter 3: Open and Linked Data Open data Linked data The case for open data The economics of open data Concerns with respect to opening data Chapter 4: Big Data Volume Exhaustive Resolution and indexicality Relationality Velocity Variety Flexibility Chapter 5: Enablers and Sources of Big Data The enablers of big data Sources of big data Directed Data Automated data Volunteered data Chapter 6: Data Analytics Pre-analytics Machine learning Data mining and pattern recognition Data visualisation and visual analytics Statistical analysis Prediction, simulation and optimization Chapter 7: The Governmental and Business Rationale for Big Data Governing people Managing organisations Leveraging value and producing capital Creating better places Chapter 8: The Reframing of Science, Social Science and Humanities Research The fourth paradigm in science? The re-emergence of empiricism The fallacies of empiricism Data-driven science Computational social sciences and digital humanities Chapter 9: Technical and Organisational Issues Deserts and deluges Access Data quality, veracity and lineage Data integration and interoperability Poor analysis and ecological fallacies Skills and human resourcing Chapter 10: Ethical, Political, Social and Legal Concerns Data shadows and dataveillance Privacy Data security Profiling, social sorting and redlining Secondary uses, control creep and anticipatory governance Modes of governance and technological lock-ins Chapter 11: Making Sense of the Data Revolution Understanding data and the data revolution Researching data assemblages Final thoughts
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“Big data have fundamentally different properties to traditional 'small' datasets, being generated and processed in real time, exhaustive in scope and having fine resolution [1].”
From The ethics of smart cities and urban science (2016) · cites this paper as [1]
In the citing paper
“it should be noted that its approach is shaped by two more recent epistemological positions [1].”
From The ethics of smart cities and urban science (2016) · cites this paper as [1]
In the citing paper
“Such data practices are now common, enabling the rapid growth of data brokers which capture, gather together and repackage data into privately held data infrastructures for rent (for one time use or use under licensing conditions) or re-sale, along with data analysis and profiles [1,69].”
From The ethics of smart cities and urban science (2016) · cites this paper as [1]
In the citing paper
“The data and services these companies offer are used to perform a wide variety of tasks for which the data were never intended, including to predictively profile, socially sort, behaviourally nudge, and regulate, control and govern individuals and the various systems and infrastructures with which they interact [1].”
From The ethics of smart cities and urban science (2016) · cites this paper as [1]
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“…“a higher proportion of male residents, of unmarried population, and minority population; a higher unemployment rate; and more limited English speakers” tend to use 311 requests less frequently even though they experience similar issues with heat and hot water problems as their counterparts [11].”
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de Freitas, E., & Dixon‐Román, E. J. (2017) · Research in Education · doi:10.1177/0034523717723384 · Cited by 11
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“…administrative (Davies, 2010; Janssen, 2012; Kornberger, Meyer, Brandtner, & Höllerer, 2017; Pereira, Macadar, Luciano, & Testa, 2017), technological (Kitchin, 2014; Reichman, Jones, & Schildhauer, 2011; Zuiderwijk, Janssen, Choenni, Meijer, & Alibaks, 2012) and cultural contexts (Brunette et al.,…”
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“…(Esty and Rushing 2007), “algorithmic regulation” (O’Reilly 2013), “data-smart governance” (Goldsmith and Crawford 2014), “smart urbanism” (Gabrys 2014; Kitchin 2014a), “data-driven urbanism” (Kitchin 2017), and “data-driven city” (Flowers 2013) generally pointing to a “revolution” or…”
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Urban sprawl and the automation of building control in the peripheries of Nairobi
Reyes-Carranza, M., & Muthama, D. M. (2025) · Urban Geography · doi:10.1080/02723638.2025.2450307 · Cited by 9
“Literature indicates that smart city initiatives continue to raise questions about whether data is genuinely used in the public interest (Batty, 2013; Cinnamon, 2020; Kitchin, 2014a; Söderström & Datta, 2023).”
“Loading, please wait” – Temporality and (bodily) presence in mobile digital communication
Sebald, G. (2020) · Time & Society · doi:10.1177/0961463x20916566 · Cited by 9
Stock, R. (2022) · Mobilities · doi:10.1080/17450101.2022.2057810 · Cited by 16
“Yet the potential and importance of elevators and media infrastructures to open data remains to be explored (Kitchin 2014, 48).”
BackgroundGoverning through metrics in the digital age
Hansen, H. K. (2023) · Globalizations · doi:10.1080/14747731.2022.2156700 · Cited by 12
“…panoptic and post-panoptic forms of surveillance, social sorting is depending on ‘ dataveillance ’ , that is surveillance enacted through the processing and analysing of data records (Kitchin, 2014), searchable databases, the operation of networked communication systems, and algorithms.”
BackgroundThe data treadmill: water governance and the politics of pollution in rural Ireland
Hesse, A., Bresnihan, P., & White, J. M. (2023) · Local Environment · doi:10.1080/13549839.2023.2169668 · Cited by 13
“Enumeration, counting, and data generation is a form of recognition and legitimation (boyd and Crawford 2012; Crawford, Gray, and Miltner 2014; Kitchin 2014).”
BackgroundQiu, D., Lv, B., Chan, C. M. L., et al. (2023) · Sustainability · doi:10.3390/su15054455 · Cited by 9
La Smart City como imaginario socio-tecnológico
González, M. F. (2016) · Cuadernos de Investigación Urbanística · doi:10.20868/ciur.2016.109.3498 · Cited by 14
Information in the ecosystem: Against the “information ecosystem”
Norris, T. B., & Suomela, T. (2017) · First Monday · doi:10.5210/fm.v22i9.6847 · Cited by 9
Perturbation Methods for Protection of Sensitive Location Data: Smartphone Travel Survey Case Study
Badu-Marfo, G., Farooq, B., & Patterson, Z. (2019) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198119855999 · Cited by 9
“At the same time, governments have been eager to adopt ‘‘open data initiatives’’ that make data available for free reuse and republishing to everyone, without restrictions related to copyright or patents (8, 9).”
BackgroundOpen Data Publishing by Public Libraries
Weber, N., & Norlander, B. (2019) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2019.00031 · Cited by 9
Why ‘clinical teaching’? An interdisciplinary analysis of metaphor in initial teacher preparation
McKnight, L., & Morgan, A. (2019) · Journal of Education for Teaching International Research and Pedagogy · doi:10.1080/02607476.2019.1708629 · Cited by 9
“The use of normative scientific language is central to education’s key tensions (Kitchin 2014); medical metaphor in education is not only familiar, but has ardently been critiqued, for example, for how it ‘uses the language of science and efficiency to enhance its own status’ (Apple 1986/2013, 142).”
BackgroundUnwaged labour intensified: Volunteer management and work targets at a UK charity
Read, R. (2020) · The Sociological Review · doi:10.1177/0038026120938291 · Cited by 10
Investigative approaches to researching information technology companies
Carter, D. L., Acker, A., & Sholler, D. (2021) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24446 · Cited by 9
Kim, D., Gil, M., Nguyen, M. C., et al. (2021) · ETRI Journal · doi:10.4218/etrij.2020-0298 · Cited by 9
Jørgensen, A. M., Webb, C., Keddell, E., & Ballantyne, N. (2021) · Nordic Social Work Research · doi:10.1080/2156857x.2021.1999846 · Cited by 9
Challenging opacity, embracing fuzziness: Geographical thought and praxis in a post-truth age
Shelton, T. (2023) · Dialogues in Human Geography · doi:10.1177/20438206231157891 · Cited by 9
“Despite the proliferation of new forms of big and open data (Kitchin, 2014), and a resurgence of interest in critical approaches to mapping and quan-ti fi cation among human geographers in recent years ( The Canadian Geographer , 2018; ACME , 2022), it seems that the data most necessary to make…”
BackgroundNews Media Monitoring Capabilities in 14 European Countries: Problems and Best Practices
Harro‐Loit, H., & Eberwein, T. (2023) · Media and Communication · doi:10.17645/mac.7199 · Cited by 9
“This hierarchical model, that has also been presented in the form of a “knowledge pyramid” (Kitchin, 2014), exemplifies that data always precedes information, which precedes knowledge, which precedes understanding and wisdom (see Figure 2).”
BackgroundCounting people in the data revolution era: challenges and opportunities for population censuses*
Aragona, B., & Zindato, D. (2016) · International Review of Sociology · doi:10.1080/03906701.2016.1244926 · Cited by 9
“That is why some talk about a ‘ data deluge ’ (The Economist 2010) able to radically change both the individual and the social behaviours and others (Kitchin 2014) label the present time as ‘ the data revolution era ’ .”
Background
Metadata
- Authors
- Kitchin, R.
- Corresponding author
- Kitchin, Rob
- ORCID
- Kitchin, Rob
- Venue
- SAGE Publications Ltd eBooks
- Publisher
- SAGE Publications Ltd
- Published
- 2014
- OpenAlex
- W584790758
- Cited by
- 1,469
- References
- 0
- Citations by year
- 2026: 170 · 2025: 150 · 2024: 120 · 2023: 126 · 2022: 156 · 2021: 143 · 2020: 113 · 2019: 123 · 2018: 99 · 2017: 108 · 2016: 100 · 2015: 52 · 2014: 8
- FWCI
- 135.08
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- SAGE Publications Ltd eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- data revolution, big data, open data, data infrastructure, data brokers, data repositories, linked data, data analytics, data-driven research, computational social science, digital humanities, data quality, data integration, interoperability, data ethics, privacy, dataveillance, profiling, anticipatory governance, technological lock-in, data assemblages
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