VivaCity Smart City Platform
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Montanari, Marco
Many big vendors are exploring the smart city concept explaining that the smart city is a city aware of the things happening in the infrastructures. Thus the vendors are pushing for a Smart Grid, Smart Metering, Smart Sensors and Smart Whatsoever. This makes the city look like a sick patient, being monitored in many ways with histograms, gauges and panels for the information to be read. In our opinion this is the most unnatural way to interact with city information. Historically the most used way to interact with citizen oriented information is the map. Even today, with the always more precise GIS tools, the map can be an important part of a city information management tool. The VivaCity Project is a platform for the data-driven smart city. The core of the platform consists of a map- based view of the city itself, with all the possible cartographic open data made available by the governance. Beyond that, various apps can contribute in a smart manner through a set of plugins and entry-points for various views of the city, enabling a deep and complex interaction with the city itself. This system is self-sustaining, considering that the city already contains its monitors, which are the citizens. They just need two sets of tools: a visualization tool enabling the citizens to understand what is being done at a given time, and a tool to express opinions, problems and proposals to the governance. Considering that an overly generic tool loses its meaning because it has no real target, the interaction with the governance is delegated to function-specific or target-specific apps sharing a common API. This way both governance and citizen gain benefits, having both sides creating new data all the time and interconnecting information from the city and its inhabitants: governance has the ability make decisions based on real-time citizen-driven data, while citizens have the opportunity to create new services using the provided data. Figure 1 - Part of the VivaCity Smart City Interface For instance, the APIs offered to external apps are aimed to the following areas of interest: Politics, political decisions Maintenance          Security City Info, Touristic, Cultural information Management, urbanistic information Urban events, Urban Acupuncture, social analysis Emergency Management, Emergency information aggregation from the many sources available Economic, Managerial information Environmental, Energy usage information The data shown in the interface is the sum and interpretation of the data provided by the local governments through open data, or applications created by third parties like OpenMunicipio in Italy, the OpenSpending platform by OKFN or even simply mash-ups with complex datasources, like the USGS earthquake map, or the various regional APIs for simple services or any other app enabling the citizen to participate actively to the activity of his government. Using the platform in different cities enables a normalization of the services offered by the cities, and the direct comparison and interconnection of cities through a distributed API supporting the governance to empower policies and improve citizens lifes.
 Thank you. Show it a few seconds. A minute. Okay, let's go. Okay. Hi everyone. I'm Marco. Hi, Marco. Hi, I'm Marco. And, well, I'm going to talk about a different way to talk about smart cities because many, many cities are publishing lots of data. I'm part of Open Knowledge Foundation Italy and I'm working with lots of many Italian municipalities to work on open data. And we all love open data. It's great. It's a great way to get transparency from the community, get transparency from almost anything. And open data are really cool because we got lots of data collections. The census says 281 are available at the moment. Lots of data sources, an incredibly growing movement. And more and more cities, regions, entities all over the world are giving out data. So we get lots of geographic data sets, lots of geologic data sets, and public transportation and so on. So, ever more interesting. Is it jumping? Somewhere? Oh. Earlier that was happening because the connection was somewhere. Yeah, yeah. Can you just say the top title in Italian? The top open data is so cool. But there's a catch. In specific, this is a photo of Central Park. And there's a catch and I want to show you what the catch is about looking at parks. Because looking at the data catalogs from three cities, New York, Chicago, and Bologna, where I come from, we see that there is something really strange going on. New York City Parks have this level of detail, organizations, status, type, and the whatsoever. Chicago, yeah. We can't even read the level of detail. Bologna. I mean, it's part of the game. I mean, everyone publishes the information he or she has. So, New York has obviously a way to look at parks in a more management kind of way, because it has information about jurisdiction, waterfront, map, if it's mapped or not, the borough, the precinct, and whatsoever. In Chicago, we can't read it, but there's a whole level of detail on the specific services available, or areas, or a lot of detail. Yeah. So, if we want to try to connect the dots and see what kind of information matches in the various views of the map, of the park concept, we have to see that, for example, the precinct or the sign name are connected to the park name in Chicago and the norma in Bologna. And the idea, the specific idea of the single row is once it's in Gis Proc NAM, here it's another code, and here code underscore UG. It's terrible. So, it's all about semantics. If we look at a data set and we don't understand the columns, we need to develop something around it to understand how we can be able to manage it. We know how we could do that. Having an application for Chicago, we would take the data sets, work on that, know the column names, and write our code around those column names. It's easy. It's elaborate, but it's easy. But let's say we want to try to take a data set from New York. We would have to add a normalization process, for example, because the address, the written address is not exactly the same format. So, we would have to really create a complete re-elaboration of the data. And it's, again, pretty easy, but quite elaborate. And doing that once means that you have to do it for every data set you want to add to your system. Or we could start looking at the whole problem at a higher level. It's all about dimensions. We have time, we have a space, and we have a topic of the data. Time, it's easy. We know, time's a line, more or less. So, it's pretty easy to manage. Space, we love space because else we wouldn't be here. What the real problem is, is the third part. It's the topic problem, because there are so many topics that are covered by open data. And there are so many data sets available all around the world that it's pretty, quite impossible to understand exactly what a specific data set covers. Again, if we talk about parks, everyone has a different view on what parks are. And that goes from parks to recycle bins to any kind of element. Probably nobody in this room would agree on what a door is. So, it's all about ontologies. We have lots of ontologies, explaining almost any kind of topic. Not every, but many. We have the DNTF for specific computer infrastructure ontologies. We have Inspire. We are Love Inspire, more or less. We have Dublin Core. We have Friend of Friend. Do we need more? We always need more ontologies, because we always need more ways to describe the world in a coherent fashion. What we see behind the whole text is the linked open data graph, the linked data graph specifically, which has lots of data providers and ontology definitions that are interconnected. And as such, the whole discussion of ontologies means basically it's like having foreign keys in our relational databases. So, if we can get the ontologies into the whole discussion on semantics, we can be basically ready to do something way more interesting with our data than we could before. But in the end, this is our discussion for developers and coders. What in the end is true is visualizing the data, and an end user doesn't like a table. It's terrible, because this is just data. This is not information. What a user wants is a map, and he's always wanted a map, because a map gives you the context for the information. And giving you the context for the information, these are all maps of Nottingham in various time periods, giving you the context for the information enables you to understand the situation, to understand where you are and what the services are around you. And it enables you to do one more thing, to elaborate on that. There are many ways to elaborate on information. There's MDX for business intelligence, there is Sparkle for the graph world, there is SQL for the relational world, and there's WFS to get the specific features. And this is great, because basically what you can get is infographics. You get the possibility to do aggregations, to do aggregations and get directly into something like a city dashboard, where you can get more information than you could ever get from just having one element in a table. And you can think about planning, and only knowing every part of the city enables you to do that. So here comes the whole Vivacity project. The Vivacity project starts taking this information, these very simple rows of CSV files, takes the concept of an ontology, this is a very simple ontology for a park, that I wanted just to show you the functionality of Vivacity. It says, yeah, it's unreadable. Okay, it says park, tree, species, because in Bologna we have specific details that the single trees are available present in every park. And then there is services, fields, ball field, basket field, then there is management of the park and phone number, because we have this information almost available here without these... The problem with these CSV files is that there is no connection between the parts. So what happens? This happens. Vivacity uses a graph database as a backend. So the information is connected in a way that enables the user to reconnect back using only the ontology as an entry point. For example, knowing this structure, we could just ask the database what the telephone number is of each of the parks and show it on the map, because we know the geometry from there we get to here, we get to the borough, the borough is this and we have the telephone number, which is connected to here. And this way we just ask a question about our ontology and we get the answer on the specific city. This generates obviously a few problems because it's not fun and games. And the raw data, the ETL is complex and we thought that having just an ETL taking only CSV files and Excel files and tables anywhere, or shape files, basically structured data with meta information is one part, but it needed something more because many cities are starting to publish APIs to get access to their information. The raw data can be taken directly from APIs with this meta information and description. The raw data collected is kept and is versioned so that we can see a given time, a given moment in time what the situation was in a very specific moment. And then there is semantics. The semantic part is basically an interpretation of each column based on the ontologies that are given. And every change in a single data set, we control the meta information about the specific data set, every change in a data set creates a new semantic model and the user has to intervene which means the mappings. It already enables just, I mean it requires the user intervention just in this case. It's not just a front end, it's not just an only forming tool. It's a way to understand and really help the understanding of the data. In the end it's a way for the city to become not just a producer of data and just someone who has the information and just gives it away. But it becomes an integrating part of the city decision making and most importantly the integration of data sets and APIs enables the city to really understand what's going on. The stack, the Vasity 1.0 had to be presented last year in Beijing. Didn't make it. I mean Beijing didn't make it. The Vasity did. Anyway, it was based on OpenLayers 2, Django and Postgres. It was just a prototype, very slow, incredibly slow, because putting a graph inside Postgres, don't do that. I mean, now the new versions are quite good, but yeah, last year wasn't that great. Now the Vasity 2, we changed to Leaflet and maybe soon to OpenLayers 3, hopefully. Again, Django as a data manager and exposes the APIs. And as I said, I spoke about MDX, Sparkle, SQL and WFS. These are supported by the backend. So Django interprets everything and manages to do the queries and transform the queries in the specific queries to the various backends. And now the two backends, MongoDB for the document approach, let's say, from the bottom up, and Neo4j for the relational part, Neo4j spatial, to get the relationships between the resources in the map. And yes, it's open source. It will be soon in November, by end of November. I wanted to show you a demo, but the server farm where it is in Germany just said, your motherboard is exploded. Okay, no problem. That's why you use server farms, right? It's somewhere else, someone has to do it. I can't make the demo, sadly. But there is version one on GitHub, and it's a prototype. It doesn't work at 100% as a prototype. Yeah, and that's it. That's it, yeah. APPLAUSE You want that? I was going to say you forgot the question. Okay. I'll handle them. Thank you. Hello. Hi. I'm Sebar Colby. I would like to see if you could go back to the slide that had all the data that looked like newspaper graphics or something. Yeah, the one that one. Okay. So that was sort of like an example. Yeah. How you can be a data integrator. And could you just talk a little bit about what those tables represent and also how these are used in decision making for citizens and urban matters? I mean, good question. This is just an example of what can be done. Basically, these are all aggregations of the information available. The more information is put into the system, the better the ontologies represent, I mean, better the semantics represents the whole system. And as such, you're able to define the specific aggregations. I don't know. Has anyone used MDX and business intelligence tools? Okay. Basically, what you can do is that is you can, you have, imagine a cube of information. Only it has not only three dimensions. You need and you think you need. As soon as you start working with lots of dimensions, you have to really understand how to get to that element you really want to look at. And MDX does just this. It's like SQL, normal database query. Only it enables you to slice this cube and take only the parts that you really want to work on. And then aggregate at the end with only a selection of elements. For example, you could say one dimension is, let's say, it's unreadable here and it's unreadable here. Great. Let's say, let's make an example, usage of buses. You know where the transport, where the bus stations are, you know where the, how the buses, the lines work. You know, if you get to the level that you know every user, when a given user uses a bus, you can be able to start aggregating on that level of detail. For example, saying how many users use that specific bus stop. And as soon as you do that, you're able to create a model for the bus stops. And this model enables you to then understand how many bus stops you really need. And this can all be done through this MDX queries. Maybe they're complex, maybe sometimes they're slow, not always that fast. But it's part of the whole idea is that usually MDX uses specific databases to work. And applying this, the MDX model to a graph database is a completely new approach. Just a very small literature on that, yeah, because it's a very new line of experiments. Actually, my question is facing a similar challenge. Probably is that we ruled out the use of ontologies because of the risk was ending up with one ontology for every dataset. Because basically, in the different sciences, so vague, there is no defined set of ontologies. So you ended up with one ontology for every dataset, or for every organization you can create a problem. Is that something? Have you experienced it? I understand it's just the version one, but how many datasets you already have been? And I think that you've experienced in this. Thank you. Thank you. The problem of the dataset is that, in fact, there is no real consensus of what is a good, the problem with ontology, sorry, is that there is no real consensus that this ontology is good enough for solving this problem, and everyone uses that ontology. And what we did was using the most used ontologies and trying to work on them, to work just with them. As soon as someone gave us data that didn't respect that ontology, there was two-sided work. On one side, we evaluated the specific dataset, if it was sensed to elaborate it and try to bring it towards that ontology. In other cases, it didn't make any sense. So it was basically an extension. And the whole thing is that the system itself contains, in part, extensions to the basic ontologies given by the classic... I mean, the standard ontologies, and there is a small extension given, created by us, just to map the additional information. And sometimes, some of the information is just mapped between the ontologies. Just to give you an idea, how many datasets do you have on the big day? We have the... at the moment, it's a good question, we have around 30... we have the datasets of Bologna installed that are around 50, 60 datasets. What are the municipalities or from other... Municipality and a few entities around the municipality. Transports. All companies, all entities starting to push open data. And we're starting to confront with other datasets, and most importantly, the CCAN and SOCRATA data collectors that have a really nice API to get directly to the metadata. And possibly, starting to import their datasets soon. Meaning, New York, Baltimore, Ann Arbor, and anywhere. That would be an interesting experiment, because then we will have really to see what kind of problems... We think we solved. Yeah, exactly. We found that with the data we were working on, we had some problems, but we were able to solve them pretty fast. Yeah. Can I just grab that? Thank you. Sorry, just before the next question, we've got about five minutes left before the next presentation, which is a start. Due to the presentation after this one, I've been told that we'll have to sort of skip to the program, because the one after this is cancelled. We're going to have to get after me, because I'm back for yours, Chris, after. So I've just given everyone a bit of a heads up about that. But we can carry on with the questions in the meantime. So sorry, who got questions? So I guess my question is, so you have Bologna, so you have a starting... You have some ontology specifically for Bologna. When you add New York, there's going to be additional data elements. Some data elements are going to have to be transformed. You may have some data for capital, and you may have to transform it to a population, something like that. So that's part of the work you do. Every time you add a new data set, there's going to be some semantic mapping that you have to do. You're going to have to perhaps extend the data model. I mean, this is structured data. We're not talking about unstructured data. So there's no magic bullet there. This is work you have to do, right? But the idea is that you're going to come up with an ontology, and over time, you're going to come up with an ontology that will be able to include Bologna, Beijing, New York, whatever. Yeah, exactly. There is one additional aspect. Thank you. Yes, exactly. There is one additional aspect. The transformation part of the data from the specific format to the ontology-like format. The idea is to have that easily created by anyone. Meaning to have a small flow editor that enables you to just do the basic operations. In fact, that part is still under heavy development because we're evaluating even the possibility to work with Google Refine and Open Refine that have a great, great tool to elaborate the transformation of information and the datasets. Having that would help us a lot because Google Refine enables you to export the transformations you make. Who knows you Google Refine? Okay. It's an amazing tool. Now it's Open Refine. It enables you to basically elaborate CSV files and Excel files and anything and clean up your data. For example, you suppose you have data collected by someone in an enormous amount of time. A given road has one name but it's spelled wrong many times. Open Refine just takes the deck column and says, hey, maybe you meant the same road. You just can clean up everything just before you put it into a more complex system to enable evaluations. We're thinking about integrating that into the system, into the platform so that it's an easy experience to clean up the data, prepare the transformation, prepare the mapping and then have everything already running. Don't you have to do the same thing with spatial data? I mean, using a tool like FME, basically, is that a rule that transforms data into something else? Yes, we do. We do. That's part of the game. We have been looking at Open Refine because there is a tool already, an extension for Open Refine, that enables you to already do at least part of that. That is one of the issues. I didn't even talk about the problem that Bologna, living in Italy, Bologna and the city beside Bologna have different projections in data sets. That's great. That's sweet. Yes? We have a set story. That's okay. We came up to when the next session is due to start. We don't have one in here. If anyone wants to go to a different talk, there's a few different ones on. Alternatively, Mark, do you want to answer more questions or do you want to have a break? No problem. You're okay. Do people want to ask a few more questions or hear their story? It's entirely up to yourselves. Okay. Go back over to you then if you're on. I'm just on water. Basically, the problem with the projections in Italy is that we had, up until last year, when the European standards had with ED50, almost every region had a specific projection. Sometimes even a modified version of classic projection then that made everything worse for people who were working on the data. There are some regions that are overlapping into other, not time zones, meridians. Basically, the government chose to get part of Italy down to Africa as measures so that everything would be on the same side. Now you mentioned OGC standards on one of the slides. For spatial representation, does that mean that you're using GML as your standard way of representing spatial data? Standard weight, yes. Standard weight, yes, because that's what more or less WFS supports. The next question is, you're talking about a whole city. So, you know, there's something called City GML which is designed, it doesn't do very well inside buildings, but outside buildings and it's been extended to include utilities and so on. Is that sort of the longer term model that you'd like to fit into? Yes, that's the, yes. We have a project working, no, we have a discussion going on with people working already on City GML and that's, yes, it's a long term, mid to long term, but yes, that's part of the plan, yes. Do you want the microphone? Well, it's part of the, I mean, it's part of the blocks, yes, scale. No, it's, but if you have the same source of data and then you want the same target, schema, you can do this mapping in hail, it's probably open source and so on, then load it, then export the mapping and do the transformation for the data set aside. So, every time you have this kind of data, you can transform that. Yeah, the idea is exactly that. I mean, you do the mapping, as I said, once the semantic is given for a given data set, that is kept and you don't even have to tell the system he has to go and get it. The idea is to have it automatically at least once every, say we have a data set that gets updated every week, once every week you get the data set into the system and it's up and working possibly, hopefully. Would this be, let me say, only a container of the data or do you think that it will be also possible to integrate some management models or some other things? At the moment, it's just a container with APIs. But the idea is to be able to develop plugins to have the APIs, I mean, already in the system to be easier to access and faster, possibly. Yeah, but the idea is, again, plan, this is not long term, but mid term, ten, even short term, but yeah, it's part of the game. Any other questions at all? I was just wondering if you could do another use case scenario, maybe using something like water. For the aggregation. Water or just another urban metric that people will look at. In this moment, we have in Italy the, I mean, it's part of the 2020 thing to have broadband in cities. So we're developing a model to see how much a city is valuable for a telecommunication company. And to do that, we need to know lots of information, basically. Amount of people in a given area, amount of infrastructures already available under the streets, amount of the kind of people, meaning the income, the meaning of a given area. These are all factors to be considered in this evaluation. And at the end, we can give a specific value to different zones. And through this system, basically, it's a project that's starting because, yeah, Europe. Things are not always as fast as we would love them to be. The whole project is just, yeah, just starting, but we were able to define two or three areas that big telecommunication companies could be interested in investing in fiber optics and installing the infrastructures. And two of these areas will be starting in a few months. So, yeah, it's a model we are using. The whole aggregation on OpenData part is something we are using, and it's useful. Sometimes it's difficult to find, to really see the connection of information, and that's in part why we wanted to do this project. Because as soon as you see how information is connected between the, how the dots are connected in the city, then the aggregation is simply deciding where to go and cut to see how the cake is made. And as soon as you see that, then the aggregations are immediate. Because you see the structure, you see the stratification of the city, the services, the infrastructures, and you can see, suddenly you can even see where a city is tendentially going to grow. Because you see the infrastructures, the transports, the quality of living in that area, the services present to kindergartens and whatsoever, and schools. And you really understand how the urban fabric is created. And as soon as you get that, the aggregations are part of the game. And the good thing is that having a standard tool, a standard language for these kind of aggregations, you basically can use a graphic tool to just play with them. And it just creates new tables. Then the problem is really understanding those tables with those numbers. And it's something really interesting because it enables you to really have a playground to work with. It's like SimCity only with real data. I just add that it allows you to compare cities in the sense that you can compare the quality of the bus system in the quality of the bus system in Milan. Having all the information connected enables you to do metrics. It's an interesting book. It's your example of your own parks. I work at Birmingham and the UK City Council. And I seem to have spent days just trying to discuss with our parks team what's a park, what's a recreation ground, what's an open space. This is just within one city municipality in effect. So in order to do that kind of international comparison, the ontology and the descriptions are going to be really, really important to allow that kind of comparison. The infrastructure I suspect is a little bit easier. I don't know because I don't work in that sector. Use of space, that's a really difficult thing to compare. Not that it shouldn't be tried down. So I don't know your parks example, as Mark took, comparing even Chicago to New York. I should imagine judging by not just the attributes that were available, which ones are minigolf. It's an interesting worldwide comparison, the best minigolf courses. That alone is getting into the fine detail. I think that's one of the factors we wanted to explore. Because it had a very practical reason that was the one I was talking about, the fact that we wanted to evaluate the value of a city for the telecommunication. But in fact it's just a metric. You did a market survey. You looked at demographics, were you doing market research or a whole composite assessment? You say, oh well, here's where we see stronger man. Here's where we see an underserved area. I mean, is that it? So you don't understand the driver's for your mouth? The answer is quite strange, so I'm just putting my hands in forward. I believe in Dr. House. He says the patient always lies. The patient lies, but data doesn't. Data is numbers, and numbers don't lie, at least, hopefully. You can manipulate them, but then we're talking about black magic. Data, they're numbers. And being able to create a model only on numbers without getting to evaluate the specifics of having people calling you, would you like to have broadband whatsoever? Has given us the opportunity to really thinking about changing that approach, because again, people lie. And on the phone, they don't even have to give the picture. Once everybody agrees on the basic data, then you can have a rational conversation about what to do. One thing you may be aware of is something called urban observatory, and one of the things that the first thing I ever saw from urban observatory, it's totally graphical from what I saw, totally graphical, was they compared parks in Paris to parks in New York or Chicago or London, and it was just incredible how much parkland there is in Paris compared to how much parkland there isn't in Chicago. And it's graphical. All they've really done is they made sure that the area cover is the same, so you're comparing apples to apples and not apples to orange. But it's exactly the kind of thing you're doing, except they're doing it totally graphically. The idea is to be able to do that. I mean, the whole platform has the scope to be able to do that numerically if you need it so, for example, having an endpoint for Excel to make MDX queries, and possibly graphically, because you could define a query beside the map, meaning in a side panel, you'd write your MDX query, run it, and the data shows up on the map, so that maybe not like that, because that requires normalization of areas, and maybe that is probably easier to do numerically, but the idea is exactly this, to be able to see what happens. I mean, this slide deck is slightly different from the one I had before, because the one I had before for version one was deeply SimCity-based, because what's open data if not the engine of SimCity? And we've been, I've been working with the municipality where I live in, it's a very small town, 15,000 people. So, and we've been, I mean, the council, the city council, really was amazed when they saw me playing at a, waiting for a meeting, playing SimCity on my laptop. They said, what's that? It's really cool, and we started thinking, that's the moment when I started thinking of, hey, SimCity, why didn't you ever play it? I mean, you're managing a city, if you can't manage a simulated city, how can you manage a real city? I mean, there are more problems there, for sure, there is more complexity, but the model below is basically the same, and the new version of SimCity is even more like that, is more a network, specifically a network of networks, and that's what cities are, and that's why, yeah, the whole thing started. I've said one thing, and once you've got the data, using a SimCity game-playing model is a really good approach, because it's something that a politician, non-technical people can do, you don't have to be an engineer to do that kind of thing. That's one thing we did with some of the members of the city council, and they played, and it was great, they had fun, and they saw that they couldn't always manage the simulated city, but it was really fun, it was really great. It's interesting, actually, from the SimCity point of view, the talk that we're supposed to be on now by E-Mart and Rob Hawkes, I don't know if you've come across the visiting cities, it's called Visit Cities, V-I-Z-I, cities all one word, they were both, well, certainly I know Rob Hawkes was a former developer at Mozilla, and they're taking the likes of OpenStreetMap data purely for London, I think, at the moment, and OSOpen data, they've made 3D visualisations that work in the browser, I think they were inspired by SimCity from what I understand, as well as making it effectively creating this SimCity game with London as the SimCity in fact. So, it's well worth going away and checking what they're doing now, I think it's not really beta, is it? Yeah, it's still in beta. Following some of their Twitter accounts at the moment, Rob Hawkes has put some images on there, just trying to deal with the Zed-hide data, like in London you've got some roundabouts that are underground and stuff like that, so they're trying to deal with underground roundabouts combined with flyovers, combined with various parts of the infrastructure, let alone the Thames running around. And on the website there is an amazing video of the underground map, and really great. It's amazing because it's a 3D visualisation of the whole underground network, and wow. With real-time data on where the underground is in a given moment, it's really great. Sorry, I'm just taking over, my mind's racing. The thing that I find interesting from the City Council point of view is, in Birmingham we broadcast on the internet the planning committee, so you've got these people, untrained, elected members who are looking at 2D plans on perhaps either on a Google map or on our own internet maps, and asking the kind of questions that the 3D visualisation and the now playing of the city would answer a lot of times, how does the life from the Zed affect things, stuff that you would normally model, but they actually just want something they can almost instantly just turn and play with, and then they can make a more informed choice about the actual planning decisions, and particularly start throwing demographic data on top of the actual physical infrastructure and the physical effect of something going on. I was hoping to meet the guys from Visit Cities, but yeah. The guys from Visit Cities are great, they're super star web developers. Amazing. Do you have any more comments, questions, or should we give Mark a break, because we've got 10 minutes before the next session, to start or we can have a... Well, I just want to say that one of the biggest problems with urban planning is, and let's just say, like you say, land use and planning committee decision making, is it's a long elaborate process because you have to integrate all this information, like the infrastructure and then the economics and the architecture and okay, and then you take every public comment that you're supposed to get, and then you respond to every public comment you get, and you know, after... It feels like home, yeah. Seven months, you know, maybe you're... Yeah, I know. You get your permit to build, let's say you were going to build a big, urban, nice new thing, it's maybe 10% affordable, right? It just takes so long. And so what you're saying is this will provide a more integrated, immediate, integrated data, and networks of data, you know, showing all the ways that something is integrated into space, and help people make better decisions. And it's a better way to argue pros and cons of a certain decision. Yeah, it's part of being informed. I mean, being informed is not just having that piece of paper, it's knowing all that's around, the decision you have to make, and all the implications. Call it like that, give me a rest, what a nice... Thank you.
 Thank you.  Show it a few seconds.  A minute.  Okay, let's go.  Okay.  Hi everyone.  I'm Marco.  Hi, Marco.  Hi, I'm Marco.  And, well, I'm going to talk about a different way to talk about smart cities because many,  many cities are publishing lots of data.  I'm part of Open Knowledge Foundation Italy and I'm working with lots of many Italian municipalities  to work on open data.  And we all love open data.  It's great.  It's a great way to get transparency from the community, get transparency from almost anything.  And open data are really cool because we got lots of data collections.  The census says 281 are available at the moment.  Lots of data sources, an incredibly growing movement.  And more and more cities, regions, entities all over the world are giving out data.  So we get lots of geographic data sets, lots of geologic data sets, and public transportation  and so on.  So, ever more interesting.  Is it jumping?  Somewhere?  Oh.  Earlier that was happening because the connection was somewhere.  Yeah, yeah.  Can you just say the top title in Italian?  The top open data is so cool.  But there's a catch.  In specific, this is a photo of Central Park.  And there's a catch and I want to show you what the catch is about looking at parks.  Because looking at the data catalogs from three cities, New York, Chicago, and Bologna,  where I come from, we see that there is something really strange going on.  New York City Parks have this level of detail, organizations, status, type, and the whatsoever.  Chicago, yeah.  We can't even read the level of detail.  Bologna.  I mean, it's part of the game.  I mean, everyone publishes the information he or she has.  So, New York has obviously a way to look at parks in a more management kind of way, because  it has information about jurisdiction, waterfront, map, if it's mapped or not, the borough, the  precinct, and whatsoever.  In Chicago, we can't read it, but there's a whole level of detail on the specific services  available, or areas, or a lot of detail.  Yeah.  So, if we want to try to connect the dots and see what kind of information matches in  the various views of the map, of the park concept, we have to see that, for example,  the precinct or the sign name are connected to the park name in Chicago and the norma  in Bologna.  And the idea, the specific idea of the single row is once it's in Gis Proc NAM, here it's  another code, and here code underscore UG.  It's terrible.  So, it's all about semantics.  If we look at a data set and we don't understand the columns, we need to develop something  around it to understand how we can be able to manage it.  We know how we could do that.  Having an application for Chicago, we would take the data sets, work on that, know the  column names, and write our code around those column names.  It's easy.  It's elaborate, but it's easy.  But let's say we want to try to take a data set from New York.  We would have to add a normalization process, for example, because the address, the written  address is not exactly the same format.  So, we would have to really create a complete re-elaboration of the data.  And it's, again, pretty easy, but quite elaborate.  And doing that once means that you have to do it for every data set you want to add to  your system.  Or we could start looking at the whole problem at a higher level.  It's all about dimensions.  We have time, we have a space, and we have a topic of the data.  Time, it's easy.  We know, time's a line, more or less.  So, it's pretty easy to manage.  Space, we love space because else we wouldn't be here.  What the real problem is, is the third part.  It's the topic problem, because there are so many topics that are covered by open data.  And there are so many data sets available all around the world that it's pretty, quite  impossible to understand exactly what a specific data set covers.  Again, if we talk about parks, everyone has a different view on what parks are.  And that goes from parks to recycle bins to any kind of element.  Probably nobody in this room would agree on what a door is.  So, it's all about ontologies.  We have lots of ontologies, explaining almost any kind of topic.  Not every, but many.  We have the DNTF for specific computer infrastructure ontologies.  We have Inspire.  We are Love Inspire, more or less.  We have Dublin Core.  We have Friend of Friend.  Do we need more?  We always need more ontologies, because we always need more ways to describe the world in a coherent fashion.  What we see behind the whole text is the linked open data graph, the linked data graph specifically,  which has lots of data providers and ontology definitions that are interconnected.  And as such, the whole discussion of ontologies means basically it's like having foreign keys in our relational databases.  So, if we can get the ontologies into the whole discussion on semantics,  we can be basically ready to do something way more interesting with our data than we could before.  But in the end, this is our discussion for developers and coders.  What in the end is true is visualizing the data, and an end user doesn't like a table.  It's terrible, because this is just data.  This is not information.  What a user wants is a map, and he's always wanted a map,  because a map gives you the context for the information.  And giving you the context for the information, these are all maps of Nottingham in various time periods,  giving you the context for the information enables you to understand the situation,  to understand where you are and what the services are around you.  And it enables you to do one more thing, to elaborate on that.  There are many ways to elaborate on information.  There's MDX for business intelligence, there is Sparkle for the graph world,  there is SQL for the relational world, and there's WFS to get the specific features.  And this is great, because basically what you can get is infographics.  You get the possibility to do aggregations, to do aggregations and get directly into something like a city dashboard,  where you can get more information than you could ever get from just having one element in a table.  And you can think about planning, and only knowing every part of the city enables you to do that.  So here comes the whole Vivacity project.  The Vivacity project starts taking this information, these very simple rows of CSV files,  takes the concept of an ontology, this is a very simple ontology for a park,  that I wanted just to show you the functionality of Vivacity.  It says, yeah, it's unreadable.  Okay, it says park, tree, species, because in Bologna we have specific details  that the single trees are available present in every park.  And then there is services, fields, ball field, basket field,  then there is management of the park and phone number,  because we have this information almost available here without these...  The problem with these CSV files is that there is no connection between the parts.  So what happens? This happens.  Vivacity uses a graph database as a backend.  So the information is connected in a way that enables the user to reconnect back  using only the ontology as an entry point.  For example, knowing this structure, we could just ask the database  what the telephone number is of each of the parks and show it on the map,  because we know the geometry from there we get to here, we get to the borough,  the borough is this and we have the telephone number, which is connected to here.  And this way we just ask a question about our ontology and we get the answer on the specific city.  This generates obviously a few problems because it's not fun and games.  And the raw data, the ETL is complex and we thought that having just an ETL  taking only CSV files and Excel files and tables anywhere,  or shape files, basically structured data with meta information is one part,  but it needed something more because many cities are starting to publish APIs  to get access to their information.  The raw data can be taken directly from APIs with this meta information and description.  The raw data collected is kept and is versioned so that we can see a given time,  a given moment in time what the situation was in a very specific moment.  And then there is semantics.  The semantic part is basically an interpretation of each column based on the ontologies that are given.  And every change in a single data set, we control the meta information about the specific data set,  every change in a data set creates a new semantic model and the user has to intervene  which means the mappings.  It already enables just, I mean it requires the user intervention just in this case.  It's not just a front end, it's not just an only forming tool.  It's a way to understand and really help the understanding of the data.  In the end it's a way for the city to become not just a producer of data  and just someone who has the information and just gives it away.  But it becomes an integrating part of the city decision making  and most importantly the integration of data sets and APIs  enables the city to really understand what's going on.  The stack, the Vasity 1.0 had to be presented last year in Beijing.  Didn't make it. I mean Beijing didn't make it.  The Vasity did.  Anyway, it was based on OpenLayers 2, Django and Postgres.  It was just a prototype, very slow, incredibly slow,  because putting a graph inside Postgres, don't do that.  I mean, now the new versions are quite good, but yeah, last year wasn't that great.  Now the Vasity 2, we changed to Leaflet and maybe soon to OpenLayers 3, hopefully.  Again, Django as a data manager and exposes the APIs.  And as I said, I spoke about MDX, Sparkle, SQL and WFS.  These are supported by the backend.  So Django interprets everything and manages to do the queries  and transform the queries in the specific queries to the various backends.  And now the two backends, MongoDB for the document approach, let's say, from the bottom up,  and Neo4j for the relational part, Neo4j spatial, to get the relationships between the resources in the map.  And yes, it's open source.  It will be soon in November, by end of November.  I wanted to show you a demo, but the server farm where it is in Germany just said,  your motherboard is exploded.  Okay, no problem.  That's why you use server farms, right?  It's somewhere else, someone has to do it.  I can't make the demo, sadly.  But there is version one on GitHub, and it's a prototype.  It doesn't work at 100% as a prototype.  Yeah, and that's it.  That's it, yeah.  APPLAUSE  You want that?  I was going to say you forgot the question.  Okay.  I'll handle them.  Thank you.  Hello.  Hi.  I'm Sebar Colby.  I would like to see if you could go back to the slide that had all the data that looked  like newspaper graphics or something.  Yeah, the one that one.  Okay.  So that was sort of like an example.  Yeah.  How you can be a data integrator.  And could you just talk a little bit about what those tables represent and also how these  are used in decision making for citizens and urban matters?  I mean, good question.  This is just an example of what can be done.  Basically, these are all aggregations of the information available.  The more information is put into the system, the better the ontologies represent, I mean,  better the semantics represents the whole system.  And as such, you're able to define the specific aggregations.  I don't know.  Has anyone used MDX and business intelligence tools?  Okay.  Basically, what you can do is that is you can, you have, imagine a cube of information.  Only it has not only three dimensions.  You need and you think you need.  As soon as you start working with lots of dimensions, you have to really understand how to get to  that element you really want to look at.  And MDX does just this.  It's like SQL, normal database query.  Only it enables you to slice this cube and take only the parts that you really want to work on.  And then aggregate at the end with only a selection of elements.  For example, you could say one dimension is, let's say, it's unreadable here and it's unreadable here.  Great.  Let's say, let's make an example, usage of buses.  You know where the transport, where the bus stations are, you know where the, how the buses,  the lines work.  You know, if you get to the level that you know every user, when a given user uses a bus,  you can be able to start aggregating on that level of detail.  For example, saying how many users use that specific bus stop.  And as soon as you do that, you're able to create a model for the bus stops.  And this model enables you to then understand how many bus stops you really need.  And this can all be done through this MDX queries.  Maybe they're complex, maybe sometimes they're slow, not always that fast.  But it's part of the whole idea is that usually MDX uses specific databases to work.  And applying this, the MDX model to a graph database is a completely new approach.  Just a very small literature on that, yeah, because it's a very new line of experiments.  Actually, my question is facing a similar challenge.  Probably is that we ruled out the use of ontologies because of the risk was ending up with one ontology for every dataset.  Because basically, in the different sciences, so vague, there is no defined set of ontologies.  So you ended up with one ontology for every dataset, or for every organization you can create a problem.  Is that something?  Have you experienced it?  I understand it's just the version one, but how many datasets you already have been?  And I think that you've experienced in this.  Thank you.  Thank you.  The problem of the dataset is that, in fact, there is no real consensus of what is a good, the problem with ontology, sorry,  is that there is no real consensus that this ontology is good enough for solving this problem, and everyone uses that ontology.  And what we did was using the most used ontologies and trying to work on them, to work just with them.  As soon as someone gave us data that didn't respect that ontology, there was two-sided work.  On one side, we evaluated the specific dataset, if it was sensed to elaborate it and try to bring it towards that ontology.  In other cases, it didn't make any sense.  So it was basically an extension.  And the whole thing is that the system itself contains, in part, extensions to the basic ontologies given by the classic...  I mean, the standard ontologies, and there is a small extension given, created by us, just to map the additional information.  And sometimes, some of the information is just mapped between the ontologies.  Just to give you an idea, how many datasets do you have on the big day?  We have the... at the moment, it's a good question, we have around 30... we have the datasets of Bologna installed that are around 50, 60 datasets.  What are the municipalities or from other...  Municipality and a few entities around the municipality.  Transports.  All companies, all entities starting to push open data.  And we're starting to confront with other datasets, and most importantly, the CCAN and SOCRATA data collectors that have a really nice API to get directly to the metadata.  And possibly, starting to import their datasets soon.  Meaning, New York, Baltimore, Ann Arbor, and anywhere.  That would be an interesting experiment, because then we will have really to see what kind of problems...  We think we solved.  Yeah, exactly.  We found that with the data we were working on, we had some problems, but we were able to solve them pretty fast.  Yeah.  Can I just grab that?  Thank you.  Sorry, just before the next question, we've got about five minutes left before the next presentation, which is a start.  Due to the presentation after this one, I've been told that we'll have to sort of skip to the program, because the one after this is cancelled.  We're going to have to get after me, because I'm back for yours, Chris, after.  So I've just given everyone a bit of a heads up about that.  But we can carry on with the questions in the meantime.  So sorry, who got questions?  So I guess my question is, so you have Bologna, so you have a starting...  You have some ontology specifically for Bologna.  When you add New York, there's going to be additional data elements.  Some data elements are going to have to be transformed.  You may have some data for capital, and you may have to transform it to a population, something like that.  So that's part of the work you do.  Every time you add a new data set, there's going to be some semantic mapping that you have to do.  You're going to have to perhaps extend the data model.  I mean, this is structured data. We're not talking about unstructured data.  So there's no magic bullet there.  This is work you have to do, right?  But the idea is that you're going to come up with an ontology, and over time, you're going to come up with an ontology  that will be able to include Bologna, Beijing, New York, whatever.  Yeah, exactly.  There is one additional aspect.  Thank you.  Yes, exactly.  There is one additional aspect.  The transformation part of the data from the specific format to the ontology-like format.  The idea is to have that easily created by anyone.  Meaning to have a small flow editor that enables you to just do the basic operations.  In fact, that part is still under heavy development because we're evaluating even the possibility to work with Google Refine  and Open Refine that have a great, great tool to elaborate the transformation of information and the datasets.  Having that would help us a lot because Google Refine enables you to export the transformations you make.  Who knows you Google Refine?  Okay. It's an amazing tool.  Now it's Open Refine.  It enables you to basically elaborate CSV files and Excel files and anything and clean up your data.  For example, you suppose you have data collected by someone in an enormous amount of time.  A given road has one name but it's spelled wrong many times.  Open Refine just takes the deck column and says, hey, maybe you meant the same road.  You just can clean up everything just before you put it into a more complex system to enable evaluations.  We're thinking about integrating that into the system, into the platform so that it's an easy experience to clean up the data,  prepare the transformation, prepare the mapping and then have everything already running.  Don't you have to do the same thing with spatial data?  I mean, using a tool like FME, basically, is that a rule that transforms data into something else?  Yes, we do. We do. That's part of the game.  We have been looking at Open Refine because there is a tool already, an extension for Open Refine,  that enables you to already do at least part of that.  That is one of the issues.  I didn't even talk about the problem that Bologna, living in Italy, Bologna and the city beside Bologna have different projections in data sets.  That's great.  That's sweet.  Yes?  We have a set story.  That's okay.  We came up to when the next session is due to start.  We don't have one in here. If anyone wants to go to a different talk, there's a few different ones on.  Alternatively, Mark, do you want to answer more questions or do you want to have a break?  No problem.  You're okay. Do people want to ask a few more questions or hear their story?  It's entirely up to yourselves.  Okay.  Go back over to you then if you're on.  I'm just on water.  Basically, the problem with the projections in Italy is that we had, up until last year, when the European standards had with ED50,  almost every region had a specific projection.  Sometimes even a modified version of classic projection then that made everything worse for people who were working on the data.  There are some regions that are overlapping into other, not time zones, meridians.  Basically, the government chose to get part of Italy down to Africa as measures so that everything would be on the same side.  Now you mentioned OGC standards on one of the slides.  For spatial representation, does that mean that you're using GML as your standard way of representing spatial data?  Standard weight, yes. Standard weight, yes, because that's what more or less WFS supports.  The next question is, you're talking about a whole city.  So, you know, there's something called City GML which is designed, it doesn't do very well inside buildings,  but outside buildings and it's been extended to include utilities and so on.  Is that sort of the longer term model that you'd like to fit into?  Yes, that's the, yes. We have a project working, no, we have a discussion going on with people working already on City GML and that's,  yes, it's a long term, mid to long term, but yes, that's part of the plan, yes.  Do you want the microphone?  Well, it's part of the, I mean, it's part of the blocks, yes, scale.  No, it's, but if you have the same source of data and then you want the same target, schema, you can do this mapping in hail, it's probably open source and so on,  then load it, then export the mapping and do the transformation for the data set aside.  So, every time you have this kind of data, you can transform that.  Yeah, the idea is exactly that. I mean, you do the mapping, as I said, once the semantic is given for a given data set, that is kept and you don't even have to tell the system he has to go and get it.  The idea is to have it automatically at least once every, say we have a data set that gets updated every week, once every week you get the data set into the system  and it's up and working possibly, hopefully.  Would this be, let me say, only a container of the data or do you think that it will be also possible to integrate some management models or some other things?  At the moment, it's just a container with APIs.  But the idea is to be able to develop plugins to have the APIs, I mean, already in the system to be easier to access and faster, possibly.  Yeah, but the idea is, again, plan, this is not long term, but mid term, ten, even short term, but yeah, it's part of the game.  Any other questions at all?  I was just wondering if you could do another use case scenario, maybe using something like water.  For the aggregation.  Water or just another urban metric that people will look at.  In this moment, we have in Italy the, I mean, it's part of the 2020 thing to have broadband in cities.  So we're developing a model to see how much a city is valuable for a telecommunication company.  And to do that, we need to know lots of information, basically.  Amount of people in a given area, amount of infrastructures already available under the streets, amount of the kind of people, meaning the income, the meaning of a given area.  These are all factors to be considered in this evaluation.  And at the end, we can give a specific value to different zones.  And through this system, basically, it's a project that's starting because, yeah, Europe.  Things are not always as fast as we would love them to be.  The whole project is just, yeah, just starting, but we were able to define two or three areas that big telecommunication companies could be interested in investing in fiber optics and installing the infrastructures.  And two of these areas will be starting in a few months.  So, yeah, it's a model we are using.  The whole aggregation on OpenData part is something we are using, and it's useful.  Sometimes it's difficult to find, to really see the connection of information, and that's in part why we wanted to do this project.  Because as soon as you see how information is connected between the, how the dots are connected in the city, then the aggregation is simply deciding where to go and cut to see how the cake is made.  And as soon as you see that, then the aggregations are immediate.  Because you see the structure, you see the stratification of the city, the services, the infrastructures, and you can see, suddenly you can even see where a city is tendentially going to grow.  Because you see the infrastructures, the transports, the quality of living in that area, the services present to kindergartens and whatsoever, and schools.  And you really understand how the urban fabric is created.  And as soon as you get that, the aggregations are part of the game.  And the good thing is that having a standard tool, a standard language for these kind of aggregations, you basically can use a graphic tool to just play with them.  And it just creates new tables.  Then the problem is really understanding those tables with those numbers.  And it's something really interesting because it enables you to really have a playground to work with.  It's like SimCity only with real data.  I just add that it allows you to compare cities in the sense that you can compare the quality of the bus system in the quality of the bus system in Milan.  Having all the information connected enables you to do metrics.  It's an interesting book. It's your example of your own parks.  I work at Birmingham and the UK City Council.  And I seem to have spent days just trying to discuss with our parks team what's a park, what's a recreation ground, what's an open space.  This is just within one city municipality in effect.  So in order to do that kind of international comparison, the ontology and the descriptions are going to be really, really important to allow that kind of comparison.  The infrastructure I suspect is a little bit easier.  I don't know because I don't work in that sector.  Use of space, that's a really difficult thing to compare.  Not that it shouldn't be tried down.  So I don't know your parks example, as Mark took, comparing even Chicago to New York.  I should imagine judging by not just the attributes that were available, which ones are minigolf.  It's an interesting worldwide comparison, the best minigolf courses.  That alone is getting into the fine detail.  I think that's one of the factors we wanted to explore.  Because it had a very practical reason that was the one I was talking about, the fact that we wanted to evaluate the value of a city for the telecommunication.  But in fact it's just a metric.  You did a market survey.  You looked at demographics, were you doing market research or a whole composite assessment?  You say, oh well, here's where we see stronger man.  Here's where we see an underserved area.  I mean, is that it?  So you don't understand the driver's for your mouth?  The answer is quite strange, so I'm just putting my hands in forward.  I believe in Dr. House.  He says the patient always lies.  The patient lies, but data doesn't.  Data is numbers, and numbers don't lie, at least, hopefully.  You can manipulate them, but then we're talking about black magic.  Data, they're numbers.  And being able to create a model only on numbers without getting to evaluate the specifics of having people calling you, would you like to have broadband whatsoever?  Has given us the opportunity to really thinking about changing that approach, because again, people lie.  And on the phone, they don't even have to give the picture.  Once everybody agrees on the basic data, then you can have a rational conversation about what to do.  One thing you may be aware of is something called urban observatory, and one of the things that the first thing I ever saw from urban observatory,  it's totally graphical from what I saw, totally graphical, was they compared parks in Paris to parks in New York or Chicago or London,  and it was just incredible how much parkland there is in Paris compared to how much parkland there isn't in Chicago.  And it's graphical. All they've really done is they made sure that the area cover is the same, so you're comparing apples to apples and not apples to orange.  But it's exactly the kind of thing you're doing, except they're doing it totally graphically.  The idea is to be able to do that.  I mean, the whole platform has the scope to be able to do that numerically if you need it so, for example, having an endpoint for Excel to make MDX queries,  and possibly graphically, because you could define a query beside the map, meaning in a side panel, you'd write your MDX query, run it,  and the data shows up on the map, so that maybe not like that, because that requires normalization of areas,  and maybe that is probably easier to do numerically, but the idea is exactly this, to be able to see what happens.  I mean, this slide deck is slightly different from the one I had before, because the one I had before for version one was deeply SimCity-based,  because what's open data if not the engine of SimCity?  And we've been, I've been working with the municipality where I live in, it's a very small town, 15,000 people.  So, and we've been, I mean, the council, the city council, really was amazed when they saw me playing at a, waiting for a meeting, playing SimCity on my laptop.  They said, what's that?  It's really cool, and we started thinking, that's the moment when I started thinking of, hey, SimCity, why didn't you ever play it?  I mean, you're managing a city, if you can't manage a simulated city, how can you manage a real city?  I mean, there are more problems there, for sure, there is more complexity, but the model below is basically the same,  and the new version of SimCity is even more like that, is more a network, specifically a network of networks,  and that's what cities are, and that's why, yeah, the whole thing started.  I've said one thing, and once you've got the data, using a SimCity game-playing model is a really good approach, because it's something that a politician, non-technical people can do, you don't have to be an engineer to do that kind of thing.  That's one thing we did with some of the members of the city council, and they played, and it was great, they had fun, and they saw that they couldn't always manage the simulated city,  but it was really fun, it was really great.  It's interesting, actually, from the SimCity point of view, the talk that we're supposed to be on now by E-Mart and Rob Hawkes, I don't know if you've come across the visiting cities,  it's called Visit Cities, V-I-Z-I, cities all one word, they were both, well, certainly I know Rob Hawkes was a former developer at Mozilla,  and they're taking the likes of OpenStreetMap data purely for London, I think, at the moment, and OSOpen data, they've made 3D visualisations that work in the browser,  I think they were inspired by SimCity from what I understand, as well as making it effectively creating this SimCity game with London as the SimCity in fact.  So, it's well worth going away and checking what they're doing now, I think it's not really beta, is it?  Yeah, it's still in beta.  Following some of their Twitter accounts at the moment, Rob Hawkes has put some images on there, just trying to deal with the Zed-hide data,  like in London you've got some roundabouts that are underground and stuff like that,  so they're trying to deal with underground roundabouts combined with flyovers, combined with various parts of the infrastructure, let alone the Thames running around.  And on the website there is an amazing video of the underground map, and really great.  It's amazing because it's a 3D visualisation of the whole underground network, and wow.  With real-time data on where the underground is in a given moment, it's really great.  Sorry, I'm just taking over, my mind's racing. The thing that I find interesting from the City Council point of view is,  in Birmingham we broadcast on the internet the planning committee, so you've got these people, untrained,  elected members who are looking at 2D plans on perhaps either on a Google map or on our own internet maps,  and asking the kind of questions that the 3D visualisation and the now playing of the city would answer a lot of times,  how does the life from the Zed affect things, stuff that you would normally model,  but they actually just want something they can almost instantly just turn and play with,  and then they can make a more informed choice about the actual planning decisions, and particularly start throwing demographic data  on top of the actual physical infrastructure and the physical effect of something going on.  I was hoping to meet the guys from Visit Cities, but yeah.  The guys from Visit Cities are great, they're super star web developers.  Amazing.  Do you have any more comments, questions, or should we give Mark a break, because we've got 10 minutes before the next session,  to start or we can have a...  Well, I just want to say that one of the biggest problems with urban planning is,  and let's just say, like you say, land use and planning committee decision making,  is it's a long elaborate process because you have to integrate all this information,  like the infrastructure and then the economics and the architecture and okay,  and then you take every public comment that you're supposed to get, and then you respond to every public comment you get,  and you know, after...  It feels like home, yeah.  Seven months, you know, maybe you're...  Yeah, I know.  You get your permit to build, let's say you were going to build a big, urban, nice new thing,  it's maybe 10% affordable, right?  It just takes so long.  And so what you're saying is this will provide a more integrated, immediate, integrated data,  and networks of data, you know, showing all the ways that something is integrated into space,  and help people make better decisions.  And it's a better way to argue pros and cons of a certain decision.  Yeah, it's part of being informed.  I mean, being informed is not just having that piece of paper,  it's knowing all that's around, the decision you have to make,  and all the implications.  Call it like that, give me a rest, what a nice...  Thank you.
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