The following was submitted to the University of Pennsylvania's School of Design as part of the Systems Thinking elective in Fall 2013.
"We’re going to find ourselves in the not too distant future swimming in sensors and drowning in data." (1)
For Viktor Mayer-Schönberger and Kenneth Cukier, authors of Big Data: A Revolution That Will TransformHow We Live, Work, and Think, the “revolution” that is currently shaping the
contemporary world has the potential to create fundamental transformations in
the way in which society operates, on a par with the introduction of the
Guttenberg Printing Press in 1450. They point to the fact that "In less than a person's life span,
the flow of information has changed from a trickle to a torrent" (2) and that throughout history people have always “opted for more information
flows rather than less." (3) The book sets out to describe big data, (4) defined as “the ability of society to harness information in novel ways to
produce useful insights or goods and services of significant value", (5) as a theoretical and practical construct that will continue, and greatly
accelerate, this trend.
 |
| Big Data: A Revolution That Will Transform How We Live, Work, and Think (Houghton Mifflin Harcourt, 2013) |
The co-authors, an academic and a journalist, share common
research interests in areas surrounding Internet governance and technology,
recurring themes throughout Big Data.
Considerable time is spent dealing with the Implications,
Risks and Control (Chapters 7, 8 and 9 respectively) of big data, which is
perhaps a testament to the fact that Viktor Mayer-Schönberger is the Professor
of Internet Governance and Regulation at the University of Oxford, and in 1986
he founded Ikarus Software, a company focused on data security. (6) With other interests surrounding innovation and intellectual property rights (both
touched upon in Big Data), Kenneth Cukier
is currently the Data Editor of The Economist, having previously been the
paper's technology correspondent. (7)
Setting out their
intentions for the book early on, the co-authors intend to “explain where we
are, trace how we got here, and offer an urgently needed guide to the benefits
and dangers that lie ahead." (8) Judged by these criteria the book is fairly successful, providing a
comprehensive yet accessible overview to both the benefits and the risks of big
data. It should be noted that early on in the book they describe
themselves “not so much big data's
evangelists, but merely its messengers”, however at times Big Data does come across as evangelical in its praise of big
data’s virtues. (9) Whilst
this is tempered by discussions about the risks associated with its adoption,
which widens the discussion beyond issues of privacy, the tone nevertheless
falls firmly on the side that big data is good and that it is here to stay. (10) When asked in an interview about the likely trajectory of big data over
the next five years Cukier predicted that it will unfold in much the same way
as the Internet, with widespread adoption of big data “around all corners of
society”. (11) Big data’s ‘breakthrough moment’ may already have happened, with Cukier drawing
comparisons between “the birth of the Web at the Netscape IPO” (1995) and the Facebook IPO in 2012: “It is a $67 billion company with very small revenues and small
earnings and all the value of its share is in the promise of what its data
holds." (12)
Datafying, then digitizing, big data
The term “big data”
emerged during the 2000s, with an ‘explosion’ of data in sciences such as
astronomy and genomics; it has since migrated across disciplinary boundaries “to
all areas of human endeavour." (13) Today big data “refers to things one can do at a large scale that cannot be
done at a smaller one, to extract new insights or create new forms of value, in
ways that change markets, organizations, the relationship between citizens and
governments, and more." (14) This relies on a dramatic increase in not only the amount of quantitative
information available but also the expertise and the tools to properly utilise
it. However, Mayer-Schönberger and Cukier go to great lengths throughout
the book to explain that whilst “changes
in technology have been a critical factor making it [big data] possible,
something more important changed too, something subtle."(15) This subtle shift is a change in mind set about data itself and how it can be
used; this is the “revolution” Mayer-Schönberger and Cukier are referring
to, not the machines. (16) An initial quantitative change (technology) has produced a qualitative change,
comparable to the difference between a photograph and a movie: “by changing the
amount, we change the essence." (17) Furthermore, today data is no longer
“regarded as static or stale, whose usefulness was finished once the purpose
for which it was collected was achieved” but is instead recognised as “a raw
material of business, a vital economic input, used to create a new form of
economic value." (18) To put it another way data is “the oil of the information economy" (19) but
whereas oil can only be used once, data can be used again and again.
That big data is
often conflated with issues surrounding technology is not surprising when you
consider that “The amount of stored information grows four times faster than
the world economy” and that this process is only speeding up with further
technological advancement, to the point where “Everyone is whiplashed by the
changes." (20) This conflation is further aggravated by the fact that big data is “described
as part of the branch of computer science called artificial intelligence, and
more specifically, an area called machine learning … [but] Big data is not
about trying to ‘teach’ a computer to ‘think’ like humans." (21) Regardless of this it is difficult not to argue that technology, and
particularly digitization, has not had an important role to play, even if “it
is important to keep them separate." (22)
Mayer-Schönberger and Cukier trace the building blocks of
big data back far beyond the realm of the digital age. (23) Ancient civilizations attempted to collect census data for entire empires but the
difficulty in the analogue world was that this was both costly and time
consuming—the intrinsic value of large datasets was never in question. The work
of Matthew Fontaine Maury in the 1800s is used to demonstrate “the degree to which the use of data predates
digitization”. (24) Alongside
his dozen “computers”, Maury revisited old logs to extract and tabulate
valuable information on temperature, wind speed, and time, which had been discarded,
and from it draw new navigational charts. (25) There are then two prerequisites to ‘datafication’: having the right set of
tools; and a desire to quantify and to record. (26)
Whilst mathematics
gave new meaning to data in centuries past, the shift from information no
longer being stored in atoms but in bit has resulted in much larger
transformational effects. (27) However, “The act of digitization—turning analogue information into computer
readable format—by itself does not datafy." (28) The differences between datafying and digitizing are epitomized, for the
co-authors, in the differences between Google’s Book Project and Amazon’s Kindle,
surmising that: "Perhaps it is not unjust to say that, at least for now,
Amazon understands the value of digitizing content, while Google understands
the value of datafying it." (29)
Valuing more, messy, correlation
At the core of the
principles underlying big data are three major, interconnected, shifts in mind
set, each serving to reinforce the others position. The first is the ability to
analyse vast amounts of data, without the restrictions of smaller sample sizes.
Second, there is a “willingness to embrace data's real-world messiness rather
than privilege exactitude.” Finally, causality is replaced by correlation as
the driving force. (30)
Historically, we
have “relied on to the barest minimum” when collecting information; this was
(and still is) “a form of unconscious self-censorship … an artificial constraint
imposed by the technology at the time." (31) Today the ‘codified practice of stunting’ is no longer necessary because "The
shortcomings in counting and tabulating no longer exist to the same extent." (32) This data is also increasingly temporal but its’ chronological contingency is
less relevant because newer data is always being gathered by passive measures, from
GPS to Twitter, replacing the outmoded data set. (33) Of course all of this requires "ample processing and storage power and
cutting-edge tools to analyze it” but with these now accessible the cost of
comprehensive data collection has fallen dramatically. (34) Furthermore, just as the Lytro Camera “records the entire light field instead
of a 2D image” enabling the viewer to refocus pictures after they are taken,
data can be revisited for entirely new reasons and purposes. (35)
The issues of
dependency, messiness and exactitude are explored through descriptions of
various translations devices that have been developed. It is argued that
previous failures at translation software can be accounted to their design
being based on a predilection with exactitude, an obsession which is “an
artefact of the information-deprived analogue era." (36) To discard, or at least lessen, the importance of exactitude relies on
embracing that we are never able to collect perfect information; therefore “as
long as it is imperfect, messiness is a practical reality we must deal
with." (37) Previous statisticians relied on imperfect information in the form of sampling,
in the hope that the accuracy would make up for the limitations of the sample
size, but today we can work with much larger samples that seem to iron out any
errors produced by inexactitude. (38)
Having embraced having more, messy data, Mayer-Schönberger
and Cukier present a new “pragmatic approach” wherein with big data “Knowing what,
not why, is good enough." (39) The pursuit of one single version of the truth is seen as a distraction, things
are far “more malleable than we may admit”, (40) and as such causation is less important than correlation. The work of Daniel Kahneman is used as a demonstration of the human obsession with causalities,
painting causation as a fast-thinking activity that jumps to conclusions far
too quickly. (41) Whilst,
correlation does not bring about certainty, only probability, when “a
correlation is strong, the likelihood of a link is high." (42) The advent of machines capable of more powerful computations means that the
limits of traditional linear correlation can be discarded in favour of more
complexity, identifying non-linear relationships among data. (43)
If these three mind
sets are fully adopted then, "in the age of big data, all data will be
regarded as valuable, in and of itself." (44) This is because information is a “non-rivalrous” good, that doesn’t wear out
and as such “The crux of data's worth is its seemingly unlimited potential for
resuse: its option value." (45) In order to unleash the option value of data three methods are presented: basic reuse; merging datasets; and finding
"twofers." (46) Wilst each of these strategies is relatively straight forward, it is when they
are applied to “data exhaust”—the by-product of people’s actions and movements
in the real and digital world—that the greatest option value can be obtained.. (47) As seen by the valuation placed on Facebook, data as an intangible asset is now
as valuable to a company as its brand, talent and strategy. (48)
Implications and controlling the risks
Currently companies
can be differentiated into three broad categories based upon the data, the
skills, and the ideas they offer, although some, such as Google, benefit from "vertical
integration in the big-data value chain, where it occupies all three positions
at once." (49) According to Mayer-Schönberger and Cukier the skills to succeed in this new big data workplace are shifting and “Today's
pioneers of big data often come from disparate backgrounds and cross-apply
their data skills in a wide variety of areas." (50) The breaking down of traditional silos and the cross-fertilisation of ideas
amongst disciplines is not a trait unique to big data however the co-authors go
one step further when discussing the possible demise of the expert, due to big
data practices. They point to a change in the way in which knowledge itself is
valued, and that expertise, like exactitude, is only appropriate for “a
small-data world where one never has enough information, or the right
information, and thus has to rely on intuition and experience to guide one's
way." (51) In this new landscape the middle of an industry will be squeezed, so that firms
will be either very large or small and nimble, recasting traditional sectors as
diverse as city planning, manufacturing, pharmaceuticals and financial services. (52)
If these are the
wider implications for a new big data world, there are three categories of risk
that we will all be faced with: issues of privacy, propensity and the
fetishization of data. (53) This is the dark side of big data; it “allows for more surveillance of our
lives while it makes some of the legal means for protecting privacy largely
obsolete … [it] renders ineffective the core technical methods of preserving
anonymity ... [and] there is a real risk that the benefits of big data will
lure people into applying the techniques where they don't perfectly fit". (54) The three core strategies of individual notice and consent, opting out, and
anonymization, are no longer effective in the big data age and as such Mayer-Schönberger
and Cukier "envision a very different privacy framework for the big-data age" (55) wherein there is “a regulatory shift from privacy
by consent to privacy through accountability" (56) They are keen to stress the importance of maintaining individual responsibility
within these new systems and that the more we attempt to reduce risk in society
by relying on “data-driven interventions” the more we devalue that
responsibility. (57) They propose a new caste of professionals, big data auditors or Algorithmists, who
“would take a vow of impartiality and confidentiality", as a means to
ensure human agency remains amid computer driven predictions. (58)
Now
Each of the
measures set out in the book are designed to put big data in its place, as
nothing more than a tool and a resource. (59) Mayer-Schönberger and Cukier talk about a new world that is taking shape
now, “already sketched in faint traces
that are discernible to those with the technology to make them apparent." (60) To them this is not a world built on "ice-cold
… algorithms and automatons” rather there is “an essential role for people,
with all our foibles, misperceptions and mistakes, since these traits walk hand
in hand with human creativity, instinct, and genius." (61) The question then is if big data is happening all around us, how is it shaping
our lives beyond the invisible infrastructures and business systems with which the
book is primarily concerned and for whom the co-authors view as its most
advanced users. (62)
Aside from fleeting
references to sensors being fixed to bridges and buildings, (63) to grey infrastructures, such as roads and vehicle tracking, (64) or manhole inspections and illegal conversions in New York City (65) (this is not to say that these are not important activities) there is little
tangible evidence presented in Big Data
with which a built environment professional can grapple. This seems odd given the assertion that the
new mental outlook of big data “may penetrate all areas of life” so that the
world is seen as information, with “oceans of data that can be explored at ever
greater breadth and depth”, and that this in turn “offers us a perspective on
reality that we did not have before.” (66) It is seems curious then that the book is lacking in any references to the smart
city concept, described by Adam Greenfield as either “urban-scale environments
designed from the ground up with information-processing capabilities embedded
in the objects, surfaces, spaces and interactions that between them comprise
everyday life” (67) or the broader “drive to retrofit networked information technologies into
existing urban places.” (68)
Perhaps there is
something to be drawn from the examples of European car manufacturers given in Big Data (69) or the business model of Rolls-Royce, (70) which could possibly be replicated by companies specialising in building
components or systems, such as façade packages. Would this lead to large scale
transformation of society? Probably not. It may alter procurement or operation
and maintenance procedures but as for the wider public, they would likely see
little difference. These ‘solutions’ have more to with the enthusiasm over the
Internet of Things (IoT) (71) or the ’smart’ offerings of IBM, Cisco and Siemens, that Greenfield calls into
question, than a revolution. (72)
If smart cities are
‘missing’ it is perhaps not surprising that there is also no mention of Building
Information Modelling (BIM). (73) As recently as June 2013, The Bartlett
Faculty of the Built Environment at University College London hosted a
conference on big data and BIM. At the conference, Andrew Hudson-Smith, Director
of the Centre for Advanced Spatial Analysis at The Bartlett, stated that big
data is the medium through which to join Building Information Modelling (BIM),
Geographic Information Systems (GIS), Citizen Science and the Internet of
Things together. (74)
As successful as Big Data is in painting a picture of the
new big data age, when it comes to the built environment the brush is broad and
the detail lacking. Those concerned with how big data is shaping the built
environment must instead turn to others.
Next
In a trilogy of
papers, published between 2011 and 2013, Nick Dunn re-imagines digital space as
a new terrain within which architects and urban planners can operate. Whilst
this in itself is not a new approach, finding precedent in Archigram’s Plug-In City (1964) or Instant City (1968-70) and Archizoom’s No-Stop
City (1969), or more recently UNStudio’s Time-based Urbanism (1997), MVRDV’s Datatown, Sector Waste (1999), and Asymptote’s New York Stock Exchange (1999), Dunn brings the discussion into the
physical world. He describes the overlaying of digital technologies upon extant
physical situations as a “multi-layered landscape”, (75) noting that “the
city as we understood it … has now changed." (76) Whilst the emergence of new city models predates
the digital age, it is clear that “The transformation of the physical landscape
towards an increasingly incoherent set of urban conditions and the
corresponding flows of endless data into an apparently infinite and united
system has implications for what we might consider to be public domain." (77)
For example, the Sensity (2004-09) project by Stanza
provides the public with “access to invisible but important qualities of the
city” and “offer[s] a rich platform across which we [might] better understand
our urban landscape”. (78) It is part of a much longer lineage, which includes the Nolli Map of Rome
(1784), in providing new representations of public and private domain. Mapping
exercises such as Stanzas are important to Dunn because they describe “the
ecological mutuality between digital and physical landscapes, especially with
regard to social behaviour and patterns." (79) Furthermore he is critical of the position that posits “digital networks and
physical conditions are distinct, as opposed to integrative” and challenges
designers to “to develop greater instrumentality that affords thick
descriptions of scenarios and enables us to develop appropriate design
strategies and responses” to deliver these multi-layered landscapes. (80)
With or without architects a new
“intelligent terrain” is emerging, based on a framework of “community-led
digital platforms that are easily accessible, robust and responsive to their
citizens." (81) Projects such as Open Raleigh (82) or Data Driven Detroit, which has
produced the D3 Toolbox, “envisioned
[supporting] communities with the data necessary for them to take action in
their neighbourhoods." (83) Large scale data collection has always been the purview of the state, (84) and whilst private enterprise may now be collecting their own big data, “Recently the idea has gained prominence that
the best way to extract value of government data is to give the private sector
and society in general access to try." (85) The future production of space and place will be dependent upon new interfaces,
with built in feedback mechanisms, that enable the general public to not simply
read a dataset but to get involved in it; this new “connective tissue, i.e. our
sociospatial relations and experiences [and will] result in a useful territory from
which to develop responsive tactics to urban space from places that are both socially
constructed and personally perceived." (86)
It has
not been possible to go into the various claims that Adam Greenfield makes
against the smart city, however central to his argument is that the ubiquitous
off-the-shelf products being sold as ‘smart cities’ are designed for “abstract,
featureless terrain” and not “actual places”. (87) He is calling for work which is “technically sophisticated and [can] take every
advantage offered us by emergent ways of doing and making." (88) I
would posit that it is the “realistic hybrid of top-down and bottom-up systems”
described by Dunn that Greenfield is arguing for, “rather than the illusion of
an always on, always ready, always connected, networked society" (89) (a description that seems to aptly fit the new world traced by Mayer-Schönberger
and Cukier).
Today then “our
cities are already densely and intimately linked with one another, bound
together by their own citizens in a constant and mutually reinforcing traffic
in atoms and bits." (90) Finally, in the age of big data the scale of the city no longer matters,
it is scale of the data that is important. (91) However, it is important to remember that
“we are the network and [we are] the data”. (92)
Notes
The following was submitted to the University of Pennsylvania's School of Design as part of the Systems Thinking elective in Fall 2013.
1. Magnuson, S., ‘Military ‘Swimming in Sensors and
Drowning in Data’’, National Defense [Online] January 2010. Available at: http://www.nationaldefensemagazine.org/archive/2010/January/Pages/Military%E2%80%98SwimmingInSensorsandDrowninginData%E2%80%99.aspx [Accessed: 26th November 2013] This quote
first came to my attention through Kumar Navulur, Director of Next Generation
Products at DigitalGlobe, during his keynote address at PennGIS Day 2013,
University of Pennsylvania, 20th November 2013, entitled “The New
Spatial World – A Vision for the Future”.
2. Mayer-Schönberger, V., & Cukier, K., Big Data: A
Revolution That Will Transform How We Live, Work, and Think (2013), New York:
Houghton Mifflin Harcourt, p. 171.
3. Ibid., p. 172.
4. Throughout the book Mayer-Schönberger & Cukier
refer to the term as ‘big data’ and ‘big-data’ interchangeably. For this paper
I will be using the un-hyphenated version.
5. Ibid., p. 4.
6. ‘Professor Viktor Mayer-Schönberger’, Oxford Internet
Institute, University of Oxford [Online] 25th November 2013.
Available at: http://www.oii.ox.ac.uk/people/?id=174 [Accessed: 25th November 2013]
7. ‘Biography’, Kenneth Cukier [Online] No date.
Available at: http://www.cukier.com/knccv.html [Accessed: 25th November 2013]
8. Mayer-Schönberger & Cukier, op. cit., p. 18.
9. Ibid., p. 7.
10. It would seem that I am not alone in questioning the
evangelical spirit with which the authors approach their subject matter. Press,
G., ‘What’s to be Done about Big Data?’, Forbes [Online[ 11th March
2013. Available at: http://www.forbes.com/sites/gilpress/2013/03/11/whats-to-be-done-about-big-data/ [Accessed: 25th November 2013] Sentences
such as “The data can reveal secrets to those with the humility, the
willingness, and the tools to listen” do not exactly help Mayer-Schönberger and
Cukier case. Mayer-Schönberger & Cukier, op. cit., p. 5.
11. Press, loc. cit.
12. Ibid.
13. Mayer-Schönberger & Cukier, op. cit., p. 6.
14. Ibid., p. 8.
15. Ibid., p. 5.
16. Ibid., p. 7.
17. Ibid., p. 10.
18. Ibid., p. 5.
19. Ibid., p. 16.
20. Ibid., p. 9.
21. If big data is not concerned with teaching machines to
think like humans it is at least creating more intelligent systems with
feedback mechanisms designed to “improve themselves over time, by keeping a tab
on what are the best signals and patterns to look for as more data is fed
in." Ibid., p. 12.
22. bid., p. 77.
23. Ibid., p. 78.
24. Ibid., p. 76-7.
25. “Computers” was the job title given to those who
calculated the data. Ibid., p. 74-5.
26. Ibid., p. 78.
27. Negroponte, N., Being Digital (1995), New York: Alfred
A. Knopf.
28. Mayer-Schönberger & Cukier, op. cit., p. 83.
29. Ibid.,, p. 86. On Google they add that: "The
company understood that information has stored value that can only be released
once it is datafied." p. 83.
30. Ibid., p. 18.
31. Ibid., p. 20.
32. Ibid., p. 26.
33. "The presence of the old data diminishes the
value of the newer data." Ibid., p. 110.
34. Ibid., p. 27.
35. Lytro, ‘You’ll never think about pictures the same
way’, Lytro [Online] No date. Available at: http://www.lytro.com/camera/ [Accessed: 25th November 2013] This is
discussed in Big Data Mayer-Schönberger, V., & Cukier, K., Big Data: A
Revolution That Will Transform How We Live, Work, and Think (2013), New York: Houghton
Mifflin Harcourt, p. 28.
36. Mayer-Schönberger, V., & Cukier, K., Big Data: A
Revolution That Will Transform How We Live, Work, and Think (2013), New York:
Houghton Mifflin Harcourt, p. 40.
37. Ibid., p. 41.
38. Although not stated directly in the book, there is an
implication that whilst the sample sizes may be ‘x’ times greater, with digital
methods, the order of inaccuracy is not ‘x’ times greater as well. The
importance of accuracy is still highly relevant though for some big data
disciplines, such as GIS, where accuracy is king.
39. Ibid., p. 52.
40. Ibid., p. 48.
41. Kahneman, D., Thinking, Fast and Slow (2013), New
York: Farrar, Straus and Giroux.
42. Mayer-Schönberger & Cukier, op. cit.,, p. 53. Of
course, there is the danger that “when the number of data points increases by
orders of magnitude, we also see more spurious correlations” p. 54.
43. Ibid., p. 61. This is taken one step further with the
suggestion “in
a big-data age, the argument goes, we do not need theories: we can just look at
the data." p. 71.
44. Ibid., p. 100.
45. Ibid., p. 101-2, 122.
46. Ibid., p. 104.
47. Ibid., p. 113.
48. As with all intangible assets the difficulty lies in
placing a stock market valuation on them. One possible solution to further
extracting the value of data is the idea of licensing the data to third
parties. Ibid., p. 120-1.
49. Ibid., p. 132.
50. Ibid., p. 131.
51. Ibid., p. 142.
The authors continue “In such a world, experience plays a critical role,
since it is the long accumulation of latent knowledge—knowledge that one can't
transmit easily or learn from a book, or perhaps even be consciously aware
of--that enables one to make smarter decisions."
52. Ibid., p. 148. "Smart and nimble small players
can enjoy ‘scale without mass,’ in the celebrated phrase of Professor
Brynjolfsson. That is, they can have a large virtual presence without hefty
physical resources, and can diffuse innovations broadly at little cost."
p. 146-7.
53. Ibid., p. 152.
54. Ibid., p. 170.
55. Ibid., p. 173.
56. This vision will require technical innovation to help
protect privacy in certain instances. Ibid., p. 175.
57. Ibid., p. 177.
58. Ibid., p. 180.
59. They describe big data “as a tool that doesn't offer
ultimate answers, just good-enough ones to help us now until better methods and
hence better answers come along.” Ibid., p. 197.
60. Ibid., p. 195.
61. Ibid., p. 196.
62. Ibid., p. 97.
63. Ibid., p. 59.
64. "Tracking individuals by vehicles also changes
the nature of fixed costs, like roads and other infrastructure, by tying the
use of those resources to drivers and others who "consume"
them." Ibid., p. 89. Whilst the
book is solely concerned with the implication for the highways industry, there
is far greater potential here for how all people interface and interact with a
wider range of public goods (infrastructure), such as schools and parks. If it
is not inconceivable that the fixed cost of consuming a road is tied to its
use, why not extend this to these other infrastructures, with the cost reflected
in the taxes a person pays or how taxes are proportioned.
65. Ibid., p. 69.
66. Ibid., p. 97.
67. Greenfield, A., Against the smart city (The city is
here for you to use), (2013) Do projects (Kindle Edition), Loc. 77-8.
68. Ibid., Loc. 119-20.
69. Mayer-Schönberger & Cukier, op. cit.,, p. 69.
70. Ibid., p. p. 146.
71. "The enthusiasm over the ‘internet of things’—embedding
chips, sensors, and communications modules into everyday objects—is partly
about networking but just as much about datafying all that surrounds us."
Ibid., p. 96.
72. Greenfield, op. cit., Loc. 159-60.
73. In reality this is hardly surprising given that it is
very much a ‘niche’ industry compared to the mass-market audience this book is
aimed at, even if Mayer-Schönberger lists ‘learning about architecture’ as one
of his interests in his spare time.
74. Hudson-Smith, A., ‘Big Data, Sensing and Augmented
Reality – New Directions for The Crowd and Industry’ [Online] September 2013.
Available at: http://www.digitalurban.org/2013/09/pedagogy-meets-big-data-and-bim-big-data-sensing-and-augmented-reality-paper-and-key-note-presentation.html [First Accessed: 25th November 2013] This
is a copy of his keynote address given at the Pedagogy meets Big Data and BIM:
Building environment education and information management Conference at The
Bartlett, 24th – 25th June 2013. On his own research he
states that: “We explore various augmented reality systems and conclude that
the next decade will see the fall of the smart phone and the rise of
electroencephalograph embedded devices with information sent directly to our
retinas – this is, we argue, the future of big data, sensing and augmented
reality in relation to the built environment”.
75. Dunn, N., Infrastructural Urbanism: Ecologies and
Technologies of Multi-Layered Landscapes. In: Spaces & Flows: An
International Journal of Urban & ExtraUrban Studies, Vol. 1, No. 1, 2011,
p. 87-96.
76. Dunn, N., The end of architecture? : networked
communities, urban transformation and post-capitalist landscapes. In: Spaces
& Flows: An International Journal of Urban & ExtraUrban Studies, Vol.
3, No. 2, 2013, p. 67-75.
77. Dunn (2011), Ibid.
78. Ibid. The project is described by Stanza as “A series
of artworks based on connecting city spaces. The results are visualisations and
sonificications of real time spaces.” ‘Sensity’, Stanza [Online] 2006.
Available at: http://www.stanza.co.uk/sensity/index.html [Accessed: 25th November 2013]
79. Ibid.
80. Ibid.
81. Dunn (2013), Ibid.
82. ‘Open Raleigh’, City of Raleigh [Online] No Date.
Available: http://www.raleighnc.gov/open/ [Accessed: 25th November 2013]
83. Dunn, Ibid.
84. Mayer-Schönberger & Cukier, op. cit.,, p. 20.
85. Ibid., p. 116.
86. Dunn, Ibid.
87. Greenfield, op. cit., Loc. 1438.
88. Ibid., Loc. 1446-1449. He cautions: “But equally, it
ought to remain profoundly informed by our understanding of the values and
processes that have enabled cities to serve as vital engines of opportunity,
platforms for personal reinvention and expressive creations in their own right
for over seven millennia.” I would counter that we must beware forgetting that
ideas about what a city is and should constitute do change, they are “engines
of opportunity” precisely because they have never remained static.
89. Dunn, Ibid.
90. Greenfield, op. cit., Loc. 1417-9.
91. Mayer-Schönberger & Cukier, op. cit.,, p. 146.
92. Dunn, 2013. Ibid.