 I'd like to speak about the Urban Big Data Center. I am the director of this center, and we are a consortium of seven universities across Scotland, England, and the United States, actually. So we are very international in scope. We have quite a few memorandum of understanding and other mechanisms of relationship with quite other international organizations in places like Australia, US, Europe, China, and India, and so on and so forth. Our main mission is to come up and do innovations for sustainable and socially just cities. So actually that is turning out to be a very big mission because by all reports and analysis, the vast majority of the world's population are going to be living in urbanized areas and already are living in urbanized areas. And so the big challenges stemming from overpopulation, economic disruptions, access to different kinds of social care and education and health care and so on and so forth are actually quite staggering worldwide. If you look at cities globally, where we come in is to create and raise awareness about a variety of innovative sources of data and methods to use this kind of data across UK stakeholders as well as stakeholders across globally in these areas. So our scope is very broad. My background, my own background is transport. We have housing researchers. We have researchers who work on education and other areas like energy, environment and so on and so forth. The center represents 10 academic disciplines in the urban big data, urban social sciences and the urban and the data sciences. And we run UK's urban data infrastructure. So basically we are funded by the same organization as Matthew to host and collect and put together a variety of exciting sources of data to study all kinds of complex problems on that's vexing urban areas. And currently our users are all from all over the UK, large numbers not only from Scotland also from England and Wales and also internationally. So we run this data service and running it is a non-trivial business because as I will say in the next few minutes we deal with a very wide variety of different types of data, really large big data sources as well as confidential data and a wide spectrum of data sources. And in addition to running the data service we are also, we have embarked on this ambitious training and capacity building program and we are running now training courses on basic methods, on basic methods related courses as well as courses on more advanced topics in urban modeling and simulations, geographic information science as well as data science and so on and so forth. So as I said earlier I think the more exciting parts of what we are doing is looking at a wide spectrum of fairly standard as well as novel sources of data. We get together data from many different sources such as different types of sensor systems that are in roads, weather monitoring systems and what have you. We also gather data from many different sources of user generated content such as social media, citizen science project, personal sensor surveys of GPS life logging and so on. We of course have access to and would continue to aspire to have more, greater access to many different forms of administrative data that have information on citizens transactions with governments in various different ways. Also private sector data of many different kinds. Some kinds of data that are personally interesting to me sitting in this building particularly is the idea that the arts and humanities communities have very interesting sources of data that are really unstructured, very novel. They could be in the form of images. They could be in the form of books. These are really exciting data sources and we want to build the tools and technologies to extract information from these data sources and make it available to a number of different types of researchers who are interested in this urban agenda. So some of the examples of data that we have at this current time are the integrated multimedia city data platform which is sort of one of a kind comprehensive database using many, many different data sources. In fact, the city of Glasgow and Mark Livingston who was a project manager for this project is sitting right over there in the blue shirt you can't hide. I pointed the yard to the audience. So it actually starts off being a social survey of about 1500 households in the city of Glasgow and also queries about not only about their social demographics and use of information and communications technology, transportation and literacy and so on and so forth. But we also queried very detailed travel patterns and also instrumented subset of these individuals with GPS and life logging data so that they were able to go around the city collecting photographic images of the city of Glasgow sort of giving us a worldview of the way they see their world. So that it's not just a static data set, it's really understanding data from images of collected by this large groups of citizens. In addition to this I am city data that has already now generated several insights relating to sort of transport as barriers to senior citizens learning and other things of that nature. Another data source that I'd like to point out and welcome you to visit our website as well is this building this big data systems to sort of monitor things like transport on a large scale using very many naturally occurring sources of data across the UK. For example, what we are doing at the current time is aspiring to build the quality of public transport availability in every bus, train and ferry stop across the United Kingdom using online data sources. Another example is that we are trying to build together using multiple sources of data access to job conditions across the UK at the level of output area so that one might be able to go in there and sort of query a local decision maker might be very interested for example to find out whether there is, if an employer locates there if they would have access to a good or adequate labour pool or if you're a citizen who lives there whether you have high quality access to jobs and or whether there are others like you who are competing for the same jobs and so on and so forth. So the idea of building this big systems taking various naturally forms of data actually allows us to answer many of the kind of questions sort of urban operations and policy questions that might be of interest to many different communities but I also wanted to point out that some of us in UBDC are very much in the business of big models not just a big data but also big computational models for instance to really stimulate and advocate blue skies thinking about what is going to happen to cities of the future for example as a result of global climate change what would happen to coastal cities in the United Kingdom or if you have massive swads of connected vehicles or connected cars on streets as is already happening in some countries what's a street system going to look like what is going to happen to land use patterns are we going to have different urban density patterns and this is not pie in the sky stuff I mean this is real world events. Increasingly automotive manufacturers are going to put in dedicated short range communications transponders inside the vehicle so that your car will be able to communicate with others around you. The whole idea being that it'll take away the task of driving from a human driver and give it to the machine the car because it is well known in many different academic literatures that the vast majority of road fatalities occur because of this last few seconds when the driver realizes that a collision is imminent but the human body is too slow to be able to do something to the braking system so these are sort of the big transformations that are coming in our cities so just as a hundred years ago the private car came and transformed the ways in many ways how cities look what are with this kind of automation and perhaps even other things like sharing economy coming into cities what are our cities going to look like how do we build systems or models to do this kind of foresight, this kind of futures that's an interest for many of us in the big data center. So I really believe that data are very interesting there is no shortage of data obviously all the other speakers talked about this and the big data challenges really I see to be fourfold. There are very significant technological challenges associated with the kinds of many of the big data sources that I talked about not just the kinds of big data sources I specifically gave examples of but many of the other examples that came out throughout the panels how do we capture this data how do you clean it, how do you curate it how do you give people access how do you do resource discovery systems so that the researcher or somewhere else in the world finds data in Glasgow and is able to crowdsource or sort of co-produce solutions in a specific neighborhood in Glasgow so those are really important technological questions that we are interested in in UBDC additionally there are very critical methodological questions that come up as well and the methodological questions that are of particular interest to me are issues of bias and uncertainty in this many of these new forms of data I was just talking to someone earlier today the idea that we roughly many of you probably use this thing called Twitter that seems to have pervaded over many of our large parts of our lives roughly 1% of Twitter data are geotagged by the user that means I will know where you are when you have actually tweeted at 99% is not geotagged but we are developing systems in order to be able to geolocate people who have not geotagged so that I can get information on a substantially larger feed of global Twitter users and where they are at the time they tweeted compared to the geotagged data that are available at the current time the reason I'm bringing this up is that already there is a literature that has really vastly said that okay social media data are not representative of the total population of people but again within social media data the people who geotag and give away their location information are different from people who don't geotag and tweet and are probably more wary of locational privacy issues so we are very interested in these issues of biases, propagation of errors and all these things that are going to make this big data economy very challenging and will be very instrumental and will pay a big role in I think in sort of the data quality issues of the future epistemological challenges is the third big data challenge I see that we necessarily don't, you know big data is not going to be the answer to everything data is not going to be the answer to everything I think there's a lot of data there's a lot of analytics we certainly do a lot of analytics but going to impact which is Matthew's original agenda here is that think about it as a sort of pyramid with this big data, big analytics but impact in fact may be quite small because unless we link really this analytics to the governance processes, the data activism the power structures that are coming up with data you know Google owns all this data right I mean how do we have access to it so all these kind of issues have to be bypassed or overcome or addressed in some way to go from data and analytics to impact lastly I am also we are also very interested in this emerging political economy of data you know data is the new oil in a sense it's going to really change the geopolitical terms of power structures think about the US elections and Russia and hacking and so on and so forth all this is really crazy stuff but at the same time when you think about it so much of our lives is already Google and you know Facebook and all these things so really this power structure this whole issue of trust management issues of responsible innovation of course issues of access and privacy and sort of this big networks that are coming up around data and access to data are really going to play a very influential role in cities of the future in governance of the future and I think we are really interested in these issues and would be very interesting to hear your thoughts on this as well because I think here listening to the public and getting feedback is very important in addressing and really comprehending fully some of these political economic questions around big data.