 Okay, so good morning everybody. My name is Carolina Arias Muñoz, I'm a PhD student from Politecnico di Milano in Italy and I'm here to present, sorry about the long title, but it's a web application basically to know, to find the best place to live within a city. The team that came up with this idea, sorry, it's not working, it's blocked, sorry, okay. So the team that came up with this idea is the one that you see here. We are three environmental and geomatics engineers and two developers and we belong to the Geomatics and Air Observation Lab at Politecnico di Milano and we basically are interested in Web GIS, volunteer geographic information systems and the big data. So what is city focus? So as I said before, it's a web-based, interactive 2D and 3D application, not only to find the best place in a city to live but also to pass a certain stain. So normally when you want to move to another city that you don't know, you go to Google Maps or hopefully Open Street Map and you check the streets and you check more or less the services that you have available because you don't know the place and then you go to another website to check the prices of the houses, the apartment and so on. So you have to do a lot of manual work, let's say. So we wanted to create an application in order to make all these in just one place and also the user can decide their different criteria and their importance and at the end to have a map that can show them the different places within a city. So how is city focus different from other apps? So actually the question that we had before was is there an app that does these things and actually we couldn't find any app that show you places within a city. I mean we find, I put here just the main ones but in this application you only, they give you a city in the world. For example you put your income, you put your preferences in temperature or services and they can tell you which city in the world you can go to live but not within the city. So this is one important question and also we try to make it as user friendly as possible, we hope it is, avoiding long and handmade search on the web. And also normally when you do this type of search you can normally not consider the environmental conditions like air pollution, temperature and so on. So we also wanted to include this into the criteria and most importantly we only use open data and open specifications. So this application was a winner for the MyGeos third call for innovative apps. I don't know if you're familiar with MyGeos but MyGeos was a project or the European Commission that finished last December and this project, the objective of this project is to create innovative apps in environmental and social domains. So they wanted to show the people that you can do stuff with Geo's data core open data and with open data in general. So basically the idea was to develop innovative applications that could be mobile or web based using only openly or freely available data or also cloud generated data in different domains but especially addressing citizen needs. So if you go to this link over here you will find all the apps that were winners in this calls and you can also download this application and modify them because they are published as free content and you can also use it for commercial uses. So as my, our application you can download them, download it and modify the application. Well in terms of data we use data specifically for the city of Milan in this case as a demo because we normally work with Milan and data from Milan but actually this can be used in whatever city you want because mainly the data that we use was from open street map. So it will depend on the availability of open street map data in a specific city. But we also use data from Lombardia region, the municipality of the city of Milan, the national statistical service and of course the geos data core. So the criteria that we consider just for this demo because you can add all the criteria that you want but we use especially environmental conditions such as air quality and the different temperatures within the city. We also use the population density because maybe some people want to live in the suburbs instead of the center of the city for example and the rest of the criteria are referred to the nearness. So nearness to transportation for example bus stops and train and metro station or the nearness to services such as ATMs, pharmacies, coffee shops, hospitals and so on. The nearness to nature so if people want to be near to a park or a water stream we also consider the land use type so if you want to live inside an industrial of our commercial type of land use and stuff like that or nearness to education so schools, universities and so on. Okay so the application principle is actually really simple in a sense that it's a map algebra so you have different criteria that are the different we call it score maps that goes from 1 to 100 or to 0 to 100 or to 0 to 1 and basically you assign weights to the different score maps then you sum and you obtain a final map so it's really simple. But before we in order to obtain these score maps we needed to make a data cleaning and data processing before just once but we needed to do it and then these score maps went into the application. So we did first of all data cleaning and then the score maps creation we did using python script and grassy IS. So first of all we did the data cleaning because we had data from different sources that was the same data for example so we tried to clean the data with buffers because we had data from open street map that normally was the majority of the data but we also have the same data from the municipality that probably is not exactly in the same position so we tried to choose one of them and we did it by buffers and for the score maps creation it depends on the type of feature that we want to create. In this case we have point layers or polygon layers or raster layers so in the case of point layers that represents like services like hospitals, banks, post office and so on we thought of let's say a walking distance so in average we know that people don't want to walk more than 15 minutes to access a service so approximately 15 minutes walking is 1200 meters so we used that distance to create a spatial concentration maps using aquatic kernel density function and then we normalize in order to have the score maps between 0 and 1. For the case of the polygon layers we have more or less the same criteria in the sense that in this case you don't have a position but you have the whole area so we rasterize the data then we create multiple distance buffers taking into account again this 1200 meter distance we divided in different categories from 0 to 100, 1200 and then we reclassified it and we created the score maps. For the raster layer was a little bit different because in this case we already have raster data we just needed to normalize it in order to have it from 0 to 1. In the case of air pollution we wanted the highest scores be on the less polluted areas of course but for the case of temperature population land use layers we tried to create three different layer categories high, medium, low or industrial continuous, discontinuous and then we create the score maps by means of reclassification. So the architecture of the application is based mainly on two components the first one is Rastaman Raster Data Management and NASA What will win. I won't enter it too much detail because I know in the afternoon professor Baumann is going to explain a lot better Rastaman but basically it's an array dbms that adds capabilities to storage and retrieval of array data or raster data and NASA will win as you know probably is a 3D beautiful world is open source and customizable and you can add it to any web application So as I said before CTFocus relies on a standard installation of Rastaman this standard installation has normally sqlite database backend for us it was okay because we only have like 20 maps so it was fine but actually you can connect it to other type of databases like Postgre and so on and basically data are accessed over the web by a pethoscope component of Rastaman and so this component is the one that translated the web coverage processing service queries from the standard of GC and it translated into the sql language and this is what allows us to create the map algebra I'm going to show you how it's done and for the client side combining jQuery and WebBorwin it is possible to retrieve maps from Rastaman to the WPCS and show them to the end user So apart from the raster data we also wanted to show the vector data so the locations of the different services because we were working with Rastaman we didn't want to access to this vector data through a database so we just added to the application using a JSON so it is only in the client side of the application and the final map as well as the different score maps were painted by coloring a grid so a vector version of the raster grid using the values of the retrieved CSV files from the WPCS request we know that there is a way to color data using the WPCS but we find it really not aesthetical so we prefer this way which was much better so the WPCS requests are done in these ways they are very simple you have to call the different layers and then you make the map algebra and you request your data in this case we use CSV but you can request it using T for JPEG so this is the idea of the application I'm going to show you anyway a demo, a video of a demo so first of all you select your criteria and assigns weights then you simply click on the bottom to find your place and then the map will appear you can use the map controls to see the map in 2D and also 3D in the case of Milan because Milan is so plain you don't see a lot of 3D but in other types of cities you could see also the topography you can check each criterion maps not only the resulting map but you can also check the different criteria you can navigate to the location of interest or the vector layers and then you can do it again so this is mainly how it works so I'm going to show you if I can sorry about that but it's completely lost I'll try it again okay so these are the different criteria the user just put the weights and for each criteria you have the caption that explains how you assign the weights to each of the criteria in this way then you click and you get your map you can sum in and check the vector layers of the different services that you choose then you can check also the individual score maps and then you can also see it in 3D but of course depending on the topography and then you can do it again so this is basically so for future developments we are thinking to the possibility for users to get a glimpse of the changing environment by giving trends of the different criteria like the temperature or other type of environmental criteria we have the final map but we don't put the name for example of the neighborhoods so we want to put also that maybe take more advantage of the 3D functionalities that elevate the sales according to the sales values for example we don't have user administration functionalities yet but the idea is to collect people's choices so in the future we can have a lot of data for research or maybe for urban management or marketing analytics we want to add information about house rent prices in the city of Milan we are trying to make contact with some real estate companies to add this information and as a first case study we consider the city of Milan but of course in the future we want to add more different cities so you can check the application on this link and you can find also the source code and the documentation in GitHub and you can download it use it as you want so thank you for your attention so there is any question? Any support for custom filtering on the layers for example everyone knows that there are schools and there are schools and if I only want to be close to the best part of it how feasible is it to add the customer? It is feasible sorry the question was if there is like filtering capabilities to the application we don't have it yet but for sure it is really feasible yes? How do you do the data like the markets they open and they close do you do a complete re-import of your data every time or do you just do the data? So the question is if we updated our data for the application and actually not yet I mean this was a demo for the Mygeos call but the idea of course is to update it to have like practically real-time update of the data especially if they are from OpenStreetMap but we don't have it yet and we are thinking on that yes? You mentioned at the beginning you moved to a city you looked at house prices and you looked at whether your heat map of what's a good place to live according to you matches house prices can you see good places to live that are more expensive? Yeah so the question is if we checked the house prices in our application and the answer is that I mean we want this data to be inside of our application but we don't have it yet because normally especially in the city of Milan all this data is private it belongs to private companies and so we don't have it yet but we are trying to negotiate to put them on the application we do it manually like just to test and yeah you can do it more or less but the idea is to include it in the application So like just by your impression it matches the pleasant places are Yeah, yeah in some cases yes so the question is that if the houses with the best prices match the options of the people and normally yes we were afraid in the future also the fact that maybe people will I mean because the idea is to create a map that then we will show like the best places that people prefer stuff like that we are afraid that this maybe change the market in the city because you see maybe the map of the areas that people prefer so we are not sure if we want that we are trying to see how to portray this data Yeah How good is the turning around that? Because I'm thinking if I can plug in Berlin there how much time it will take me how much effort it will take me should I sanitize the data myself or I can just plug it into the open-suit map source and other sources? No, I mean we did it very manually in a sense that we sorry so the question is that if we can replicate this application to another city easily and not I mean it depends because we actually take open-suit map data and we download the data so we didn't use any API or anything and we did it very manually so I guess if you use the APIs it will be easier so but we didn't do that Okay, thank you