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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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Dixon‐Román, E. J. (2017) · Research in Education · doi:10.1177/0034523717723387 · Cited by 44
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“This later point emerges well in Kitchin (2014a) work on smart cities and real time as well as in Lyon’s (2018) latest work on surveillance culture.”
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Olugbade, T., Bieńkiewicz, M. M. N., Barbareschi, G., D’Amato, V., Oneto, L., Camurri, A., Holloway, C., Björkman, M., Keller, P. E., Clayton, M. R. L., et al. (2022) · ACM Computing Surveys · doi:10.1145/3534970 · Cited by 39
“Data is central to the majority of scientific and engineering endeavor [54] with the area of movement analysis and modelling being no exception.”
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BackgroundLyon, D. (2022) · Internet Policy Review · doi:10.14763/2022.4.1673 · Cited by 51
Au, Y. (2022) · Territory Politics Governance · doi:10.1080/21622671.2022.2153160 · Cited by 36
“The focus here is on digital data assemblages, or the socio-political, technical and economic apparatuses that constitute the production, circulation, and deployment of digital data in complex and intertwined ways (Iliadis & Russo, 2016; Kitchin, 2014).”
BackgroundPiedra-Castro, W. I., Cajamarca-Correa, M. A., Burbano-Buñay, E. S., & Moreira-Alcivar, E. F. (2024) · Journal of Economic and Social Science Research · doi:10.55813/gaea/jessr/v4/n3/123 · Cited by 36
Severo, M., Feredj, A., & Romele, A. (2016) · Policy & Internet · doi:10.1002/poi3.127 · Cited by 41
More bark than bytes? Reflections on 21+ years of geocomputation
Harris, R., O’Sullivan, D., GAHEGAN, M. N., Charlton, M. E., Comber, A., Longley, P., Brunsdon, C., Malleson, N., Heppenstall, A. J., Singleton, A. D., et al. (2017) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/2399808317710132 · Cited by 34
Trepal, D., Lafreniere, D. J., & Gilliland, J. (2020) · Historical Archaeology · doi:10.1007/s41636-020-00245-5 · Cited by 37
“…of CC-HSDI is freely available, questions of how to provide access to costly, restricted-access, and/or sensitive data (de Kleijn et al. 2014; Kitchin 2014) are a shared concern with all SDI projects and will require further evolutions of the HSDI interface and sharing model as those types…”
Backgroundvan der Sloot, B. (2015) · Utrecht Journal of International and European Law · doi:10.5334/ujiel.cp · Cited by 36
Morawiec, P., & Sołtysik-Piorunkiewicz, A. (2023) · Sustainability · doi:10.3390/su15054646 · Cited by 36
Swetnam, T. L., Gillan, J. K., Sankey, T. T., et al. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2017.02144 · Cited by 35
Robertson, C., & Feick, R. (2018) · Transactions in GIS · doi:10.1111/tgis.12321 · Cited by 34
Imagining big data: Illustrations of “big data” in US news articles, 2010–2016
Pentzold, C., Brantner, C., & Fölsche, L. (2018) · New Media & Society · doi:10.1177/1461444818791326 · Cited by 37
Naraine, M. L., & Wanless, L. A. (2020) · Sports Innovation Journal · doi:10.18060/23898 · Cited by 43
“Email: wanless@ohio.edu SIJ be the most significant (Kitchin, 2014).”
BackgroundInvestigating Algorithmic Misconceptions in a Media Context: Source of a New Digital Divide?
Zarouali, B., Helberger, N., & de Vreese, C. H. (2021) · Media and Communication · doi:10.17645/mac.v9i4.4090 · Cited by 48
“In addition, algo‐ rithms are constantly being tweaked, tuned, re‐written, repaired, or deleted; as such, they are not fully indepen‐ dent technical objects (Kitchin, 2014; Seaver, 2018) and co‐evolve in their interactions with humans.”
BackgroundJefferson, B. J. (2018) · Urban Geography · doi:10.1080/02723638.2018.1446587 · Cited by 38
“Kitchin (2014) explores “data ontologies” toward this end, understood as the sociotechnical conditions that undergird the harvesting, analysis, and circulation of data.”
BackgroundInfluentialCharacterizing Disinformation Risk to Open Data in the Post-Truth Era
Colborne, A., & Smit, M. (2020) · Journal of Data and Information Quality · doi:10.1145/3328747 · Cited by 34
“Publication date: June 2020. applications [Jetzek et al. 2014], including applications driven by Big Data analytics and machine learning [Kitchin 2014].”
MethodActivity Spaces and Big Data Sources in Segregation Research: A Methodological Review
Müürisepp, K., Järv, O., Tammaru, T., et al. (2022) · Frontiers in Sustainable Cities · doi:10.3389/frsc.2022.861640 · Cited by 34
“Despite the challenges (Zook et al., 2017), big data sources are increasingly mobilized in many social sciences (Kitchin, 2014; Halford and Savage, 2017) for “supplementing existing data sources and providing a richer understanding of the multiple social and spatial processes” in our cities (Zook…”
BackgroundInfluentialUrban Echoes: Exploring the Dynamic Realities of Cities through Digital Twins
Boccardo, P., Riccia, L. L., & Yadav, Y. (2024) · Land · doi:10.3390/land13050635 · Cited by 35
Algorithms, ontology, and social progress
Iliadis, A. (2018) · Global Media and Communication · doi:10.1177/1742766518776688 · Cited by 33
“…and Powell, 2014; Crawford et al., 2014; Dalton and Thatcher, 2014; Dencik et al., 2016; Hoffmann, 2017; Iliadis and Russo, 2016; Johnson, 2014; Kitchin, 2014a; 2014b; Kitchin and Lauriault, 2014; Markham, 2013; Mayernik and Acker, 2018; Milan and van der Velden, 2016; Neff et al., 2017;…”
Digital Disruption In Teaching And Testing
Wyatt‐Smith, C., Lingard, B., & Heck, E. (2021) · Routledge eBooks · doi:10.4324/9781003045793 · Cited by 42
Ethnographic data in the age of big data: How to compare and combine
Bjerre-Nielsen, A., & Glavind, K. L. (2022) · Big Data & Society · doi:10.1177/20539517211069893 · Cited by 32
“Kitchin (2014) calls these data types secondary data.”
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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