 Mae gwirionedd'n ni, Gillian Rose, a rhai am y project down cause Foundation investing program. Yn gweithi nhw'n rhai o gweithio cyffredinol Sqad. Rhaid oedd yn gweithio cyffredinol Sqad yw heddiw'r sydd arrol gwneud allan brwyth ngair sydd y archwil am ffordd cyffredinol. Gall ychydigol mae'n rhai o cyffredinol'r gyrsiau efo'u rhai i cyfredinol iawn, i gweithi a'r hyffredinol. The other reason we really wanted to explore how these particular various smart city projects were working out in the city is that it's also very socially divided and also diverse city. Milton Keynes has some very deprived neighbourhoods and also some very wealthy ones. ac y cwestiynau yna mewn gweld, a dyma'r cwestiynau ar gyfer jygoleis, yw y cwestiynau ar hyn sy'n tynnu cyfnolol, cyfweld cyflym, cyfweld cyfweld, cyfweld cyflym. Mae'r cyflwyffydd i gwybod iawn i'r cyfrifiad o'r cyflwyffydd, y gallwn cyllidau cyllidau cyrryffydd i'r cyflwyffydd. Mae'n ffair i'r gyflwyffydd, ac mae'n gweithio'r cyflwyffydd for many years as a sort of class, gender, race, sexuality and so on. But we're also particularly interested in exploring what new forms of social differentiation might be emerging alongside these various kinds of technologies, devices and data interventions. So, thank you, trying to work through that, as I said in my brief opening remarks, data is really the core of the smart city. Data is a highly diverse range of things, but we were particularly interested in the ways in which data travels through the city, as indeed the smart policies and other smart devices. I'm trying to think about how they are engaged with particular users, but also had a kind of agency in generating other forms of identities and so on. And we were particularly inspired by a couple of approaches. Baker and McGurk in particular thinking about circulations of policy. A lot of smart stuff is around as much policy as it is around the kind of technologies and the data. And so we were interested in looking at circulations of data, but also Joe's project with her colleagues thinking about data circulation specifically. And we've got those three broad approaches that we were pulling out in terms of thinking about methodology for this. One was to go to the places where things are done with data and policy and observe with a kind of ethnographic sensibility what's happening. And we were particularly interested in thinking about the kind of work that has to be done to make these kinds of devices function or not, or to make data or policies circulate or not. And that notion of the kind of labors of pulling these things together and making them hold. Again, we travelled a similar trajectory to Joe's project, which is that once you start looking at that, you also see how these things fall apart for all sorts of reasons as well. There are frictions of many kinds, technical funding frictions, stakeholders disagreements and so on and so forth. And tracing the site situations, circulations of these things is a really good approach to beginning to map this particular configuration of SMART. So, I just want to talk through briefly one of our case studies and then Miguel will talk in more detail about another. I've got a particularly interest in visual culture and what digital technologies are doing to visuals. Kind of ironic that I'm getting more interested in spatialities and circulations and sonic things, which certainly in SMART cities a whole range of different kinds of digitally produced visuals are absolutely crucial to how SMART cities work, both in the kind of hype of what they're about. The big corporations put a lot of money into their advertising, into their YouTube channels, into their digital animations that show you how marvellous the SMART city is going to be. But there's also a lot of very what we might call operative images as well in dashboards. We might think of interfaces kind of moving away from the representational into maybe you do something rather than give you meaning and so on. So, to address those sorts of issues, I just want to go through these extremely briefly. We've tried to pull out three different kinds of approaches, one of which is a kind of content analysis of the range of images that the key stakeholders of SMART in Milton Keynes put on their websites, their social media sites and other kind of promotional materials, brochures, policy statements, vision statements and so on and so forth. We've gathered a corpus of about, I think it's about 600 images at the moment, which we have scanned, but actually that's manageable for us to do that content analysis using conventional methods manually. I've also experimented using Lev Manovich's software called Image Plot, scraping images attached to tweets about SMART cities and trying to think through the kind of configurations, or particularly of colour actually across there and how those different sort of tweeting streams might make you feel about being SMART. And then finally something that Ed Wigley, the third quarter of this paper has been particularly interested in and I'm afraid I've not been peering over his shoulder in any detail in this, so any technical questions may have to be addressed to him directly. He's really interested in picking out some of these key images and this is one produced by Arup, a very large global consultancy but very heavily involved in the SMART city market and trying to do using Google image search, reverse image search to find out where these images get circulated. A previous project of mine suggested that digital visualisations, there's quite a clear global division of labour and global divisions of circulation if you like, between the kind of, although many cities in the global south are going SMART, you can see here the distribution of that particular image seems to have landed in Western Europe, in the Eastern Western seaboard of the US and not really many other places, which suggests perhaps the kind of particular dynamic of visual circulation as much as it does a policy circulation as well. And some questions that that prompts me and that map is relatively recently produced, so I'm afraid I don't have any very clear answers to this yet, but I'm interested in who's creating these images, who is recirculating them, what kind of spatially located networks are embedded and kind of highlighted by that pattern of circulation. And maybe, as someone who's interested in visual culture, but again starting to rethink how we think about the visual, perhaps it's a circulation, it is that dynamic of their movement that's maybe more important than kind of trying to do a sort of semi-logical reading of what they show, and particularly because I think certain places are shaping that visual language more than others. And finally a question I want to raise, because again I wonder, it's a puzzling one for me the reception of these images, when we show them to audiences talking about smart cities, people are incredibly sceptical about them, obviously as you could see that our point, flying cars and glossy skyscrapers, and everybody finds it very easy to critique those sorts of images, but nonetheless the companies keep on producing them, they keep on being circulated. There's an interesting question about what kind of audience it is happening there. OK, thank you. So, moving on to our second case study. Oh yes, our second case study, it's about the story of two data infrastructures, because smart cities are supposed to be all about using big data for making decisions about the urban, but that is not new. People have been using data in cities from ages ago, so for me this case study is about the kind of data that people were using in milk pumpkins before smarts happened, then smart lands into the city, and it assimilated what was there before, and in assimilating it it made it different, changed its priorities. So I will come back to this for know what is important, is that we had an intelligence observatory for social difference in 2003, and then smart landed in 2014, and in 2017 smart assimilated what was there before, so the observatory did not exist anymore. But going back to how did we went about unraveling this story, what we are trying to do is to follow the journeys of data and of policy, as data went from one infrastructure to another, and I keep talking about data and policy, because in this case when you talk about smart city policy, you cannot make policy without the infrastructure. Usually smart city policies assume the existence of a certain data hub, of a certain sensors system for monitoring traffic in real time, for example, of a certain set of sensors data, a certain application, so when data travels, policy travels with data as well. And well, here for talking about what we did methodologically, we took policy documents as our starting point, particularly the smart city project in Miltonkins is organized around a set of core documents, like the MK 2050 vision. So if we go through that document, we look at all the other documents that are referenced there, we look at all the people who had an input into that document, and all the data sets that's fed into the making of that document, then we can jump to the next step in this journey, and then we have to follow an iterative process, and look at this new document and do it all over again, see which documents, which data sets, which actors are referenced there, and once we keep tracking step by step, we can start putting together a map and a sort of oral history of how smart landed into and changed Miltonkins and made some things new and destroyed some others. This was made very easy, the documentary part at least, because the city council in Miltonkins keeps very good track of its documents, you can enter MK 2050 vision and see minutes of all the city council meetings where they discuss something about it, and all the big documents, the small documents, the boring documents they produced. Eventually we connect that in a map, we tried to use post-its initially, and it quickly got out of control, so not that I want to publicize it particularly, but we found it very useful to use this particular data visualization platform, which was almost like using post-its, except that when you want to shuffle things around, you can just steal the system. Please, centre on this so we can see everything that is emanating from here and now, or please look at everything that is emanating from here and now. But well, actually it was post-its, just made easier with software. And okay, so now that's about it for our method, we, sorry, going back to one slide, please. As I said, sometimes tracing the next step involved looking at the next set of documents, sometimes it was about interviews. We got to interview some people, sometimes it was looking at the metadata to see where that bit of data had been imported from, and sometimes when everything else failed, we could even look at the newspaper's clippings. We got to situations with the council person who had made some key decision, was not around anymore, but they had granted an interview to some newspaper 10 years ago, so our method was whatever it takes to get from here to here and to see how they landed here. So okay, now this is the story, the kind of oral history of data in Milton Keynes that we managed to assemble together. So 2003, the intelligence observatory is commissioned with one specific goal, understanding difference in health outcomes through data. All the data from the city, they could get their hands on at a high resolution level. It was important to them that they could understand health inequality at a postcode level. And gradually it developed for the functionalities because they kept accumulating more and more data to understand health outcomes. They found that a lot of it had to do with multiple deprivation index and that they could start correlating health deprivation with all the other bits and pieces that they had lying around. And well, this is what deprivation looks like in a map of Milton Keynes and the reason why a lot of people became interested in this is because voluntary organisations, community organisations, the fire department, I don't know, everybody who wanted to do some kind of intervention in Milton Keynes could say, we don't have to cover the whole of the city. Look like fishermen is where they really, really need our help. And well, maybe it is quite obvious but having this map at very high spatial resolution created a very tight coalition of actors over time because well, this data infrastructure was originally used by the city council. It was hosted in the council and the idea was that anyone in the council would know where to find what they needed. But they made available to a lot of people over time the scope became not only that of a data hub but something of a data-driven ecosystem that was connecting authorities, citizens, communities. And it sounds smart in many ways and we are talking about 2003. By the time this ecosystem had developed, it may be 2007, say, which is roughly 10 years before smart cities became fashionable, wish our point is in a way that smart is a new label for things that people have been doing all along. And an important thing that we discovered in trying to trace this journey is that a lot of the jumps of the data were not made through computers, through applications. Again, because we were talking about 2007, but it was done face-to-face by people, initially by people from the city council, by the Milton Keynes Intelligence Unit, and then later by volunteer community organisations like Community Action, MK. So we had this computer, this infrastructure, these data sets being aggregated in servers, but some human factor, some human network has to actually make sure that it has some impact on what's going on on the streets. And then 2014 happens, smart city happens, the city council gets about 16 million for this smart city project, MK Smart. And by 2017, the data infrastructures for this smart city project replaced the MK Intelligence Observatory, the old one, and this was largely because austerity. We got to see the minutes for the council meeting when they decided we don't have money to keep the observatory open anymore and the open university can do this instead. And they thought that it was equivalent in a way, they say, we have this server, this data hub, to make all the information available in one stop and it makes no difference if that data hub is sitting in the city council or if it is sitting in the open university. Data is data, they assume that it would flow like oil frictionlessly, so it doesn't matter where you store it. But what we have realised is that the logic of the smart city is very different to that of the observatory. As I said, the old observatory was looking into very high spatial resolution. It wanted to be able to understand what was happening in this particularly deprived state, council state, this particularly deprived city block and the new logic of smart turnouts not to be so much about spatial resolution, but about temporal resolution, about real-time data, urban flows and largely for technology development, knowledge economies and increasingly efficient use of infrastructure. Here, then, data is used, for example, so the driverless car can choose the optimal route to drive through the city without bumping into congestion. So it needs to know where people are, but not if people are in good health or not, or if they have employment opportunities or not, unless they happen to be knowledge economy, employment economies. So the observatory and MK Smart represented made visible and make possible different versions of Milton Keynes. Now I think I am almost running out of time, so I will rush through this one, but the point is, as I mentioned, MK Smart is hosted in the Open University, where fortunately we happen to be working in the Open University. So we were able to go to them and tell them, you know what this data infrastructure was working in a different way in the past, and as a consequence of that, we are able to use what we gathered from the data journey's approach to tell them, hey, let's retrace this journey because it was a good journey, it was a good network, let's put it back together the way it was, but using the Smart City technologies now. So now we are trying to find the people who work in the old intelligence unit, and try to figure out what were the matters of concern, the data journeys, the flows, so that we can reconnect it and refacilitate the making of such a difference visible and actionable in the way it were. We are finding the challenge that the insight team wants to replace human facilitators with automated processes, because that is the logic of Smart, everything has to be automatic, everything has to be artificial intelligence, so it's hard to find the logic of Smart, even if we are trying to reconstruct what was there before. And well, this is our conclusion so far, that Smart cities are a convergence of journeys, difficult to disentangle from each other, of policies, of imaginaries, of infrastructures, people and data. The observatory, MK Smart, makes different forms of urban life visible and actionable, so data here is not value neutral, it makes different things possible, and we are trying to figure out how to use different methodologies to disentangle these assemblages and these entangled journeys. And in the case of Smart cities in the making, this is not just an academic concern of understanding how the Smart city is made. We are, because the Smart city is being made, right now we are trying to collaborate with those that are enacting this new journeys to ensure that the consequences of the new forms of data, the new mobilities, the new visibilities are taking into account and hopefully are made more fair and more inclusive. And I think that's about it, so we can take questions for all the presentations I think at this time. Thank you.