The DOI matches its Crossref record.
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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cited by
Sorted by citations
Items 301–400 of 1,001
Global networks of money and information at the crossroads: Correspondent banking and SWIFT
Robinson, G., Dörry, S., & Derudder, B. (2022) · Global Networks · doi:10.1111/glob.12408 · Cited by 32
Accounting for the social: Investigating commensuration and Big Data practices at Facebook
van der Vlist, F. (2016) · Big Data & Society · doi:10.1177/2053951716631365 · Cited by 43
“…terms – one such assemblage ‘composed of a set of apparatus and elements that are variously scaled (e.g., from local organisations and materialities to dispersed teams, national and supranational laws, and global markets) but are nonetheless bound in a unique constellation’ (Kitchin, 2014b: 186).”
Gellert, R. (2018) · Regulation & Governance · doi:10.1111/rego.12349 · Cited by 35
“In this sense, data has a representational dimension (see, e.g. Kitchin 2014), and is a conveyer of information.”
BackgroundBig Data: A New Empiricism and its Epistemic and Socio-Political Consequences
Rieder, G., & Simon, J. (2017) · Springer eBooks · doi:10.1007/978-3-658-12153-2_4 · Cited by 38
Evaluating gender representativeness of location-based social media: a case study of Weibo
Yuan, Y., Wei, G., & Lu, Y. (2018) · Annals of GIS · doi:10.1080/19475683.2018.1471518 · Cited by 32
“These studies naturally raised concerns that LBSM data sets may not be accurate, objective, or representative of the entire population (Kitchin 2014).”
BackgroundSeven reasons to question the hegemony of Visible Learning
McKnight, L., & Whitburn, B. (2018) · Discourse Studies in the Cultural Politics of Education · doi:10.1080/01596306.2018.1480474 · Cited by 33
“These are examples of the conflation of data volume with insight (Kitchin, 2014) as addiction intensifies.”
BackgroundHow are information deserts created? A theory of local information landscapes
Lee, M. A., & Butler, B. S. (2018) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24114 · Cited by 35
Georgiou, M. (2019) · Television & New Media · doi:10.1177/1527476419857683 · Cited by 36
“…(Hintz et al. 2019; Lyon 2017), or about decisions made on people’s right to work or consume based on the analysis of data they have no control over (Kitchin 2014), assume preexisting freedoms that the urban or migrant subaltern, always oversurveilled and controlled (Hall et al. 1978; Mattern…”
BackgroundWyatt, D., Lampon, S., & McKevitt, C. (2020) · Sociology of Health & Illness · doi:10.1111/1467-9566.13101 · Cited by 31
GEOGRAPHY IN THE BIG DATA AGE: AN OVERVIEW OF THE HISTORICAL RESONANCE OF CURRENT DEBATES
Ferreira, D., & Vale, M. (2020) · Geographical Review · doi:10.1080/00167428.2020.1832424 · Cited by 37
“On the one hand, it has been argued that big data allows a new form of doing science based solely on data processing, often referred to as data-driven science, which has the capacity of explaining everything through data analysis (Kelling and others 2009; Miller 2010; Kitchin 2014b).”
BackgroundInfluentialSpatio-temporal dynamics in airport catchment areas: The case of the New York Multi Airport Region
Teixeira, F. M., & Derudder, B. (2020) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2020.102916 · Cited by 33
Dingelstad, J., Borst, R. T., & Meijer, A. J. (2022) · Public Personnel Management · doi:10.1177/00910260221111744 · Cited by 32
Modeling behavior dynamics using computational psychometrics within virtual worlds
Cipresso, P. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01725 · Cited by 33
Digital humanities, knowledge complexity, and the five ‘aporias’ of digital research
Edmond, J., & Lehmann, J. (2021) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqab031 · Cited by 34
“…in opposition to the methodologically controlled navigation through terra incognita which characterizes scientific research processes, rather than based upon ‘hypotheses that are more widely tested, which in turn are used to build and refine a theory that explains them’ (Kitchin, 2014, p. 135).”
BackgroundPhilosophy of Big Data: Expanding the Human-Data Relation with Big Data Science Services
Swan, M. (2015) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice.2015.29 · Cited by 32
A social cartography of analytics in education as performative politics
Prinsloo, P. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12872 · Cited by 35
Rummens, A., Snaphaan, T., de Weghe, N. V., den Poel, D. V., Pauwels, L. J. R., & Hardyns, W. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10060369 · Cited by 45
“In this data-intensive era, the global adoption of mobile information and communication devices and human interaction through these mobile devices create digital footprints of people, both in space and time [55,56].”
BackgroundBüchner, S., & Dosdall, H. (2021) · KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie · doi:10.1007/s11577-021-00752-0 · Cited by 36
“Im Zuge der Digitalisierung exploriert der Österreichische Arbeitsmarktservice wie andere Organisationen auch, wie sich die eigenen Datenbestände unter den neuen technologischen Bedingungen nutzen lassen (Kallinikos et al. 2013; Kitchin 2014).”
BackgroundFunding models for Open Access digital data repositories
Kitchin, R., Collins, S., & Frost, D. (2015) · Online Information Review · doi:10.1108/oir-01-2015-0031 · Cited by 32
“…curated and managed, staffed by dedicated and specialist personnel who are dealing with multiple depositors, diverse interlinked data sets, and varying formats, standards, protocols and technologies, and seek to add value and ensure continuity (Borgman, 2007; Lauriault et al., 2007; Kitchin, 2014).”
BackgroundSelf-citationPutting big data in its place: aRegional Studies and Regional Scienceperspective
Rae, A., & Singleton, A. D. (2015) · Regional Studies Regional Science · doi:10.1080/21681376.2014.990678 · Cited by 32
“The critiques of big data alluded to above have been explored elsewhere at greater length (e.g. Kitchin, 2014b; Lazer, Kennedy, King, & Vespignani, 2014) so in this Editorial we wish to position the contribution in relation to the big data backlash that has emerged in recent months (e.g.”
BackgroundThinking Critically About and Researching AlgorithmsPreprint
Kitchin, R. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2515786 · Cited by 32
Self-citationSetting data free: The politics of open data for food and agriculture
Fairbairn, M., & Kish, Z. (2023) · New Media & Society · doi:10.1177/14614448231174520 · Cited by 30
“Nonprofits were founded to advocate for open data, such as the Open Knowledge Foundation and Sunlight Foundation (Kitchin, 2014).”
BackgroundThe Internet of Things: Social dimensions
Lupton, D. A. (2020) · Sociology Compass · doi:10.1111/soc4.12770 · Cited by 31
Järv, O., Tominga, A., Müürisepp, K., & Silm, S. (2021) · Journal of Location Based Services · doi:10.1080/17489725.2021.1887526 · Cited by 37
“Novel big data sources have diverse characteristics (Kitchin 2014), whereas sources that provide information on people from spatial and temporal perspectives are also referred to as mobile big data (Poom et al. 2020).”
BackgroundHalpern, O., Jagoda, P., Kirkwood, J. W., & Weatherby, L. (2021) · Critical Inquiry · doi:10.1086/717320 · Cited by 34
The use of photos to investigate ecological change
Depauw, L., Blondeel, H., Lombaerde, E. D., Pauw, K. D., Landuyt, D., Lorer, E., Vangansbeke, P., Vanneste, T., Verheyen, K., & Frenne, P. D. (2022) · Journal of Ecology · doi:10.1111/1365-2745.13876 · Cited by 31
“Social media data are generally referred to as ‘big data’ (Crampton et al., 2013; Kitchin, 2014), and the volume of usergenerated data worldwide is overwhelming (Toivonen et al., 2019).”
BackgroundIssar, S. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2233138 · Cited by 50
“Narratives of data are crucial for control over information asymmetries (Kitchin, 2014), while data-driven narratives are important for addressing the risk of being subjected to asymmetry-driven algorithmic invisibility, or to put it more simply, the risk of being “ irrelevant ” to an algo-rithm,…”
Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS
Canali, S. (2016) · Big Data & Society · doi:10.1177/2053951716669530 · Cited by 42
“…raised questions about these claims, by highlighting the difficult and complicated tasks needed for the use of Big Data in the sciences (Leonelli, 2014a), the technical impossibility of many data-driven claims (Kitchin, 2014b) and the role of hypotheses in research based on Big Data (Ratti, 2015).”
BackgroundTsatsou, P. (2017) · New Media & Society · doi:10.1177/1461444816688274 · Cited by 31
“Such barriers include the narrow scope of data sets; limited access; poor data quality, veracity and fidelity; ill-defined lineage; poor interoperability; suspect analytics and ecological fallacies; and a weakly developed skills base and organisational receptiveness (Kitchin, 2014b).”
InfluentialPersonal Data Practices in the Age of Lively DataPreprint
Lupton, D. A. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2636709 · Cited by 31
Digital analytics in professional work and learning
Edwards, R., & Fenwick, T. (2016) · Studies in Continuing Education · doi:10.1080/0158037x.2015.1074894 · Cited by 30
“…the growth and unexamined nature of these sorts of analytics, as they permeate professional practice, is creating a particular form of rationality, and potentially a new epistemological order (Kallinikos 2010; Kitchin 2014) and this needs explicit discussion to try to ensure beneficial effects.”
BackgroundOpen government data: critical information management perspectives
Shepherd, E., Bunn, J., Flinn, A., Lomas, E., Sexton, A., Brimble, S., Chorley, K., Harrison, E., Lowry, J., & Page, J. (2019) · Records Management Journal · doi:10.1108/rmj-08-2018-0023 · Cited by 30
Tan, G. K. S. (2021) · Urban Studies · doi:10.1177/00420980211039407 · Cited by 30
“Understanding the development of smart cities as ‘a set of interlocking discourses that justifies and sus-tains new developments and naturalises and reproduces their use’ (Kitchin, 2014: 113) is helpful in tracing their development.”
BackgroundSqueaky wheels: Missing data, disability, and power in the smart city
Deitz, S. L., Lobben, A. K., & Alferez, A. (2021) · Big Data & Society · doi:10.1177/20539517211047735 · Cited by 33
“There are no established metadata standards for open data and few guidelines about what to include, making them a wild west of localized quantification (Kitchin, 2014).”
BackgroundSustainable biobanks: a case study for a green global bioethics
Samuel, G. N., Lucivero, F., & Lucassen, A. (2022) · Global Bioethics · doi:10.1080/11287462.2021.1997428 · Cited by 31
“10 The advent of biobanking ’ s growing popularity at the beginning of the century was accompanied by an explosion in data management and storage capabilities that are now a prominent feature of contemporary society (the “ big data revolution ” (Kitchin, 2014)).”
Big data phenotyping in rare diseases: some ethical issues
Hallowell, N., Parker, M. J., & Nellåker, C. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0067-8 · Cited by 32
“Anticipatory value is not just a form of performative value, but also relates to the economic opportunities that data afford.(14) Private companies, nation states, public institutions (health systems, universities, biobanks), and academic researchers are increasingly aware of the value accumulating within big datasets…”
Background‘I like to run to feel’: embodiment and wearable mobile tracking devices in distance running
Toner, J., Allen‐Collinson, J., Jackman, P. C., Jones, L., & Addrison, J. (2023) · Qualitative Research in Sport Exercise and Health · doi:10.1080/2159676x.2023.2225516 · Cited by 37
Sabbata, S. D., Ballatore, A., Miller, H. J., Sieber, R. E., Tyukin, I., & Yeboah, G. (2023) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2023.2279978 · Cited by 33
“The widespread availability of open satellite imagery and the increased digitalisa - tion of our daily lives have led to the emergence of large ‘exhaust’ datasets (Kitchin 2014) that can be reused.”
Kassen, M. (2018) · Canadian Public Administration · doi:10.1111/capa.12251 · Cited by 29
Bartlett, A., Lewis, J., Reyes-Galindo, L. I., & Stephens, N. (2018) · Big Data & Society · doi:10.1177/2053951718768831 · Cited by 32
“Over the past decade, ‘Big Data’ has been positioned as the indispensable mode of 21st century research across academia (Boyd and Crawford, 2012; Kitchin, 2014a).”
BackgroundBig data: perspectives of using in urban planning and management
Ivanov, N., & Gnevanov, M. (2018) · MATEC Web of Conferences · doi:10.1051/matecconf/201817001107 · Cited by 29
Velichety, S., Ram, S., & Bockstedt, J. C. (2019) · Journal of Management Information Systems · doi:10.1080/07421222.2019.1598692 · Cited by 28
Data with its boots on the ground: Datawalking as research method
van Es, K. F., & de Lange, M. (2020) · European Journal of Communication · doi:10.1177/0267323120922087 · Cited by 34
“New technical media and platforms capture nearly every aspect of everyday life in a wide variety of environments as data (Kitchin, 2014; Mayer-Sch € onberger and Cukier, 2013; Van Dijck 2014).”
BackgroundOverton, M., & Kleinschmit, S. W. (2021) · Teaching Public Administration · doi:10.1177/01447394211004990 · Cited by 30
“Capture is purposefully measured in surveys, sensors, etc. while digital exhaust is the by-product of a separate data generation process not centered around an application’s primary purpose (Kitchin, 2014).”
BackgroundThe role of sensors in the production of smart city spaces
Enlund, D., Harrison, K., Ringdahl, R., Börütecene, A., Löwgren, J., & Angelakis, V. (2022) · Big Data & Society · doi:10.1177/20539517221110218 · Cited by 27
“…and citizen-centric models, recent scholarship on smart cities has coined a number of hybrid terms such as ‘socio-technical assemblages’ (Kitchin, 2014) or ‘technopublic assemblages’ (Cowley et al., 2018), which understand these spaces as comprising a heterogeneous collection of…”
BackgroundMapping information wealth and poverty: the geography of gazetteers
Graham, M. J., & Sabbata, S. D. (2015) · Environment and Planning A Economy and Space · doi:10.1177/0308518x15594899 · Cited by 29
The digital mundane: social media and the military
Maltby, S., & Thornham, H. (2016) · Media Culture & Society · doi:10.1177/0163443716646173 · Cited by 30
“…places (Hjorth, 2011), issues of surveillance and the disciplined or quantified subject (Burrows, 2012; Dodge and Kitchin, 2005; Foucault, 1977; Kitchin, 2014; Van Dijck, 2014) or university students checking social media during lectures (Bundgens-Kosten and Preussler, 2013), this is not in…”
BackgroundClayton, J., Donovan, C., & Macdonald, S. J. (2016) · Geoforum · doi:10.1016/j.geoforum.2016.07.001 · Cited by 29
Offsite Manufacturing Research: A Systematic Review of Methodologies Used
Ehwi, R. J., Oti‐Sarpong, K., Shojaei, R. S., & Burgess, G. (2021) · Construction Management and Economics · doi:10.1080/01446193.2021.2007537 · Cited by 28
“Data relates to raw material produced by abstracting the world into categories, measures and other representational forms such as numbers, characters, symbols, images, etc. (Kitchin 2014a).”
BackgroundThe (im)possibilities of open data?
Jamieson, D., Wilson, R., & Martin, M. (2019) · Public Money & Management · doi:10.1080/09540962.2019.1611240 · Cited by 28
“Kitchin (2014) echoes this, stating that attention has been drawn to data produced by state agencies as they have already been funded by the public purse.”
BackgroundInfluentialCieslik, K., & Margócsy, D. (2022) · Progress in Development Studies · doi:10.1177/14649934221076580 · Cited by 35
“While a number of scholars praise big data especially in relation to economic planning and policy-making (Kitchin, 2014; Kleine and Unwin, 2009), others point to its many limitations as well as the threats it poses to personal freedom and democracy (arora, 2016; Crawford and Schultz, 2014; O’Neil,…”
BackgroundThe future of smart glasses: An essay about challenges and possibilities with smart glasses
Due, B. L. (2014) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 27
Algorithmic vulnerabilities and the datalogical: Early motherhood and tracking-as-care regimes
Thornham, H. (2019) · Convergence The International Journal of Research into New Media Technologies · doi:10.1177/1354856519835772 · Cited by 27
“But I also want to suggest that the data of both are ‘constitutive’ (Kitchin, 2014: 21) and ‘framing’ (Gitelman and Jackson, 2013: 5) within a much wider datalogical system of healthcare and intervention.”
BackgroundAlgorithmic governance in public sector: is digitization a key to effective management
Pūraitė, A., Zuzevičiūtė, V., Bereikienė, D., Skrypko, T., & Shmorgun, L. (2020) · Independent Journal of Management & Production · doi:10.14807/ijmp.v11i9.1400 · Cited by 32
Data diaries: A situated approach to the study of data
Tkacz, N., Martins, M. H. D. M., de Albuquerque, J. P., Horita, F., & Dolif, G. (2021) · Big Data & Society · doi:10.1177/2053951721996036 · Cited by 31
“Finally, without wishing to revisit debates about what constitutes Big Data, in Kitchin’s influential writing on the topic he notes that ‘Big Data has existed in some domains, such as remote sensing, weather prediction, and financial markets, for some time . . . ’ (2014a: 2).”
BackgroundInfluentialThe Evolution of Data-Information-Knowledge-Wisdom in Nursing Informatics
Ronquillo, C., Currie, L. M., & Rodney, P. (2016) · Advances in Nursing Science · doi:10.1097/ans.0000000000000107 · Cited by 27
Smith, K. L., & Shade, L. R. (2018) · Big Data & Society · doi:10.1177/2053951718805214 · Cited by 31
“In our analysis, we are also informed by political economy, which is identified by Kitchin (2014) as an apparatus to interrogate the broader context in which a data assemblage emerges.”
MethodPorlezza, C. (2023) · Journalism · doi:10.1177/14648849231190232 · Cited by 32
“(Kitchin 2014: p. 80) showed that the rapid growth of data in society is due to the simultaneous development of different technologies, infrastructures, techniques and processes, but also their implementation and embedding in social practices, spaces, and businesses: “ This new knowledge…”
BackgroundDeep Bayesian network architecture for Big Data mining
Njah, H., Jamoussi, S., & Mahdi, W. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4418 · Cited by 26
Why EdTech is always right: students, data and machines in pre-emptive configurations
Witzenberger, K., & Gulson, K. Ν. (2021) · Learning Media and Technology · doi:10.1080/17439884.2021.1913181 · Cited by 27
Contextualizing realism: An analysis of acts of seeing and recording in Digital Twin datafication
Korenhof, P., Giesbers, E., & Sanderse, J. (2023) · Big Data & Society · doi:10.1177/20539517231155061 · Cited by 25
“Data are produced “ by abstracting the world into categories, measures and other representational forms — numbers, characters, symbols, images, sounds, electromagnetic waves, bits ” (Kitchin, 2014: 1).”
BackgroundMethodInfluentialPrototyping sustainable mobility practices: user-generated data in the smart city
Valdez, M., Cook, M., Langendahl, P.-A., Roby, H., & Potter, S. (2017) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2017.1297399 · Cited by 26
“The specific mechanisms through which such technologies may develop and the promised benefits may be delivered, however, remain vague (Greenfield 2013, Kitchin 2014b, p 163).”
BackgroundDigital subjectivation and financial markets: Criticizing Social Studies of Finance with Lazzarato
Christiaens, T. (2016) · Big Data & Society · doi:10.1177/2053951716662897 · Cited by 23
“If CDS theorists are right to suggest that data are never raw, but always dependent on social contexts that render data meaningful (Kitchin, 2014: 20), then the question arises what kind of subject is needed to cook the data.”
BackgroundInfluentialNew geographies of platform capitalism: The case of digital monopolization in Turkey
Yeşіlbağ, M. (2022) · Big Data & Society · doi:10.1177/20539517221124585 · Cited by 27
“…political mobilization, from governance and fi nance to urban development and labor relations, it is hard to imagine a domain that has not been subject to the multifaceted implications of digital platforms (Beraldo and Milan, 2019; Dazzi, 2019; Hendrikse et al., 2021; Kitchin, 2014; Zuboff, 2019).”
BackgroundDove, E. S., & Özdemir, V. (2015) · Laws · doi:10.3390/laws4030515 · Cited by 27
Rhizomatic data assemblages: mapping new possibilities for urban housing data
Dalton, C. M. (2019) · Urban Geography · doi:10.1080/02723638.2019.1645553 · Cited by 26
Wigley, E., & Rose, G. (2020) · Geografiska Annaler Series B Human Geography · doi:10.1080/04353684.2020.1747943 · Cited by 26
“…in urban environments, and visions of CAVs are often part of, or at least very similar to, prevalent depictions of the smart city as entrepreneurial, efficient and frictionless (Kitchin 2014; Goodspeed 2015; Luque-Ayala and Marvin 2016); and Milton Keynes is one of the UK’s leading smart cities.”
BackgroundFabricated Data Bodies: Reflections on 3D Printed Digital Body Objects in Medical and Health Domains
Lupton, D. A. (2014) · Social Theory & Health · doi:10.1057/sth.2015.3 · Cited by 25
Sarkar, D., & Chapman, C. A. (2021) · Tropical Conservation Science · doi:10.1177/19400829211014740 · Cited by 25
Open health data: Mapping the ecosystem
Heijlen, R., & Crompvoets, J. (2021) · Digital Health · doi:10.1177/20552076211050167 · Cited by 24
The Continuing Evolution of Official Statistics: Some Challenges and Opportunities
MacFeely, S. (2016) · Journal of Official Statistics · doi:10.1515/jos-2016-0041 · Cited by 25
Electricity as (Big) Data: Metering, spatiotemporal granularity and value
Kragh-Furbo, M., & Walker, G. P. (2018) · Big Data & Society · doi:10.1177/2053951718757254 · Cited by 23
“Our main concern is with electricity as Big Data and it is only with the arrival of digital devices, infrastructures and analytical capabilities that key Big Data qualities (Kitchin, 2014) begin to materialise.”
BackgroundOut of the loop? On the radical and the routine in urban big data
Barns, S. (2021) · Urban Studies · doi:10.1177/00420980211014026 · Cited by 26
“There is much attention today towards the many radical implications of big data in cities (Allam and Dhunny, 2019; Bettencourt, 2013; Kitchin, 2014; Thakuriah et al., 2017), but less is said about what is more quotidian, and the role of the routine in the production of big data.”
BackgroundStudying Up Machine Learning Data: Why Talk About Bias When We Mean Power?Preprint
Miceli, M., Posada, J., & Yang, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08131 · Cited by 25
“Technological development is sociotechnical in nature and data, as an abstraction [28, 55], is not given but created through human discretion [68] and shaped by power dynamics [62].”
BackgroundThe insurgent smart city: How a social movement created an alternative imaginary of the smart city
Stokols, A. (2023) · Journal of Urban Affairs · doi:10.1080/07352166.2023.2216887 · Cited by 26
“Telegram’s combination of channels and groups, its automated bot functions, and encrypted capabilities are ● Real-time data collection (Kitchin, 2014a) ● Data from mobile phones to analyze urban systems and improve their efficiency (Ratti et al., 2006; Townsend, 2000) ● Real-time monitoring of…”
InfluentialSmart Policing: A Critical Review of the Literature
Afzal, M. Y., & Panagiotopoulos, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57599-1_5 · Cited by 30
“Themes were compared and grouped as follows: (1) sources of data under Kitchin’s [19] typology, (2) analytical techniques were inductively grouped under roles to policing (3) policing activities under Braga & Schnell’s [20] intervention dimensions (environmental, enforcement and community outreach)…”
Infrastructures of tracking: Mapping the ecology of third-party services across top sites in the EU
Helles, R., Lomborg, S., & Lai, S. S. (2020) · New Media & Society · doi:10.1177/1461444820932868 · Cited by 32
“Critical research into datafication has also been pursued in infrastructure and data governance studies (Flyverbom, 2016; Just, 2018; Kitchin, 2014) concerned with issues of power, regulation and communication policy.”
BackgroundKwaśnicka, D., ten Hoor, G. A., van Dongen, A., Gruszczyńska, E., Hagger, M. S., Hamilton, K., Hankonen, N. E., Heino, M. T. J., Kotzur, M., Noone, C., et al. (2020) · Health Psychology Review · doi:10.1080/17437199.2020.1844037 · Cited by 25
“Educating future researchers to pursue the standards of transparency and openness of data and materials provide vast opportunities for the scientific progress (Kitchin, 2014).”
BackgroundDis/organising visibilities: Governmentalisation and counter-transparency
Weiskopf, R. (2021) · Organization · doi:10.1177/1350508421995751 · Cited by 24
Empowering Citizens with Open Data by Urban Hackathons
Concilio, G., Molinari, F., & Morelli, N. (2017) · doi:10.1109/cedem.2017.28 · Cited by 24
Falco, E., Malavolta, I., Radzimski, A., Ruberto, S., Iovino, L., & Gallo, F. (2017) · Journal of Urban Technology · doi:10.1080/10630732.2017.1362901 · Cited by 24
“BusOnAir’s REST API publicly exposes transit data by following the principles of the open data movement (Kitchin, 2014).”
BackgroundInfrastructuring “data-driven” environmental governance in Louisiana’s coastal restoration plan
Nost, E. (2020) · Environment and Planning E Nature and Space · doi:10.1177/2514848620909727 · Cited by 25
“…I refer to data infrastructures—“institutional, physical and digital means for storing, sharing and consuming data across networked technologies” (Kitchin, 2014: 32)—in the sense of archives, repositories, and cyber-infrastructures (Kitchin and Lauriault, 2015), rather than server farms (e.g.…”
Backgroundvan Es, K. F., Schäfer, M. T., & Wieringa, M. (2021) · Medien & Kommunikationswissenschaft · doi:10.5771/1615-634x-2021-1-46 · Cited by 28
A new era for digital twins: progress and industry adoption
Hasan, S., & Crawford, C. W. (2025) · Digital Twin · doi:10.1080/27525783.2025.2555877 · Cited by 34
TAKING CITY RANKINGS SERIOUSLY : Engaging with Benchmarking Practices in Global Urbanism
Acuto, M., Pejic, D., & Briggs, J. (2021) · International Journal of Urban and Regional Research · doi:10.1111/1468-2427.12974 · Cited by 23
Gawlak, A., Matuszewska, M., & Ptak-Wojciechowska, A. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18094519 · Cited by 23
Diffracting Enfolding Futures: Critical Inquiry in Quantitative Educational Research
Dixon‐Román, E. J. (2015) · Critical education · doi:10.14288/ce.v7i14.186147 · Cited by 23
A Case for the Case Study: How and Why They Matter
Longhofer, J. L., Floersch, J. E., & Hartmann, E. (2017) · Clinical Social Work Journal · doi:10.1007/s10615-017-0631-8 · Cited by 24
Representationalism at work: dashboards and data analytics in urban education
Crooks, R. N. (2017) · Educational Media International · doi:10.1080/09523987.2017.1408267 · Cited by 23
“Big Data is often defined in terms of increased velocity, volume, and variety: data have become superabundant, produced continuously by new sensors, devices, and platforms (Kitchin, 2014; Matzner, 2016).”
BackgroundMethodInfluentialGiardullo, P. (2023) · Sociology Compass · doi:10.1111/soc4.13100 · Cited by 21
A Survey on Large Language Model-based Agents for Statistics and Data Science
Sun, M., Han, R., Jiang, B., Qi, H., Sun, D., Yuan, Y., & Huang, J. (2025) · The American Statistician · doi:10.1080/00031305.2025.2561140 · Cited by 57
“Despite its widespread utility, data analysis is often perceived as a challenging field with a significant “entry barrier” (Jordan and Mitchell 2015; Cao 2017), typically requiring knowledge in areas such as statistics, data science, and computer science (Kitchin 2014).”
Algorithmic Decision-Making, Spectrogenic Profiling, and Hyper-Facticity in the Age of Post-Truth
Weiskopf, R. (2020) · Le foucaldien · doi:10.16995/lefou.62 · Cited by 22
Design-based research in mathematics education: trends, challenges and potential
Fowler, S., Cutting, C., Fiedler, S. H. D., & Leonard, S. N. (2022) · Mathematics Education Research Journal · doi:10.1007/s13394-021-00407-5 · Cited by 35
‘Real-time’ air quality channels: A technology review of emerging environmental alert systems
Schulte, K. (2022) · Big Data & Society · doi:10.1177/20539517221101346 · Cited by 25
“While AQ channel trace data presents a promising opportunity to examine artefacts of patterns of interaction with AQ information, speci fi c ethical and interpretive issues warrant careful methodological consideration (Amoore, 2011; Kitchin, 2014; Neff et al., 2017).”
BackgroundIs smart city a slogan? Evidence from China
Zhou, Y., Xiao, F., & Deng, W. (2022) · Asian Geographer · doi:10.1080/10225706.2022.2052734 · Cited by 23
Seeing into hearts and minds: Part 2. ‘Big data’, algorithms, and computational counterinsurgency
González, R. J. (2015) · Anthropology Today · doi:10.1111/1467-8322.12188 · Cited by 22
Characterizing the Spatial Structure(s) of Cities “on the fly”: The Space‐Time Calendar
Arribas‐Bel, D., & Tranos, E. (2017) · Geographical Analysis · doi:10.1111/gean.12137 · Cited by 22
Gillingham, P. (2016) · Journal of Public Child Welfare · doi:10.1080/15548732.2016.1255697 · Cited by 22
“Serious concerns have been raised about the practical problems associated with the processes of generating, extracting and analyzing data (boyd & Crawford, 2012; Bollier, 2010; Kitchin, 2014).”
BackgroundAffect and value in critical examinations of the production and ‘prosumption’ of Big Data
Cockayne, D. (2016) · Big Data & Society · doi:10.1177/2053951716640566 · Cited by 24
“Yet these inquiries often focus on Big Data as an already-constituted object, and it is less common for scholarly research to examine the relationship between the production of data, digital forms of labor, and the value of those data (though see Beer and Burrows, 2013; Kitchin, 2014).”
BackgroundRemote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks
Mandryk, R. L., Birk, M. V., Vedress, S., Wiley, K., Reid, E., Berger, P., & Frommel, J. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.767507 · Cited by 23
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review