The bottom-up smart city: Local and non-governmental data in urban environments.

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University of Amsterdam

June 2015

The bottom-up smart city:

Local and non-governmental data in urban environments.

MA Thesis

Presented to the Faculty of Humanities

Department of Media Studies New Media and Digital Culture

Supervisor: dhr. dr. J.A.A. Simons

Second reader: dr. Sjoukje van der Meulen

Gabriel Souza Reis

10883452

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Introduction

Life in the beginning of the 21st

century is being more and more influenced by its digital representations and translations in databases. Digital technology has become one of the main actors in everyday life and the multifold debates surrounding data represent some of the main discussions in our society and culture as a whole. The different methods of data collection, storage and usage; the role and power relations of the multiple stakeholders involved in the practices of data governance and ownership; the privacy and surveillance issues and the ideologies behind the algorithmical design of the tools that make use of the data are some of the debates with many questions yet to be answered.

Many authors have discussed the ubiquitous presence of computational processes in our daily lives since the “The Computer on the 21st century” by Weiser (1991), the seminal article that intended to describe a future picture of the pervasive presence of computers in many different activities and moments of life, the so called ‘ubiquitous computing’. Even though the arrival of the 21st

century did not make many of the activities presented by Weiser a reality, at least not in the lives of the big majority of the population, some theorists recognize that we already live in environments of ubiquitous computing (or simply ‘ubicomp’) due to the growing presence of smartphones, tablets and other devices connected to 3G and wi-fi networks. As pointed out by Dourish and Bell: “ubiquitous computing is already here, in the form of densely available computational and communication resources.” (2007 140)

This pervasive however ‘hidden’ aspect of software and data in our lives characterizes it as one of the main infrastructures of our society, and thus, requires an approach that grasps its social and political importance. As every infrastructure, code is ‘black-boxed’ and disappears from the eyes of the majority of the people. The experiences with the activities mediated by it become basically the inputs and outputs of the processes, ignoring the inner operations of the systems. The computational systems are “silent” and “familiar” (Thrift 2004) pre-conditions to the activities we do every day. However, infrastructures have a constitutional, but many times forgotten,

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a lens through which the world is encountered but remains in the background.” (2011 112) By its mediational aspect, it can determine and influence perceptions and meanings, and “drive and maintain standardization, reflect and embody historical concentrations of power and control.” (2011 112)

Furthermore, the ever-growing presence of software and code is grounding studies that point towards a more dynamic and fluid relationship with space. Instead of understanding space as the formal and steady stage that waits for life to be enacted, studies that suggest a theoretical approach to it as a dynamic construction that is being constantly shaped and shapes life seem more adequate to the analysis of computational work nowadays. Castell’s “Spaces of Flows” (1999) affirms, “spatial forms and processes are formed by the dynamics of the overall social structure.” (441), Kitchin & Dodge’s “Code/Space” (2005) states “new software-enabled technologies make a difference because they alternatively modulate the form, function and meaning of space”, Thrift’s (2004) “flow-world” (590) in which the cybernetic feedback loops have become the natural way of dealing with data in “a plane of endless calculation and recalculation” (591), and Flusser’s “flections on fields” (2005) are some of the authors and concepts that support the approximation of our conceptions of space to the dynamic aspects of networked computational systems.

As the growing pervasiveness of ICTs (information and communication technologies) in people’s lives has broken the barriers of the private domicile and the Personal Computer is not the only point of access to the Internet anymore, these broad concepts of dynamic space and its relations with computers and data can be easily grasped by the common observer in the daily relationship with the city, as the urban life already represents the majority of people’s situation on the planet today - 54% of the world population already live in cities and it is expected that this number reaches 66% by 2050 (UN 2014). As put by Andersen and Pold, “the computer is moving out into physical and urban reality” (2011 110) and adds “new layers to the already multilayered urban scripted space”. (2011 113)

This is the scenario that gave birth to the now popular concept of the “smart city”. According to Rob Kitchin, smart cities are “places being increasingly composed

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of and monitored by pervasive and ubiquitous computing and whose economy and governance is driven by innovation, creativity and entrepreneurship, enacted by smart people.” (2014) Frank Kresin, Research Director of the Waag Society of Amsterdam, states that “Smart Cities are essentially networks of sensors strewn across the city, connected to computers managing vast flows of data, optimizing urban flows like mobility, waste, crime and money. They promise to make governance more efficient, and turn cities into safer, cleaner and more enjoyable places”. (2013)

This idea that city management can be optimized via data analysis and algorithmical programming is encouraging different forms of collection, storage and usage of data in diverse ways that vary within a spectrum from governmental totally top-down approaches, with masterplans of efficiency and control rooms to observe and take actions upon the city immediately, to local scale citizen’s bottom-up initiatives, created and executed by the population. In any of the approaches that might take place, the management and control of data becomes vital to the debates about the endless negotiations between the actors of a city, and society in general.

The wide spectrum of top-down and bottom-up projects of smart cities currently presents a concentration of attention in the initiatives of the top-down side. Entire cities built from scratch in masterplans of efficiency and sustainability are advertised in the news as the state of the art of how the technological advances can be applied in favor of society and huge systems that integrate all the operations of a city in a central control room promise to improve the quality of life of the citizens through the powerful knowledge of hidden in complex data analysis. However, most of these solutions exclude the active agency of the main targets of their efforts, the citizen. Considered only as sources of data and mere users of the public projects, the citizen is often eclipsed in the complex fabric of interests that form top-down politics. In parallel, as an alternative effort, bottom-up initiatives blossom in a totally decentralized way, created in a smaller (sometimes even individual) scale, address local issues with the new possibilities brought by the popularization of social networks, open and shared libraries of code for mobile and web development and falling prices of hardware and cloud storage.

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The combination of both governmental resources and citizen motivation and capability for developing solutions for the city makes the current urban environment a field for new negotiations between its actors, through the negotiations present in the governance and ownership of the data flows that occur in a contemporary city. The three main forms in which the data flows take place nowadays are: governmental open data initiatives, private data and local non-governmental data.

In the ‘open data’ initiatives, the state makes governmental data available for citizens to consult and work on top of it, with the believe that the transparency in the management will empower the citizens and balance the forces derived from data usage. According to the National League of Cities, open data is defined as “data that can be freely used, reused, and redistributed by anyone. This freedom of use is a lens into the city that creates transparency and engagement opportunities with citizens. It also provides a resource for the city to function more efficiently, and generates economic development opportunities for new companies to incubate and expand.” (2014) In the other hand, the critics of these initiatives argue that the simple act of opening the data does not automatically mean a benefit for the population via citizen empowerment due to the technical complexity of the datasets, that wards off the layman, and the common lack of interoperability of the data provided, that hinders complex crossing between different datasets.

The second, and most popular form of data in the current urban flow is the ‘private data’, provided by an enormous variety of companies that offer products and services that translate in data aspects of the life in the city and manage privately these datasets. The major technology infrastructure companies (such as Google and Amazon) and social media giants (such as Facebook and Twitter) are the most important players of this data flows. With geographical information attached to their services, whether on navigation maps or simple latitude coordinates, these companies have built their own layers of data throughout the city, using it to provide information that affects the relationship of the citizens with the urban space. This way, the debate about this type of data flow address the opposition between the public and personal value of these datasets for the citizens and the condition of private property of the companies that collected them.

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In both types presented above, the presence of large-scale players controlling all of the collection and availability of data, end up distancing the citizen from fundamental parts of the process. This way, alternative initiatives, developed in local and smaller scale contexts arise as a third form of data flow in the urban environment. Many projects such as the ‘Smart Citizen Kit’ and ‘Ring Ring’, the case studies of this research, can be considered an emerging force in the power relations within the city. Solutions for specific problems and localized situations are also creating another layer of data in the city, an even more decentralized and messy, which adds more fuel to the debate of data governance in the urban environment.

This objective of this research is to investigate how are the bottom-up projects affecting the negotiations in the urban environment in regard to the dynamics of the data flows generated and managed by its main actors: the government, private companies and the citizens. The first chapter will address the general debate of data studies, discussing the presence and importance of data in the contemporary society and developing on some of its main concepts. The second chapter will discuss the idea of smart cities, its most popular approaches and the complimentary concept of smart citizens. The third chapter will analyze two projects developed and managed by citizens and how they exemplify and extend the presented debates.

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Chapter 1 – Data studies

“A data revolution is underway, one that is already reshaping how knowledge is produced, business conducted, and governance enacted”, states Rob Kitchin in his book Data Revolution (2014). boyd and Crawford assert: “The era of Big Data has begun.” (2011). In an article on The New York Times, David Brooks affirmed, “If you asked me to describe the rising philosophy of the day, I’d say it is Data-ism” (2013). His colleague Bruce Feiler reinforces that “we are awash in numbers. Data is everywhere” in the article “The United States of Metrics” (2014).

The argument that life in the first decades of the 21st century is being characterized by the pervasive and growing presence of data flows in our societies has being discussed by different authors in academia and lately, especially after the privacy issues involving the National Security Agency (NSA) of the United States and the declarations of Edward Snowden in 2013, the topic gained major public attention among the general public. The popularization of quantified-self initiatives, with its self-tracking tools and wearable devices to measure exercise, sports performance and health index, such as Strava, Fitbit and Apple Health, also help to spread among various types of people the idea that the collection and analysis of data can unveil valuable knowledge and “make the invisible visible” (Kresin 2015) . It is the “faith in numbers” identified by Nigel Thrift (2004 592).

Rob Kitchin conceptualizes data as the “raw material produced by abstracting the world into categories, measures and other representational forms – numbers, characters, symbols, images, sounds, electromagnetic waves, bits – that constitute the building blocks from which information and knowledge are created.” (2014) It is important to notice that Kitchin’s definition do not reduce data to digital registers, but to any abstraction or representation of the world in a language. This way, it is natural to consider that we humans have practically always been working with data in order to produce “information and knowledge”.

However, the fast advances in digital technologies of the last decades have multiplied exponentially the amount of data collected and stored, and the possibilities

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computers in a global-scaled network, the flows of information are increasingly growing and being understood as one of the most important media in the contemporary society, therefore “determining our condition” (Kitler 1987) and shaping how we live our lives.

1.1 – Social and political importance of data

In the words of Kitchin and Laurialt, “Data provide the key inputs to systems that individuals, institutions, businesses and science employ in order to understand, explain, manage, regulate and predict the world we live in, and are used to create new innovations, products, and policies.” (2014 2) The importance of data is continuously growing once “analysts and consultants argue that advanced statistical techniques will allow the detection of ongoing communicative events (natural disasters, political uprisings) and the reliable prediction of future ones (electoral choices, consumption).” (Burgess & Puschmann 2014 44)

Playing such a big role in many different aspects of today’s society, data studies have the need for horizontal analysis of its implications that need “to cut across conventional social boundaries. In different contexts, database technologies have been understood as media, infrastructures, products, and tools.” (Driscoll 2012 p7) The multifold aspect of data is reinforced by boyd and Crawford when they mention the “deep government and industrial drive toward gathering and extracting maximal value from data, be it information that will lead to more targeted advertising, product design, traffic planning or criminal policing.” (boyd & Crawford 2011 p13) Lessig, also defends the importance of a watchful eye towards contemporary technologies, because “rules built into software and hardware functions as a kind of law. That we should understand code as kind of law, because code can restrict or enable freedoms in just the way law should”. (2005 355) Kelty also emphasizes the importance of the relations between technology and society: “the mode of existence of any live technology (as opposed to those dead ones no longer maintained) is not singular or stable. It depends on how people and practices exist, how organizations and laws exist, how ideologies and discourses exist; and it is in constant motion.” (2013) In other words, as much as one of the main characteristics of digital data

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collection is its constant dynamic nature, it’s feedback loops of continuous learning, the analysis of the social implications of it must also be thought of as an ongoing process of negotiations and ‘beta-testing’.

The understanding of the multiple aspects and implications of data is crucial to the study of its influences in the urban environment, a field historically marked by the complex negotiations of interests between actors and technologies. Moreover, this complexity requires a critical look to escape the optimistic bias that tends to look at data purely as a source of knowledge and improvement nor the pessimistic one, over-concerned about the ways in which surveillance and control issues are ending the real freedom of our behaviors. It is important to have in mind the complexity and dual character of it. Again in the words of boyd and Crawford “Data is increasingly digital air: the oxygen we breathe and the carbon dioxide that we exhale. It can be a source of both sustenance and pollution.” (2011 p2)

‘Ubiquitous computing’, ‘internet of things’ and ‘big data’ are buzzphrases that have already surpassed the technological realm to become part of daily conversations in different circles, such as business, health, sports, entertainment and governmental management. The implications of the pervasiveness of computers and macro analysis of enormous amounts of data are increasingly visible in society, whether in the shape of personalization practices on social networks and online retail or at the more and more complex systems of homeland security in airports. The development and of specific or personalized gadgets and datasets have also given birth to the term ‘small data’, in which the analysis of the information can be done in a smaller scale, with potential gains in contextualization and accuracy. Consequently, the more relevant the data is in society, the more the questions about it become important and urgent, and concepts such as ‘data ownership’ an ‘privacy’ need to be discussed among the different actors and disciplines that it involves, once the constant data trace left in most of the platforms existent nowadays make the lines between public and private blurry and more complicated. The next session will go through the concepts of these terms and address its relationship with urban environments and local bottom-up initiatives.

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1.2 – Ubiquitous computing, big data and small data

This sub-chapter will address three of the main concepts of data studies related to the dynamics of collection and management of data that can lead to a better understanding of the scenario that made bottom-up initiatives in urban environments possible.

1.2.1 – Ubiquitous computing

In one of the first and most influential articles about the distribution of computation in the daily lives of people, Mark Weiser stated, “specialized elements of hardware and software, connected by wires, radio waves and infrared, will be so ubiquitous that no one will notice their presence” (1991 94). This invisible but constant presence is the main reasoning behind the ubiquitous computing systems that have been developed ever since. The idea of a network of distributed computers performing the most varied activities in a discrete way in the ‘background’ of our lives has driven many technological developments in the last years with the mindset of a world where automated algorithms will solve problems and optimize individual and common lives.

The main manifestations of ubiquitous computing take place both in the domestic and in the urban environment. The domestic landscape is more and more inhabited by “coded objects” that “are helping to reshape domestic living and its spatialities by, on the one hand, augmenting and supplementing domestic tasks and, on the other, plugging the home into new, extended, distributed networks.” (Dodge & Kitchin 2009 13452) The electronic devices that compose the average Western household, from the PC to the TV set, the sound system to the microwave, operate with internal computational products and are constantly evolving towards more integrated capabilities, especially by connecting with bigger external networks, usually via Internet connection; e.g.: the refrigerator that recognizes the lack of specific products and sends a signal to a central control room that warns its owner in a mobile app or the TV that connects to content streaming services via specific apps. In the urban environment, space of more complex relations between actors and systems,

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the ubiquitous computing is characterized by the pursuit of optimization of processes such as transportation (e.g.: coordinated traffic lights and digital cards for trains and buses) and law enforcement (e.g.: CCTV cameras in strategic points).

Despite of the general tendency to picture ubiquitous computing always in a near future yet to come, Bell & Dourish declare, “ubiquitous computing is already here, in the form of densely available computational and communication resources” (2007 140), in other words, we are already living in the world in which we can solve problems and gain efficiency via computational processes running under the conscious surface of the daily life. Their argument is that the evolution of digital technology, and infrastructure in general, always happens in a messy way, and although the popular images may suggest a totally planned landscape with digital processes conducting every interaction with the world in an integrated and harmonic way, “The lesson of the real world of ubiquitous computing, then, is that we will always be assembling heterogeneous technologies to achieve individual and collective effects”. (Bell & Dourish 2007 139) Their argument is that, if we better observe it, we can see that the contemporaneity is already surrounded by pervasive digital technologies, the difference is that it usually happens in a fragmented and disorganized way. The omnipresence of mobile phones, wi-fi networks, 3G antennas, CCTV cameras, ATMs, and other examples that have inhabited the urban landscape in the latest years.

This way, this research is based on the idea that ubiquitous computing is already alive and active in the urban environment. It is a combination of multiple and not necessarily integrated devices and networks performing specific activities and generating diversified data flows in the background of life in society.

1.2.2 – Big data

The assemblage of devices and platforms that structure what we call ubiquitous computing, as described above, in general perform on top of or at least leave a trace of data behind its activities, either data produced by people or computers. Consequently, the more the computational pervasiveness grows, the more

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data is collected and stored, to the point that the management and interpretation of it can only be done by computers bigger and more powerful than the household standard. This is the idea behind “big data”. The Wikipedia defines it as “a broad term for data sets so large or complex that traditional data processing applications are inadequate.” (2015) Rob Kitchin points out that despite of the popularity of the term, “there is no agreed academic or industry definition of big data” (2014), however, he suggests the 3Vs, “volume, velocity and variety” (Laney 2001; Zikopoulos et al. 2012 in Kitchin 2014) as parameters to characterize it. In this definition, big data is “huge in volume, consisting of terabytes or petabytes of data; high in velocity, being created in or near real-time; diverse in variety in type, being structured and unstructured in nature, and often temporally and spatially referenced.” (Kitchin 2014) Moreover, he points out other characteristics from more recent literature, such as exhaustiveness, fine-grained resolution, indexicality, relationality, flexibility and scalability (boyd and Crawford 2012; Dodge and Kitchin 2005; Marz and Warren 2012; Mayer- Schonberger and Cukier 2013 in Kitchin 2014). All these characteristics help us to define what big data is and also understand its contemporary practices and motivations.

In the first place, the general interest is in being the most accurate representation of the world possible, in order to gain control of the complex phenomena that composes reality. Second, it is also structured in order to make possible dynamic possibilities of usage according to the interest in question. This is stressed in boyd & Crawford: “Big Data is notable not because of its size, but because of its relationality to other data.” (2011 1)

1.2.3 – Small data

In parallel with big data, the concept of small data has its importance increased as ubicomp becomes more fragmented and distributed. Whereas big data is concerned with the representation of all of the possible phenomena of the world, small data rises as a more contextualized and specific approach to the possible translations of reality into quantitative information.

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The Open Knowledge Foundation blog defines it as “the amount of data you can conveniently store and process on a single machine, and in particular, a high-end laptop or server” (Pollock 2013) This definition points to one of the most important characteristics of small data, its distributed aspect. The mention to the possibility of processing it in a personal device is a reflex of the bigger scenario of more distributed and democratic (Pollock 2013) devices of data collection, process and access, as addressed above in the ubiquitous computing debate, such as mobile phones, sensors and cameras. The current advances in computation are all about “more people than ever being able to collaborate effectively around a distributed ecosystem of information, an ecosystem of small data.” (Pollock 2013)

Small data also rises as a critique on big data by targeting the impossibility of a full translation of the world into data, which accordingly, leads inevitably to gaps and intervals opened to subjective interpretation. Which almost plays against the main idea of a translation of reality into quantitative and objective information. Once “data is never raw”, “big data approaches falter with issues of interpretation and context” (Dalton & Thatcher 2014). The grandiosity of the possibilities of big data may have dazzled the debate about the role of data in our society and the increase of the attention on ‘small data’ can be seen as a further step in the evolution of data management, once it embraces a more complex scenario of possibilities, in different sizes according to the specific situation and context. Following this trend, in the Open Knowledge Foundation blog, Pollock also states: “Size in itself doesn’t matter – what matters is having the data, of whatever size, that helps us solve a problem or address the question we have.” (2013) Dalton & Thatcher can add to this argument when they take big data away from the position of some kind of ultimate panacea to give light to what it can not do: “’big data’ analytics are better suited to quantitative questions of what, where, and when than to questions of how, and why.” (2014) The problem addressed is inherent to any purely quantitative approach to knowledge and although small data is no different than big data in regard to its form per se, the smaller size can lead to an easiness of an interpretation in relation to other information not represented in the dataset itself. The multiple factors and circumstances that compose life and can never be grasped by any data collection process, once “big isn’t everything” (Dalton

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& Thatcher 2014), are of fundamental importance in the understanding of the studied phenomena, whatever that is.

The distributed aspect of small data is fundamental to the study of local non-governmental data, once it is a ‘solution’ more adapted to the infrastructural and financial situation of the majority of the bottom-up initiatives for their data management. Not only it is simply easier and cheaper to have small datasets, but also the small scale of the studied phenomena and the small number of participants in the collection are factors that lead to the creation of “small data “packages”” instead of “big data monoliths” in methodologies that prioritize “partitioning problems in a way that works across people and organizations, not through creating massive centralized silos.” (Pollock 2013) This way, data becomes more relevant to the specific situations and problems in question. As pointed out by boyd and Crowford: “Research insights can be found at any level, including at very modest scales.” (2012 8)

Small data is currently being used together with big data practices as well, in order to enhance the accuracy and sophistication of the analysis with more contextualized data. “Big data in the form of behaviors and small data in the form of surveys complement each other and produce insights rather than simple metrics.” (Peysakhovich & Stephens-Davidowitz 2015) As the possibilities of data collection increase, the probable direction taken by the industry is the assemblage of every possible data available.

1.3 – Data ownership and privacy

This sub-chapter addresses the most controversial concepts of the data studies debate. Both data ownership and privacy have not reached a common sense on their practices and there is an ongoing debate over what are the limits and what is the ethics that govern these practices. Thus, both topics cannot be developed in their entirety, what follows is an overview of the debate focused on the most relevant topics for the study of bottom-up initiatives in urban environments.

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1.3.1 – Data ownership

‘Who owns data’ is a question with no easy answer and the ethics and regulations of the current practices are still unclear. In one hand, the companies that build the services and platforms in which data are collected and stored claim that the collection and usage of data are done in agreement with the user, who allows it in exchange for the free usage of the services provided, in the other, the highly personal aspect of the information in question lead to defenders of user total ownership and control.

In order to understand the debate, it is important to realize that the value that it generates creates a power relation between the actors involved in it. The power of data is the power “linked with knowledge” (Foucault 1982 781). With data representing more and more human behaviors and activities, the aggregation of these traces will also represent whole individuals and “while the human subject is placed in relations of production and of signification, he is equally placed in power relations which are very complex.” (Foucault 1982 778) The complexity of these relations is the reason why the debate on data ownership has not yet reached a point of general understanding and agreement about its modus operandi. With the increasing pervasiveness of data, the value that can be extracted from it increase in the same proportion, thus “data constitute an economic resource, one that is a key component of the next phase of the knowledge economy” (Kitchin 2014). The growth of importance in the value of data can be attributed not only to the fact that it translates the world in a sort of structured and organized way, but also to its potential to generate even more knowledge if more efforts of interpretation are invested on it.

The knowledge pyramid (Kitchin 2014) is a graphic representation of the potential evolution of ‘data’ into ‘wisdom’ in the process of value aggregation into it. The route passes from ‘data’ through ‘information’, ‘knowledge’, ‘understanding’ until ‘wisdom’. (Adler 1986; Weinberger 2011 in Kitchin 2014) and shows the linear steps in the addition of “meaning and value by revealing relationships and truths about the world” (Kitchin 2014).

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The conversion of this aggregated value into financial assets is the objective of the majority of the private companies working in the data business. The most common and debated examples are private companies such as Google and Facebook applying these aggregation processes in order to enhance the efficiency of their advertising features and reach a more and more accurate and refined audience for every specific message. Data enhances the belief that if the advertising message is delivered to the perfect customer in the perfect timing it will lead to conversion (a click, a visit to the website, a sale), consequently, the more data these media companies can gather and analyze, the more valuable their services are for advertisers. In regard to the relationship with its customers, Christian Rudder, the founder of the dating website ‘OkCupid’ and the blog ‘OkTrends’, that became famous for analyzing the data from the website, states: “Basically, it's a trade-off. In exchange for your data, Facebook lets you use their site for free.” (Big Think 2014) and recognizes that the practice is the industry’s standard: “You can assume that almost any website you visit for free is gathering information about you.” (Big Think 2014) According to Kitchin (2014), this “trade-off” is part of the inherent complexity and dual character of data usage, there are “opposing outcomes” that liberate and coerce people simultaneously (2014), they gain personal benefit at the same time as they become enmeshed in a system that seeks to gain from their participation.” (2014)

In parallel to the collection practices, as not every company can collect data but practically all of them can benefit from it, a billion-dollar market has been developed around the trade of datasets. Companies known as ‘data brokers’ “(sometimes called data aggregators, consolidators or resellers) capture, gather together and repackage data into privately held data infrastructures for rent (for one-time use or use under licensing conditions) or re-sale on a for-profit basis.” (Kitchin 2014) Their clients are usually companies that offer products and services that benefit directly from big analysis of customer behavior, such as “retail, financial, health, tourism, logistics, business intelligence, real estate, private security, political polling, and so on.” (Kitchin 2014) This way, data itself became a “new currency” (Lawrence 2015) and can be a “significant stream of revenue for many companies.” (Kitchin 2014)

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In the other hand, defenders of more open policies of copyrights and intellectual property allege that “information wants to be free” (Brockman 2011) and that data accumulation and protection can lead to a “next round of uneven development” as it becomes “an agent of capital interests” (Kitchin 2014). This side of the debate usually defends the user ownership of the data. Tim Berners-Lee is one of the biggest sponsors of the idea that “the data we create about ourselves should be owned by each of us, not by the large companies that harvest it” (Berners-Lee in Hern 2014). For him, the general usage of data for advertising is not any interesting for the user and, also for privacy reasons, suggests a less invasive standard: “I think they should be set up to be the minimal collection as needed, as opposed to the maximal collection possible.” (Berners-Lee in Hern 2014) Pursuing an alternative praxis as well, Hern proposes a more transparent collection: “I think they should have a quid pro quo, which is ‘you're giving up this particular piece of personal information and you're getting this benefit in return’, as opposed to the current status quo, which is "we will collect anything we can and not tell you what the benefits are." (Hern 2014-2) Lawrence also questions if the current scenario of “giving it away so freely to social networks, supermarket loyalty cards and internet search engines” is not a contemporary form of a “Faustian pact” (2015) in the sense of a general and exaggerated belief in the gains of the collection of more and more data.

The unbalance of power in the data scenario is leading to what Lawrence calls a “data-oligarchy”, according to him, we are witnessing a big concentration of power “that comes with data in the hands of a few.” (2015) The financial value of data addressed above also helps to create concentration due to the high costs of data collection in general, “historically speaking, collecting data has been hard, time consuming, and resource intensive” (boyd and Crawford 2012 12), and the commercial interests behind the scarcity that comes with this scenario create even more unevenness in the system: “those with money – or those inside the company – can produce a different type of research than those outside.” (boyd and Crawford 2012 12).

This concentration is molding the ways in which the majority of the data flows in society: “There is a fine balance then between using data in emancipatory and

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empowering ways.” (Kitchin 2014) The empowerment through data is mainly done via access. Basically, more access to the data means more access to the knowledge, and consequently, to the power. “Whoever then has access to high-quality and extensive data has a competitive advantage over those excluded in being able to generate understanding and wisdom.” (Kitchin 2014) This is the rationale that grounds open data initiatives and its democratic intentions that will be addressed in the next chapter. The idea that “we need to form a data-democracy: data governance for the people, by the people and with the people’s consent.” (Lawrence 2015) is the basis for the bottom-up initiatives in urban environments analyzed in this research.

1.3.2 – Privacy

In order to grasp the scenario of data ownership, it is also important to discuss the issue of privacy. Especially after Edward Snowden’s declarations regarding the controversial practices of the National Security Agency (NSA) of the United States, the topic went mainstream in news and consequently became part of public discussions outside of the academic or technical realms. The disclosure of the practices of the governmental agency also brought light to the private companies’ proceedings (such as Google and Facebook) and the general situation of privacy nowadays. The ubiquity state of computational processes and data flows in today’s world changes the way we conceptualize and negotiate privacy and, as much as the discussion of data ownership, we have not yet reached any kind of general agreement. As boyd states, we live in “a constant state of confusion about privacy” (2010).

The innumerous possibilities of data collection available nowadays and the lack of transparency in the collection and usage performed by most of the companies and governments give rise to new questions and discussions about a new form of relationship towards privacy. “Privacy is completely intermingled with Big Data” (boyd 2010) and the rise of social networks and ubiquitous systems brought us to “much more open and transparent societies than we used to. Information that was previously considered private is being more freely shared” (Kitchin 2014). The voluntary exposure of social networks users in their profiles and conversations and the algorithms with veiled forms of data collection are making people “subject to much greater levels of scrutiny and modes of surveillance than ever before” (Kitchin

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2014). The surveillance practices in the contemporary networked society have been discussed by Deleuze in his theory of ‘societies of control’ (1990) in which he discusses the database representations of society and states: “We no longer find ourselves dealing with the mass/individual pair. Individuals have become "dividuals,"and masses, samples, data, markets, or "banks.” (1990 5) The resultant complexity of such a decentralized and fragmented social scenario leads to new forms of relating to privacy. “In an increasingly networked world, privacy protection is an ever-present concern.” (Dourish & Palen 2003)

To boyd, “[Privacy] will always be a process that people are navigating” (2010) and Dourish and Palen also promote the idea that privacy is a dynamic issue and requires “continuous negotiation and management” (2003) according to the situation in question. The privacy regulations should not be thought in a “static nor rule-based” (2003) fashion but contemplate the multiple contexts of today’s scenario. This way, the idiosyncrasies of the data flows should be taken in consideration in the debate: “[Privacy] is the continual management of boundaries between different spheres of action and degrees of disclosure within those spheres.” (Dourish & Palen 2003) For example, sharing personal data can be considered civic duty if done in a municipality census practice and a privacy invasion if the same municipality access data from the same user generated in a private social network. The situations are thought of in a ‘spectrum’ in which the “boundaries move dynamically as the context changes” (Dourish & Palen 2003)

The small-scale aspect of local collections of data of bottom-up activities can be seen by citizens as a form of empowerment in relation to the bigger and more powerful companies and governments, the usual holders of data today. The proximity to its final users can also lead to a bigger consideration to what Dourish and Palen emphasize as the importance of the platform design to the form that privacy will actually be negotiated: “the active process of privacy management takes place in the context of the possibilities that are offered by one or another technology” (2003) and the responsibility of the platform owners over what is made ‘possible’. (2003) This way, local initiatives can also be understood as alternatives to the current privacy issues of data management.

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Chapter 2 – Data in urban spaces (smart cities)

As mentioned in the sub-chapter 1.3.1, the ubiquitous computing scenario of the contemporary urban space is characterized by data flows generated and managed by complex and messy assemblages of devices and datasets, performing mostly independently from each other.

The attempt to coordinate these flows towards a more efficient management of the city resources gave birth to the term ‘smart cities’, also known as ‘intelligent cities’ or ‘digital cities’. In this paper, the term ‘smart city’ will be used once it is the most commonly adopted in the industry and media. According to Donelly & Harrison, “the term has been adopted since 2005 by a number of technology companies [Cisco, 2005], [IBM, 2009] [Siemens, 2004] for the application of complex information systems to integrate the operation of urban infrastructure and services such as buildings, transportation, electrical and water distribution, and public safety.”

2.1 – Smart cities

The concept of smart cities is one of the main examples of the changes that digital data flows are performing in today’s society. Smart cities are usually taken as a form of revolution that will benefit almost every area of urban life. In the words of Batty et al: “The convergence of information and communication technologies is producing urban environments that are quite different from anything that we have experienced hitherto.” (2012) The current and future challenges for city administrators and inhabitants will inevitably have to take into account the influence of technology in the daily dynamics of its processes.

The administration of the cities’ common goods and services and the strategies to increase the quality of life and well being of the citizens are increasingly being performed with the support of the collection and analysis of digital data, collected by the most varied sources and being used for the most varied activities. Smart cities can be described as “places being increasingly composed of and monitored by pervasive

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and ubiquitous computing […] that provide real-time analytics to manage how aspects of the city function and are regulated.” (Kitchin 2014), as “a vast, active livable information surface” (Greenfield 2011) and as “a fusion of ideas about how information and communications technologies might improve the functioning of cities, enhancing their efficiency, improving their competitiveness, and providing new ways in which problems of poverty, social deprivation, and poor environment might be addressed.” (Batty et al 483) Mitchell, adds an even deeper layer to the analysis of the impact of current technology is our relationship with the cities by affirming that it establishes a “cyborg condition” with the different infrastructural networks as they “extended the capabilities of human bodies” through “the combination of digital telecommunication networks (the nerves), ubiquitously embedded intelligence (the brains), sensors and tags (the sensory organs), and software (the knowledge and cognitive competence).” (2007), an approach clearly based on Latour’s ‘Actor Network Theory’ that understand that “contemporary life is seen to be made up of complex and heterogeneous assemblies of both social and technological actors” (Murdoch 1995 in Marvin and Stephen 2002 185).

In general, all of these descriptions and concepts of smart cities have in common the background idea that, by increasing and better managing the information flows between its systems and actors, cities can improve its operations, which, consequently, will lead automatically to more efficiency in its administration and a better life for its citizens.

This discourse is also the cornerstone of huge business opportunities in an industry expected to grow “from $8.8 billion annually in 2014 to $27.5 billion in 2023.” (Navigation Research 2014). Technology giants such as IBM and Cisco, taking advantage of the opportunity, have developed systems that provide technological infrastructure to increase the ‘smartness’ of cities. The IBM® Smarter Cities® solutions (a registered brand) “can provide city leaders with the tools and deep insights they need to help develop and implement a vision, build a resilient and sustainable infrastructure, and improve individuals’ health and productivity.” (IBM Smarter Cities brochure 2014) Their methodology is based on applying IBM technology with the objective of “Capitalize on new insights; Create system-wide

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efficiencies; Collaborate in new ways.” The field of work is broad and addresses three different categories of city services: ‘Planning and Management’ (Public Safety, Government and Agency Administration, City Planning and Operations and Buildings); ‘People’ (Social Programs, Smarter Care and Education) and ‘Infrastructure’ (Energy, Water and Transportation). (IBM Smarter Cities brochure 2014) In general, the IBM solution is a system of services designed with the vision of municipal governments as clients interested in improving their administration of the city, in the same rationale used in their product design for private companies.

However, the general concept of smart city must not be presented without a critique in regard to its centralized, top-down, masterplan-based practices. The idea that the ‘smartness’ of the city must exist only through efforts performed by the municipality with the help of big global corporations should be questioned once it leaves almost no room for influence and agency of the citizens. In general, the most popular concept of smart city contains this paradox: at the same time that the technology is implemented in a citizen-centered reasoning, aiming for better conditions for the city inhabitants in various ways, often little or no room is left for them to influence directly on this technology. Different authors have addressed this issue: “The phrase [smart city] itself has sparked a rhetorical battle between techno-utopianists and postmodern flâneurs: should the city be an optimised panopticon, or a melting pot of cultures and ideas?” (Poole 2014); Hemment & Townsend add that “the big technology companies are selling ‘smart city in a box’ solutions to cities” (2013 1) and that despite of advertising that their solutions can “Infuse your city with charisma, resiliency and vitality” (IBM Smarter Cities brochure 2014) they are “walled gardens that prevent scalable local business innovation.” (Hemment & Townsend 2013 1) The Dutch architect Rem Koolhaas adds: “by calling it smart, our city is condemned to being stupid.” (2014) According to him, “the citizens the smart city claims to serve are treated like infants” and continues: “Why do smart cities offer only improvement? Where is the possibility of transgression?” (2014) Dan Hill develops, “Cities, like actual natural ecosystems, are not steady state systems […], they tend towards disequilibrium. […] But this is precisely why they work, and why they attract people.” (2013) In general, more humanistic points of view criticize the simplistic and optimistic efficiency discourse and the absence of deeper political and

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social analysis. “We don’t make cities in order to make buildings and infrastructure. We make cities in order to come together, to create wealth, culture, more people”. (Dan Hill 2013) Again in Koolhaas words: “Because the smart city movement has been apolitical in its declarations, we also have to ask about the politics behind the improvements on offer. A new trinity is at work: traditional European values of liberty, equality, and fraternity have been replaced in the 21st century by comfort, security, and sustainability. They are now the dominant values of our culture.” (2014) Sassen adds: “The challenge for intelligent cities is to urbanize the technologies they deploy, to make them responsive and available to the people whose lives they affect.” (Sassen 2011) This ‘apolitical’ and optimistic bias in the discourse of smart cities can be analyzed not only as a consequence of the belief in the inherently positive aspect of the technological advances but also as a reflex of the commercial interests and PR work of technology giants that popularized the concept in order to promote their services. It is important to emphasized, though, the importance of citizen influence in the making of the solutions for the city, in order to minimize the power unbalance that technology can create.

2.2 – Different approaches to smart cities

The different approaches of smart cities existent worldwide nowadays are going to be analyzed in regard to the management of the data flows that make the city ‘smarter’. Basically the initiatives happen in a spectrum from top-down to bottom-up, with the first as the purely governmental actions without influence of the citizen and the latter as the popular initiatives, idealized and performed by the cities’ inhabitants. The first two examples discussed below, “smart cities built from scratch” and “smart cities ‘in a box’ solutions”, exemplifies the top-down approach, and the third, “mixed top-down and bottom-up activities”, represent the form in which governments and citizens can work together. The latter is the main object of study of this research, thus will receive more attention in a sequential sub-chapter.

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2.2.1 – Smart cities built from scratch

The most flagship examples of the totally top-down approach towards a smart city are the cities of Songdo in South Korea and Masdar in the United Arab Emirates, both built entirely from scratch following masterplans of efficiency, eco-friendlyness and optimization of resources and processes via digital technologies of data collection and algorithmical automation. Songdo is presented as “the city of the future” (Cisco) and Masdar as “a model for sustainable urban development regionally and globally, seeking to be a commercially viable development that delivers the highest quality living and working environment with the lowest possible ecological footprint.” (thefuturewewant.org) In other words, both of them are presented as two versions of the state of art of the relationship between technology and human life in society.

As commented in the last sub-chapter, the optimistic point of view in which these initiatives are advertised is mainly a result of the private interests that characterize their projects. These approaches are criticized in regard to the lack of citizen consultation and participation in the process of the making of the city. The excess of planification ends up eclipsing the dynamic and unpredictable forces that take place in a city, factors such as serendipity, chance, randomness and other gains of the unplanned in people’s lives and their interaction with it. According to Jordi Borja, Director of the City management postgraduate programme of the Universitat Oberta de Catalunya, the chance factor is “an indispensable complement of need, and therefore the claim made for public space and for the intensity of urban life” (2007 11) He claims for the necessary accidental chance encounters between people and the clash of ideas in order to promote innovation, new discoveries and new knowledge. “The danger now is that this information-rich city may do nothing to help people think for themselves or communicate well with one another,” claims Richard Sennett in the article “No one likes a city that's too smart” (The Guardian 2012), also putting light in the citizen agency and autonomy in the process of urban life. To Greenfield, “if you have a city without informal activity you don’t have a city at all” (2013) and Saskia Sassen, reinforces the humanist approach adding, “It is the need to design a system that puts all that technology truly at the service of the inhabitants—and not the other way around.” (2011) In general, what these critics have in common is the idea

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active role not only in the everyday usage, or even consumption of it, but in the building and constant evaluations of its foundations as well. This is the point of view that led to the creation of the many bottom-up initiatives of the current scenario. This discussion does not only happen in the academic environment, but in the societal level as well, with communities of citizens understanding that designing cities from scratch is not the best solution for their interests.

2.2.2 – Smart cities ‘in a box’ solutions

The other main type of top-down smart city plan is the ones developed by companies such as IBM, Cisco and Siemens and sold as products ‘in a box’ (Hemment & Townsend 2013 1) to municipalities. One example is the system currently underway in Rio de Janeiro, Brazil, developed by IBM. In this case, the city government hired the services of the company to develop an operational control room of the city (the ‘Centro de Operações Rio’) with the intention of optimizing the city management via technological solutions that captures data in sensors and cameras spread throughout the city and that connects different departments within the municipality through database intelligence. IBM’s website describes the project in its press release as a center to “integrate and interconnect information from multiple government departments and public agencies in the municipality to improve city safety and responsiveness to various types of incidents, such as flash floods and landslides.” (IBM 2010)

As addressed in the last sub-chapter, these solutions merely serve the commercial interests of the technology giants, “The notion of the smart city in its full contemporary form appears to have originated within these businesses, rather than with any party, group or individual recognized for their contributions to the theory or practice of urban planning” states Adam Greenfield (2013). He also defends a closer look at the importance of the interests behind smart city planning, based on the idea that “the design of object models, APIs and other such specifications to be an inherently political act” (2011 6) and the importance of the implications that they can bring to the city and its inhabitants: “Our ability to use the city around us, our flexibility in doing so, just who is able to do so, will be shaped by decisions made

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about the technical design of objects, their interfaces and the precise ways in which they are connected and made visible to the network.” (2011 6) With the overall centralization resultant from these solutions, the concentration of the management of the flows of data in the hands of the government and the companies that design the solutions can be seen as an unbalance in the power relations in the city, also eclipsing the citizen participation and agency. The bottom-up projects can also be seen as a response of the inhabitants of the city to these overly centralized solutions.

2.2.3 – Mixed top-down and bottom-up activities

The third approach towards smart cities addressed in this research is the one that mix top-down with bottom-up activities. This is the approach chosen to be studied with more depth once it is the one with more possibilities of meeting the interests of the different actors in the city. Not only the interests of the governments and private corporations are considered, as the case of the two examples showed previously, but also the citizens’. This way, the bottom-up activities mixed with top-down polices end up at least increasing the influence of the inhabitants, the actor that was supposed to be the priority of the efforts that take place in the city, in the active process of making the urban environment. Moreover, the diversity of the characteristics of the bottom-up initiatives and the different approaches of the governments towards them tend to show a more complex and interesting scenario for research.

In general, the main characteristic of the mixed activities is, consequently, the resultant increase of balance between the existing forces in the urban data flows. “Alongside ‘top-down’ master-planning, we need to enable ‘bottom-up’ innovation and collaborative ways of developing systems out of many, loosely joined parts.” (Hemment & Townsend 2013 2) By sharing the responsibility and work of the collection and/or usage of data between citizens and the government, the city have gains in spontaneity, liveliness and strength in its more contextualized efforts. This way, the technological advances invested in the cities can actually be used to the common good and perhaps achieve the ultimate goal of quality of life improvement. “If we dovetail active citizens with active governments, building the interactions of

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both around these new logics but balancing their inherent biases, we might discover better cultures for producing good, sustainable decisions.” (Dan Hill 2013) But first, in order to better study these initiatives, it is important to address the emerging concept of smart citizens.

2.3 – Smart citizens

The critique on smart cities gave birth to the term ‘smart citizens’, a concept that opposes the techno-centrism of the current ‘smartness’ applied to the urban environment. Celebrating the idea that “the city is its people” (Dan Hill 2013) and that citizens should “take the fate of the places we live in into our own hands” (Kresin 2013 91), many scholars have increased the complexity of the debate by proposing a shift in the balance of the urban power relations by making the governments more accessible and transparent and mainly by increasing the responsibilities and agency of the citizen.

In the text “A Manifesto for Smart Citizens” (2013), Kresin establishes a framework for the smart citizen and lists twelve responsibilities and attitudes expected from them. These twelve items can be summarized in a more active approach towards the city in general and a sense of collectiveness and community, through the exchange of knowledge among the citizens. The citizen as a “co-creator” (Hemment & Townsend 2013 2) that “refuse[s] to be consumers, client and informants only, and reclaim agency towards the processes, algorithms and systems that shape our world” (Kresin 2013 93) is what makes him ‘smart’ and this idea grounds most of the bottom-up initiatives that blossom in the urban environments nowadays. This active condition of the smart citizen is the answer against the idea of the “passive citizen”, described by Dan Hill (2013). According to him, the ideal smart city is inhabited by “smart, engaged, aware and active citizens, rather than the passive systems that smart city visions are often predicated upon.” (2013) On his critique of the passive citizen, the unbalanced power relations in the urban environment are also addressed once they “remain intact; if not made worse as citizens devolve their decision-making and responsibility to software, as well as city government” (2013).

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The initiatives that mix top-down with bottom-up approaches are usually, consciously or unconsciously, maybe with a different label, based on the concept of smart citizens, once the participation of the population is fundamental for the very existence of these projects, as it will be addressed in the following sub-chapter.

2.4 – Different forms of mixed top-down and bottom-up data

management

This session will present and discuss three different forms of data management that mix top-down and bottom-up approaches in an urban environment: governmental open data initiatives, private data and local non-governmental data. Whereas in the first, the government actively provides the tools that ignite the ideas and the work of the bottom-up projects, in the second and third examples, the design and implementation of the idea is done totally by the citizens backed by legal and promotional opportunities given by the government. The local non-governmental data will be developed via two case studies in the following chapter.

2.4.1 – Governmental open data initiatives

Open Data initiatives are grounded in the idea that governments should open their datasets in order to allow citizens to freely consult and use the data, and creatively build tools and solutions on top of it. The Open Knowledge Foundation conceptualizes ‘Open knowledge’ as “any content, information or data that people are free to use, re-use and redistribute — without any legal, technological or social restriction”. This model of governance became popular in the last decade and researches affirm that “40 national governments now offer data on matters like population and land use” (Hardy 2013).

The idea behind these initiatives is that the opening of information will empower citizens to better understand the society and the work of governments, strengthen the basis of their demands and also give them the opportunity to actively engage with the public data in order to produce information and solutions for the city by themselves. There is a “tendency to advocate some form of enhanced citizen

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participation and empowerment with respect to the state and/or corporate power” (Bates 2012). The Open Knowledge Foundation claims that Open Data will increase transparency, the release of social and commercial value and the participation and engagement of citizens. Adam Greenfield also defends the idea that “all public infrastructure capable of data collection of any type should, as a matter of policy and law, be furnished with open, read-access APIs” (2011 8) and stresses that information (and consequently, data) means power when he advocate for the idea that open data can reshape the balance of forces between governments and its populations: “The primary advantage of open data in this context is that it resists attempts to concentrate power by leveraging asymmetries of information and differentials of access.” (2011 6) For Bates, “open initiatives, through breaking down knowledge to the raw data and code, and abandoning models of false scarcity that restrict access, interpretation, and re-use, suggest the possibility of a significant reconfiguration of modes of understanding and production that have previously been shaped by dominant interests.” (2012) In other words, according to these academics, opening the governmental data for the population can drive significant changes in the structure of society.

The whole debate on open data is surrounded by a very optimistic idea that many different aspects of society will benefit from it, and the more it happens, the more the life in the society as a whole will be better. “The greater the number of resources available, the greater the extent to which they are described properly and are capable of being used without further configuration, the better off we’ll all be.” (Greenfield 2011 13-14) Many of the main ideas and innovative projects of the Internet culture developed in the last 30 years are grounded by similar ideas, such as the Open Source and Free Software movement that claims that every software design should be exposed to society by its developers and claims for an understanding of ‘openness’ “as a powerful new form of political desire in network cultures.” (Tkacz 2012) The concept of the “commons”, highlighted by Lessig (2005), as the space where “anyone can draw […] without the permission of anyone else” (352) and that led to the creation of the popular intellectual property law alternative ‘Creative Commons’. The idea of the ‘Wisdom of the Crowds’, as presented by Surowiecki (2005), argues that “the many are smarter than the few” and led to the invention of

The bottom-up smart city: Local and non-governmental data in urban environments.

University of Amsterdam

June 2015