 Does anyone love Superheroes? Even older people love Superheroes. So, yeah. With our conversation, adding not just Superheroes. So, I said not just Superheroes. Then who else it could be? Those else could be our VLS Superheroes. Like our defence organisations. ATU agencies. Government officials. And such kind of different different organisation that works from human safety, security perspective. They help us to make... So, they help us in resolving the issues that we face from a natural disaster or from a management disaster. That's what our VLS Superheroes do. We can call them Superheroes. Though they don't have a superpower, but still we can call them Superheroes. How come data visualisation will help them? Basically, data visualisation is a technique. It's a method to which we can understand data and we can play with the data. We can... There are multiple ways of data visualisation. The way different user interact with the data, they find, they can derive their own insight out of it. So, that's why we say data visualisation is for big data. And it covers a broader spectrum of data. So, here we say how data visualisation will help our Superheroes because it gives them any way to communicate with the data. Right? And through the data, different user identify patterns, different trends or correlations. So, some of might... There will be might to identify trends. So, they will go with those kind of visualisation. If somebody want to identify some kind of pattern, they will go with those kind of visualisation. So, there are multiple things to it. But we will just only cover the part which is relevant to our talk. So, people who utilise data visualisation, 28% feel that they can easily find the information without... like the people who use normal dashboards or reporting tools, they will find it... they find very delay in terms of insight they want to infer or the information they are seeking for. But people who use data visualisation, they can quickly find the relevant information and take that further access accordingly. Similarly, 48% of people say... people who use data visualisation, they are saying without any technical support, they can find the relevant information or data. So, we have just to connect with the data visualisation with our Superheroes, how data visualisation helps them. So, we saw the recognition and other things. But now, if we have seen like how an only star is juggling and playing with the visualisation on air and he freaks and he manipulates it and whatever information you want to infer from it, he takes. So, the next step is taking based on whatever you infer information or insight that you derive from the particular piece of data. So, that's also all this kind of a data visualisation where you see the vivid information. Similarly, the output that we have, it got many other multiple devices. The devices that help you to identify location or the context, the goal and the sensor display of the information. From that, the insight that he infer is he can easily understand what is the charging percentage of his suit that he is wearing so that he can find an alternate source of charges in terms of some task that he is performing. He can also identify where the target object is, what is the position, what is the distance of an object, weight analysis and all those kind of... there are multiple insights, but this is just few of them which I have highlighted. From the insight, what is the action that he takes? He can instantly take any decision with the help of this data and with the help of this insight so that any unexpected problem that has arise, he can take a quick decision. That's why data visualization is meant for. So, that user, whoever uses the data visualization, they can take an immediate action and proceed further. Similarly, identify the target and shoot remotely instant analysing problems. All these are actions that we perform after analysing the data. Similarly, in various fields like our healthcare, transport, business, politics, social life, data visualization, analysing data visualization or understanding data visualization is a big challenge. So, how do we come to resolve that problem? So, since we all are US people, we are designers, we think from a design perspective, so how our design can make a really impact with the help of data visualization to make our city smarter, to make our city better, place to live. So, as in yesterday's commission said that the traffic issue needs to be resolved. So, some kind of issues can also be resolved with the help of data visualization if we go with the proper process of channel. So, now we will come to the case study or a concept which is related to our real-life super-elows, not with the real-life super-elows. So, participatory surveillance and participatory sensing, these are the two patents that DCS has published. There are many patents that DCS has published, which are relevant to our super-admin for our city administrators who have to manage or optimize our plan of city better. These two are the relevant patents that we have identified and we integrated these two story lines together and created a concept that would really help our city administrator to plan things better. So, these are the evidence of the in-case if you want to go through the, what exactly these patents are, I want to even go into the detail of it. So, when we went to the patent, so what that patent handles is, these are the challenges that what it handles. Like there is no system versus government and different organizations. There are systems, but they are not so effective which will really help them to quickly solve their problem because this is mainly related to the disaster or either an actual human disaster or man-made disaster. But people, our city administrators cannot easily identify the things when the system is not available. So, it takes time for you to take further action. So, there is a delay in it. So, delay in this case is very, very crucial. And if there is a delay, then there is a big loss for us. There is a natural loss for us, there is a property loss for us, there is a casualty loss for us, the human loss. Then, the participative surveillance that the correct system has, it gives a loss of frequent false evidence. So, the system itself was not so built enough, capable enough to give you a real kind of data. So, a lot of analytics and improvement was needed from the system side. So, that's how the correct system is. So, it used to give false alarm which really triggered an unnecessary operation for the administrator to take and again have to bear the false unnecessary cost. So, it goes to the government part that they have to bear the unnecessary cost and do the other false alarm. Then, analyzing data becomes tedious for them because there is a manual intervene over here where they have to go through all the data and analyze things and again take the delay taking further actions or decisions which we have to avoid. Then, unavailability of crucial information. So, event or incident or disaster, this can happen at any, you cannot pre-depend on where it is going to happen. So, the surveillance capital that has landed in the city, that has placed in the city, you, it notices that it will happen in that area as it can happen. It's an arbitrary situation. The sites can be arbitrary. So, you might not get a crucial or relative information because of that. So, after all these challenges and understanding this to fit and we came up with a concept where this is to pass, where you see the first two parts, the city we can find the well is a part of participatory surveillance and the second part, sensor monitoring system is part of our second fit and that is participatory sensing. So, that is a, so multiple sources and the middle layer is the analytical engine and this is the advantage mode. So, I will quickly explain you. The information from the multiple resources goes to the respective analytical engine and it gives you the filter, the mind, the refined data to the administrator so that you can perform this task easily. Now, we'll go with the resolution, what solution, how the admin goes and he sees the dashboard and the dashboard, what are the other actions that he needs to perform. So, this is the dashboard where users get to see the real-time and the tranquility of both the type of disaster, natural disaster or man-made disaster. The other is a notification up in this grid over here. For a time when he clicks on man-made disaster, he can see and another thing goes to man-made disaster. We take a geographical view to the geographical view. Presenting all this information in a form of tabular or some kind of sheet could be very difficult for him to scan through and identify what's where are the highlighted area or what is the problem. It will take a lot of time. So, given the geographical view where the incidents have happened, it shows in a form of e-map when he clicks with the largest impacted area, he gets to see the tweets, Facebook tweets and apps, which is part of that part of the surveillance app. He gets to see information related to those incidents. Here, the three incidents that user has selected is of terror and blast and fire. So, the color represents all these three different incidents. This is the area where the user has zoomed in when he clicked on that e-map here when he zoomed in and he gets to see the number of photos, the number of videos and what is uploaded by the citizen. Since it is a part of the surveillance that means the citizen of the country or location, they have to participate. They install the app, they conduct it wherever they see the incident, they click photos, they upload it, they record the video, they upload it. So, all those videos, photos, audio, everything goes to that analytical agent and that came in a diagonal and this is what the screen is where you get to see the photos, videos and audios of those whatever a citizen has uploaded. Here, they have a nearby service area where once he analyzed all this data and he clicks on the nearby service area he gets all those surveillance images related to that location. So, now user has clicked on Twitter if you want to go for a further detail analysis he goes through each tweets and identify what is the problem and what other steps he has to take. Then he clicks on photos, you get to see the photos which are refined and filtered not all the photos that citizen after the analytical agent has refined the data those data have been displayed over here not all the data that citizen has uploaded. Then similarly we can see the audio when you click on the audio you get to see the audio he can, the user can hear the audio what it is talking about what are the incidents what is the background how the background noise is he can analyze that which location it's a terror attack or something what is the next time all these things he can analyze by listening to videos if needed. Then that was the citizen contribution now if you want to see the sensory contribution which is the second patent the same thing that is converted into a heat map now we give you another kind of information it's talked about the temperature of that particular location where the blast and fire has been occurred and at the same time it also give you the direction that we take the next step for action if you want to see a live CCTV footage he clicks on that option and you get to see live CCTV footage over here that's a live thing he can get to see only the sensory not in the citizen contribution when you click on the nearby area you get to see those emergency details which I was talking about earlier ambulance fire building and police station then he goes to the trend view now whatever we saw these were the real-time data now if you want to plan for the future you want to take some actions you want to improvise the CT you want to plan better for the CT so for that you need to understand what is the trend of the data so if you see here we have two axis in both the axis we have numerical data one is both are talking about the timeline is talking about the years and the horizontal part is talking about the timeline for how long the event or incident has been offered so for all these incidents you will always find a temper at the end why? because at the end the incident will definitely get resolved and the scrutiny are there for that obviously the administration are there for that so whichever data comes away you will find a temper at the end but the distance is telling for how long that incident offered and for when it got mitigated so if you see in 2011 there were lot of incidents that happened but as you keep progressing you saw you can see that there are less incidents happening while that will come to know so when you click on one of this ribbon kind of visualization it has a matter for doing it has a it's not that it's just for a visual representation but this ribbon has some meaning to it we'll get to know when you click on this ribbon it gives you the detail of that incident like the bulge area will tell how many casualties how many injuries how many property loss and natural loss happened in that particular period of time and the slightly thinner area will tell during that period some kind of this way has happened some kind of problem has happened but again if you see a bulge again over there and you see some more problems arise but that's also probably it won't be a kind of ribbon kind of a this wave shape or fire shape but it would be definitely a ribbon kind of visualization but again it's in the metaphor of fire yeah so it's in the metaphor of fire which gives you a feel of the blast incident or terror event has offered so it's it's been given in a fire manner so in 20 level if you see the contribution from this app or the pageant that I was talking about it was less so that's why you see the incidents are still high whether but as you keep progressing if you keep click on 25 then you see the contribution is more so the incident has offered so those are less so this is the desired outcome of this visualization of this design solution that we provided it helps to reduce the force alarm because the analytical engine the technology and the design both help that understand that this is the force alarm whenever there is a real alarm it will give them an indication that only those part will come from that dashboard so that's how it reduces the force alarm it reduces the effect of disaster it gives you both a real-time view and then you can just click on it to the nearby area you can initiate the initiation please or that's it so this is let's be a super hero we are designers we can utilize our design power to create such kind of solution that will really help society and we can create a good social impact from our design power thank you sir you can validate the incident this is a video how you can validate the voice resources which leads this data into the analytical engine based on some algorithm that algorithm that that analytical engine has it runs it sees that what are the relevant information it validate amongst all those different scenarios or all the different resources that it will check the relevant information on the dashboard not all of them that's how it will work so that's why I said if the technology and what design will go hand in hand and then we can create a solution how did you solve the direction of fire in terms of vibration so there are two questions one is fire surveillance which is those are the sensory devices placed within the seat and within the locality which gives you those reading and data but requires chance to give a piece of token of appreciation so sir requires Rajeev