Keywords

1 Introduction

As global urbanisation and urban sprawl intensify, the United Nations projects that over 70% of the world’s population will live in cities by 2050 (Department of Economic and Social Affairs 2016). And with rapid urban population growth comes corresponding stress on physical urban infrastructures, such as increased pressure on limited capacity power grids, greater traffic flow volume, faster road degeneration, and greater pollution. In parallel with global urbanisation, approximately 90% of city residents are also now connected to mobile networks (International Telecommunication Union 2015). And cities make use of these mobile networks to gather large amounts of data to help organise and manage urban life and identify solutions to the problems that come along with increased population trends. Additionally, the ubiquity of mobile technologies also can lead to increased citizen access to city services and the potential for a greater voice in governance. But because of the inherent invisibility of big data flows, it becomes crucial to consider the consequences, particularly when unequal power differentials are reified and strengthened.

While the issues explored within this chapter are not limited strictly to city initiatives, data-saturated urban ecosystems have become harbingers of smart city and data-driven experimentations that not only help to inform our collective and individualised urban identities but also consistently reshape physical urban space and our experiences within it. It, therefore, becomes increasingly crucial, in our era of fake news, search engine optimisation, and confirmation bias, to be critical of state-sponsored rhetoric, especially claims that the use our digital devices and collected data are being used for national security and safety purposes. De Waal (2018), for instance, suggests that many urban planning and governmental policies that occur as a result of analysed data “could lead to new spatial regimes” (my italics). Big data practices have indeed become embedded in the everyday life of cities, but the use of analysed data is not so much creating “new” spatial regimes as functioning to reinforce existing ones. As this chapter argues, while some data-driven smart city practices can certainly lead to policies and programs that can help ease the strains of increasingly stressed urban ecosystems, these same data practices may also function to construct and reinforce existing socio-economic boundaries and (re)spatialise historically marginalised identities.

Recently, the German Chancellor, Angela Merkel was quoted as saying that “Data will be the raw material of the 21st century” (World Economic Forum 2018). Data “are to this century what oil was to the last one” (The Economist 2017, para. 4). While scholarship on the phenomenon of big data increases daily, and many work to better understand the role big data play in our daily lives, current scholarship primarily addresses its potential, whether for business, medicine, smart city planning or economies at large. And scholarship that criticises the use of big data has focused primarily on issues of hidden bias (i.e. Crawford 2013; Thatcher et al. 2018) or privacy and surveillance concerns (i.e. Christl 2017; Horvitz and Mulligan 2015). Less explored are the real material consequences of smart data practices on everyday experiences, spatialised identities or physical urban spaces, which this chapter aims to address. Looking at several international urban case study examples, this chapter explores various data-driven constructions of geographic knowledge and state power, particularly as manifested through certain affective technologies of safety and security, such as crowdsourced smartphone applications, the digital visualisation of crime and disease, and the more recent phenomenon of predictive policing. This chapter argues that some urban data practices often function to not only aggravate existing racial–spatial divisions and construct and reinforce existing topologies of power but also deleteriously mediate and threaten geographically situated identities, from local neighbourhoods to nation-states, fuelling both material and discursive hegemonic geopolitics.

2 Data Ecosystems and Inherent Bias

In order to better understand how big data practices potentially mediate geographical boundaries and reinforce socio-economic identities, it is first important to understand how data is collected and interpreted. Governmental data collection is not a new phenomenon, nor are the co-constitutive relationships between information gathering, knowledge, and power (Foucault 2005). Throughout the seventeenth and eighteenth centuries, for example, data collection practices occurred regularly throughout Europe, and state officials collected a wide variety of demographic data in an effort to gather information of “interest to the state” (Ross 2010, p. 7). Historically, governments collected data on everything from population trends to numbers of marriages and deaths in an effort to not only inform state policies and actions, but also predict future needs (Ross 2010). While the methods have changed, and data are now collected in unprecedented quantities through a variety of digitalised tools and strategies, governments continue to collect data in the interest of the state and for the purpose of acquiring knowledge that will help inform urban policies and practices. The associated power that comes with such knowledge, however, does not simply lie within the quantity of information gathered, but how it is collected, interpreted, and then used—in other words, what kind of “knowledge” is produced, by whom, and for what purposes?

Urban residents also are often encouraged, through the use of various apps and websites, to give officials feedback on city services and offer suggestions and ideas that will help shape or benefit their communities. Through the use of city-sponsored smartphone applications, for example, such as BOS:311, a non-emergency smartphone reporting app for Boston residents, one can quickly report quality of life issues, such as potholes, broken traffic signals or broken metres. Unbeknownst to the average user, however, city goverments also regularly collect and aggregate a variety of personal data, from internet browsing habits to geo-tracking and even with whom people meet up or interact (Degli-Esposti and Shaikh 2018). And many data sets are then sold or given to third-party corporate entities (Vallina-Rodriguez and Sundaresan 2017). Conversely, according to one recent source, approximately 70% of commercial smartphone apps are tracking and reporting personal data to third-party companies, many of which then sell their data to city governments (Vallina-Rodriguez et al. 2016). Thus, governments and corporate entities often are in partnerships, sharing a variety of data collected from our devices. Rio de Janeiro, for instance, was reported to be the first city in the world that collected real-time data of drivers from the Waze application to aid in urban planning efforts (Olson 2014, para. 4). And London, Amsterdam, Barcelona, and nearly 30 other cities started using Strava app data to track cyclist movement, along with age and gender, throughout urban streets (Walker 2016). While this kind of data collection may seem somewhat benign, the practice raises a myriad of concerns and can have far-reaching implications. Smartphone apps, for instance, typically gather small bits and pieces of information and send data to different trackers, after which users’ unique identifiers can then be patched together (Rathi 2016). Therefore, while one app alone may not collect much personal information, when the data is aggregated, users or a particular demographic area can be profiled more fully (Vallina-Rodriguez and Sundaresan 2017).

Furthermore, and frequently in attempts to seem transparent, many cities host their gathered data on open data platforms to make information publicly accessible and useable; but the whole system is exceedingly flawed. Major cities like New York, London, Brussels, Sydney, Berlin and Colombia, for instance, have open data platforms that are, at least theoretically, available for public viewing and use (Data Portals, n.d.). Yet one recent study found that 9 out of 10 government datasets are not actually “open;” only 7% of the total data was even accessible, and only 1 in 2 of those datasets were machine readable (World Wide Web Foundation 2017, p. 12). The World Wide Web Foundation (2017) also concluded that government-sponsored open data platforms are not only often incomplete, but of low quality and frequently fragmented, making those data sets more likely to have multiple errors in other sets. Data also are not easily interpreted by the average citizen. In most instances, the interpretation or analysis of data requires certain expertise, licence or set of professional skills or software, thus further decreasing public access. Additionally, raw data can easily contain subjective, false or repetitive information; and because data collected and fed into software algorithms are chosen by people, the entire process is vulnerable to human error and bias.

3 Data-Driven Geographical Mediations

As if the inability to trust public data were not problematic enough, power is often spatially and racially organised through data-driven public platforms and apps that then help shape physical environments. Consider, for instance, the digital visualisations of crime, public health issues or disease distribution. Cities around the world frequently visualise data (or linked to commercially visualised data) to inform its citizenry of risks and threats, but often to the detriment of already-marginalised neighbourhoods. And many of the data visualised, function to construct perceptions of unsafe spaces. Not only can one likely find an online crime map (or dozens) for practically any major city in the world, one can also choose from a growing plethora of crime mapping smartphone apps. There are also interactive maps that visualise specific crimes, such as homicide (Keng 2016) or show criminal activity on entire continents, allowing the user to zoom into a particular geographical area to learn the specifics of recorded criminal acts (CrimeReports 2017). Worldwide, many governments and official agencies also publish interactive maps that visualise and spatially represent various public health and policy issues, such as disease (U.S. Centers for Disease Control and Prevention 2016; Institute for Future Cities 2018), worldwide drug addiction (Bouchareb 2015), as well as refugee resettlement (Dupere 2017).

But these kinds of persistent digital visualisations of data on crime, disease, poverty or even something as seemingly inconsequential as property values, can heighten the public’s perception of geographical-based disorder, neglect or chaos, which in turn further constructs these elements through our responses and behaviours. Simply put, people tend to avoid areas believed to be unsafe. Furthermore, security and safety become bound up in urban political efforts to (re)construct and manage urban space. While these kinds of maps are often described as or presumed to be merely informative, allowing residents, prospective homeowners and tourists to know what areas are deemed safe or prosperous, these visualisations also help construct and reinforce existing racialised demographics and sociopolitical boundaries by profiling neighbourhoods (Scott 2016). Data-driven neighbourhood profiling, furthermore, is frequently obscured by reference to the collection and aggregation of commonplace data. “Officially lifted from the demographic variables of race,” Rice and White (2010) argue, and placed on “less controversial variables,” such as building inspection reports, tax records or property values, information can be patched together in such a way as to profile certain neighbourhoods as more dangerous than others, which in turn make them supposedly in need of greater policing and surveillance.

To further explain how this cycle of neighbourhood profiling operates, Wilson and Kelling (1982) used the metaphor of a broken window, which draws negative attention to a particular area. As Wilson and Kelling argued (1982), broken windows indicate potentially unsafe areas and signal neighbourhood neglect, which then lead to an actual rise in crime. By applying this theory to contemporary visualisations of crime, disease or low property values (the digital equivalent of broken windows), Scott (2016) explains how similar perceptions of a particular space as unsafe help to further construct those spaces as dangerous. In other words, if a neighbourhood is perceived to be dangerous, it actually becomes dangerous, thereby drawing increased policing and surveillance, entrenching that community in a vicious downward cycle and further marginalising its inhabitants.

Madanipour (1996) argues that urban practices of segregation and exclusion of those considered a threat to the larger public have always been a “socio-spatial phenomenon,” constructed through both physical organisation of space, as well as social control. While the methods vary over the years, the rhetoric of reclaiming public space from those who are perceived to threaten it has been a consistent strategy of geospatial marginalisation efforts. And as Sampson and Raudenbush (2004) point out, when we see or perceive disorder, we make certain socio-economic and cultural assumptions about race and class and generate meaning that then serves to perpetuate those stereotypes. Zukin (1995) furthermore argues that rather than working to ameliorate the causes of socio-economic inequalities, state entities (from cities to nation states) rather tend to fortify barriers and strengthen geospatial divides (p. 39). Therefore, data-driven interactive maps that visualise various urban problems, as they draw attention to one’s proximity to criminals or areas of disorder or crisis, function to exacerbate existing racialised anxieties and reproduce prevailing geospatial inequalities (Scott 2016).

Foucault’s (1988) work on the biopolitics of spatial knowledge, mapping and surveillance, helps to clarify how this power is constructed and maintained through the socio-spatial effects of data visualisation, as he noted the rise of the connections between mapping, surveillance and policing practices. When Foucault (1988) cites the increase of ‘new mapping [techniques] and the closer surveillance of urban space,’ he infers that maps are not only a product of political knowledge, but an intervention or method of constructing political knowledge (p. 142). Today, digital maps of urban degeneration or degradation (whether visualising crime, disease or poverty, for example) become similar examples of biopower, as they function to both encourage self-regulation (i.e. productive citizens should stay away from these areas) and exert power and control over those within. By mapping “deviants,” these geospatial boundaries are thus reified. Urban planning theorists refer to this phenomenon as “negative institutionalised oppression” (Flyvbjerg and Richardson 2002). By identifying problem people and areas, the state can better exercise its disciplinary and regulatory power. As Pløger (2008) points out in regards to Foucault’s discussion of the politics of urban health that seek to reproduce self-regulating and productive citizens, the various spatial mechanisms put into place are not attempts to “care for the population” (or help residents feel safe), but are rather apparatuses of power maintained through socio-spatial order and security (p. 64). And through biopolitical normalisation processes, the identification of risks and dangers and management of crises (in this instance, through the use of data) become established, though often through invisible, power mechanisms (Šupa 2015, p. 88).

Furthermore, these kinds of data visualisation practices also are representative features of what Zukin (1995) refers to as the “institutionalisation of urban fear.” Zukin theorised that the “politics of everyday fear” is constructed, in part, by amplifying existing public anxieties (p. 39). Explicit examples of this can be found on websites with maps that are accompanied by alarming headlines, such as “The world’s most murderous places and other lessons from a killer map” (Byrne 2015) or “Watch the world’s health crises in REAL TIME: Outbreak map reveals spread of deadly diseases around the globe” (Griffiths 2014). While some of these examples are overly dramatised, they are at least explicit; the institutionalisation of fear that results from data-driven processes, however, operates almost imperceptibly and often serves to normalise such fears. Importantly, the institutionalisation of fear cannot be disarticulated from racism, as the data visualisations that work to reinforce the public’s “knowledge” of certain areas as dangerous tend to be homogenously racialised with people of colour. As Pasquale (2015) argues, “Algorithms are not immune from the fundamental problem of discrimination, in which negative and baseless assumptions congeal into prejudice” (p. 38). So not only are the data chosen prone to inaccuracies and bias, as earlier discussed, the motivations behind the aggregation of that data (i.e. to identify hot spots for crime or areas with increased building code violations), along with often prejudiced interpretations or vested interests, proxy discrimination more easily.

The institutionalisation of fear, particularly on the national level, though trickling down into small rural communities at an alarming pace, works in tandem with what Grusin (2010) calls “premediation,” which he describes as a recent media shift from mediating past events to premediating future ones. These premediations, Grusin (2010) argues, function to encourage public fear and insecurity by predicting future disasters or major threats to our security and safety. The institutionalisation of fear has increased by staggering proportions in the United States since the election of Donald Trump. The Trump administration, for instance, along with right-wing conservative media, daily promotes fear of the “illegal” immigrant, women (and the #metoo movement) and minorities. What makes premediation particularly effective, according to Grusin, is that as public fear and insecurity increases, the public then often looks to national governments to then quell that fear. Take, for example, the 2018 rhetoric from Trump and his administration about the “caravan of immigrants” that were about to “invade” the country (Mealer 2018). Trump and his administration were the ones to create this particular premediation of fear, while also offering the supposed solution—vowing to send thousands of military troops to the border to defend against the oncoming “invasion” (Mealer 2018).

On a daily basis, the U.S. Immigration and Customs Enforcement agency (ICE) has become particularly insidious, as the department works to offer affects of security and safety through illusions of governmental intervention, watchfulness or policing, which in turn helps the federal government to justify the increase in surveillance and raids. And as Šupa (2015) points out, once modalities of power are normalised, sovereign power becomes further legitimated (p. 87). ICE also regularly works to analyse data in government databases in an effort to predict a potential immigrant’s likelihood of committing criminal or terrorist acts (Biddle 2017). Part of that initiative involves also collecting and analysing data from “social media sites, blogs, public hearings, conferences, academic websites,” and so forth (Biddle 2017, para. 8). Additionally, ICE recently signed a $2.4 million contract with a data surveillance company that collects a wide variety of personal information from the nation’s networked users, most notably real-time tracking of people through smartphone GPS location data, particularly within urban areas (Da Silva 2018), signalling a national imperative of predictive policing.

By manufacturing new and reinforcing existing public anxiety about immigrants, refugees, Muslims and any other marginalised group being targeted in any given week, Trump’s administration regularly premediates catastrophe to come by promoting a collective sense of insecurity of and anxiety about what might happen—if these people are allowed to come into or remain in the U.S. And the fearmongering rhetoric (particularly on social media) about what might happen is increasingly geared towards the supposed prevention of what Blow (2018) calls “white extinction anxiety,” or the fear that whiteness, “white culture,” or white dominance and power is threatened. It is important to note, however, that this kind of institutionalisation of fear is neither new nor only a U.S. problem. However, as the institutionalisation of fear of “the other” becomes increasingly explicit and visible to a larger Internet public and thus increased violence and racism normalised, geospatial identities become further entrenched.

4 Predictive Policing

The more recent phenomenon of predictive policing, primarily spearheaded by the U.S. and U.K., has also emerged from urban data practices and cities around the globe are increasingly using aggregated data to predict the occurrence of future criminal behaviour and then targeting specific neighbourhoods for increased surveillance. Crime prediction software, most often created and maintained by for-profit companies, has been found to be inherently prejudiced against blacks because of the kind of data fed into the system from the start (Angwin et al. 2016). And because governments either buy their data and/or algorithms from private businesses, this “often means the algorithm is proprietary or ‘black boxed,” and government officials have limited knowledge about how the software makes decisions (Tashea 2017, para. 6). Along with giving new meaning to the phrase “crime pays,” the for-profit nature of predictive software raises serious ethical questions. The Human Rights Data Analysis Group reports that data fed into crime prediction software is already biased, as crime committed in historically heavy crime areas is more likely to be recorded than in other areas (Lum 2016). Because law enforcement officers tend to record crime unevenly, an algorithm designed to predict urban crime is most likely to find patterns of higher crime patterns in these over-represented areas. And as more police that are sent to patrol these areas, the more they observe criminal or suspicious behaviour, which then is reported back into the already biased system, creating a “vicious cycle” of misleading data, increased neighbourhood profiling and arrests—all of which then inform and subtend data-driven institutionalised racism.

Additionally, most crime prediction software is focused on place-based predictions of criminal behaviour, rather than people; therefore, certain kinds of crime that can be better tracked by place, such as robbery or domestic violence, are more highly represented in the data and preventative policing practices, than other crimes, such as white-collar crimes or drunk driving (Moses and Chan 2016, paras. 30–33). Since “surveillance of racial and ethnic minority groups tend to be grounded in specific and bounded locations,” those who are less mobile (or geographically bound due to socio-economic statuses) are likely more vulnerable to increased geographically targeted policing (Byfield 2018, para. 1). Place-based predictive policing also relies on historical data of particular neighbourhoods (assuming crime will happen again where it has in the past), which in turn signals (and often visualises) an unsafe neighbourhood. This system seems to unfairly oppress certain neighbourhoods, making it extremely difficult for community members and local business owners to improve socio-economic revitalisation efforts. While many law enforcement agencies argue that predictive policing is precisely aimed at this effort (to make communities more safe), most research thus far seems to suggest that predictive policing, relying as it does on racially biased data, is highly problematic.

Also of concern is how these data algorithms can follow offenders or arrestees into court, as courts are increasingly adopting predictive software to help judges make data-centric sentencing decisions on issues such as bail, sentencing and parole. In the UK, as part of a research study, the Durham Constabulary used a predictive algorithm software for several years called the Harm Assessment Risk Tool (HART), which would rate people as crime risks, based on several data points such as a person’s age, previous criminal history, gender and postcode. After a review of the software’s results, Oswald et al. (2017) found that the system not only perpetuated existing neighbourhood stereotypes but also further exacerbated inequalities within the criminal justice system, particularly in regards to race and class. Another report that examined the COMPAS recidivism algorithm, which has become one of the most popular software products for pretrial and sentencing analysis, argues that it is biased against blacks and black defendants were twice as likely to be classified as being a “higher risk of violent recidivism” than whites and were, overall, more likely to be rated higher risk scores (for likelihood of repeat offenses) than whites (Angwin et al. 2016).

4.1 Re-territorialisations and the Rise of Nationalism

The Internet has been historically encoded with democratic ideologies and frequently described as a sort of digital bridge between people, cultures and borders. Negroponte (1995) predicted the Internet would bring about communities that would supersede national boundaries, making space irrelevant. Bell and de-Shalit (2011) suggested that with the decrease of national attachments and an increase in cosmopolitan ideals, the intersections between the city and cyberspace offered unique opportunities to offset homogenising forces. And De Souza e Silva (2006) theorised that as the Internet gave rise to connected, mobile and social hybrid digitalised spaces, users increasingly interacted with their digital–physical environments and the blurring of “traditional borders between physical and digital spaces” were even more pronounced by the unprecedented, though often invisible, flow of data (pp. 261–262).

Warf (2009), however, early on argued that all the “utopian hype” about a borderless Internet was deceptive and that “data-driven cartographies” were consistently (re)defining and (re) constructing borders (p. 67). And certainly, a deeper analysis of contemporary cyberspatial practices, such as digital visualisations of data and various cyber communication flows discussed within this chapter, suggests a phenomenon of physical border re-territorialisations and reified constructions of socio-spatial relations. While more people than ever have access to global networks, we nevertheless are in an era of increasingly tightened borders, higher surveillance and decreased governmental transparency. The World Resources Institute (n.d.) reported that more than 100 governments “took steps to close civic space” in 2015 and that civil liberties violations were on the rise, fuelling worldwide “feelings of exclusion” and increased “nationalism, populism and authoritarianism around the world” (my italics, para. 1). A recent report by CIVICUS (2016) on the state of civil society around the globe similarly concluded that the Internet has become “the new frontier in the global campaign to silence civil society” (p. 3). As actual physical spaces are “increasingly constrained,” monitored and policed, the report suggests, more activism occurs online; and yet, with greater opportunities for online activism come increased risk of Internet surveillance (CIVICUS 2016, p. 3). Amongst the governments listed by CIVICUS (2016) as having “significantly violated” civil rights were democratic countries such as France, Spain, Germany and the United States (pp. 4–22).

While social mobilisation, on the one hand, may sometimes flourish through Internet pathways, increased surveillance, control and online censorship may have the opposite effect of constraining our physical movements. And as flows of information increasingly transcend national boundaries and potentially threaten nation-state control, countries are reinforcing their physical borders. In Lund’s (2013) exploration of mobility as an inherent characteristic of the new economy, she argues that market-led economies create “spatial fixations” that limit people to specific geographical locations, rather than liberate them. And a pervasive and dangerous rhetoric around national security and safety has surfaced in many countries, worldwide and functions not only as a justification for increased cybersecurity and surveillance, but also subtends governmental efforts to maintain and secure physical border integrity. In 2017, for example, U.S. Customs and Border Protection officials began demanding that some travellers coming into the U.S. (including, at times, its own citizens) hand over their digital devices, in part to examine their social media accounts and cloud data to determine whether they constituted any sort of threat. And in the fall of 2018, the Trump administration, citing a supposed rise in “citizenship fraud” started denying or taking away existing passports of thousands of its own American citizens living near the Mexican border, those almost exclusively of Latino heritage, born in the U.S. and possessing birth certificates (Sieff 2018; Grinberg et al. 2018), which has had the devastating effect of calling into question these peoples’ citizenship and setting a precedence for the rescinding of U.S. citizenship.

5 Conclusion

Abundant with socio-economic and cultural diversity, cities hold great potential for the emergence of intersectional urbanism, wherein urban diversity is not only recognised and valued, but also critically considered when governmental officials are considering certain urban policies and practices that could potentially oppress marginalised members. As the economic power of cities continues to outrank those of nations, cities altogether possess the major portion of the global economy. According to a study by the McKinsey Global Institute, cities were projected to generate more than 60% of global wealth by 2025 (Dobbs et al. 2011, para. 1), while another study conducted by the World Bank suggests that “more than 80% of global GDP” is currently generated by cities (Urban Development 2018). And as the percentages of foreignborn residents in many cities around the world are also rising (and contributing to the economic growth of cities), more cities are pushing back on national policies and practices that are detrimental to immigrants. For instance, many cities in the U.S. are now refusing to cooperate with federal or national immigration officials, declaring their cities to be “sanctuary cities.” In New York City, for example, nearly 40% of the population are reported to be comprised of foreignborn immigrants (City of New York 2017, para. 1). So when confronted with the possibility of having to turn over information about undocumented residents of New York City to the federal government, the mayor declared that NYC, as a sanctuary city, would refuse to cooperate (Tharoor 2016, para. 11). Therefore, the significant political clout that comes with that greater economic power clearly can be leveraged against the kinds of federal practices described above (Florida, 2017). Tharoor (2016) also argues that “metropolises such as London seem increasingly detached from the right-wing populist surge in the hinterlands around them,” where we increasingly hear calls to “take our country back” or, as is the case in the U.S., to “make America great again” (para. 7). However, as these cities increasingly work against the current nationalised, white supremacist agenda, they also often become targets of nationalised and state-sponsored retribution—such as through increased urban militarisation, decreased national funding or economic sanctions.

As this chapter has discussed, cities are often complicated, chaotic, contradictory and highly racialised. Historically, cities have been and still are inscribed by contentious economic and racial borders, mapped by geospatial divisions of injustice, inequality and white privilege. Cities also are spaces of increased militarisation and securitisation. And discursive urban data practices, enabled in great part by smartphone user-generated data, are both palpable and indiscernible, material and ideological, public and private. And while cities often may be perceived to be the safest and most equitable geographical spaces of diversity, institutionalised racism still exists, and the invisibility of certain data-driven practices, as earlier discussed, continue to construct and reinforce existing racial bias, socio-economic boundaries and reify existing geographically based marginalised identities.

And yet, cities are also radicalised spaces of subversion and resistance that often challenge nation-state ideologies and policies. Cities are inherently paradoxical, complex hybrid ecosystems of digital and concrete flows, simultaneously reflecting nationalised identities and yet also always composed of pluralised and diverse cultures. As Cowen (2014) explains, the urban paradox necessarily involves these contradictory tendencies: while marginalising, fragmenting and segregating, cities are also often perceived and enacted as spaces of “human resilience and innovation […] that can mitigate the oppressive character of capital-led urban growth” (para. 7). And while cities often espouse the rhetoric of “renewal,” “regeneration,” and “progress,” urban residents are “utilising their own produced spaces to obstruct, expel and resist the devastating effects of the urban paradox” (Cowen 2014, para. 9). Ultimately, a thriving and productive urban public depends on the collective, active engagement of individual residents with multiple viewpoints, backgrounds and experiences.

As this chapter illustrates, opportunities for agency and resistance of digital socio-spatial injustices lie with those at the heart of the city, in its people. While cities are implicated in the mediation and reinforcement of geospatial marginalisations and spatially bound identities, the inhabitants of networked cities also may be the best hope for the break down of these digitalised socio-spatial regimes, the transcendence of nation-state disciplinary power and the key to neutralising right-wing nationalism, worldwide. Furthermore, political progressiveness appears to depend upon a new form of digital-transurbanism that not only puts more communicative power in the hands of residents, but challenges growing nation-state isolationism. Dobbs et al. 2011 for example, argue that the “reclamation of public space” already is happening in “cities all around the world,” and in part through citizen-sourcing projects that focus on the needs of all urban residents. When citizen-sourcing techniques are disarticulated from commercial interests, they have the potential to leverage the collective communicative power of urban residents in such a way as to reclaim public space and the development of bottom-up politics.