10.6092/issn.2038-3460/17377
In this article, I develop the concept of the “urban stack“ to elucidate how urban data infrastructures gain legitimacy and produce value in capitalist cities. Using two case studies, I study how the stack can incorporate both digital and non-digital components into its hierarchical topology. Heterogeneous components are strung together not only through technological means, as might be inferred from the emphasis on digitality in smart city literature, but also through the “soft infrastructures“ of legal designations, franchise agreements, privacy policies, and info-graphics. A topological comparison between the case studies yields three novel insights: first, urban data infrastructures exploit extant infrastructural conditions; second, technical and protocological operations at the control layer can be used to legitimate ontological claims; and third, technology producers employ a selective and asymmetrical display of information at the level of the interface in order to manage mobile urban populations in real-time. From these insights, it is possible to reach a more abstract conclusion: value production for urban data infrastructures hinges on their producers’ ability to enroll heterogeneous elements into their stacked configuration and to then use this configuration to control the flow of information.
application/pdf
2038-3460
17377
2
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Aaron Shapiro
https://creativecommons.org/licenses/by-nc-sa/2.5/
Tecnoscienza – Italian Journal of Science & Technology Studies
urban data
The Urban Stack. A Topology for Urban Data Infrastructures
Text.Serial.Journal
https://tecnoscienza.unibo.it/
8
Essays
2018-01-29
2023-06-25
2018-01-29
2023-08-24
61-80
TS
Aaron Shapiro
2017
10.6092/issn.2038-3460/17377
61
University of Pennsylvania
2038-3460
2
urban data
en
80
The Urban Stack. A Topology for Urban Data Infrastructures
https://tecnoscienza.unibo.it/article/download/17377/16234
8
                                                                                 Essay

The Urban Stack
A Topology for Urban Data Infrastructures
Aaron Shapiro
University of Pennsylvania (USA)




   Abstract: In this article, I develop the concept of the “urban stack” to eluci-
   date how urban data infrastructures gain legitimacy and produce value in cap-
   italist cities. Using two case studies, I study how the stack can incorporate
   both digital and non-digital components into its hierarchical topology. Heter-
   ogeneous components are strung together not only through technological
   means, as might be inferred from the emphasis on digitality in smart city liter-
   ature, but also through the ‘soft infrastructures’ of legal designations, franchise
   agreements, privacy policies, and info-graphics. A topological comparison be-
   tween the case studies yields three novel insights: first, urban data infrastruc-
   tures exploit extant infrastructural conditions; second, technical and proto-
   cological operations at the control layer can be used to legitimate ontological
   claims; and third, technology producers employ a selective and asymmetrical
   display of information at the level of the interface in order to manage mobile
   urban populations in real-time. From these insights, it is possible to reach a
   more abstract conclusion: value production for urban data infrastructures
   hinges on their producers’ ability to enroll heterogeneous elements into their
   stacked configuration and to then use this configuration to control the flow of
   information.

   Keywords: interface; infrastructure; on-demand economy; stack; urban data.

   Submitted: October, 5 2016 – Accepted: May 30, 2017

   Corresponding author: Aaron Shapiro, Annenberg School for
   Communication, University of Pennsylvania, 3620 Walnut St., 200 Philadelphia,
   PA 19104, United States. Email: ashapiro@asc.upenn.edu




1. Introduction: Beyond the Urban Interface

    Our representational lexicon of the smart city is populated by all sorts
of digital, touchscreen interfaces. As a nominal resource to citizens, these
interfaces offer visitors “personalized streams of city data are rendered into


 TECNOSCIENZA
 Italian Journal of Science & Technology Studies
 8 (2) pp. 61-80 – ISSN 2038-3460
                                                   2017
 www.tecnoscienza.net
62                                                              Tecnoscienza – 8 (2)

‘actionable’ information” (Mattern 2014). More often than not, however,
the information on offer is pre-processed and presented to users as colour-
ful info-visualizations that boast of the efficiency of city services but offer
“little understanding of how and where the mediation of urban systems
takes place within the city itself” (ibid). As Shannon Mattern argues in her
critique of the smart city, if we truly want to learn about the politics of
urban data and actually existing smart cities (Shelton et al. 2015), we need
first to see these interfaces as the mere surface of vast, digital-material in-
frastructures that work by rendering the city as data.
    Getting at these infrastructures means studying the composition of the
operating systems and material supports that lay hidden, beneath and be-
hind the interface: the vertical formation of interdependent layers of hard-
ware and software that are stacked together, materially and protocologi-
cally, to produce the digital-material assemblage of the city (McFarlane
2011; Galloway 2004). This is what Mattern terms the “urban stack”. Its
hardware includes switches, wires, and cables; pipes, telephone poles, and
gas lines; the transmitters and receivers of mass communication broadcasts,
as well as wi-fi internet connections and 4G cell networks; the dirt, con-
crete, plastics, rubber, metal, and flesh that are the city’s core materials. Its
software involves elements of the digital interface − “all those zoomable
maps and apps that translate urban data into something useful” (Mattern
2014) − but also other kinds of interfaces that need be neither public nor
digital: the paperwork of the police officer, the ticket punch of the train
conductor, the analogue clock atop city hall, the route of the post-
man/woman; the inscription devices that enable or constrain mobility (Pe-
ters 2013; Rose-Redwood 2006; Valverde 2011). Taken together, these as-
semblages of humans and their social practices, objects and their materials,
infrastructured technologies and their interfaces, are what make the city an
urban space, “not simply a context for the support or appropriation of spe-
cific lives,” but “the provisionally stitched together, jigged up intersections
of bodies and materials upon which things are both moved and caught”
(Simone 2011, 356).
    This article develops and extends Mattern’s concept of the urban stack
in order to advance our understanding of how and why the composition of
digital urban formations matters. It focuses on two case studies of urban
data infrastructures and the composition of their stacked assemblages: a
public wi-fi infrastructure currently under construction in New York City,
and the worker-facing apps employed in the “on-demand economy”. I con-
clude by arguing that value production in the urban stack hinges on urban
technology producers’ ability to enrol heterogeneous elements into a hier-
archical flow of information and, through this enrolment (Law and Mol
2001), to effect forms of control.
Shapiro                                                                     63
2. The Urban Stack

     The concept of “the stack” is borrowed from software production,
where it refers to a specific, hierarchical assemblage of hardware, network
protocol, and software (Solomon 2013). Theorists of software and power
have applied the stack as topology for mapping how digital media relate to
and affect the material, cultural, legal, and political worlds in which they
are embedded (Bratton 2016; 2014; Solomon 2013; Straube 2016). The
stack itself, however, is a somewhat ambiguous analytic object. As Solomon
(2013) writes, the stack topology conflates the “operative structure that ex-
ists materially within the program code of software systems” with the “class
of diagrams used to explain both these operative structures and software
systems more generally”. Without being able to fully disentangle these two
dimensions, the slippage between material structuring and diagrammatics
is nonetheless productive; it reflects both the ways in which practitioners
conceptualize the integration of software and hardware as well as the top-
ological relationships within their integration. These analyses suggest that,
while the stack is a specific type of assemblage, its specificity is revealing
for data infrastructures that bridge material-digital divides − exactly what
is at stake in the urban stack.
     Here I follow Mattern’s (2014) more liberal and heuristic use of the
stack in order to conceptualize the relationship between data and materi-
ality in the smart city. When applied to urban systems, the stack as a heu-
ristic allows seemingly disparate data infrastructures to be juxtaposed in a
meaningful way. More precisely, it can illuminate how data infrastructures
enrol and assemble various objects, materials, human practices, technolo-
gies, and infrastructures into a looping structure of data flow (Kitchin and
Lauriault 2014). Its implicit topological orientation reflects topological
spatial thinking, “that some spatial problems depend not on the exact
shapes of the objects involved but on the ways that they are put together,
on their continuities, and cuts” (Secor 2013, 431). As I argue here, urban
data infrastructures gather together digital and non-digital infrastructural
components that, in their topological ordering, effect a privatization of ma-
terial and infrastructural public goods. The protocological control that they
perform works to enact a proprietary claim to that data (Thatcher et al. 2016).
     Figure 1 shows how smart city practitioners adapt a stack topology from
software development and apply it to the smart city imaginary. At the bot-
tom of this diagram are various devices used to collect data about urban
populations, spaces, and processes. In the smart city imaginary (Söder-
ström et al. 2014), data collection is accomplished through the use of ubiq-
uitous digital sensing and urban informatics − devices that are impercepti-
bly embedded within the urban landscape (Shepard 2011). In practice,
however, much data collection involves both digital and analog data, as
64                                                                 Tecnoscienza – 8 (2)

well as a combination of automated and manual collection processes1.




             Figure 1 – The urban stack. Source: Liu and Peng 2014.


    We can imagine this base layer as a distributed infrastructure, tethered
together into a coherent program through its stacking. Urban data infra-
structures rely on already-existing infrastructural conditions. Data collec-
tion and aggregation is highly opportunistic in this sense (Thatcher 2014).


     1
      Data about bicycle ridership in New York City, for example, utilize traffic
cameras with algorithmic sorting systems to automatically tag bike riders, but for
sites where data collection is deemed valuable and such technologies don’t yet exist,
the Department of Transportation still places a staffer on the corner, manually
counting cyclists by hand (cfr. NYC DOT 2016).
Shapiro                                                                           65
Infrastructures erected for one purpose are coopted for another. The same
goes for networks and protocols, digital and otherwise. A company doesn't
have to reinvent GPS, traffic systems, census tracts, or government bureau-
cracies in order to implement a new urban technology. But of course, such
opportunistic cooptation of existing infrastructures is hardly acknowl-
edged by smart city boosters.
     In the middle is the control layer2. This is the level at which analogue
and native-digital data are aggregated together, processed, standardized,
and analyzed. It is also at this layer that most proprietary software systems
take hold (Kanngieser 2013; Rossiter 2014). Regardless of whether the
source is public or private, data at the control level becomes privatized
through its analysis. Machine learning techniques employ data to train al-
gorithms for improved accuracy, or to discover non-obvious relationships
between disparate phenomena. As the cloud icons at both the Transmis-
sion and Processing layers in Figure 1 suggest, control is opaque to non-
experts and outsiders. It involves code and interfaces that are not oriented
toward end users, making them difficult to represent iconically (Chun
2011; Galloway 2012). Cloud icons stand in for the proprietary analytics
that are so central to how value is imagined to be produced in capitalist
systems (e.g., Mayer-Schonberger and Kukier 2012) − to how citizens are
dispossessed of their data (Thatcher et al. 2016).
     At the interface, processed data are presented to end-users through
platforms that are both informational and informatic3. Interfaces are dou-
bly communicative in this sense: they both gather and display information
(Halpern et al. 2013). Notable is the highly selective and asymmetrical way
that information is presented to different types of users, and the effects that
such asymmetries can have. As Galloway (2012, vii) writes, “Interfaces are
not simply objects or boundary points” but “autonomous zones of activity
[…] processes that effect a result of whatever kind”. The selectivity by
which information is communicated to different users is derived from a set
of decisions made by technology producers to achieve desired effects from
users’ interactions. Such decisions are thus an important source of control
in urban space and a key objective amongst urban technology producers
who utilize urban data. The uneven distribution of information, which hails
different user-types as subjectivities (see Dalton et al. 2016) and augments
patterns in urban mobility, is similarly an oft-neglected component of dig-
ital urbanism.



    2
      The term “control layer” is borrowed from early descriptions of how GPS
technologies work (Kaplan, cited in Kanngieser 2013, 604).
    3
      The present discussion is limited to the types of urban interfaces that Mattern
(2014) considers as the “points of engagement” through which smart city practi-
tioners imagine citizens will interact with smart city operating systems − screens,
dashboards, displays, graphical user interfaces, etc.
66                                                          Tecnoscienza – 8 (2)

3. The City of the Future: Two Case Studies

     The hierarchical topology of the urban stack helps to elucidate how
value is produced and control exercised through urban data infrastruc-
tures. How technology producers construct new stacks, or take advantage
of stacked assemblages already in use, affords control over the flow of data
and the production of new data ontologies (Kitchin and Lauriault 2014).
Through a consideration of two case studies of urban data infrastructures,
I illustrate how value is produced and legitimacy ensured by controlling
the flow of information. These case studies share a number of similarities
in how heterogeneous elements are assembled together to effect a stacked
topology: each relies on the affordances of externalized infrastructures;
each utilizes technical and protocological operations at the control layer,
not only to extract value but also as a form of technological legitimation;
and each employs a selective display of information on the urban interface
as a way to manage mobile urban populations in real-time (Kanngieser
2013; Levy 2015; Rossiter 2014).

3.1 The Future of Public Spaces

    In 2014, New York City Mayor Bill De Blasio announced that a con-
sortium of private companies had won a Request for Proposal (RFP) to
implement a vision of what the future of public spaces would look like
(NYC.gov 2014b). This vision came in the form of an infrastructure for
free public wi-fi called LinkNYC, slated to become the world’s fastest mu-
nicipal wi-fi infrastructure and largest outdoor advertising network
(ScreenMedia 2014). Intersection, the for-profit conglomeration of two ex-
isting companies − Titan and Control Group, an out-of-home advertising
firm and technology design company, respectively, along with consulting
by technology giants Qualcomm and Comark (NYC.gov 2014a) − was now
licensed to implement, operate, and maintain the LinkNYC infrastructure.
The potential advertising revenue generated by LinkNYC, to be split with
the City of New York, makes the infrastructure an attractive model for
other cities. Much larger players, including Google’s Sidewalk Labs,
quickly garnered interest in the project (Ingraham 2015), and there is al-
ready talk of replicating LinkNYC in other cities (Kinney 2016; Tadena
2016).
    LinkNYC utilizes the city’s extant payphone infrastructure to create a
network of kiosks, called “Links,” that provide free wi-fi access with a ra-
dius of at least 150 feet (and up to 500), free telephone calls to anywhere
in the United States, and, through the touch-screen interface, free access
to information about city services. One of LinkNYC’s key features is that
users receive a unique token that allows them to move within and across
network nodes without having to log back into the network each time their
device is “handed off,” meaning that this meshed coverage has the poten-
Shapiro                                                                             67
tial to be extensive in certain areas (I Quant NY 2014). One estimate sug-
gests that LinkNYC’s overall coverage will include more than a third of
New York City’s land area4. Construction of the Links began in late De-
cember, 2015 in a rush to meet the stipulations of the service agreement
(Brandom 2015). Several hundred Links now dot Manhattan, the Bronx,
and Queens, with between 7.500 and 10.000 planned for implementation
across the five boroughs. Each Link comes equipped with two 55-inch dig-
ital, LCD signage displays dedicated to advertising (ScreenMedia 2014).
The expected windfall of advertising revenue is slated to pay for the infra-
structural overhaul and to yield an approximate $500 million for both the
public and private entities involved over the next decade (Department of
Information Technology and Telecommunications [DoITT] 2014).
     LinkNYC’s potential for generating urban data has been celebrated as
invaluable for urban planning purposes (Fung 2016; Hotz 2015; NYC.gov
2014b). Despite repeated concerns about privacy infractions on the net-
work (e.g. NYCLU 2016), the promise that LinkNYC will provide real-
time data about mobile urban populations to institutional actors − includ-
ing real estate developers, city planners, app developers, advertisers, met-
ropolitan police, transit authorities, etc. − is an important mechanism for
establishing the infrastructure’s legitimacy (cfr. Gustin 2016). In the words
of Intersection’s Chief Strategist Dave Etherington:

When you think about LinkNYC and the 7.500 or so fairly evenly distributed nodes
across the five boroughs, then that does represent a really interesting opportunity
to learn about the city, the behaviours of the city, that could lead directly to health
benefits, more efficient use of traffic – being able to sense, are trucks idling near
these things illegally? Is there congestion? Is there a traffic jam? Is there noise pol-
lution, air pollution? All of these things, by microlocation, could really empower
some really interesting insights about the city that will make it a kind of more en-
joyable place to live (Behind the Numbers 2016).

     To quell lingering privacy concerns, Intersection developed a concise
(if still vague) privacy code (NYC.gov 2016) outlining the technical protec-
tions in place. Data shared over the networked will be encrypted and au-
tomatically anonymized by unique, randomized keys for each MAC ad-
dress that logs onto the wi-fi network. LinkNYC also promises not to track
web browsing histories on devices connected through the wi-fi. However,
even if these technological solutions and protections for privacy prove ef-
fective, data generation will continue apace. This is because LinkNYC’s
most valuable data-infrastructural affordance is its ability to simply count
people: “We do not collect information about your precise location. How-
ever, we know where we provide Wi-fi services, so when you use the Ser-
vices we can determine your general location” (NYC.gov 2016). By virtue


   4
     Manhattan’s coverage may be as high as 50% while other areas could be as
low as 16% (I Quant NY 2014).
68                                                                Tecnoscienza – 8 (2)

of logging into the LinkNYC wi-fi, users will be counted, in place, in real-
time. The system may “combine Technical Information or non-Personally
Identifiable Information about your use of the Services with similar infor-
mation about other users in an aggregate or anonymous manner” in order
to “measure or understand the effectiveness of advertising we serve to you
and other customers like you, and to deliver relevant advertising to you” (ibid).
    There is also the potential for LinkNYC to count people who are not
logged onto its wi-fi (cf. Musa and Eriksson 2012). Evidence of this can be
gleaned in documentation of the LinkNYC technical capabilities as well as
by considering how companies in the Intersection consortium have be-
haved historically. In 2014, Titan, Intersection’s advertising arm, installed
Bluetooth low energy (BLE) beacons on New York’s payphones, which are
capable of counting all devices with wi-fi and Bluetooth connection capa-
bilities within its range. When it was made public that Titan had installed
these devices without notifying citizens, the New York City Department of
Information Technology and Telecommunications (DoITT) required Ti-
tan to remove the beacons (Bernstein and Ryley 2014). This same technol-
ogy is built into the Link system (Intersection 2016), although representa-
tives from Intersection claim that they have not yet been turned on (Gustin
2016).
    The technological capacity to silently count readable devices is not new,
nor is it limited to LinkNYC5. What is new about the LinkNYC’s potential
data collection is the granularity and penetration that it achieves. With
smart phone penetration reaching 80% of New Yorkers in 2015 and still
growing (NYC Dept. of Consumer Affairs 2015), LinkNYC is poised to
generate real-time locational and mobility data on a majority of New York’s
population. The uses towards which this data might be put are, at present,
limited to the twinned domains of advertising and urban planning. Where
the urban planning uses of data legitimates LinkNYC’s silent locational
data collection, the advertising revenue generated for the City likewise le-
gitimates the public-private partnership between Google-backed Intersec-
tion and the City of New York. The normalized dwindling of public service
provision in neoliberal or entrepreneurial cities (Harvey 1989) opens a
market-space for private companies to capitalize on infrastructure and to
label it “innovation”. In Intersection’s Chief Strategist Etherington’s words:

The advertising concessions related to this infrastructure are seen as vehicles for
innovation and that's really where we’re at in our focus from the media side – that,



    5
      In 2013, an IT worker discovered that the New York Department of Trans-
portation had been silently scanning drivers’ EZ-Pass tags (RFID cards for auto-
matic toll collection), in order “to monitor the flow of New York City traffic […]
scrambl[ing] the serial numbers to anonymize vehicles and their owners.” See
http://www.popsci.com/article/diy/ezpass-hack-covert-scanning (retrieved April
30, 2016).
Shapiro                                                                         69
with these advertising contracts, we’re able to introduce not just increased adver-
tising revenue for cities, but we can bring in new technologies and new innovation
(Behind the Numbers 2016).

    The desired outcome of LinkNYC is to transform public spaces into
sites of real-time data generation that can be capitalized on through adver-
tising sales. The release of this data to urban planning agencies legitimates
not only the City’s involvement, but also the private company’s right to
silently collect and analyse urban data without even tacit consent.

3.2 The Future of Work

    Recent discussions about the future of work have emphasized the role
that app-based platforms will play in making labour economies more flex-
ible (e.g., Hanrahan 2015). This debate is especially important for cities,
given the growth and concentration of the service sector as a major local
economic industry in urban areas in the U.S. and elsewhere (Lopez-
Cermeño 2015), as well as the impact that work platforms have already had
on cities (Zumbrun 2016). Prominent examples include informal taxi ser-
vices Uber and Lyft, and courier services like Caviar and Postmates. These
companies profit from the algorithmic management of fleets of independ-
ent contractors who, through worker-facing apps loaded onto their mobile
smartphones, connect with customers seeking delivery or taxi services.
Fleet management apps work as semi-automated systems for labour assign-
ment and oversight (Rosenblat and Stark 2016, 2). They use closely-
guarded algorithmic calculations to set prices for both customers and pay-
outs for workers. If described at all, explanations of these algorithmic cal-
culations are cloaked with vague terminology about the distance of a deliv-
ery or a taxi fare, or even shifting levels of demand. Neither the customer
nor the worker has access to the full information (Kirchner and Mattu 2015).
    The term “on-demand economy” describes the experiences of both
customers and workers for these platforms. What these companies deliver
is quasi-luxury, hyperlocal mobility – the movement of goods (as in the
food courier platforms Caviar and PostMates) or people (as in the taxi and
black car services Uber and Lyft): door-to-door service, ordered with the
push of a button, just-in-time and on-demand (Ruckelshaus 2016). Work-
ers for these companies, designated as independent contractors rather than
employees (Scheiber 2015), are enticed with the promise of flexibility −
working whenever they choose, deciding whether to accept or deny any job
in the form of delivery or ride request. Work, like the service, is available
on-demand: workers log on whenever they want and choose which jobs to
accept or reject. But labour is also on-demand: workers are not paid with-
out completing an order or a fare, and order allocation is dictated by the
same opaque algorithmic calculations that determine the payment for a
70                                                                   Tecnoscienza – 8 (2)

given job (Rosenblat and Stark 2016)6.
    Despite the promise of flexibility, in practice, on-demand platforms
employ numerous disciplining techniques (such as ratings systems and ac-
countability indices) to cajole workers into adhering to some sort of sched-
uling system or acceptance rate − the same rigid components from which
flexible working was meant to depart (cfr. Graboyes 2016). For instance,
Caviar, an upscale food delivery service available in fifteen of the country’s
largest metro areas, sends an automated weekly email to its workers with a
breakdown of completed orders and payments7. The company recently in-
troduced a new component to this email, an index of workers’ scheduling
reliability, which calculates the ratio of time spent logged into the app dur-
ing a scheduled shift. Workers are contractually not obliged to commit to
scheduled shifts, but for management, having a schedule helps plan for
predicted ebbs and flows in demand. The index impresses upon the worker
his or her standing as reliable, despite its contractual irrelevance. Several
indicators are excluded from this index that could just as easily reflect a
worker’s reliability. For example, when understaffed, the company sends
out a notice to encourage couriers to sign on; the scheduling reliability in-
dex does not account for how often a courier responds to these emergency
requests8. Nor is there a calculation of what percentage of time a worker
sat idle during his or her scheduled shift − logged on, but not receiving
orders and thus not getting paid.
    What is most striking is the opacity around whether or not this ratio
affects one’s rankings in the algorithmically-defined queue of couriers used
for dispatching orders. As one Caviar courier explained during an inter-
view:

When I first started working for Caviar, I was told that we weren’t obligated to
accept orders. It’s completely at our discretion when we want to work and what
orders we want to accept. That was a big selling point for them looking for couriers
[…]. Now, they're doing this [scheduling] reliability system […]. It feels like Caviar
is trying to guilt trip us for not showing up for our shifts, which are not obligatory,
and whether or not we’re being penalized for showing up for our shifts is kind of
unclear. But whether or not they’re penalizing us, it seems like they're asking us to
penalize ourselves9.

     Another example is Uber’s policies for deactivating drivers. Prior to the

     6
      Contrary to this model, some have argued that since Uber and other compa-
nies do profit off of drivers even when they are not delivering a passenger, workers
should be paid for their time.
    7
      My methods for this research include working as a Caviar courier for 12
months; I received these emails while working for Caviar.
    8
       A typical notice, which is called “the bat signal” by management, reads
“Lunch is busy NOW and we are understaffed! Go online NOW to take full ad-
vantage of this lunch bizness, Philly!” (received 5/3/2016).
    9
      Interview conducted March 7, 2016.
Shapiro                                                                   71
settlement of a class action lawsuit (Isaac and Scheiber 2016), Uber was
opaque about its deactivation policy. Rationales ranged from inactivity (not
working for 90 days) to low acceptance rates (the ratio of how many rides
a driver accepts to how many requests he or she receives) (Dough 2016).
These disciplinary techniques are automated and incorporated into the
technological fabric of workers’ day-to-day labor practices.
    In addition to mechanisms that belie the flexibility of on-demand work,
on-demand platforms are also characterized by their highly selective and
asymmetrical display of information within the worker interfaces (see also
Rosenblat and Stark 2016). Lyft drivers see maps that show them areas
where surge pricing (or Prime Time) is in effect. In these areas, passengers
are subjected to higher rates due to local distributions of demand (or algo-
rithmically-predicted distributions of demand) at a given time interval
(Chen and Sheldon 2015; Kirchner and Mattu 2015; Rosenblat and Stark
2016). Workers argue that dynamic pricing is a fleet management tech-
nique used to incentivize drivers to go to busier areas. But since the algo-
rithmic calculations that determine surge pricing are opaque to drivers, as
are the number and whereabouts of other drivers on the road at the same
time, there is no guarantee that going to a surge zone will mean getting a
well-paying job. In another example, the courier apps often obscure the
address of a delivery drop-off when the worker is prompted to accept or
reject an order. High rise apartments or office buildings can be unattractive
to couriers, since payment is calculated based on the ground-distance be-
tween the restaurant and the delivery address and not on how much time
is spent getting to an apartment or office. Knowing that a drop-off location
is on the 25th floor might thus be a disincentive for a courier to accept the
job; the company’s interest is thus to omit this information until a courier
has already accepted the order. Such informational asymmetries give work-
ers just enough information to complete a task, but obscure enough infor-
mation that the company’s interests appear to be in the workers’ as well.

4. Topologizing Urban Data Infrastructures
    The case studies presented here differ in interesting ways. LinkNYC is
a large infrastructural overhaul managed by private firms and marketed as
a public good in the form of free wi-fi; apps in the on-demand economy
are much more distributed and explicitly focused on extracting value from
workers. Despite their differences, the two cases share much in common,
and their similarities can be fruitfully highlighted by employing the topol-
ogy of the urban stack. Using the stack as a heuristic, these cases can be
bent and stretched to facilitate comparison (Secor 2013), which, in turn,
can help to expand our understanding of how “actually existing smart city”
interventions (Shelton et al. 2015) are legitimized, and how controlling the
flow of information can produce capitalist value.
72                                                           Tecnoscienza – 8 (2)

4.1 Distributed Infrastructures

    Both case studies rely on a distributed infrastructural base upon which
other elements are stacked to create small monopolies of data collection,
storage, and analysis. This distributed base externalizes costs and mitigates
risk by taking advantage of extant infrastructural conditions. With
LinkNYC, there are two ways that extant infrastructure is enrolled into the
network. First, LinkNYC exploits the sunk cost telecommunications infra-
structure already in place in New York City, constructing its hardwired
connections between the Links within the conduits built beneath the sur-
faces of New York’s most densely packed pockets (PlaNYC 2013). Fiber
optic connections can be strung through conduits, which can be accessed
simply by opening a manhole cover. LinkNYC is being built without hav-
ing to break ground. Second, LinkNYC relies on the growing penetration
of smartphones amongst New Yorkers. Smartphones and other readable
devices, such as tablets or laptops, even if not actively connected to the
LinkNYC wi-fi networks, serve as de facto sensors for LinkNYC’s produc-
tion of real-time data about urban populations. This data collection is in-
tegral to its legitimacy.
    In the on-demand economy, the most profound way that companies
take advantage of distributed infrastructures hinges on the legal designa-
tion of workers as independent contractors rather than workers. The ques-
tionability of this designation was recently deferred by the settlement of a
class action suit involving Uber workers in Massachusetts and California
(Isaac and Scheiber 2016). For on-demand services like Uber, Lyft, or Cav-
iar, this deferment is a boon: not only do the companies remain free from
being required to cover employee expenses such as Social Security and
workers’ compensation, they can continue requiring workers to provide
their own means of communication and transportation – typically a
smartphone and a bicycle or car. Employees are left to cover the costs of
data bills and fuel, as well as for any upkeep and repair to vehicles due to
wear and tear incurred while working on the road. Further, companies are
legally prohibited from providing tax education to workers, as this would
breach the legal distinction between independent contractor and employer
(Mishel 2016). On-demand economy companies have proved successful at
enrolling workers who are willing to supply their own means of transpor-
tation and communication − costs that employees typically do not cover.
The infrastructural conditions that facilitate that rapid, on-demand move-
ment of people and things in this sector is thus outsourced to the workers
themselves, both in terms of their own bodies and labor (including risk of
injury, healthcare coverage, fatigue, etc.) and in terms of their privately
owned consumer technologies, which serve as networked infrastructural
components10.

   10
      The outsourcing of labor in the on-demand economy is reminiscent of 19th
century telegraph messenger boys, who, as Downey (2003, 134) argues, were both
Shapiro                                                                            73
4.2 Control

    Control is exercised in both the LinkNYC and on-demand economy
examples through the effects of black-boxed regimes of calculation. In the
case of LinkNYC, two functions at the control layer will be key to its suc-
cess. The first is the hidden protocological activity that randomizes or
anonymizes user identification in order to ensure privacy. These protocols
transform aggregated user data into a format that can become information-
ally meaningful while simultaneously providing a technological solution to
concerns over privacy. The second function at the control layer involves
the dynamic, algorithmic calculation of pricing for advertising that will be
based on this information (Behind the Numbers 2016). Once a real-time
count of devices is in place, algorithms will not only “allow advertisers to
deliver highly targeted content to passers-by, [which] works similarly to
ad-targeting algorithms users encounter while surfing the Web” (Campbell
2016), but also to create a dynamic pricing model such that ad space costs
more when more people are around to view them (Shpanya 2014). As one
online advertising trade magazine explains, the Links’ “strategically placed,
networked digital signage displays” are situated within “a larger mul-
tiscreen ecosystem that effectively amplifies brand messages to create a
deeper level of engagement with active consumers […] with highly targeted
messages” (ScreenMedia 2014). LinkNYC’s real-time data on the ebbs and
flows of urban populations will be able to make already-valuable out-of-
home advertising space even more profitable by charging advertisers more
during periods of high traffic.
    With on-demand economy smartphone apps, the control layer is largely
hidden from workers, effecting an informational asymmetry that can be
leveraged to manage large fleets of workers in real-time (Rosenblat and
Stark 2016). Control is manifest in the proprietary algorithms that deter-
mine which couriers or drivers should be matched with which deliveries or
riders, where, when, and at what price (Chen and Sheldon 2015). The pro-
prietary nature of these algorithms is central to the profitability of compa-
nies in the on-demand economy. But it is also important in legitimizing
claims that on-demand companies are not service providers, but rather
technological platforms that serve merely to connect supply and demand.
Such claims are important, since they legitimate the designation of workers
as independent contractors, who supply their own modes of transport,
communication, health insurance, etc. As one Uber engineer wrote in a
widely read forum about Uber on Quora:
A taxi company contracts drivers, deals with vehicles, pre book rides [sic], etc.
Uber deals with building data centres, running real time software services, facilitat-



“active components” of telegraphy as a technological system and “laboring agents
within produced urban spaces”.
74                                                                    Tecnoscienza – 8 (2)

ing payment and conducting research into the economics of real time transporta-
tion automation, among solving all sorts of other interesting technological problems
− all things that are not done by a taxi service. It’s a totally different operation from
what a taxi company or a transportation service does […] Uber is not a taxi com-
pany, but a technology company that provides solutions for people's transportation
needs [just like] eBay is not a shopping mall, but a technological platform that en-
able [sic] private sellers to find buyers for what they have to offer (Tal 2015).
    Activity at the control layer allows for dynamic pricing models that ex-
ploit surges in demand, allocates orders to the lowest-costing courier or
driver based on the distance to the customer, and, ultimately, serves as a
justification for the companies’ designation as technology producers rather
than urban transportation or logistics services, which would be regulated
more stringently.

4.3 Interface

    Finally, the selective display of information at the interface level is key
to the functioning of both LinkNYC and on-demand economy companies.
On the one hand, the omission of information can be used tactically to
realize certain effects. This is clear in the on-demand economy’s worker-
facing apps. For example, the Caviar Courier app has a sequence through
which couriers must step through when accepting, picking up, and deliv-
ering an order. Throughout this sequence, certain bits of information are
given while others remain omitted. The need-to-know basis of information
here is productive: it gives workers just enough information to complete
the task at hand, but not enough for them to gain a full understanding of
how the system works and thus optimize their output in the form of pay-
ments. The same could be said of LinkNYC’s interfaces and the “larger
multiscreen ecosystem” (ScreenMedia 2014) into which they fit. This eco-
system is both informational and informatic: usage generates data. But
these data are systematically excluded and consciously hidden from inter-
face displays (Chun 2011). The doubly-communicative interfaces are de-
signed such that individual users can never access the full scope of infor-
mation relevant to the landscape in which they’re operating, but are ex-
pected nonetheless to use the information that they do have to maximize
private gain. This reflects Mattern’s (2014) point about the trade-offs im-
plicit in the smart city interface; they “suggest that we’ve traded in our en-
vironmental wisdom, political agency and social responsibility for corpo-
rately-managed situational information, instrumental rationality and per-
sonal consumption and convenience. We seem ready to translate our messy
city into my efficient city” (original emphasis).
    But if certain information is selectively omitted or excluded, other in-
formation is strategically included in order to achieve certain effects. In the
on-demand economy’s worker-facing apps, information about worker reli-
ability and productivity is tactically deployed in an effort to discipline
workers into conforming to the rigid elements of supposedly flexible work.
Shapiro                                                                       75
Indices and info-graphics about the worker’s performance are described
by workers as “mind games” or “guilt trips”. These techniques are com-
mon to the integration of digital surveillance mechanisms within the work-
place (Kanngieser 2013; Levy 2015; Rossiter 2015). With LinkNYC, the
potential for advertising displays to direct the attention of passers-by to
local consumer points of interest is a subtler form of managing mobility.
Hyperlocal, modular ad displays can be designed to steer potential custom-
ers to local restaurants, cafes, shopping centres, department stores, with
the promise of discounts or coupons: “You can expect the [LinkNYC] ki-
osks to start telling you there’s a table for two open at the French bistro
down the street, for instance. Or that the subway station nearest you is of-
fering limited service due to repairs” (Fung 2016). This kind of hyperlocal
notification allows those with access to the network’s counting capabilities
to produce market value through the targeted modulation and steering of
mobile urban populations.


5. Conclusion

    The urban stack is a productive heuristic with which we might better
understand how urban data can be made a valuable commodity. Using the
two case studies of LinkNYC as a data-generative municipal wi-fi infra-
structure and the worker-facing apps of the on-demand economy, I showed
how the stack can incorporate both digital and non-digital components
into its hierarchical topology, including telecommunications conduits lo-
cated beneath the streets in Manhattan and other parts of New York City,
as well as mobile fleets of drivers and bikers, as infrastructures for facilitat-
ing or steering the movement of goods and people. I have also illustrated
that heterogeneous components are strung together not only through tech-
nological means, as might be inferred from the emphasis on digitality in
smart city literature, but also through the “soft infrastructures” of legal
designations, franchise agreements, privacy policies, and info-graphics, as
well as the dispersed infrastructure of transportation and communications
maintenance.
    Using the urban stack to construct comparisons across urban data in-
frastructures yields novel insights. Here I have shown how urban data in-
frastructures rely on the affordances of externalized infrastructures by ex-
ploiting extant infrastructural conditions. Further, both LinkNYC and
companies in the on-demand economy utilize technical and protocological
operations at the control layer to extract value from digitally-mediated in-
teractions. LinkNYC legitimates its data collection practices through tech-
nological means to secure privacy and the promise of sharing this data with
urban planning actors, while for companies in the on-demand economy,
control layer activity legitimates claims about the status of workers as inde-
pendent contractors rather than employees. Finally, in both cases, technol-
ogy producers employ a selective display of information at the level of the
76                                                               Tecnoscienza – 8 (2)

urban interface to manage and capitalize on the movements of urban pop-
ulations in real-time. From these insights, it is possible to reach a more ab-
stract conclusion: value production for urban data infrastructures hinges
on their producers’ ability to enrol heterogeneous elements into their
stacked configuration, and then use this configuration to control the flow
of data and information.


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