Category Archives: analysis

Smart spaces and smart citizens?

I attended the Smart Cities and Regions Summit in Croke Park, Dublin, today and took part in the ‘smart spaces and smart citizens?’ panel. We were asked to produce a short opening statement and thought I’d share it here.

I’m going to discuss smart citizens by considering Dublin as a smart city. To start, I want to ask you a set of questions which I’d like you to respond to by raising a hand. Don’t be shy; this requires participation.

How many of you have a good idea as to what Smart Dublin is and what it does?

How many of you feel you have a good sense of smart city developments taking place in Dublin?

Would you be able to tell me much about the 100+ smart city projects that are taking place in the city in conjunction with Smart Dublin and it four local authority partners?

Would you be able to tell me much about the extent to which these projects engage with citizens?

Or how the technologies used impact citizens, either in direct or implicit ways?

Or whether Smart Dublin and the four local authorities have a guiding set of principles or a programme for citizen engagement or smart citizens?

You’re all people interested in smart cities. You’re here because it relates to your work in some way. You have a vested interest in knowing about smart cities.

Do you think that citizens in Dublin know about these projects, which might be taking place in their locality?

Do you think that they have sufficient knowledge to be able judge, in an informed way, a project’s merits?

Do you think they have an active voice in these projects’ conception, their deployment, the work that they do? In how any data generated are processed, analysed, shared, stored, and value extracted, etc.?

Do local politicians – citizen representatives – know about them? And do they have an active voice in smart city development in Dublin?

This panel is titled ‘Smart spaces and smart citizens’.

What is difficult to see in most smart city initiatives is the ‘smart citizen’ element. It seems that what is implied by ‘smart citizen’ is simply being a person living in a city where smart city technology is deployed, or being a person that uses networked digital technology as part of everyday life.

To create a smart citizen, all a state body or company apparently needs to do is say people should be at the heart of things, or enact a form of stewardship (deliver a service on behalf of citizens) and civic paternalism (decide what’s best for citizens), rather than citizens being meaningfully involved in the vision and development of the smart city.

In our own research concerning networked urbanism and smart cities from a social sciences perspective we have been interested in exploring these kinds of questions, and how the citizen fits into the smart city. It’s a central concern in our latest book published next month, ‘The Right to the Smart City’, which explores the smart city in relation to notions of citizenship and social justice.

What our research shows is that citizens can be varyingly positioned, and perform very different roles, in the smart city depending on the type of initiative.

ladder

It is perhaps no surprise then that citizens in numerous jurisdictions have started to push back against the more technocratic, top-down, marketised versions of the smart city – the on-going protests in Toronto over the Sidewalk Labs waterfront development being a prominent example. Instead, they demand more inclusive, empowering and democratic visions, with Barcelona’s notion of technological sovereignty often providing inspiration (see my recent piece comparing Toronto and Barcelona and links to articles and organisation websites).

It is difficult to argue that we are enabling ‘smart citizens’ if they are not informed, consulted or involved in the development and roll-out of smart city initiatives. As such, if we are truly interested in creating smart citizens then we need to make a meaningful move beyond the dominant tropes of stewardship and civic paternalism to approach smart cities in a smarter way.

For a fuller discussion see the opening and closing chapters of The Right to the Smart City, which are available as open access versions.

Kitchin, R., Cardullo, P. and di Feliciantonio, C. (2018) Citizenship, Social Justice and the Right to the Smart City. Pre-print Chapter 1 in The Right to the Smart City edited by Cardullo, P., di Feliciantonio, C. and Kitchin, R. Emerald, Bingley.

Kitchin, R. (2018) Towards a genuinely humanizing smart urbanism. Pre-print Chapter 14  in The Right to the Smart City edited by Cardullo, P., di Feliciantonio, C. and Kitchin, R. Emerald, Bingley.

Rob Kitchin

Queering code/space: special section of GPC

There a new special section of Gender, Place and Culture on queer theory and software studies and the queering of code/space edited by Dan Cochayne and Lizzie Richardson. I’ve not had anything to do with the issue other than to referee one paper. It’s nice to see the code/space concept though being re-worked with queer theory and software studies and the digital thought about with respect to sexuality and space because in many ways that is its origin and its publication provides the opportunity to provide a short anecdote of our initial thinking. Myself and Martin Dodge started our work on the first code/space paper in 2002/3.  At that time I was finishing an ESRC-funded project on homophobic violence in Northern Ireland and had been using Michel Foucault, Judith Butler, Gillian Rose and queer theory in general to frame this material. One of the papers I drafted at the time with Karen Lysaght was titled ‘Queering Belfast: Some thoughts on the sexing of space’, which was published as a working paper. Our initial working of code/space was rooted in this work, with the term ‘code/space’ echoing Foucault’s power/knowledge (in terms of being a dyadic relationship). Martin then discovered Adrian Mackenzie’s use of transduction and technicity (borrowed from Simonden), which was also ontogenetic in conception and more centrally focused on technology. I seem to remember us trying to blend performativity and transduction together, then moving to favour transduction. It’s nice to see those ideas now coming together in productive ways. Check out what are a fascinating set of papers.

Rob Kitchin

 

The limits of social media big data

handbook social media researchA new book chapter by Rob Kitchin has been published in The Sage Handbook of Social Media Research Methods edited by Luke Sloan and Anabel Quan-Haase. The chapter is titled ‘Big data – hype or revolution’ and provides a general introduction to big data, new epistemologies and data analytics, with the latter part focusing on social media data.  The text below is a sample taken from a section titled ‘The limits of social media big data’.

The discussion so far has argued that there is something qualitatively different about big data from small data and that it opens up new epistemological possibilities, some of which have more value than others. In general terms, it has been intimated that big data does represent a revolution in measurement that will inevitably lead to a revolution in how academic research is conducted; that big data studies will replace small data ones. However, this is unlikely to be the case for a number of reasons.

Whilst small data may be limited in volume and velocity, they have a long history of development across science, state agencies, non-governmental organizations and business, with established methodologies and modes of analysis, and a record of producing meaningful answers. Small data studies can be much more finely tailored to answer specific research questions and to explore in detail and in-depth the varied, contextual, rational and irrational ways in which people interact and make sense of the world, and how processes work. Small data can focus on specific cases and tell individual, nuanced and contextual stories.

Big data is often being repurposed to try and answer questions for which it was never designed. For example, geotagged Twitter data have not been produced to provide answers with respect to the geographical concentration of language groups in a city and the processes driving such spatial autocorrelation. We should perhaps not be surprised then that it only provides a surface snapshot, albeit an interesting snapshot, rather than deep penetrating insights into the geographies of race, language, agglomeration and segregation in particular locales. Moreover, big data might seek to be exhaustive, but as with all data they are both a representation and a sample. What data are captured is shaped by: the field of view/sampling frame (where data capture devices are deployed and what their settings/parameters are; who uses a space or media, e.g., who belongs to Facebook); the technology and platform used (different surveys, sensors, lens, textual prompts, layout, etc. all produce variances and biases in what data are generated); the context in which data are generated (unfolding events mean data are always situated with respect to circumstance); the data ontology employed (how the data are calibrated and classified); and the regulatory environment with respect to privacy, data protection and security (Kitchin, 2013, 2014a). Further, big data generally capture what is easy to ensnare – data that are openly expressed (what is typed, swiped, scanned, sensed, etc.; people’s actions and behaviours; the movement of things) – as well as data that are the ‘exhaust’, a by-product, of the primary task/output.

Small data studies then mine gold from working a narrow seam, whereas big data studies seek to extract nuggets through open-pit mining, scooping up and sieving huge tracts of land. These two approaches of narrow versus open mining have consequences with respect to data quality, fidelity and lineage. Given the limited sample sizes of small data, data quality – how clean (error and gap free), objective (bias free) and consistent (few discrepancies) the data are; veracity – the authenticity of the data and the extent to which they accurately (precision) and faithfully (fidelity, reliability) represent what they are meant to; and lineage – documentation that establishes provenance and fit for use; are of paramount importance (Lauriault, 2012). In contrast, it has been argued by some that big data studies do not need the same standards of data quality, veracity and lineage because the exhaustive nature of the dataset removes sampling biases and more than compensates for any errors or gaps or inconsistencies in the data or weakness in fidelity (Mayer-Schonberger and Cukier, 2013). The argument for such a view is that ‘with less error from sampling we can accept more measurement error’ (p.13) and ‘tolerate inexactitude’ (p. 16).

Nonetheless, the warning ‘garbage in, garbage out’ still holds. The data can be biased due to the demographic being sampled (e.g., not everybody uses Twitter) or the data might be gamed or faked through false accounts or hacking (e.g., there are hundreds of thousands of fake Twitter accounts seeking to influence trending and direct clickstream trails) (Bollier, 2010; Crampton et al., 2012). Moreover, the technology being used and their working parameters can affect the nature of the data. For example, which posts on social media are most read or shared are strongly affected by ranking algorithms not simply interest (Baym, 2013). Similarly, APIs structure what data are extracted, for example, in Twitter only capturing specific hashtags associated with an event rather than all relevant tweets (Bruns, 2013), with González-Bailón et al. (2012) finding that different methods of accessing Twitter data – search APIs versus streaming APIs – produced quite different sets of results. As a consequence, there is no guarantee that two teams of researchers attempting to gather the same data at the same time will end up with identical datasets (Bruns, 2013). Further, the choice of metadata and variables that are being generated and which ones are being ignored paint a particular picture (Graham, 2012). With respect to fidelity there are question marks as to the extent to which social media posts really represent peoples’ views and the faith that should be placed on them. Manovich (2011: 6) warns that ‘[p]eoples’ posts, tweets, uploaded photographs, comments, and other types of online participation are not transparent windows into their selves; instead, they are often carefully curated and systematically managed’.

There are also issues of access to both small and big data. Small data produced by academia, public institutions, non-governmental organizations and private entities can be restricted in access, limited in use to defined personnel, or available for a fee or under license. Increasingly, however, public institution and academic data are becoming more open. Big data are, with a few exceptions such as satellite imagery and national security and policing, mainly produced by the private sector. Access is usually restricted behind pay walls and proprietary licensing, limited to ensure competitive advantage and to leverage income through their sale or licensing (CIPPIC, 2006). Indeed, it is somewhat of a paradox that only a handful of entities are drowning in the data deluge (boyd and Crawford, 2012) and companies such as mobile phone operators, app developers, social media providers, financial institutions, retail chains, and surveillance and security firms are under no obligations to share freely the data they collect through their operations. In some cases, a limited amount of the data might be made available to researchers or the public through Application Programming Interfaces (APIs). For example, Twitter allows a few companies to access its firehose (stream of data) for a fee for commercial purposes (and have the latitude to dictate terms with respect to what can be done with such data), but with a handful of exceptions researchers are restricted to a ‘gardenhose’ (c. 10 percent of public tweets), a ‘spritzer’ (c. one percent of public tweets), or to different subsets of content (‘white-listed’ accounts), with private and protected tweets excluded in all cases (boyd and Crawford, 2012). The worry is that the insights that privately owned and commercially sold big data can provide will be limited to a privileged set of academic researchers whose findings cannot be replicated or validated (Lazer et al., 2009).

Given the relative strengths and limitations of big and small data it is fair to say that small data studies will continue to be an important element of the research landscape, despite the benefits that might accrue from using big data such as social media data. However, it should be noted that small data studies will increasingly come under pressure to utilize the new archiving technologies, being scaled-up within digital data infrastructures in order that they are preserved for future generations, become accessible to re-use and combination with other small and big data, and more value and insight can be extracted from them through the application of big data analytics.

Rob Kitchin

Emerging Technological Responses in Emergency Management Systems

The advent of discourses around the ‘smart city’, big data, open data, urban analytics, the introduction of ‘smarter technology’ within cities, the  sharing of real-time information, and the emergence of social media platforms has had a number of outcomes on emergency services worldwide. Together they provide opportunities and promises for emergency services regarding efficiency, community engagement and better real-time coordination.  Thus, we are seeing a growth in technologically based emergency response. However, such developments are also riddled with broad concerns, ranging from privacy, ethics, reliability, accessibility, staff reluctance and fear.

This post considers one recent technological push for the re-invention of the emergency call system (911bot) and another for the sharing of real-time information during a major event (Smartphone Terror Alert).

911bot

In recent years, there has been a significant move away from voice calls towards texting and internet based platforms (eg.WhatsApp and Twitter)(see figure 1), this is tracked regularly by the International Smartphone Mobility Report conducted across 12 countries by the data tracking company Infomate. In 2015, they found that in America the average time spent on voice calls was 6 minutes as opposed to 26 minutes texting, and worldwide,  internet based platforms were the main form of communication (Infomate, 2015 and Shrapshire, 2015).

 

cell phone communication

Figure 1: Cell phone Communication. Source: Russell (2015).

In light of this, there is a push by both the private sector and entrepreneurs to utilise mobile phones and  social media platforms in new ways such as within the emergency call system. Within my own field research, I have questioned first responders in Ireland and the US regarding the use of social media and apps as alternative means to the current telephone system.  For the most part, this was met with disdain and confusion from first responders.  Strong arguments were made against a move away from a call-dominated response system. These included:

a)      Difficulty in obtaining relevant and accurate information regarding the event, including changing conditions and situations.

b)      Not able to provide the victim or caller with accurate instructions and information.

c)      Restrictions in contacting the caller.

d)     The system would need an overhaul for it to work, i.e. a dedicated team ensuring that these messages are not missed, and require staff training.

e)      Call systems are established mechanisms for contacting the emergency services, why change it when it works?

f)       If you use something like Twitter or Facebook to report an emergency how do we ensure that it is reported correctly and not just tweeted or messaged to an interface which is not monitored 24/7?

And as can be seen through the following conversation with two operational first responders in Dublin, Ireland, they want new technology but are also highly hesitant as to its ability to ensure a quick response.

Conversation between researcher and two first responders

R1: See the problem with a tweet and a text, I can’t get any information out of that, like I could tweet and back and then you are waiting for them to send something back, when I have you on the phone, I can question you, “What is it?”, “What is wrong?”, “What is the problem?”.

R2: If you did go with something like [social media platform for emergency call intake], you would have to have the likes of, if you are the tweet man then you would have to be 100% on the phone looking at it

R2: It probably would work if it wasn’t an emergency as such, not a full emergency

R1: I think people need tobe re-assured that someone has seen it and really knows what is happening.

R1: Jesus you could have everyone tweeting saying I have a sore stomach and that would register as a call for us so the calls would just get worse and worse. [...] I think if you ring Domino Pizza now, it will know who you are, where you are and your order

R2: They can read the caller ID coming

R1:We haven’t got that

All of these are understandable concerns, but they also illustrate a resistance to innovative change that may result in cultural and institutional change which they oppose due to highly legitimate fears of effectiveness and reliability. Even so, they are welcoming of technology which has obvious benefits for them such as the “Domino’s Pizza” caller ID system, but are more reluctant towards innovations such as the 911bot whose value is overshadowed by fears of inefficiency, information gaps and reliability. However, the 911bot does potentially address some of these issues within its design.

The 911bot (figure 2) was developed during TechCrunch’s Disrupt Hackathon in New York in 2016.  It works through Facebook Messenger, which had a reported 1 billion users in July 2016 (Costine, 2016), to allow users to report an emergency.  Initially, one would be forgiven for immediately thinking of the arguments made against a transformation of the current system as presented above. However, the messenger app already offers location services based on the phones GPS thus, when reporting an incident, your exact location is immediately sent (although you can turn off your GPS signal and restrict your location being sent, when using this bot there is potential for that to be overridden).  The person reporting the incident can also send pictures or videos and the bot can provide information on what you should and shouldn’t do in that situation such as, how to do CPR during a cardiac arrest (Westlake, 2016).

Further, this bot has potential to feedback the location of the first responders to the reporter. It provides the control room with more accurate information coming from real-time videos and pictures meaning that they are not relying wholly on information from untrained and scared people.  And, most importantly, this system doesn’t take away from the control room interacting with the caller. From the information provided by the developers, it appears that once the messenger sends the request, the control room calls the phone and resumes their role but with more information.   Possibly, going forward this could even be done through Facetime so that the control room has live interaction with the event prior to the arrival of the first responders.  Although, the 911bot has only been developed and not deployed, in time and after much consultation and experimentation, it could prove very beneficial within emergency response.  For instance, if the control room operator can actually see how the person is conducting CPR, can see and hear their breathing, see the extent of the injury, fire, or road traffic collision in real time, it would inform decision-making that could create better and more efficient responses.  However, it would be remiss to discuss this without noting that there are potential privacy issues with the mass use of this type of technology outside of the remit of this post, that would need to be considered.

911BOT

Figure 2: 911bot. Source: 911bot online.

Smartphone Terror Alert

Another new use of mobile technology was the mass terror alert issued on September 17th 2016, after Chelsea, Manhattan was hit with an explosion.  The alert (figure 3) was issued by the Office of Emergency Management, New York Police Department and the FBI through all phone networks. It was received by an unknown number of people and provided information about the key suspect – Ahmed Khan Rahami.  The Press secretary for New York Mayor Bill de Blasio stated that it was the first use of this alert at a “mass scale” and as the suspect was caught within 3 hours, it presented the appearance that this alert was effective, with New York’s Police Commissioner stating “it was the future”(Fiegerman, 2016). Yet there is no evidence that the alert had anything to do with the catching of the suspect; these two factors could be circumstantial.

SMARTPHONE TERROR ALERT

Figure 3: Smart phone terror Alert. Source: published in Fiegerman (2016).

Further, as illustrated by Anil Dash in Fiegerman (2016) how effective was it actually?  “Is there evidence that low-information untargeted push notifications help with any kind of crime? Seems they’re more optimised for panic”.  This is compounded by the lack of an all-clear alert, which would work to ease tensions and potential panic.  We live in a socially constructed risk society (Beck, 1992; 2009) and with innovations such as this, even if the intention is good, the potential for mass panic is created, which raises questions regarding the appropriateness of this mechanism. In this instance, sending an alert with little information, using just a name, makes everyone who could fit that name a potential target, and is an action that could create panic, fear and racial attacks under the illusion of “citizen arrest”.  However, this system has potential especially if it were utilised during severe weather events to provide information on evacuation centres and resources rather than during more sensitive events such as a manhunt.  Essentially, though, before it can be deemed thoroughly effective and safe there needs to be stringent supportive policy and agency and community training to ensure that response from agencies as well as communities is coordinated and effective rather than panicked and uninformed. So, I wonder, is this really the future, and indeed, does it need to be the future? Is it already the present with no sense of reflection on the potential consequences of such a system by the lead federal and local emergency agencies and institutions?  I don’t have the answers to these questions but examining the operational use of this alert even, at its small scale of use, provides opportunities to begin to tease out the danger of a dichotomy between effectiveness and panic and to explore issues around privacy, fear, reliability and usefulness.

In conclusion, this post has provided two different innovations within emergency management, one being experimented with and one which has been implemented. But what is clear is that changes in how we engage with control centres and emergency services are taking place, albeit slowly. But, one can only hope, especially in relation to the alert system, that lobbied criticisms will be engaged with and solutions sought.

 Bibliography

911bot (2016) 911bot. [Online]. Available at: http://www.911bot.online/) (Accessed 9th November 2016).

Beck, U., (1992). Risk Society: Towards a New Modernity. London: Sage.

Beck, U., (2009). World of Risk. Cambridge: Polity Press.

Costine, J. (2016) How Facebook Messenger clawed its way to 1 billion users. [Online].  Available at: https://techcrunch.com/2016/07/20/one-billion-messengers/ (Accessed 8th November 2016).

Fiegerman, S.(2016) The story behind the Smartphone Terror Alert in NYC. [Online].  Available at: http://money.cnn.com/2016/09/19/technology/chelsea-explosion-emergency-alert/ (Accessed 9th November 2016).

Infomate (2015) The International Smartphone Mobility Report [Online]. Available for download at: the International Smartphone Mobility Report (Accessed 7th November 2016).

Russell, D. (2015) We just don’t speak anymore. But we’re ‘talking’ more than ever. [Online].  Available at: http://attentiv.com/we-dont-speak/ (Accessed 9th November 2016).

Shropshire, C. (2015) Americans prefer texting to talking, report says. Chicago Tribune [Online].  Available at: http://www.chicagotribune.com/business/ct-americans-texting-00327-biz-20150326-story.html (Accessed 9th November 2016).

Westlake, A. (2016) Finally, there’s a chat bot for calling 911. [Online].  Available at: http://www.slashgear.com/finally-theres-a-chat-bot-for-calling-911-08439211/ (Accessed 7th November 2016).

 

Smart Docklands in a word, and smart city bingo

A couple of weeks ago I published a list of words that members of the Smart Dublin Advisory Network felt represented qualities they hoped Smart Dublin would fulfil.  At a recent meeting about a proposed Smart Docklands initiative attendees were asked to perform the same task – use one word to describe a desirable quality for the area/initiative.  Here is that list of aspiration words:

Co-creation                  Innovation                  Collaboration
Best practice               Showcase                    Testbed
Quality of Life             Community                 Engagement
Smart energy              Telecoms                     Internet of Things
Data                              Open                            Bright
Intelligent                    Optimized                    Autonomous system
Sustainability              Safety                           Resource efficient
Industry                       Startups                       Opportunity
Alignment                    Integrated                   Deploy and forget
Electricity                     Battery                         Energy
Connectivity                Smart mobility

While there is some overlap in the lists, it’s interesting to note the differences between the aspirations expressed at the two meetings.

Here are the words in the Smart Dublin list that are not in the Smart Docklands one:

Networking, Collaborative, Cooperation, Sharing, People, Well-being, Accessible, Diversity, Insight, Problem-solving, Strategic, Joined-up, Agile, Transformative, Future-proofing, International, Socio-technical, Curiosity, Easy

And here are the words in the Smart Docklands list not in the Smart Dublin one:

Co-creation, Best practice, Showcase, Smart energy, Telecoms, Internet of Things, Open, Bright, Intelligent, Optimized, Autonomous system, Resource efficient, Industry, Opportunity, Alignment, Deploy and forget, Electricity, Battery, Energy, Smart mobility

And here is the overlap:

Innovation, Collaboration, Testbed, Quality of Life, Community, Engagement, Data, Sustainability, Safety, Startups, Integrated, Connectivity

Perhaps not unsurprisingly the Smart Docklands list has more economic aspirations, but does still contain ambitions concerning community, engagement, quality of life and sustainability.  Adding the two list together, I sense, provides a kind of ‘smart city bingo’ – a full house of smart city goals.

Thanks for Jamie Cudden and Réka Pétercsák for compiling and sending the Smart Docklands list to me.

Rob Kitchin

Smart Dublin – in one word

The first Smart Dublin Advisory Network meeting took place on the 12th October in the Mansion House.  The plan is for the network to meet every six months to help guide the work of Smart Dublin as it develops and implements its strategy and programmes.  The first meeting mainly focused on introducing Smart Dublin and undertaking some initial workshop exercises to brainstorm initial ideas and feedback and to do so preliminary backcasting.  The first task was a quick introduction and for each person to say in one word a quality they hoped Smart Dublin would fulfil.  Here’s a list of those aspirational words – which I have grouped into triplets – a list against which to judge over the next few years how successful Smart Dublin has been.

Connectivity              Networking              Integrated
Collaborative            Cooperation             Sharing
People                       Community              Engagement
Well-being                 Safe                           Quality-of-life
Accessible                 Sustainable              Diversity
Data                           Insight                       Problem-solving
Strategic                    Joined-up                  Agile
Transformative        Future-proofing       International
Innovation                Start-ups                   Testing
Socio-technical        Curiosity                    Easy

Interestingly, efficiency, economy and open – which are three of the four key terms that have to date underpinned Smart Dublin’s work (along with engagement) – were not suggested. Personally, I think it’s a fascinating list in terms of what it prioritizes as key attributes of a successful smart city and it would be interesting to compare this list to other lists produced by stakeholder groups in other cities.  A brief post about the advisory board meeting and the Smart Dublin showcase that followed its first meeting can be found here.

Rob Kitchin