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Barriers to the Implementation of Big Data

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Big Data for Urban Sustainability

Abstract

This chapter sounds a cautionary note about big data applications. In general terms, it discusses, in turn, the potential serious challenges to its use, including privacy, data security, reliability, cost, technical challenges, and potential barriers to its acceptance, which will need to be overcome. The barriers to acceptance and use vary greatly from one application to another, being close to zero for some applications (for example, urban weather forecasting), to possibly serious for more sensitive applications that involve even anonymised personal data. We conclude that big data is not a panacea for all urban problems—some important areas of urban sustainability are probably best tackled by traditional small data approaches or a judicious use of both big and small data. The barriers for some applications, particularly those based on personal data, will for some time be greater in the cities of many industrialising countries than in OECD cities.

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References

  1. Acquisti A, Brandimarte L, George Loewenstein G (2015) Privacy and human behavior in the age of information. Science 347(6221):509–514

    Article  Google Scholar 

  2. Adlee S (2017) Your entire Internet history is now for sale. New Sci 234(3120):25

    Article  Google Scholar 

  3. Allen N 2016. Cybersecurity weaknesses threaten to make smart cities more costly and dangerous than their analog predecessors. http://bit.ly/1Q33UTK

    Google Scholar 

  4. Anon (2017) Worldwide cyberattack. New Sci 234(3126):4

    Google Scholar 

  5. Athey S (2017) Beyond prediction: using big data for policy problems. Science 355:483–485

    Article  Google Scholar 

  6. Anderson C (2008) The end of theory: will the data deluge makes the scientific method obsolete? Wired. June 23. http://www.wired.com/science/discoveries/magazine/16-07/pb_theory

  7. Baraniuk C (2015) Forget carjacking, car hacking is the future. New Sci 227(3033):19

    Article  Google Scholar 

  8. Bin Abdul Rahman MF (2016) Threats of driverless vehicles: leveraging new technologies for solutions. (RSIS Commentaries, No. 138). RSIS Commentaries. Nanyang Technological University, Singapore

    Google Scholar 

  9. Boulos MNK, Al-Shorbaji NM (2014) On the Internet of Things, smart cities and the WHO Healthy Cities. Int J Health Geogr 13(10):1–6

    Google Scholar 

  10. Boyd D, Crawford K (2012) Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inf Commun Soc 15(5):662–679

    Article  Google Scholar 

  11. Brynjolfsson E, Hitt L (1998) Beyond the productivity paradox. Commun ACM 41(8):49–55

    Article  Google Scholar 

  12. Cartwright J (2016) Smartphone science. Nature 531:669–671

    Article  Google Scholar 

  13. Cavoukian A, Jonas J 2012. Privacy by design in the age of big data. Accessed on 12 June 2017 at https://datatilsynet.no/globalassets/global/seminar_foredrag/innebygdpersonvern/privacy-by-design-and-big-data_ibmvedlegg1.pdf.

    Google Scholar 

  14. Crawford K, Calo R (2016) There is a blind spot in AI research. Nature 538:311–313

    Article  Google Scholar 

  15. de Montjoye Y-A, Radaelli L, Singh VK et al (2015) Unique in the shopping mall: on the reidentifiability of credit card metadata. Science 347:536–539

    Article  Google Scholar 

  16. Faghmous JH, Kumar V (2014) A big data guide to understanding climate change: the case for theory-guided data science. Big Data 2(3):155–163. https://doi.org/10.1089/big.2014.0026

    Article  Google Scholar 

  17. Fisher S 2016. Gartner Hype Cycle 2016: blockchain, no big data. Accessed on 2 March 2017 at https://www.laserfiche.com/simplicity/gartner-hype-cycle-2016-blockchain-no-big-data/.

    Google Scholar 

  18. Fox P, Hendler J (2014) The science of data science. Big Data 2:68–70

    Article  Google Scholar 

  19. Ganguly AR, Kodra EA, Agrawal A et al (2014) Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques. Nonlinear Processes Geophys 21:777–795

    Article  Google Scholar 

  20. Glaeser EL, Kominers SD, Luca M et al (2016) Big data and big cities: the promises and limitations of improved measures of urban life. Econ Inq 56:114–137. https://doi.org/10.1111/ecin.12364

    Article  Google Scholar 

  21. Glasmeier AK, Nebiolo M (2016) Thinking about smart cities: the travels of a policy idea that promises a great deal, but so far has delivered modest results. Sustainability 8:1122. https://doi.org/10.3390/su8111122

    Article  Google Scholar 

  22. Gudivada VN, Baeza-Yates R, Raghavan VV (2015) Big data: promises and problems. Computer 48:20–23

    Article  Google Scholar 

  23. Gumbus A, Grodzinsky F (2015) Era of Big Data: danger of descrimination. SIGCAS Comput Soc 45(3):118–125

    Article  Google Scholar 

  24. Haight R, Haensch W, Friedman D (2016) Solar-powering the Internet of Things. Science 353:124–125

    Article  Google Scholar 

  25. Hargittai E (2015) Is bigger always better? potential biases of big data derived from social network sites. Ann Am Acad Pol Soc Sci 659:63–76

    Article  Google Scholar 

  26. Hvistendahl M (2016) Crime forecasters. Science 353:1484–1487

    Article  Google Scholar 

  27. Jagadish HV, Gehrke J, Labrinidis A et al (2014) Big data and its technical challenges. Commun ACM 57(7):86–94

    Article  Google Scholar 

  28. Kitchin R (2016) The ethics of smart cities and urban science. Philos Trans R Soc A 374:20160115. https://doi.org/10.1098/rsta.2016.0115

    Article  Google Scholar 

  29. Kshetri N (2014) Big data’s impact on privacy, security and consumer welfare. Telecommun Policy 38:1134–1145

    Article  Google Scholar 

  30. Kshetri N (2016) Cybersecurity and development. Markets Global Dev Rev 1(2):3. https://doi.org/10.23860/MGDR-2016-01-02-03

    Google Scholar 

  31. Lazer D 2015. Issues of construct validity and reliability in massive, passive data collections. The Cities Papers.. Accessed on 7 March 2017 at http://citiespapers.ssrc.org/issues-of-construct-validity-and-reliability-in-massive-passive-data-collections/.

    Google Scholar 

  32. Lazer D, Kennedy R, King G et al (2014) The parable of Google Flu: traps in big data analysis. Science 343:1203–1205

    Article  Google Scholar 

  33. Lee BG, Galli S, Brunner M et al (2011) New technical areas: exploring our future. IEEE Commun Mag 49(10):12

    Article  Google Scholar 

  34. Mai J-E (2016) Big data privacy: the datafication of personal information. Inf Soc 32(3):192–199

    Article  Google Scholar 

  35. Martínez-Ballesté A, Pérez-Martínez PA, Solanas A (2013) The pursuit of citizens’ privacy: a privacy-aware smart city is possible. IEEE Commun Mag 51:136–141

    Article  Google Scholar 

  36. Mayer-Schӧnberger V, Cukier K (2014) Big data. Mariner Books, Boston, New York, NY

    Google Scholar 

  37. Montgomery B (2015) Future shock: IoT benefits beyond traffic and lighting energy optimization. IEEE Consum Electron Mag 4:98–100

    Article  Google Scholar 

  38. Moriarty P (1999) Don’t depend on IT. Aust Quart 71(6):16–20

    Article  Google Scholar 

  39. Mowshowitz A (2013) The end of the information frontier. AI Soc 28:7–14

    Article  Google Scholar 

  40. Neff G (2013) Why big data won’t cure us. Big Data 1(3):117–123

    Article  Google Scholar 

  41. Obermeyer Z, Emanuel EJ (2016) Predicting the future — big data, machine learning, and clinical medicine. N Engl J Med 375(13):1216–1219

    Article  Google Scholar 

  42. Oboler A, Welsh K, Cruz L (2012) The danger of big data: social media as computational social science. First Monday 17(7):60. https://doi.org/10.5210/fm.v17i7.3993

    Google Scholar 

  43. O’Grady M, O’Hare G (2012) How smart is your city? Science 335:1581–1582

    Article  Google Scholar 

  44. Ornes S (2016) The Internet of Things and the explosion of interconnectivity. Proc Natl Acad Sci U S A 113(40):11059–11060

    Article  MathSciNet  Google Scholar 

  45. Perera C, Zaslavsky A, Christen P et al (2014) Sensing as a service model for smart cities supported by Internet of Things. Trans Emerg Telecommun Technol 25:81–93

    Article  Google Scholar 

  46. Qualtrics 2010. The 1936 election – a polling catastrophe. 12 October. Accessed on 9 March 2017 at https://www.qualtrics.com/blog/the-1936-election-a-polling-catastrophe/.

    Google Scholar 

  47. Revell T (2017) Be careful what you wish for. New Sci 234(3125):22–23

    Article  Google Scholar 

  48. Reynolds M (2017) Bias test to keep algorithms ethical. New Sci 234(3119):10

    Article  Google Scholar 

  49. Rutkin A (2016) Medicine by machine. New Sci 231(3089):20–21

    Article  Google Scholar 

  50. Salvatore C 2017. FDA reports hacking risk in St. Jude medical heart devices. Law 360. Accessed on 28 February 2017 at https://www.law360.com/articles/879525/fda-reports-hacking-risk-in-st-jude-medical-heart-devices.

    Google Scholar 

  51. Schmitt M, Marks P (2013) The right to bear cyber arms. New Sci 218(2912):26–27

    Article  Google Scholar 

  52. Shah NH (2015) Using big data, Chapter 7. In: PRO P, Embi PJ (eds) Translational informatics: realizing the promise of knowledge-driven healthcare, health informatics. Springer, London. https://doi.org/10.1007/978-1-4471-4646-9_7

    Google Scholar 

  53. Shapiro A (2017) Reform predictive policing. Nature 541:460–462

    Article  Google Scholar 

  54. Smith M, Szongott C, Henne B et al 2012. Big data privacy issues in public social media. 6th IEEE DEST Conference. Accessed on 27 March 2017 at http://ieeexplore.ieee.org.ezproxy.lib.monash.edu.au/stamp/stamp.jsp?arnumber=6227909.

    Google Scholar 

  55. Spratt S, Baker J 2015. Big data and international development: impacts, scenarios and policy options. Accessed on 25 April 2017 at https://opendocs.ids.ac.uk/opendocs/bitstream/handle/123456789/7198/ER163_BigDataandInternationalDevelopment.pdf?sequence=1.

    Google Scholar 

  56. Statistica 2017. Number of mobile phone users worldwide from 2013 to 2019 (in billions). Accessed on 16 March 2017 at https://www.statista.com/statistics/274774/forecast-of-mobile-phone-users-worldwide/.

    Google Scholar 

  57. Stutzman F, Gross R, Acquisti A (2012) Silent listeners: the evolution of privacy and disclosure on Facebook. J Priv Confid 4(2):7–41

    Google Scholar 

  58. Taleb N 2013. Beware the big errors of ‘big data’. Wired blog. Accessed on 15 December 2015 at http://www.wired.com/2013/02/big-data-means-big-errors-people/

    Google Scholar 

  59. Tankard C (2012) Big data security. Netw Secur 2012:5–8

    Google Scholar 

  60. Utz S, Krämer NC (2015) The privacy paradox on social network sites revisited: the role of individual characteristics and group norms. Cyberpsychology 3(2):article 2

    Google Scholar 

  61. Wang W, Kennedy R, Lazer D et al (2016) Growing pains for global monitoring of societal events. Science 353:1502–1503

    Article  Google Scholar 

  62. Waldrop MM (2016) More than Moore. Nature 530:144–147

    Article  Google Scholar 

  63. Weinberg BD, Milne GR, Andonova YG et al (2015) Internet of things: convenience vs. privacy and secrecy. Bus Horiz 58:615–624

    Article  Google Scholar 

  64. Wikipedia 2017 Closed-circuit television. Accessed on 24 February 2017 at https://en.wikipedia.org/wiki/Closed-circuit_television.

    Google Scholar 

  65. Wikipedia 2017. Criticism of Facebook. Accessed on 29 February 2017 at https://en.wikipedia.org/wiki/Criticism_of_Facebook.

    Google Scholar 

  66. Wong S (2015) How to take out a city. New Sci 227(3033):18–19

    Article  Google Scholar 

  67. Ziegeldorf JH, Morchon OG, Wehrle K (2014) Privacy in the Internet of Things: threats and challenges. Secur Commun Netw 7:2728–2742. https://doi.org/10.1002/sec.795

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Design, Royal College of Art, Kensington Gore, London, SW7 2EU, UK

    Stephen Jia Wang

  2. Department of Design, Monash University-Caulfield Campus, Caulfield East, VIC, Australia

    Patrick Moriarty

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  1. Stephen Jia Wang
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Wang, S.J., Moriarty, P. (2018). Barriers to the Implementation of Big Data. In: Big Data for Urban Sustainability. Springer, Cham. https://doi.org/10.1007/978-3-319-73610-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-73610-5_4

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  • Print ISBN: 978-3-319-73608-2

  • Online ISBN: 978-3-319-73610-5

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