Welcome to Exploring Our World, Maps and Geospatial Science. Now before we embark on this journey, let's ask the following question. What is geospatial technology? Well, geospatial technology includes geographic information systems, remote sensing, global positioning systems, cartography, spatial analysis, and location-based services. Now as you're reading through that list, it might seem that many, if not all of those names, are foreign to you. But the fact of the matter is, you actually know more about geospatial technology already than you think. Geospatial technology is all around us. It's in our phones, in our cars, on the news, in the movies, and yes, even in our video games. For you fans out there of Wii Sports, here's a lovely picture of Woohoo Island. And for you fans of Grand Theft Auto, well, here's an image taken from Grand Theft Auto. And as you can see in these video games, they are completely different than Pac-Man, and Pong, and Tetris, and Asteroids, and all those cool games that I grew up with. Today's video games are founded in virtual reality. And when we're talking virtual reality, we're talking landscape, topography, maps, and really spatial awareness. Frankly, geospatial technology has almost become ordinary. And for that, that's a good thing. So with that said, what will you learn in this course? Well, certainly we're going to learn the basic elements of GIS, remote sensing, cartography, global positioning systems, spatial analysis, and location-based services. All of the items that make up the field of geospatial technology. We'll also learn about geospatial software and its functionality. We'll take a look at how geospatial data is captured, modeled, stored, analyzed, and ultimately displayed. We'll explore how basic GIS web mapping applications can be used, such as Google Earth and ARP GIS online. And perhaps most importantly, we'll take a look at what geospatial technology does in certain areas. That is, how can we use geospatial technology to solve problems? In what areas can we use geospatial technology? And what are today's present-day applications? And then finally, we're going to explore the future of geospatial technologies. After we talk about what's going on today, we're going to take a look at what we can do perhaps tomorrow. And with that, there's going to be some interesting ethical questions that we're going to need to explore. So let's get started. Before we begin, let's take a quick look at some important and universal geographic concepts. How do we think geographically? How do we use mental maps and concept models to highlight the importance of asking geographic questions? Why is this important? And how does it relate to geospatial technology? Let's take a look at mental models versus conceptual models. A mental model is an explanation of someone's thought process about how something works in the real world. It will evolve with experience. A conceptual model's primary objective, on the other hand, is to convey the fundamental principles and basic functionality of the system in which it represents. So what's the difference between the two? Well, a mental model is the representation that a person has in his or her mind about the object he or she is interacting with. A conceptual model is the actual model that is given to the person through the design and interface of the actual product. Ultimately, both types of modeling add to our understanding of place and help to form our decision-making. This segues into mental maps. Mental maps are really maps of the environment stored in our brains that reflect the amount and the extent of geographic knowledge and spatial awareness that we possess or lack thereof. Now, you'll notice I have my frowny face here and I apologize for that. But the fact of the matter is we can turn that frown upside down with more experience. So our environment and our geographic knowledge of the environment and spatial awareness can increase with experience and with time. Here's a nice example of mental maps of Los Angeles and I have three maps as an example here. First off, notice that all the maps share something in common. That is north is up. So whatever mental map we do and whatever real map we have, we have to have certain standards and in this regard standards is north is up. We're going from simplicity toward complexity. In our first map, we have the Pacific Ocean bordered by Santa Monica. That's all we know is that Santa Monica relative to the Pacific Ocean in Southern California sorta looks like this. But as we get more complex, as we get more experience, we can start adding items to the map. So in our higher complexity map, we can now add land features. For example, here's UCLA. Here's LAX. Here's downtown. Here are the Santa Monica mountains and here's Highway 101. As we gain even more experience, we can now start adding more landmarks. For example, we have Staple Center and Dodger Stadium and the Coliseum to go along with the LA River. More freeway systems as well. So again, we increase our experience. We increase our understanding and now we can increase our complexity of our mental maps. Again, in common, all maps are oriented so that north is up. Also in common, all but the first map identifies some prominent feature in landmark because geographically speaking, that's sorta how we think. Wherever there's a geographic landmark or a prominent feature, we tend to remember that. Not common is one map highlights Staple Center, Dodger Stadium and the Coliseum. Basically, again, we're moving from complexity to more complexity. You know, what's really interesting is I have two children. Both are pre-teen. One is eight years old and the other is 12 years old. And I've made them a promise a couple of years ago that we will take our family and go to one Laker game per year. Well, Lakers play up in Los Angeles at Staple Center. My children are huge Lakers fans, but it's incredibly expensive to go to a game. But I made them a promise, one game per year. Now, if I sat down and I gave a piece of paper, a blank piece of paper to each of my kids and I said, draw me a map of Los Angeles. Front and center will be Staple Center. And in fact, the oldest kid might add some more stuff. He might actually add the Ritz-Carlton Hotel because the Ritz-Carlton is right next door to Staple Center.  They are prominently thinking of a certain feature. That is their experience. Now, my hope is over time, as we go up there, they'll start including more stuff in their map because what one includes and what one emits on the map by choice or not speaks volumes about one's geographical knowledge and spatial awareness. My kids right now are not aware. All they're aware of is getting to Staple Center, getting inside that arena, getting a hot dog or a slice of pizza, sitting down and saying hello to a Lakers player, right?  And so over time it'll change. And in a couple of years, I can say, okay you kids, where is LAX? I can say where is the Coliseum? I can say where is Dodger Stadium? I can say where is Groman's Chinese Theater? And by that time, as we continue to go on our journey, and as I continue to point out them, our confetures, and they remember them, their mental map of Los Angeles will grow and grow and grow as their experience develops even further. So what are we really doing? We're really asking geographic questions. And the first is location. And when we discuss location, whether absolute location or relative location, location is going to answer the question of where. We look at region. Region is the portion of the Earth's surface with uniform characteristics. And we can study how they form and change over space and over time. And frankly, we can look at how they relate to other regions. We look at human-Earth relationship. Humans and the environment, such as resource exploitation, hazard perception, environmental modification. That is, how do humans affect the environment? And a couple of really big issues today are ozone depletion and climate change. And frankly, we really ought to take a look at this human-Earth relationship to see or to try our best to understand the extent to which humans are modifying the environment. We look at place. The characteristics that make each place unique. We look at movement. We're talking about movement of communication, movement of circulation in terms of people, airflow, water movement. All of this movement and diffusion across Earth's surface becomes a big player when we start talking about geographic questions. And finally, we take a look at relationships. What is the relationship between natural systems, geographic areas, society, cultural activities, and finally the interdependence of all of these variables over space and time? You know, one of the things we do in science, which is very, very important, is use something called the scientific method. Like all other sciences, geospatial professionals and ancillary users use the scientific method. And the method is based on observation, reasoning, hypotheses, and predictions to develop theories and to help solve problems. We collect data. We then try to come up with questions that we need answering and use that observational data to answer those questions. There's reasoning in scientific method. There's explanation and interpretation. We build useful models of real systems, conceptual models, numerical models also for prediction to try to explain the observation. We then create hypotheses. And hypotheses is a general statement summarizing the observed data and the model simulations. So we don't just make blanket statements. In the scientific method, there must be observation. There must be reasoning. And from the observation and reasoning, then we can create a hypothesis. And from that hypothesis, via experiments conducted, we can create predictions. And the more data gathered through observation and measurement, the more we find the hypothesis becomes that could ultimately lead to theory. And a theory then becomes a real-world understanding of something, of an event. It's the knowledge of how things happen and behave as part of broad general principles. So really the scientific method is a continual, continual process of observing, asking questions, trying to answer them, collecting more data, trying to solve problems to the best of our ability. So when we talk about geospatial technology, just like we talk about other sciences, the scientific method is going to be a prominent feature in our problem-solving. Now that we've explored some geographic concepts, as well as the scientific method, let's get back to our original question. What is geospatial technology? Remember, geospatial technology includes cartography, remote sensing, GPS, spatial analysis, and GIS. Let's define each component. But before we do, let's take a break from me for the next five minutes or so. Sit back, relax, and watch the Geospatial Revolution Project Trailer video produced by Penn State University. For those of you following along in the PDF version of this lecture, please click on the link below for direct access to the YouTube video. A house is susceptible to a wildfire. So we put sensors, like our eyes, on satellites, we collect information, and then computers create maps. Okay, now you have a map, so if you want to analyze that map, well, you take the information about the slope. Are you on a dead-end street? Do you have a lot of fuel around your house? Put all that information into a computer, and it can tell you how at risk you are for losing your home to a wildfire. Ever since the Babylonians etched the lay of the land on clay tablets in 2300 B.C., mankind has needed accurate representations of the Earth. Maps used to be made from horseback in the 1800s. They took a long time to make, so we evolved to aerial photography, and that's made a huge difference with how humans understand the Earth and how it's showing the terrain. In the 60s, people began to think about the notion of encapsulating or abstracting geography in a computer, and people could look at the database and visualizations or analytics, and that was just a magical idea. Barack Hussein Obama would solemnly swear that I will preserve, protect, and defend the Constitution of the United States. So help your God. So help me God. Congratulations, Mr. President. The Islamic campaign took to a new level their use of technology with respect to mapping. They knew what voters to target. They knew where the marginal voter was, and frankly the ones that use it the most effectively get elected. After 9-11, U.S. troops went into Afghanistan and they went in with Russian maps, because who would ever think you'd have to have maps of Afghanistan? Just facial intelligence has become really the foundation for just about anything that happens in the military. It has to do with understanding a very time-sensitive fashion, things that may be developing in different parts of the world. It's an ability to enable decision-makers, whether they're someone sitting in a White House or someone sitting in a Foxhole. More than half the world's population now lives in urban areas. 13 of the 20 largest cities are on coastlines. So how do you model in the potential rise of sea levels because of climate change? We simply could not know how the earth works without geospatial technologies telling us where things are, how they're related, how it's put together to tell us the story of what really is happening. We have the advantage of protecting the innocent life. The conflict in Darbov is over five years old now, somewhere around 400,000 people have died. We wanted to go to the place, collect testimony, take photographs, assuming the government had very little interest in having us on the ground. So we purchased satellite imagery and we saw whole villages destroyed. We took those images to the Sudanese government to let them know that people around the world were watching these villages remotely. For the insiders, the transition to digital geography has been truly revolutionary. We can navigate our world with much greater confidence than we could have before. It's changed the science agenda. It's changed the technology. It's created new occupations. But for those outside who may not even be aware that there is a field called geospatial, it has made geography ordinary, which is the most revolutionary thing of all. That was pretty good. In about a five minute video clip, we now have a pretty good idea of what geospatial technology is, what it entails, and some of the things that we can do with it. Now, let's spend the next few moments talking about the major components of geospatial technology. There is cartography, which is the key to visualization and presentation, i.e. mapmaking. There's remote sensing. Remote sensing is the collection of wavelength-specific data about features or phenomena of the Earth's surface and near surface without being in direct contact. And when we chat about remote sensing, there's really two major ways we look at it. There's active remote sensing and there's passive remote sensing. Active remote sensing really is a direct beam of energy pointed toward a surface and then we analyze this energy that's reflected back, and that's going to tell us about the surface. A good example of active remote sensing would be radar, which is radio detection and ranging. As opposed to active remote sensing, there's passive remote sensing. And in a passive remote sensing system, you're going to record energy that's actually radiating itself from the surface. And typically, we're looking at visible light or infrared radiation. And once again, the whole goal is to try to find out information on the surface looking at this radiated information. In addition to cartography and remote sensing, there's GPS, Global Positioning Systems. And with GPS, we're going to be able to find our location on the planet, pinpointing the location of objects and phenomena. There's also spatial statistics and analysis, which is the ultimate brain behind geospatial technology. Now, when we make our maps that's collected from GPS data and remote sensing information, for example, what we can do now is look for a point pattern analysis, auto correlation, area patterns, clusters, networks. The goal is to take all of our information, we have it on our map, now let's do some modeling and see if there's anything statistically viable on the map that we're looking at. This ultimately is going to be how we solve problems. And finally, the last component of geospatial technologies is GIS. So, what is GIS? Well, Ron Abler of Penn State in 1988 made the following statement. GIS's are simultaneously the telescope, the microscope, the computer, and the Xerox machine of regional analysis and synthesis of spatial data. Basically, GIS is the tool. It is the primary tool of regional analysis and synthesis of spatial data. In fact, GIS is so important we're going to spend the remaining portion of this lecture really focusing on GIS. Geographic information systems or computerized systems designed for the storage, retrieval, and analysis of geographically referenced data. GIS uses advanced analytical tools to explore the spatial relationships, patterns, and processes of cultural, biological, demographic, economic, geographic, and physical phenomena. There's a lot of stuff there and all of those items that have in common is spatial relationship, spatial context. Anything that has a spatial context associated with it, we can use GIS to study it. So, GIS is a computer software. Google Earth, for example, can be considered a nice simple but nice GIS software package. When we explore further in this course, we'll explore ArcGIS and ArcGIS is produced by a company called ESRI, Environmental Systems Research Institute. They're the number one producers of GIS software in the industry, but there's other software packages that we'll explore as well. GIS is a collection of computer hardware. It's a service that can be distributed and accessed via the Internet. It's a tool and then finally GIS is a system and a science. And I really, really want to highlight the term science. There is so much behind GIS today. The tools of GIS and the tools of geospatial technology really lends itself to be a science unto itself and the beauty of this science is you can have specific GIS technicians and GIS users, but then you can also have ancillary users. For example, someone who is in business will use GIS as a tool for their business. Someone who is in politics will use GIS. Someone who is in the physical sciences will use GIS. So GIS can be used to study all of these different problems across all of these different fields and genres. You know, we talk about tools. It's really cool is the tools today are really different than maybe the tools a decade ago. Of course we still use computers, but the computers today are so much more powerful, so much faster and have so much more memory than the computers a decade ago. It really has revolutionized the entire genre of geospatial analysis. Other tools are the digitizer, scanner, printer, plotter, GPS units, and now of course we see tablets. And with tablets running GIS software we've really brought a mobility to the science that we didn't have just a handful of years ago. As far as software goes there's a variety of different types of GIS software. There's desktop GIS software such as ArcGIS desktop version there's internet based GIS such as ArcGIS online Google Earth and other products. And of course there's CAD software, computer assisted drafting and design. But more importantly than the hardware or the software are people. People are really the mainstay of GIS. People collaborating together, sharing information, sharing data putting it all together in the quest to solve problems. I'd like to show you another short video. This one is an interview with Richard Feynman Nobel Laureate of Physics in 1965 discussing knowing something versus knowing the name of something. Again for those of you following along in the PDF version of this lecture please click on this link below in order to access the interview directly from YouTube. All the kids were playing in the field and one kid said to me see that bird? What kind of a bird is that? And I said I haven't the slightest idea what kind of a bird it is. He says it's a brown-throated thrush or something. He says your father doesn't tell you anything. But it was the opposite. My father had taught me, looking at a bird he says, you know what that bird is? It's a brown-throated thrush. But in Portuguese it's a ontarapero. In Italian a chuterapita. He says in Chinese it's a chungo in Japanese it's a patata etc. He says now they have all the languages you want to know what the name of that bird is. And when you finish with all that he says you'll know absolutely nothing one ever about the bird. You only know about humans in different places and what they call the bird. Now he says let's look at the bird and what it's doing. He knew the difference between knowing the name of something and knowing something. Now, I understand that Richard Feynman was not specifically talking about geographic information systems. Nevertheless, the premise is true. Knowing something versus knowing the name of something. We need to remember this because as we proceed through this class and we start to really study and get our hands dirty with geospatial technology, you need to know that we're not just making maps. We're not just putting a tree on the map and saying hey, the oak tree is over there for example. We are trying our best to gather a lot of information about something and solve a problem. So let's take a look at geographic information systems. With GIS we can describe any element of our world in two ways. The first way of course is simply location. Where is it? So in my example, I have an oak tree. We put it on the map. Voila, we know where it is. But with GIS we can also add attribute information. What is it? So this is an oak tree. The height is 15 meters and the age is 75 years old. We're no longer dealing with static maps. We're no longer looking at a map of something on the map and maybe it's labeled. Now what we have is something on the map that we can actually query and find out more information about it. And then once we have that on the map we can add other information to the map. For example, weather data, soil data, geologic information, wind patterns, every element that we can think of that we have data for we can put on the map. And then we can start to seek relationship between the variables that are on the map together. Let me give you a quick example. We can go to ArcGIS Online and here's ArcGIS Online and I have a vineyards project up. And what you see here is a map of the world. Now I've got a number of layers as well that I could add to this map. Let's add vineyards. So now these red dots pop up and what you see are vineyards of the world. And we can click on a vineyard and the information regarding that vineyard is going to pop up giving us the name of the vineyard as well as its latitude and longitude. Well let's say that we wanted to see whether or not all of these vineyards has something in common. So we can take a look at soils. So here's our European soils and we'll put that on the map and we'll zoom into that area so that you can see it a little bit further. And there are the vineyards. Now when we click on a soil pattern as well so we're going to get all of the information regarding that soil. Now these are European vineyards. What if we wanted to look at vineyards for example in the United States of America and we wanted to compare soils. Well we can click on the Soils of the United States map and let's go to the United States and we'll fly ourselves right to it. So here's the United States and we'll wait for those soils to pop up and here they are and when they do we can query and we can have a comparison so I can take a look at different vineyards in the United States of America and compare those vineyards to Europe. I can of course do the same with South American soils. So let's go to South America and compare those vineyards in South America as well. So here are the South American vineyards and here are the soils and again we can click on those soils and figure out if indeed we have a pattern or a relationship between soils and vineyards. Of course we know them we're growing grapes it's not just the soils precipitation matters. So now we can take a look at global precipitation patterns and see whether or not there's a relationship between these different locations their soils and precipitation. Of course we're not done yet all of these different places have unique ecosystems potentially as well so now we can do an eco region map of the world and once again we can see whether or not the variables match from place to place. Now if we're really smart we can make a model. And in our model we can weight things or weight items. So let's say I'm really knowledgeable about growing grapes and let's say water, moisture accounts for 40% of good growth. And maybe a certain type of soil accounts for 20% of good growth aspect. The direction with which the vineyard is pointing relative to the sun also plays a big role maybe 10-20%. We can make a model and weight all of these different variables together and then try to come up with our best location to grow another vineyard. Once again when we're talking about GIS geographic information systems we're no longer talking about a static map but rather we're talking about a dynamic map. GIS because of this has really unique capabilities. We're able to answer questions about where and when and why. GIS is going to store the coordinates of graphic features and other attributes as map layers and these map layers can then be reused easily and assembled into any number of map compositions to answer any number of questions. In the example we have up here we have a series of layers we have a watershed map for example a map of slope a map of soil a map of land use and a map of animal loading and we can put all of these different layers together to try to come up with a solution for an agricultural pollution potential. So once again we're taking variables of information we're combining it together and from that we're trying to analyze something or solve a problem. So what are the major questions then for a GIS? Well there's the what question. What exists at a certain location? Where are certain conditions satisfied? What has changed in a place over time? What spatial patterns exist? And what if this condition occurred at this place? You know I have a number of examples listed here and what I'm going to do is go through them relatively quickly and I'll ask you to review the PDF file on your own leisure to explore these examples a little bit further. But in any GIS we undertake we're really looking at two things. The first thing is the question. The question of where, when, why, how, what. So in this example regarding fire management the question becomes where are areas of high fire hazard? Once we have the question we then look at the variables that could feed the question. So when we're seeking high fire hazard the variables that we're looking at are for example potential ignition sources, power lines, roads, industrial areas, housing areas. We're also looking at factors such as vegetation types, slope, aspect, natural or man made barriers and historical weather patterns. We can take all of this information and integrate it into predictive models. And once the predictive models run the end game is going to be a map which shows high risk fire area. We can then take that information and maybe go in and remove dead brush. We could also figure out where the best locations for fire stations are supposed to be. So again we ask the question then we ask what do we need to solve the problem. Agriculture. What's the question? How can I improve food production? What are the map layers needed to try to solve that problem? Healthcare. In this example the question was what communities are at risk from disease? What are the map layers needed? And the example that I have up here on the screen is a Guatemalan epidemic of river blindness disease. As it turns out a few years ago in Guatemala there was this epidemic. Mosquitoes were the carriers of the disease. Health officials were able to use GIS to map out the area and they plotted the location of the mosquito breeding sites. Communities surrounding the sites were then assessed to determine their chances of getting affected by the disease. As a result they were able to manage the healthcare treatment facilities much much more effectively. Superstorm Sandy mashup. You know by the way the term mashup refers to a community project. So when we talk about Superstorm Sandy after the storm the question becomes where are their food shortages? Where should we put mobile food sites? The community then came out and said this area needs help. This area needs help. Everybody contributed and all of this information got purged into Google Maps and when the map was made it was really easy or easier I should say to have an emergency management system. So where are the mobile food sites? What are the map layers needed? Marketing. Again the question becomes how can I optimize my marketing campaign? What are the map layers needed? Real estate. Where is my dream home? What are the map layers? Of course everybody's dream home needs to be subdued a little bit by your maximum amount of money that you can pay but beyond that you can look for houses of a certain size. You can look for houses in a certain community with relationship to school districts for example or crime. There's a variety of variables that one can look at to determine the best location to buy your home. You know these have all been pretty good examples of GIS in action but let's go back to 1854 and explore one of the very first examples of GIS and spatial analysis. Sit back and enjoy the 10 minute discussion of Stephen Johnson's Ghost Map. Again for those of you following along in the PDF version of this lecture you can access the discussion directly by clicking on this link below. What I want to do is take you back to 1854 in London for the next few minutes and tell the story in brief of this outbreak which in many ways helped create the world that we live in today and particularly the kind of city that we live in today. This period in 1854 in the middle part of the 19th century in London's history is incredibly interesting for a number of reasons but I think the most important one is that London was a city of two and a half million people and it was the largest city on the face of the planet at that point but it was also the largest city that had ever been built and so the Victorians were trying to kind of live through and simultaneously invent a whole new scale of living, the scale of living that we now call metropolitan living and it was in many ways at this point in the mid-1850s a complete disaster. They were basically a city living with a modern kind of industrial metropolis with an Elizabethan public infrastructure so people for instance just to gross you out for a second had cesspools of human waste in their basement like a foot to two feet deep they would just kind of throw the buckets down there and hope that it would somehow go away and of course it never really would go away and all of this stuff basically had accumulated to the point where the city was incredibly offensive to just walk around and it was an amazingly smelly city not just because of the cesspools but also the sheer number of livestock in the city would shock people not just the horses but people had cows in their attics that they would use for milk that they would kind of hoist up there and keep them in the attic until literally their milk went out and they died and they would kind of drag them off to the bone boilers down the street so you would just walk around London at this point and you would be overwhelmed with this stench and what ended up happening is that an entire kind of emerging public health system became convinced that it was the smell that was killing everybody that was creating these diseases that would kind of wipe through the city every three or four years and cholera was really the great killer of this period it arrived in London in 1832 and every four or five years another epidemic would take 10,000, 20,000 people in London throughout the UK and so the authorities became convinced that this smell was this problem we had to get rid of the smell and so in fact they concocted a couple of early kind of founding public health interventions in the system of the city one of which was called the nuisance act which they got everybody as far as they could to empty out their cesspools and just pour all that waste into the river because if we get it out of the streets it'll smell much better but oh right, we drink from the river so what ended up happening actually is they ended up increasing the outbreaks of cholera because as we now know cholera is actually in the water it's a waterborne disease not something that's in the air it's not something you smell or inhale it's something you ingest and so one of the founding moments of public health in the 19th century effectively poisoned the water supply of London much more effectively than any modern day so this was kind of the state of London in 1854 and in the middle of all this kind of carnage and offensive conditions and in the midst of all this kind of scientific confusion about what was actually killing people there was a very talented classic 19th century multidisciplinary and John Snow who was a local doctor in Soho in London who had been arguing for about four or five years about what cholera was in fact a waterborne disease and had basically convinced nobody of this the public health authorities had largely ignored what he had to say and he'd made the case in a number of papers and done a number of studies but nothing had really kind of stuck and part of what's so interesting about this story to me is in some ways it's a great case study and how cultural change happens how a good idea eventually comes to win out over much worse ideas and Snow labored for a long time with this great insight that everybody kind of ignored and then on one day August 28th of 1854 a young child, a five month old girl whose first name we don't know we know her only is Baby Lewis somehow contracted cholera came down with cholera at 40 Broad Street you can't really see it in this map but this is the map that becomes the central kind of focus in the second half of my book when this working class neighborhood this little girl becomes sick and it turns out that the cess pool that they still continue to have despite the nuisance act bordered on an extremely popular water pump, local watering hole that was well known for the best water in all of Soho that all the residents from Soho and the surrounding neighborhoods would go to and so this little girl inadvertently ended up contaminating the water in this popular pump and the outbreaks in the history of England erupted about two or three days later literally 10% of the neighborhood died in seven days and much more would have died if people hadn't fled after the initial kind of outbreak kind of kicked in so it was this incredibly terrifying event you had these scenes of entire families dying over the course of 48 hours of cholera alone in their one room apartments in their little flats just an extraordinary terrifying scene snow lived near there heard about the outbreak and this amazing kind of active courage went directly into the belly of the beast because he thought an outbreak that concentrated could actually potentially end up convincing people that in fact the real menace of cholera was in the water supply and not in the air. He suspected an outbreak that concentrated would probably involve a single point source, one single thing that everybody was going to because it didn't have the kind of the traditional slower path of kind of infections that you might expect. So he went right in there and started interviewing people he eventually enlisted the help of this amazing other figure who was kind of the other protagonist of the book this guy Henry Whitehead it was a local minister, it was not at all a man of science but it was incredibly socially connected he knew everybody in the neighborhood and he managed to track down Whitehead did many of the cases of people who had drunk water from the pump who hadn't drunk water from the pump and eventually Snow made a map of the outbreak he found increasingly that people who drank from the pump were getting sick people who hadn't drank from the pump were not getting sick and he thought about kind of representing that as a kind of table of statistics of people living in different neighborhoods, people who hadn't percentages of people who hadn't but eventually he hit upon the idea that what he needed was something that you could see, something that would take in a sense a higher level view of the activity that had been happening in the neighborhood and so he created this map which basically ended up representing all the deaths in the neighborhoods as black bars at each address and you can see in this map the pump right at the center of it you can see one of the residents down the way had about 15 people dead and the map is actually a little bit bigger as you get further and further away from the pump the deaths begin to grow less and less frequent which is something poisonous kind of emanating out of this pump that you could see in a glance and so with the help of this map and with the help of kind of more kind of evangelizing that he did over the next few years and that Whitehead did eventually actually the authority slowly started to come around, it took much longer than sometimes we like to think in this story but by 1866 when the next big collar outbreak came to London the authorities had been convinced in part because of this story on the map that in fact the water was a problem and they had already started building the sewers in London and they immediately went to this outbreak and they told everybody to start boiling their water and that was the last time that London has seen a collar outbreak since so part of this story I think well it's a terrifying story it's a very dark story and it's a story that continues on in many of the developing cities of the world it's also a story really that is fundamentally optimistic to solve these problems if we listen to reason, if we listen to the kind of wisdom of these kinds of maps if we listen to people like Snow and Whitehead if we listen to the locals who understand what's going on in these kinds of situations and what it ended up doing is making the idea of large scale metropolitan living a sustainable one when people were looking at 10% of their neighborhoods dying in the space of 7 days there was a widespread consensus that this couldn't go on to live in cities of 2.5 million people but because of what Snow did because of this map because of the whole series of reforms that happened in the wake of this map we now take for granted that cities of 10 million people cities like this one are in fact sustainable things we don't worry that New York City is going to collapse in on itself quite the way that Rome did and be 10% of its size in 100 years or 200 years and so that in a way is the ultimate legacy of this map of deaths that ended up creating a whole new way of life the life that we're enjoying here today thank you very much you know Dr. John Snow's London street map of the cholera outbreak of 1854 really proved itself to be a good example of spatial analysis and mapping and how these techniques and technology can be used to solve problems of course back then there were no fast computers with fast processors and lots of memory to download data today we do and with the advent of the computer in the 1960s this whole idea of spatial analysis and mapping and geographic information systems really took off then in the early 1970s new satellites such as Landsat began monitoring the surface and atmosphere and started generating huge amounts of data for analysis which also subsequently continued to spur further development of GIS techniques mapping software and analytical software as well in the late 1970s and early 1980s we begin to see this nice push for integrated software in publicly and commercially available packages so now the users became people like me and you not just academics and not just professionals currently there's a real big push towards open source GIS platforms that means the free stuff so again we now have this ability where GIS can be pushed across the entire spectrum of users geospatial technologies industries and applications are across virtually any spatial genre so wherever spatial data analysis is needed geospatial technology is there whether it's in business for site location delivery systems and marketing government, local state, federal and military economic development such as population studies emergency services such as fire police and first responders environmental studies including monitoring and modeling industries such as transportation communication, mining pipelines, healthcare public health urban planning land use, historic studies housing studies, criminology politics elections and reappointments and certainly education research used as a teaching tool and certainly in the administration of educational institutions as well again wherever spatial data analysis is needed geospatial technology is there geospatial technology is considered a department of labor high growth industry and I encourage you all to check out two websites one of them is careervoyages.gov and when you go to careervoyages.gov you can explore in-demand careers one of them will be geospatial technology and when you visit the site there's an industry overview and the in-demand occupations and it tells you what jobs are available in the industry in addition to that website visit the department of labor site and there you can read about how geospatial technology is a high growth industry and what the future outlook for the industry is as well because of the growth of the geospatial technology industry there was a real need to develop a competency model for geospatial technology a competency model is a collection of competencies that together define successful performance in a particular work setting the model serves as a resource for career guidance curriculum development and evaluation career pathway development recruitment and hiring continuing professional development certification and assessment development apprenticeship program development and outreach efforts to promote geospatial technology careers specifically the geospatial technology competency model has been developed by researching and analyzing publicly available resources existing skill standards competency based curriculum and certifications to provide an employer driven framework of the skills needed for success in geospatial technology our course curriculum for this class is based upon the GTCM now what is the GTCM and what does it include well you can actually click on this link that we have here and it will take you straight to the career one stop dot org website and when you get to the website you can see that you can view an industry model and there are 22 models listed here one of them being geospatial technology so we click on that and it takes you right to the GTCM and when you get to the GTCM you notice the GTCM pyramid and let's say that you are interested in the industry why technical competencies are for this industry what you can click on this part of the pyramid and from that you are given the core geospatial abilities and knowledge so you have critical work functions and they involve earth geometry and geodesy for example data quality satellite positioning and other measurement systems remote sensing and photogrammetry cartography GIS programming application development and geospatial information technology and professionalism and then along with those cores you have technical content areas as well now as you go through this GTCM and all of the core competencies associated with it take a look at our outline for our course because the outline for our course is going to mimic quite a bit the GTCM we are going to make ourselves aware of those fundamental core concepts in the geospatial technology industry so let me give you a quick summary and some additional thoughts first of all geospatial technology when done right can help us save time money and lives we saw this going all the way back to 1854 with Dr. John Snows cover a map of London we also see that today geospatial technology is a tool that has been used by all the emergency responders and first responders in addition the geospatial industry is rapidly growing geospatial is both an industry and an ancillary tool for spatial disciplines such as political science or criminology or real estate or business or physical sciences geospatial technology is shifting from a closed centralized GIS architecture to an open distributed geospatial information services framework for all to use the field is much more than learning software and pressing buttons it includes analysis spatial awareness pattern recognition and critical thinking this is going to be a fun journey when we get back to our next lecture we're going to start to explore the real fundamentals of maps and map making and the question we're going to ask ourselves is how can you take a three dimensional world that we live on and put it on a two dimensional plane i.e. a map that we can study we'll talk about that next time


Hey Moz fans, welcome back to another edition of Whiteboard Friday. My name is Megan and I'm on the learning team here at Moz, and today I'm going to talk to you about harnessing the power of feedback when it comes to content iteration. So one of the projects that I contribute to in my position here is taking care of our Customer Help Center, which we call the Help Hub. And if you're not familiar with the Help Hub, this is where we house all of our how-to guides, tips and tricks, workflows, and troubleshooting guides for the Moz tools. And I do encourage you to check it out if you have some time later or if you have questions about the tools. A key part of maintaining the Help Hub includes gathering, monitoring, and implementing customer feedback. And this is a crucial component for us. Why is that? Well, because we want to be sure that we're providing quality, helpful content to our customers. In addition, this process allows our customers to find answers to their questions quickly and easily at any time. And it does take some of the lift off of our help team as well by reducing the number of tickets that they receive asking these very questions. And so I'm going to go ahead and take you through the process that we use to implement customer feedback in hopes that you can take it and apply it to your own content creation and maintenance strategies. So what is the first step? Well, first we gather data because we don't know what feedback people have if we don't ask for it, right? So if you are familiar with the Moz Help Hub, you may have seen our surveys that are at the bottom of all of our articles. And here we ask if the article was helpful. And we do this with a series of emojis that indicate if it met their needs or not. And if the customer indicates that it did not, they do have the option to enter a comment letting us know why. And when it comes to gathering data on your own content, you may opt to add a survey like this, or there are plenty of other ways that you can start to gather data to work with. So if you have a social media presence, you can start keeping track of feedback there or ask your followers directly for insight into what they find most helpful and least helpful about your content. Or you can send a survey out via email, ask your customer service team for feedback, look at customer emails and tickets to see what questions the customers may be asking, which you're not currently answering on your site. And we do that as well. We work closely with our help team to ensure that everything that we can answer is answered in the Help Hub. So once you have that data to work with, it's time to analyze it. So we review both vote counts, meaning helpful versus not helpful, as well as comments here at Moz. And when comments are left, we look to see if there's an opportunity to implement a change in that guide based on the feedback. Additionally, if we're seeing a trend in votes for a particular page or section, we will take a look at how we can improve or reword, update the content to better serve the customer. One thing to keep in mind during this step, however, is that not all feedback you receive will be actionable, and that's okay. A few questions that I like to ask myself when looking at feedback for an article are, was the customer on the right page to find their answer? And if not, how did they get to this page, and is there an opportunity to help them find their way to the correct page, whether that's through links or additional resources, et cetera? Is there a question that I can answer on this page, or should this question have its own dedicated page? Sometimes we end up writing whole new guides based on feedback that we get from customers. And what was the customer trying to achieve? How did this guide fall short in helping them achieve that goal? So now that we've identified areas for improvement, it's time to implement changes to that content. So this may be as simple as adding an FAQ to answer a specific question, or as involved as writing a new workflow or troubleshooting guide, as I mentioned previously. And just as an example, some specific things, some specific changes that we implemented based on customer feedback include adding quick links to all of our pages for easier navigation, creating separate pages for each of our keyword metrics, and building out multiple workflows based on questions that customers have asked. This step in the process may look different depending on the type of content that you create and the type of feedback that you receive. For example, if you primarily work in creating video content and you receive feedback that customers wish the videos had subtitles, you may opt to implement those on past videos as well as any that you release moving forward. However, if you have a blog or a newsletter or some other type of long-form content, it may not make sense to use resources to update older pieces of content. Instead, you may opt to start implementing those changes in your content moving forward, and it may be a combination of those. Maybe you have some really popular articles that can be updated from the past and start implementing those changes in your content moving forward. So after implementing your changes, you want to be sure to track your results. We track our votes and survey responses regularly to help monitor for update opportunities and to see if the responses have changed for that particular piece of content. And finally, we start the cycle all over again, gathering more data, analyzing it, implementing changes, and then tracking the results. Implementing this process here at Moz has allowed us to see a correlation between changes that we've made to the help of content and the number of helpful votes that we receive. We treat this part of our content library as a living document that is always evolving to not only account for tool changes, but also to take into account customer feedback. Gathering feedback on your content can help to identify trends in what your customers are engaging with and how you can further improve your offerings moving forward. And that's key. You want to always be improving. It can also help to identify resources that may need updating or ideas for future content. For example, if you have a blog post about how to bake a cake and your readers are commenting that they don't know how to pick the right kind of pan for the recipe, there may be an opportunity to publish a new blog post about the best types of cake pans. Or if you publish help guides like myself, if a customer says they couldn't find the answer to their question in that article, there may be an opportunity to look into questions like the ones that we outlined earlier in our analyzed step. You know, what are they trying to achieve? How do they end up on this page and how can I help them to reach their goal? I hope that you found this helpful and that you're ready to get out there and start harnessing the power of feedback. And thank you so much, Moz fans. We'll see you next time.


Before we start applying the concepts that we learned about magnetic forces to start building something cool, we need to first get some practice done. So here's our first practice question. A proton and a helium nucleus are shot into a magnetic field at right angles to the field with the same speed. Find the ratio of the radii of their part taken. So we have protons and helium which are thrown into a magnetic field perpendicular. As you can see the magnetic field is coming out of the screen. And they're thrown with the same speed. And we have to find the ratio of the radii of their part taken. So apparently they go in circles. They take circular parts. So this is the part of a circle and we'll see why that is. We'll recall that. And so we basically need to find out what the ratio of those radii are. So let's begin. Before we begin, the first question I have is why does it go in circles? Let's try and remind ourselves why does that happen? To find that out we need to think about how to calculate part taken by any object. How do you do that? Well for that you need to first investigate the forces acting on it. See if there are no forces acting on a particle then it would just continue to go in a straight line. But if there are forces acting on it then we can use Newton's laws to figure out how does the force affect the path. So what's the force acting on these particles? Well there's only one force because it's entering a magnetic field. That is the magnetic force. This means that the magnetic force, the Lorentz force, is given by the expression f equals q times v cross b. This is the force that's acting on them. Let's look at the direction very quickly and I want you to pause and see if you can quickly figure that out. We've done this before. The direction of the magnetic force will be in the direction of v cross b. So if you take the proton, the velocity is to the right, the magnetic field is coming out of the screen. And so if you cross from the velocity vector to the magnetic field vector v cross b, the thumb points downwards with your right hand. And so the magnetic force will be acting downwards. So over here the magnetic force acts downwards and that makes the proton turn downwards. But the beauty about this magnetic force is that it will always be perpendicular to the velocity vector. Right? As a result of that, over here when the velocity is this way, the magnetic force will be perpendicular to that. Over here the magnetic force will be perpendicular to the path. So it will always be perpendicular to the path, the velocity vector. And as a result you can see it will always point towards the center. And so this magnetic force acts as a centripetal force. And so immediately we know that's why it goes in a circular path. And of course if you need more clarity on this, we've talked about this in great detail in previous videos. So feel free to check those videos out. Okay, now that we understand why it goes in a circular path, the next step is how do you calculate the ratio of the radius? For that I need an equation for the radius. Now this is also something that you've derived in previous videos.  And I actually recommend you not to remember the results. Physics is not about remembering equations. It's about taking some fundamental equations and deriving everything else. That's where the fun lies. So let's see if we can re-derive the expression for the radius very quickly. How do I do that? Well, some of the fundamental equations I know is, one is I know the expression for the magnetic force. But I also know the expression for centripetal force from mechanics. From mechanics I know that centripetal force has to equal mv squared over r. And since I know that this centripetal force is provided by the magnetic force, I can now figure out what the magnitude of that is because the direction is already taken care of. And I can plug that in over here and then I can solve for the radius. And that's how I get the radius. And then I can take the ratio. So it'll be a great idea if you're excited as I am. Great idea to pause and see if you can derive it yourself first. Okay, let's do it. So let's first take the magnitude of the magnetic force. So that's going to be magnitude of this. That's qvb sin theta. Where theta is the angle between the velocity vector and the magnetic field vector. And that angle is 90 degrees. It's given to us. You can see it from the diagram as well. Velocity is to the right. Magnetic field is coming out. So it's 90 degrees. Sin 90 is 1. So this thing is just 1. And so now I can plug this in over here and what I end up with is qvb sin this is 1. That is our magnetic force. That's our centripetal force. That equals mv square over r. And therefore if I simplify, I get r to be mv divided by qb. And there you go. That's the expression for the radius. And look, I don't need to remember this equation. I can always derive it. That's what I love about this. Okay. Now all I have to do is figure out what the radius for the helium for the proton is and divided by the radius of the helium. So let's do that. So radius of the proton divided by the radius for the helium. That will be this for the proton divided by this for the helium. So this is going to be mass of the proton times the speed of the proton divided by the charge of the proton times the magnetic field in which the proton was thrown divided by mass of the helium nucleus into the velocity of the helium nucleus divided by the charge of the helium nucleus into the magnetic field in which the helium nucleus was thrown. Okay. Now a few things are common. They're coming in the same speed. They're thrown with the same speed. So the speed cancels out. They're throwing the same magnetic field. So these magnetic fields are the same. That cancels out. I know their masses are different. I know all their charges are different. That's the final piece. So if I simplify, I get mp by qp into qhe, sorry, qhe divided by mhe. Now let's look at their masses and their charges. So I know that the helium nucleus is made of what? It's made of two protons and two neutrons. And protons and neutrons pretty much have the same mass. Unless you're doing nuclear physics and you're talking about their precise processes, we don't have to worry about the difference. Pretty much have the same mass. That means helium has four times the mass of the proton because it has four particles. And so I can write that over here. This is four times the mass of the proton. So if I cancel this, I get four. What about the charge? Well, helium, neutrons don't have charge. Helium has two protons. Therefore it has twice the charge of a single proton. And so this should be twice as much as this. So I can cancel this and I get two. And we're done.  So one over two is the answer. So the radius of the proton divided by the radius of the helium nucleus happens to be one over two. And you can indeed see helium ends up taking a larger circle compared to the proton. Let's solve one more example. And let me take this radius equation. It might be handy again. All right, this time we are given there are two protons that are short into the magnetic field at right angles with their velocities in the ratio one is to two. Now this time both are protons, but their velocities are different. Find the ratio of the time periods of their circular paths. So what's going on over here? Well, now we already know that these particles are going to go in circular paths and we can now assume that the magnetic field is large enough that they can actually traverse these circles. Then what we need to find out now is the expression for the time it takes for them to complete one full circle and find the ratio of that. And again, this is an expression that we have derived before, but I don't remember the result. I don't recommend you to remember those results as well. Let's start thinking from first principles again. Okay, if I want to figure out how long it takes for this to complete a full circle, how do I do that? Well, because we are dealing with uniform speeds and uniform motion, I go back to my speed equals distance over time, and therefore to calculate the time, I need to figure out what this distance is. Speed is known to me. How do I calculate the distance? Hey, this distance is just the circumference of this circle, and I know the circumference is 2 pi r and the radius is given to me. Okay, cool. So again, great idea to see if you can put it together and figure out what the time period is and then find the ratio. All right, let's do this. So I know that speed equals distance over time. So time, let me use red for that, time or the time period over here, that's going to be speed equals distance over time, time equals distance by speed. That's going to be distance divided by speed. So what's distance going to be? Distance is 2 pi r and r is mv by qv. Divided by what's the speed? Well, speed is just v. So what I get is that the v cancels out when I simplify, and immediately I have my time period equation. And again, this is what I love. See, I don't need to remember any equations. Just imagine how horrible it is to try and remember all these equations. No, you don't need them. So that's the expression for time period, and now I can compare them. What I find is interesting is that there is no speed in the time period, which means if I compare them, they have the same mass, they have the same charge because they are both protons, and they are thrown in the same magnetic field. Their speeds doesn't matter, which means the time it will take for this is exactly the same as the time it will take for this. And so immediately the answer is 1. The ratio is just 1. So t1 to t2, or t1 divided by t2, whatever that is. It's just 1 is to 1. But I'm a little curious as to why is this happening? Let's see if we can intuitively make sense of it. Why? This is thrown at twice the speed, right? So why is it taking the same time as this one to complete a circle? Well, because if it's thrown at twice the speed, if you look at the radius equation, you can see the speed doubles, and everything else remains the same, the radius doubles. So that means this thing is going to go in twice the big, this thing will go in a circle which is twice as big as the other one. Okay? So now think about it. You're traveling twice as much distance, but which twice as much speed, so the time it will take for this person to complete will be the same as this person to complete, and therefore the time period is the same. So it's a wonderful consequence that we see is that in the magnetic fields when charges are thrown, the time it takes for them to complete a circle is independent of the speed.


So thank you everyone. Thanks for being here through the day. I understand you've gone through a lot of content since morning. Um, and everybody's talking about new age technology, how you can use technology for your marketing campaigns. And what's technology without AI today, right? If we don't talk about AI, probably there is no conference that goes ahead without speaking about AI. Um, before we move further into my presentation, I just want to spend a minute talking about what we do, right? Probably some of you would have heard what double verify does, but double verify as introduction says is a guardian of media quality. And what I mean by matter of media quality is we measure where you're buying impressions where you're buying inventory weather is going in a brand safe environment, whether the placements are done in a content which resonates your brand ethos, whether the impression you're buying is a human impression or is a fraudulent impression, right? Whether the impression you're buying is delivering the viewability or it's not even delivering the viewability or the opportunity to be seen by a real human, right? And whether it's delivered in the right geo, so brand safety, ad fraud, viewability in geo and obviously optimizing your spends to meet the KPI and deliver efficiencies in your campaign, right? So that's what we do. So next my 10, 10, 15 slides that I have will mainly touch upon AI in these realms and how we as an organization using AI to make it more relevant for our customers and clients so that they bring in efficiencies in their media plans, end up saving money, reach to right audience in the right context and in the right environment, right? That's where we are. So that's me, I lead the India business for double verify as a sales. Broadly, there are there are two agenda points which we touch upon, right? Perils and profits in the age of AI and obviously the opportunity for us that AI brings a to increase or or better the media quality and be bringing in more efficiencies in your media that that you buy. So we'll first start with perils and profits in the age of AI. We're talking about how consumers are changing, right? We all know that there is no longer a linear journey of a consumer, right? It's complex at one point of time you're reading a review at another point of time you're going on on e-commerce site to buy stuff. Other points are going to OTT. So earlier there was an IDA model that one used to follow, but there is no IDA model anymore, right? Every single user is multitasking consuming different set of content. And what are they doing, right? So about 74% Indians buy more items online today, right? Compared to couple of years back or pre pandemic stage, right? That's some massive, massive change. And how are they buying it, right? It's not, it's not only about their searching at one place going to e-commerce buying it, right? The search behavior has also changed. It's no longer only the search engines. It's the social media. It's the recommendation from your friends and family. It's the reviews that you see on social media. So there are multiple touch points for them to look at the information. So about 52% Indians use social media as a source of news too, right? And we know how it can be manipulated using AI. So we'll touch upon that soon. That's a massive number. 47% of Indians look for information about brands and products on social media. It's like no brainer, right? And very interestingly, from entertainment perspective, right? About 74% Indians subscribe to one more streaming service in past two years. That's massive, right? So if there were 10 people who are looking at one OTT in the year back, there are 20 people who are looking at two OTTs now, right? So number has literally doubled. So what it has done, it has kind of fueled content generation, content consumption, which also puts brands at risk because you never know where the content is coming from. You never know where the ad is going to go and whether that placement of my ad is in a right environment and in front of the right audience or not, right? So it kind of brings its own risks. Now before we move ahead, right? From AI perspective, I just keep the floor open for 10 seconds. Anyone of you can just shout out. In your perspective, in your organization, how you're leveraging AI? Anybody, I think it's not a right idea to throw open question in front of a large audience, but anybody who wants to give a shout to speak about, are you aware of how AI has been used in your respective organizations or your respective organizations. Anybody, any random answer is fine. Just 10 seconds I have. No. So let me, let me put it there, right? So there are multiple ways in which you can use AI and essentially the baseline for using AI is bringing in efficiencies across different work streams, right? So to begin with operational efficiencies by automating your repetitive tasks. So it can be generating your frequently required reports or it can be creating insertion orders or POs and stuff like that. So AI is bringing in more efficiency there. Smarter strategic planning, faster decision making. So AI is literally now doing your base level analysis where you used to need one analyst to do that. AI is doing it at one go at a scale and returning the outputs probably in few seconds. Impactful and compelling ads. Generative AI is the buzzword, right? Everyone knows Generative AI. Now, what you need, the 10 operators, what you should do now is being done by AI in a minute's time. And of course, from campaign perspective, meaningful insights to drive campaign effectiveness. We spoke about insights and analytics, right? Now it's also creating challenges for marketers in different spheres. Content quality, fraud, scale and complexity. And I'll talk about each of it. How? So if you look at what used to happen few years back, couple of years back, not even few years, couple of years back is probably they're aware about dozens of articles on a website, right? And it's to take certain time to create a piece of article and upload it on your website. So there were limitations to create content there. With the help of AI, now you can literally create thousands of articles in a span of a minute and put that as a piece of content on a destination. Look at the scale. If you have hundreds of thousands of websites bringing in 1000 pieces of 10,000 pieces of content in short span of time, imagine where the world is going, right? Estimates are about 99% of digital content will be AI driven by the end of 2030. And that's massive. And it's almost 100% of the content will be AI driven. And about 19% of programmatic bit streams are going to be AI powered. And why we say that? Because once this scale of content is generated, the AI will have to play a role to bring in more transparency in the programmatic ecosystem as well. Because that's where the supply will open up significantly. The demand probably will not be as much. And obviously the fraudsters will start playing games around it. And as we're talking about fraud, right? So fraud follows money and AI, the fraudsters are also leveraging AI big time. And it's across everywhere. Mobile app schemes where we've seen two times more fraud signatures coming in, under 37% more ad fraud schemes coming in on audio devices. And CTV obviously is a buzzword. And we see a lot of fraud and activities happening on CTV inventory as well. So about 15% of your CTV impressions are served when the screens are literally off, right? And we are not aware of these facts. It's Reels, the era of Reels. It's the era of shots. Obviously, TikTok is not in India anymore. But yes, this is the era of the youth who is a Gram generation as everybody calls it. And it's UGC content. UGC content can become really, really difficult to analyze and becomes really difficult for brands to stay away from. Because it's about 160 million hours of video viewed per minute only on TikTok, right? Probably we see similar behavior on Reels and shots in India if you draw parallels. And there is complexity of content, right? There is text, there is video, there is audio. How you as a brand will know what content is good for me, what content is bad for me, right? So these are the challenges which AI is throwing in front of us as a marketer. And that's where, if there are bad elements were using AI, then probably as guardians we also need to take help of AI to kind of counter that program, right? So how we as an organization are capturing this opportunity. So using AI to detect fraud at scale. How we're doing it? We'll explain that. So machine learning models paired with human insights is something which is helping us to detect fraud at scale, report fraud at scale and help advertisers save their media money. Classification, when I say classification, it's how we categorize different pieces of content in different categories, right? So there is a piece of content which talks about, for example, alcohol, right? Now alcohol can be a cocktail recipe or alcohol can also talk about drink and drive issue, right? So how that AI really captures the context of it and puts in a bucket which is about cocktail recipes or about vehicle disaster. Right? And how we can help our customers and brands today to safeguard their investments and not show their ads in vehicle disaster category, but they're okay with the recipe category, right? And obviously predictive AI, which is the next big thing which helps us optimize the campaigns at scale and bring in more efficiencies, right? So if you're buying something at X cost today, how you can save probably X percentage of that cost by optimizing it without any manual intervention and meet the KPIs that you've already said, right? I'll touch upon it. I'm just running out of time, but I'll touch upon it quickly. So from classification perspective, how we classify a video, right? It's slightly hazy image, but if you see how AI reads this as a video. So AI sees there is a wine bottle out there, right? AI sees there is a glass. AI sees there is a small handbag lying on the table. AI sees it's a table set. So probably what it will do is it will classify this video in alcohol low risk, which is a pink box that you see. If it would have been a car, it would have been an accident, then this classification would have been alcohol high risk because it talks about vehicle disaster or an accident where no brand wants to be seen on, right? So you can simply remove that from your campaigns. What's the sentiment? The sentiment is positive. So it will also categorize that video as positive sentiment. It will also put it into contextual categories like travel or food and drink or luxury and things like that, right? And then obviously as a marketer, you can pick and choose whether you want to be or your ad can be shown in this kind of content or can't be shown in this kind of content. You can literally block that piece, right? So that's how the classification has been done by the AI at scale, yeah? And how do we kind of, you know, activate AI for dynamic optimization, right? So there are multiple inputs that goes in the AI system, right? So we call it AI as a dragon, right?  And the more you train it, the more it starts following you, listening to you and doing things the way you want it to do, right? So there are multiple inputs today from campaign level that you can give to AI, right? So there can be something like as you can set campaign objectives as reduce my ECPM or you can say that maximize my convergence or increase variability to a certain stage, right? That goes as an input from K-Pay perspective. Then there is cost data that goes in, right? What bid you want it, maximum bid that you want to set it up for? There is measurement data from brand safety perspective, variability perspective, NGO, fraud, all that data kind of goes in this AI system, right? There are first party data that also can be fitted in and there is DSP data, right? Now AI gets all this data starts analyzing on more than probably 200,000 parameters for which it can bid on. And it bids on the best quality impression basis the KPI that you have, right? And it starts dynamically optimized. Now what used to happen so far is you have a set of traders or agency experts who are sitting on a desk looking at the campaign and they start optimizing the campaign on their own, right? But it has its limitation. They can't do it real time. They can't do it probably every hour. AI works continuously, looks at more than 20000 parameters and then bids the best quality inventory that you deserve and also pay probably lesser and give the fair price. I wouldn't say pay lesser, but give a fair price for that transaction. Which is what gives you outputs for you to measure on and that output again is works as an input for the AI to start optimizing it on further, right? Slightly complex, but from AI's perspective it kind of works to bring in more efficiencies and what efficiency drives, right? Media efficiency, right? Maximize campaign reach on quality media inventory by optimizing your quality CPM, right? We are not talking about CPMs anymore. We are talking about quality CPMs now, right? Because as a marketer, if you are paying X money for an impression that you are buying, you get 100% of the quality impressions in return of that money. There is no compromise on that. There is 0% tolerance there, right? Increase return on ad spends, right? Everybody, every CMO has this as a KPI probably on top of their minds, right? How do I increase return on my ad spends? AI kind of helps you increase that return by focusing on impressions that work and give fair price for that transaction, right? And obviously, attention measurement is a buzzword now, right? So how do I measure attention and go beyond just standard measurable viewability now? And how do I optimize on attention? So in real time, AI now starts optimizing on attention, which again works to deliver better return on ad investment for customers. Simple one case study I have. You can't name the brand. It's an automotive brand which using AI, how did we bring in more efficiencies there? 124% increase in attention index, 340% CTR improvement, 7% less CPM. And obviously, if this data can be fed into your brand list studies and stuff, you will get more enriched insights basis data. Yes, that's it from me. This was my last slide. So what we do is not only just keeping your investment safe, but drive outcomes by building in efficiencies and performance in your media plans. This was the only sales slide that I have. But thank you so much for listening to me patiently. I hope it was useful. I'm available backstage if you want to talk. Thank you so much. Thank you. Thank you so much, Nachik.


Alrighty, you got a construction update for you guys today, if you look really close to Tundra Twister they're installing all that electrical work going from the op booth and exit slash loading platform there over to the other parts of the ride, you can see them heading over to the pillars if you look really closely, it's the grey little pipes. Also some news is Vortex ran its first test cycle as the Patreon saw in my update yesterday, Vortex was on the top of the lift hill and it's no longer up there, thus it ran its test cycle. You'll see Vortex, the fly, and a bunch of flat rides and some Planet Snoopy rides doing some test runs starting tomorrow leading up to Friday as maintenance gets the rides ready and then they'll start actually cycling them this weekend with the crews starting their training. Every Friday, Saturday, Sunday you'll see an ride group testing as you've seen us previously clever every year for the last three years. Nothing really going on with Snoopy's Racing Railway other than it looks like they're getting the foundations for the buildings poured now so the launch shed and the second launch shed looks like they're working on that and then they'll come in and install some more track. Outside of that you can see some windows in the station there it's again it's gonna look a little bit almost like a barn and yeah clockworks coming along not much has changed there again you'll see a fresh coat of paint there same with Swings of the Century it's gotten some refurbishment this offseason but yeah a little bit of information is when they're running Vortex they actually sometimes will have someone up in a cherry picker just analyzing any noises or the parts of the track for anywhere and tear. Vortex is one of those attractions that requires some extra love and care. Anyways that's it for an update today we'll be flying almost every day now I will be taking this weekend off because we'll be on vacation anyways thanks so much for watching have a good one bye