Cited by 2,794Smart Cities and TechnologiesUrban Planning and GovernanceGeographic Information Systems Studies

The Real-Time City? Big Data and Smart Urbanism

Open access
Kitchin, Rob·2013·SSRN Electronic Journal

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2139/ssrn.2289141

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Big Data stored in the Cloud provide novel and powerful insights regarding behaviour, business transactions and human impacts, enabling real-time decision-making.

    “Often referred to as Big Data and stored in data warehouses called the Cloud, these data streams provide novel and powerful insights regarding behaviour, business transactions and human impacts, enabling real-time decision-making (Kitchin, 2013).”

    From Smart tourism destinations: ecosystems for tourism destination competitiveness (2016) · cites this paper as Kitchin, 2013

    The Conclusion and “The real-time city” section say that big-data analytics provide insights into city life and support real-time analysis and decision-making. “Big data and cities” also describes data on people’s behaviour and transactions. The paper discusses distributed storage, but does not identify the Cloud as a data warehouse or say these streams are stored there; that part of the claim is unsupported. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 31

  1. Allwinkle, S., & Cruickshank, P. (2011). Creating smart-er cities: An overview. Journal of Urban Technology, 18(2), 1-16.
  2. Bates, J. (2012). ''This is what modern deregulation looks like'': Co-optation and contestation in the shaping of the UK's Open Government Data Initiative. The Journal of Com- munity Informatics, 8(2). http://www.ci-journal.net/index. php/ciej/article/view/845/916. Accessed 6 Feb 2013.
  3. Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., et al. (2012). Smart cities of the future. European Physical Journal Special Topics, 214(1), 481-518.
  4. Bowker, G., & Star, L. (1999). Sorting things out: Classification and Its consequences. Cambridge: MIT Press.
  5. Boyd, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication and Society, 15(5), 662-679.
  6. Dodge, M., & Kitchin, R. (2004). Flying through code/space: The real virtuality of air travel. Environment and Planning A, 36(2), 195-211.
  7. Dodge, M., & Kitchin, R. (2005). Codes of life: Identification codes and the machine-readable world. Environment and Planning D: Society and Space, 23(6), 851-881.
  8. Dodge, M., & Kitchin, R. (2007a). The automatic management of drivers and driving spaces. Geoforum, 38(2), 264-275.
  9. Dodge, M., & Kitchin, R. (2007b). Outlines of a world coming in existence': Pervasive computing and the ethics of for- getting. Environment and Planning B, 34(3), 431-445.
  10. Ferro, E. & Osella, M. (2013). Eight business model archetypes for PSI re-use. Open Data on the Web workshop. http:// www.w3.org/2013/04/odw/odw13_submission_27.pdf. Accessed 10 May 2013.
  11. Gitelman, L., & Jackson, V. (2013). Introduction. In L. Gitel- man (Ed.), ''Raw data'' is an oxymoron (pp. 1-14). Cam- bridge: MIT Press.
  12. Graham, S., & Marvin, S. (1999). Planning cybercities: Inte- grating telecommunications into urban planning. Town Planning Review, 70(1), 89-114.
  13. Hancke, G. P., de Carvalho e Silva, B., & Hancke, G. P, Jr. (2013). The role of advanced sensing in smart cities. Sen- sors, 13(1), 393-425.
  14. Hannah, M. (1997). Imperfect panopticism: Envisioning the construction of normal lives. In G. Benko & U. Strohmayer (Eds.), Space and social theory (pp. 344-360). Oxford: Blackwell.
  15. Hollands, R. G. (2008). Will the real smart city please stand up? City, 12(3), 303-320.
  16. Ishida, T., & Isbister, K. (2000). Digital cities: Technologies, experiences, and future perspectives. LNCS: Springer. 1765.
  17. Johnson, J.A. (2013). From open data to information justice. Paper presented at the Annual Conference of the Midwest Political Science Association, April 13, 2013, Chicago, Illinois. http://papers.ssrn.com/abstract=2241092. Acces- sed 16 Aug 2013.
  18. Kitchin, R., & Dodge, M. (2011). Code/space: Software and everyday life. Cambridge, MA: MIT Press.
  19. Kloeckl, K., Senn, O., & Ratti, C. (2012). Enabling the real-time city: LIVE Singapore! Journal of Urban Technology, 19(2), 89-112.
  20. Komninos, N. (2002). Intelligent cities: Innovation. Routledge: Knowledge Systems and Digital Spaces.
  21. Kourtit, K., Nijkamp, P., & Arribas-Bel, D. (2012). Smart cities perspective-A comparative European study by means of self-organizing maps. Innovation, 25(2), 229-246.
  22. Lauriault, T.P. (2012). Data, Infrastructures and Geographical Imaginations: Mapping Data Access Discourses in Can- ada. PhD Thesis, Carleton University, Ottawa.
  23. Lyon, D. (2007). Surveillance studies: An overview. Cambridge: Polity.
  24. Marz, N., & Warren, J. (2012). Big data: Principles and best practices of scalable realtime data systems. Manning: MEAP edition.
  25. Miller, H. J. (2010). The data avalanche is here. Shouldn't we be digging? Journal of Regional Science, 50(1), 181-201.
  26. Ribes, D., & Jackson, S. J. (2013). Data bite man: The work of sustaining long-term study. In L. Gitelman (Ed.), ''Raw data'' is an oxymoron (pp. 147-166). Cambridge: MIT Press.
  27. Rosenberg, D. (2013). Data before the fact. In L. Gitelman (Ed.), ''Raw data'' is an oxymoron (pp. 15-40). Cambridge: MIT Press.
  28. Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., & Oliveira, A. (2011). Smart cities and the future internet: Towards cooperation frameworks for open inno- vation. In J. Domingue, et al. (Eds.), Future Internet Assembly (pp. 431-446). LNCS: Springer.
  29. Shepard, M. (2011). Sentient city: Ubiquitous computing, architecture, and the future of urban space. Cambridge, Mass: MIT Press.
  30. Sui, D., Elwood, S., & Goodhild, M. (Eds.). (2012). Crowd- sourced geographic knowledge: Volunteered geographic information in theory and practice. Dordrecht: Springer.
  31. Zikopoulos, P. C., Eaton, C., deRoos, D., Deutsch, T., & Lapis, G. (2012). Understanding big data. New York: McGraw Hill.

Authors
Kitchin, R.
Affiliations
National University of Ireland, Maynooth (IE)
Corresponding author
Kitchin, Rob
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2013
Volume
79
Issue
1
Pages
1-14
ISSN
  • 0343-2521 (ISSN-L)
  • 1556-5068
OpenAlex
W3125866593
Cited by
2,794
References
0
Citations by year
2026: 299 · 2025: 250 · 2024: 194 · 2023: 216 · 2022: 196 · 2021: 262 · 2020: 310 · 2019: 327 · 2018: 280 · 2017: 205 · 2016: 152 · 2015: 78 · 2014: 19 · 2013: 1
FWCI
288.78
Type
book chapter
Language
en
Open access
Locations
  • GeoJournal (publishedVersion)
  • SSRN Electronic Journal (publishedVersion)
  • Maynooth University ePrints and eTheses Archive (Maynooth University) (acceptedVersion)
Funders
Durham University
Topics
  • Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
  • Social Sciences › Urban Studies › Urban Planning and Governance
  • Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
Keywords
smart cities, big data, smart urbanism, urban governance, ubiquitous computing, technocratic governance, technological lock-in, urban development

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