So this is the second paper, a very interesting one, very relevant, how you can construct a DSG model where information is a state variable, and where you can test whether that makes the economy subject not to increasing or decreasing returns, so very, very interesting. Mariam Farboudi is the author and presenter here, so please, Mariam, I invite you to the podium and you have 30 minutes as you know, and the discussion is Eduard Schall, we will have a follow-up with 15 minutes. Great, thanks so much for having me, and this is joint work with Laura Welkem, who's at Columbia. So, I want to talk about a model of the data economy, and I kind of want to start by re-itrating what John talked about yesterday, which is there are things in the economy that is changing, and the same way that the policy that was used to regulate large, like, two-sided platforms does not work clearly for different reasons, it's unclear whether the ways that we used to think about different parts of the economy, like the production economy so far, are exactly relevant for the similar tech giants or the data economy still. The point is that if you think about it in a very large scheme of things, largest firms are very heavily valued for their data, and that raises the question whether and in what frequency or horizon the common ways that we used to think about capital is relevant for data as just an alternative asset, and where we should change our thinking, and I want to start by saying that this is kind of challenging because, one, data, and that lies at the core of the paper, that data is a byproduct of economic transactions, and it's difficult to measure. We still, as a profession, have not, I think, settled on a way to say, okay, this is how we measure data, so that makes life hard, and you can think about this as production is a form of active experimentation for firms, because that produces them with data that we usually think about, we used to think about it as a different thing, you experiment, you go and produce, now you produce and experiment at the same time. The second thing which, I mean, this is not, data is not the only non-rival or non-exclusive good, but it is one, and it's very, very important, okay. However, I want you to, as I go on, think about this as like a semi-rival good, so data value falls as more people have it, okay, it can be because of competitive forces, it can be because of a different source of regulation that does not allow a data seller to fully use it, and so on and so forth. Then the other thing which is important about data valuation is that data is the same piece of data that you produce, you can use it for multiple periods, not forever, so in that sense, there is some similarities to capital in that you have to depreciate it, but how do we depreciate the same piece of data that we cannot very well measure, okay. And the other thing related to this depreciation is that data depreciation rate depends on a lot of economic conditions, and both of these facts, and the fact that data is a long-lived asset basically gives rise to the point that you raise that we need like a dynamic programming framework to be able to think about data. Static methodology probably does not work well. So here, what we really want to do is we just want to provide a theoretical framework to think about these key economic forces, okay. So I'm not going to show anything rocket science here, okay. Everything that I show you've seen somewhere in some context, but what we are hoping that the framework that we put forward is simple enough, and you can see like 500 million simplifying assumptions that I'm going to make, okay. It captures the main forces about data, and it allows us to think about important things that are also very important for regulation, in particular data markets, okay. Although I'm not going to have time to talk about it here, because I've got to focus on long run and short run properties of the economy on policy and on measurement, okay. And kind of what I think is quite interesting is that this model as simple as it is, it has very realistic predictions that we see in everyday life around us. So the model is a recursive framework, and it is really as tractable as a standard DSG model. Of course, if you want to do measurement, you should not use this model because it's too simplified. But it's very like the other DSG frameworks, we are hoping that you can add as many complications as you wish to it, and then go and numerically measure it. So it allows us to value the data and data intensive firms, okay. It values data that is transacted at zero price, as well as like relevant digital services, kind of things I want you to have in mind, things that John talked about yesterday. And also hopefully it can inform us about GDP measurements. So part of the GDP measurement that is missing, because we don't measure data. All right, so let me jump into the model. And I'm going to kind of go fast on some pieces, but it's, anyways. So there's got to be, so I want to be again upfront. I'm going to try, in this paper, we're going to try to focus on what is data good for, okay. We know a lot about what is data bad for, so I'm going to focus on about what is data good for. In particular, I'm going to shut down any competition, okay. So not that we think there's no competition, but let's shut it down. So there's a continuum of competitive firms. Each firm uses capital, capital firm I at time t is KIT to produce with the Concave Technology, KIT to the Alpha, and every good has a quality, okay. And the quality, I'm going to call it AIT. You can think about it as a productivity, but let's call it quality. Now I need to talk about the output and the demand curve. So first simplifying assumption, output, all goods quality adjusted are perfect substitutes. This is not true, okay. But this makes my life a lot easier, okay. And you can use Dixit Ciclet preferences to change it, okay. Then we're going to assume a downward sloping demand curve, okay. So and then at the end, hopefully I'm going to have time when I want to talk about efficiency to give you a micro foundation of it. But for now, take it as given. So you can see there is no notion of data here. So I have to introduce data to you. So what's important about data, I want to, like going into this thought, I want you to think about data. And when you think about data, think of three things. One, data is a byproduct of economic activity. Two, data is semi-rival. And three, data is used for prediction. Not all the data in the world is used for prediction. So patents can be thought about a form of data. Not all patents are about prediction. There are patents that talk about, that are about prediction. And there we have to say something to say about them. But not all the patents and patents are data. So this paper is not about all the type of data in the world. It's about data that is used for prediction. Why? Because a lot of technologies that are being developed now, AI and ML technologies, that are tools for prediction. So that is a large part of the ongoing debate. So because data is used for prediction, it is used to improve forecasts. So I have to introduce forecasts into my models. And quality is where forecasts come in. So good quality of each firm depends on the forecasts. How do we think about this? Imagine that each firm has an optimal technique to produce it. You can think about this as customer taste. Like whether we write like next year, we would like running shoes, we would like blue shirts. You can think about Uber, where am I going to send the cars? OK, so that's also the optimal technique. You can think about what technologies has to be incorporated into self-driving cars. OK, so all of these are different optimal techniques. The optimal technique has two parts. One part is predictable. That's theta t. The second part is not predictable. It's completely IID. The predictable part is AR1 and has an innovation. OK. And the unpredictable part is unlearnable. That's what I mean by unpredictable. Then what about the quality? The quality depends on the chosen production technique by the firm and how much it is different from the optimal technique. So if people are going to like blue shirts, the closer you produce the blue shirts, OK, your quality is better. OK. So you can see that A, which is the quality, is a G function of the square difference of the chosen production technique and the optimal technique. Square difference for all the information people expected square is variance, so that's beautiful. OK. What's important is that G is monotonically decreasing. What does that mean? That means accuracy is good. You want your production technique to be as close as optimal technique. OK. All right. Now, this is what forecasts are used for. Now, where does data go? Data is information that is used for forecasting. OK. Now you can see the fact that I said data is a byproduct of production. OK. So a firm that produces KIT to the alpha produces NIT data points. OK. These data points are about the future optimal technique. And data is basically a byproduct of the amount of production times the technology that you have for data mining. How much data you can extract from the same amount of production or transactions? You can think about Walmart as a company which has terrible ZI versus Amazon as a company that has great ZI. OK. So firms can be different in how good they are in the technology to extract data. Of course, thinking about the evolution of Z and how Z is determined is very important. That's outside the scope of the paper. I'm going to take it as given. Now, each data point is just a normal signal about the realization of the future optimal technique. All right. So this already gives you something that we've all heard about very often, which is the data feedback loop. If a firm has more transactions, it has more data. It has higher quality or efficiency. Then it has more customers and more transactions. And this goes on and on and on. And the whole thing should explode. OK. I want to argue for you that this, although the state of feedback loop is in action during when firm is young in the short run, if you want, it's not the dominant force in the long run. In the long run, there is another force, at least for data that is used for prediction that kicks in and is stronger than this force. OK. So before going to that, let me talk about one last piece that is important to think about data. So so far, most of the things that I said, you're like, OK, this is learning by doing. OK. And this is where data is very different from learning by doing that is data is tradable. OK. And that's an important key part of the paper is the market for data. OK. So let's delta IT, the amount of data that's traded by firm I at time T for simple. And then delta IT is positive if firm I is purchasing data and it's negative if it's selling data. For simplicity, let's assume a firm can buy or sell, but not both. If that's not that relevant. OK. I won't have time to tell you what changes, but anyways. Then also, in the spirit of saying that thinking about good things about the data, assume that there is a comparative market for data trade that clears at the price pi. OK. Now, as I said, the other important feature of the data is that data is multi-use. What does that mean? That means data is non-arrival or some, I want to call it semi-arrival. Because firm can sell the data and still use some of it. OK. So let me introduce this parameter, IOTA, which is the fraction of sold data that is lost to the seller. If IOTA is one, that means data is perfectly rival. Think about capital. OK. I have it or you have it. If IOTA is zero, that means data is perfectly rival. We, for technical reasons, we cannot handle IOTA at zero. But you can go as much as you want to close to zero. And it's not such a bad assumption because many data contracts include like prohibitions on seller use of data, as well as it just can simply stand for competitive forces at the value of data falls. Now, because it's a dynamic model, I just don't want things to converge instantaneously. I need an adjustment cost for the data. All right. OK. Now, let me very quickly tell you what I'm going to show you. One, I'm going to show you that, well, data is an asset because it's long-lived. How we should depreciate it and value it. Then I want to argue that in the long run, there's a second force that dominates data feedback loop and that is diminishing return. And that means that in the long run, there cannot be any, in this model, there cannot be any growth without innovation. And innovation, I'm going to talk about innovation too. And innovation can be done purely by data that is used for prediction. And the formulation that I'm going to use, which leads to endogenous growth, looks like a data ladder. OK. But the reason for diminishing return is very simple. As you saw, data is to reduce variance in this model. OK. And variance is a concave function. OK. The first unit of data that you have tells you a lot about how you should move your actions, adjust your optimal actions. OK. But once you have 500 million points of data, the 500 million plus one point of data doesn't help you that much. And that means that in the very long run, when there's a lot of data, decreasing return to scale kicks in. But in the short run, there is increasing returns. And in fact, we show that every firm in this economy needs to make initial negative profits. That can lead to, because they buy data on the open market, that can lead to data poverty traps for the firms. We can also talk about two things that we see every day around those data borders, the apps that you all have on your phones, which are given to you for free. Because the firm wants to basically exchange the good that is producing and giving you with the data that it can get from you to improve its own quality going forward. And then we can talk about book-to-market dynamics. At the very end, I'm going to quickly talk about welfare in this model and how you can very simply introduce business ceiling externalities. All right. So the first, let me talk very quickly about Bayes' law and how to use that to think about data depreciation as an asset. Remember, the goal is to forecast the optimal production technique tomorrow. And the priors about the optimal production technique today has, I mean, an variance. And think about the inverse of the variance, which is the precision as the stock of knowledge of a firm. This is how much the firm knows. And then you can see that why I built in all this beautiful linearity, because Bayes' law says that for normal variables, posterior precision is additive. It's prior precision plus all the signals that you receive from the data that you have. So the posterior precision of a firm about its optimal technique is its prior precision plus the precision that it gets from all the signals that we are going to talk about. And that tells you when you have discount is previous stock of knowledge more. When the persistence of the optimal technique is lower, then the previous data is not that useful. Also, when the economic environment is very volatile, so there is a lot of noise to the optimal technique, maybe because new technologies are arriving. Then also, you need to discount the previous knowledge more. And now, that gives me all the grounds to think about valuing data. As I said, the optimal technique that the firm has to produce with is the expectation that it has with all of the data that it has and all of the precision that it has about its optimal technique. So the quality is basically a function of the square forecast errors that gives me my state, my single state variable. So think about to put in a DSGA model, which is the stock of knowledge. It's the inverse variance of the belief of the firm about its optimal production technique. And that makes my life very easy. I can reduce this huge model into a value function of the firm, and the only state variable is the stock of knowledge of the firm. Now, the firm chooses a time key, its capital, and data traded to maximize the sum of a bunch of terms, the amount of profits that it makes on the goods market. And the quality of the goods is determined by how much stock of knowledge it has to produce better goods. The firm is price taker. Then the firm has to rent the capital. The firm has to pay the adjustment cost. Importantly, the firm can trade.  It can use the data and add it to whatever, to the signal that he has. Or it can sell it on the data market and makes profits. So you can see that data has two different uses. One is that the firm can use it itself, or it can sell it on the data market. And this is really an important force to think about regulating data market among firms. So another point which I think is important is that a lot of regulation efforts has gone into regulating the data market between customers and the firms. This is a different data market. This is the data market across, if you want, firms, different firms, or firms and platforms, which are firms. And there is less work in regulating or thinking about those markets. So let me say one last point about this notion of semi-rivalry and data markets. And that gives rise to this very strange property of the data market. So think about what is the benefit to buying one unit of data? You get the marginal benefit of increasing your stock of knowledge, minus you have to pay for it. What about the cost of selling one unit of data? The cost, you only lose a euro fraction of it. You don't lose all of it. But you gain the whole price. So you can see that this is negative bid-ask spread. Negative bid-ask spread is something that we are not used to. And I say, what does it do? It puts a compare, it gives firms an incentive to participate in the data market, OK? Because they lose less than buying the data, OK? And so that means, and that is the part that has to go into thinking about how to design this policy. In fact, in this model, you can see at the very end, I'm going to show you a very simple case where firms oversell data, OK? So you have to stop selling a little bit, OK? So now, in terms of the long-run growth, the long-run growth with the quality function that I showed you is very simple in that the data inflow is concave because of the force that I told you about the long-run. While the data outflow is depreciation is almost linear. This is not exactly linear, OK? So in this case, the growth would stop. Now, you can say that, OK, this really depends is very specific to all the assumptions that you make, and there is some specificity. And here is the specificity. You need two things at the same time for the growth to stop. One thing is that, first, for the growth not to stop, sorry, one thing is that, well, infinite quality should be possible to reach when you have a lot of data, OK? That's kind of a little bit like, obviously, you would need that for permanent growth. The second thing is that you cannot have any fundamental randomness. So that you can achieve perfect quality if you can perfectly hit the optimal technique, OK? But you can only hit the optimal technique with all the data in the world if there is no fundamental randomness. You have to be able to learn everything. So to the extent that we think there is fundamental randomness, like COVID, with this type of quality, permanent growth is not possible. However, you can have endogenous growth. As I said, think about a lot of quality. The quality today is the quality yesterday plus the quality of a new technology, I call it Delta Heart AIT, if the firm chooses to incorporate it, that's the max, OK? Now, the quality of this new technology depends, that's what depends on the data. That is, again, this is similar to the G that I used to have. This is decreasing in the error, OK? So data that is used for prediction improves the forecast, decreases the quadratic error, or if you want, decreases the risk. You can think about decreases the variance or decreases the risk. Think about self-driving cars. The technology is about decreasing the risk of an accident. If the risk of an accident is very high, the profitability, the efficiency of this new self-driving car technology is negative, you're not going to incorporate it. Once the prediction is good enough, then it's good and we incorporate it. And that can change the frontier of technology. This is still a purely predictive technology, but it can lead to endogenous growth. So in that sense, in the long run, data does look like capital. It depends on what data in its purely predictive role looks like capital. In the short run, things are different. In particular, there are parameters that when knowledge is scarce, the stock of knowledge of the firm is small, then the net data inflow is actually convex. So it increases over time. You can see to the left of the graph and that's the red line. So you can see that there is a solid red line. That's the total data of the firm. The dashed red line is the data that comes from firm's production. And the gray area is what the firm buys on the data market. And this, in the beginning, you can see the firm has to either produce data or buy on the data market. And that leads to losses. The firm, in the very early part of the life of the firm, the firm doesn't have any data. So its quality is low. If it wants to produce, it has to rent capital, make losses to get data. Or it has to go buy data on the data market to improve its quality. So in the early phases, there are profit losses. But there are an investment in data by buying data from the data market, from other firms, or getting data from production. So and that rings a bell, like Amazon has been making. Amazon has been making losses for too long. But anyways, so that is something that we have been seeing very often. And the thing is that by accounting rules, at least in the US, the book value only includes purchase data. And a lot of data of the firm is produced as a byproduct of its own economic transaction. And that leads to an undercounting of the book value. So that means a very large market to book. Then the other thing I want to talk about is the data barter, which is basically why do you want to produce at a loss? And the reason is that, effectively, when a firm is producing at a loss, it's exchanging data for the good at price of good of PT. It gives you the app so that it can attract your data and then produce a better quality good and then better quality app. And then come up with subscriptions and get people paid for it. And that rises in the earlier life of the firm because the valuation of the firm is increasing in its stock of knowledge. And this also leads to a lot of missing GDP value because this digital economic activity is undercounted. So let me use my last one minute to very quickly say that you can all see that I've made everything in the model perfectly competitive, okay? So the market, the model, the equilibrium is efficient, okay? Let me talk about the simplest way on earth to incorporate inefficiency in this model, which is think about it like a lot of data is used for advertising. Maybe not all the advertising is actually quality enhancing, okay? How can I think about it? A simple way to think about it is that data processing helps the firm that uses it but hurts others. You can, there is this framework thing introduced by Morris and Schir which is called business feeling, okay? So you can very simply incorporate it in the model. The quality of the firm is downward sloping in its own forecast error but it's increasing in other forecast error. So this is very similar to think the concept of keeping up with Jones's in consumption, okay? The firm's quality is high only if it is better than its other competitors or other firms in the market, okay? And basically you can see that there's an integral, blah, blah, blah, so a firm's choices and firm dynamics and aggregate quality are unchanged. What is changed is welfare. In fact, in the data market in this case, there is over trade because firms do not incorporate the fact that when they're selling data, they're hurting themselves. So thanks so much for having us. Thank you, thank you, Mariam. Adwoa, please. All right, let me first thank the organizers for giving me the opportunity to discuss the paper. This is a paper that's been around already for quite a while. I knew it already. I've seen it many times but still there is a lot to say about the papers. I'm really glad to be given the opportunity to discuss it. So what does the paper do? So the paper first asks a very important question which is how is big data transforming our economy? And what the main objective of the paper then is going to provide a baseline framework that everybody can use to start formalizing a little bit the debate. In doing so, they're gonna choose a very particular approach which is to choose one particular way in which data can be used and that way is gonna be that data is used in forecasting.  Firms are gonna be trying to track consumer taste and they're gonna try to, knowing that, propose services or goods that match this taste the best as they can, okay? Now, the second key idea of the paper is that data is a byproduct of economic activity. So here, transactions generate data. As we're gonna see, this is gonna be really the core of most of the predictions of the model that are gonna be unusual. Data is also a non-rival thing and as I'm gonna show you, I'm gonna discuss, at least in the data, there is a lot of trade in data and so that's also something that is allowed in the paper. So the model that they propose is a very tractable model. It is basically a very standard firm dynamic model with decreasing returns in the long run. So it looks a bit like a standard open hind type of model. So it's something that we know very well and therefore can be very easily introduced into a more standard DSG model. So the model is simple, but it's already able to generate a lot of predictions. The main prediction is, I would say, on the short run. So the presence of this feedback from data, this idea that economic activity generates data, is going to produce some feedback in the short run that's gonna lead to increasing returns. Basically, firms are gonna start producing low amount. They're gonna collect more users, more customers, more data. That's gonna allow them to increase their productivity. They'll collect even more data, et cetera. So initially, we're gonna have this phase of increasing returns. Now these increasing returns are not gonna last forever because this is data used in forecasting. At some point, as you accumulate a lot of information, you're basically gonna know the truth and certainty is gonna be low. So a marginal increase in information is not gonna increase your TFP forever. So in the long run, basically we go back to a standard model of firm dynamics with decreasing returns. So that's really one of the key features of the model that is then gonna generate a lot of the rest of the predictions. So some of those predictions, for instance, is that the model can generate negative profits. That's not surprising because we have increasing returns. What is more surprising here is that the model can explain the concept of data barter. So the idea that these firms can actually still exist even if the products or the services they're proposing has a zero price, just because firms are actually collecting data and selling it on the other market. So something that connects, again, to Jean's discussion yesterday about two-sided markets. The model, the paper also covers a bunch of discussion about measurement because data, of course, is an abstract thing. It is hard also to measure. So there's a lot of mismeasurement and the paper goes over different issues about book values, market values of firms. There is also the question of missing GDP because of data barter. We might be missing a lot of potential GDP. And then the paper turns to long-run issues. So it's gonna talk about whether or not data can generate long-run growth. The answer here is that in the baseline model, it is not possible. This is because here mostly data in the baseline model has a level effect on TFP, not a growth effect. And it also turns to welfare. So here the model is really a simple benchmark, frictionless, perfect competition. There is no externality. Data is non-rival, but there is a market for it to show no externality. So the model is actually efficient. Despite the fact that you have non-convexities, this is not something that leads to a failure of the first welfare theorem. So the model is efficient. Of course, this is not necessarily something that we believe and the authors agree. So that's why then they turn to, they introduce some relevant externality, which is this business stealing externality. So that's a quick summary of what the paper does. Let me now turn to my comments. First of all, this is a thought-provoking paper. So this is an important paper because this is really, maybe with Jones' eternity, one of the few papers that really opened this new research agenda on the data economy. So this is a big picture paper. Maybe we're gonna see, we're gonna discuss that, maybe people in the audience might disagree about some forces that might be missing, but this might be true, but this is not the point. This is a paper that is a big picture paper that tries to derive strong insights out of simple assumptions. And I think on that, the paper does a very nice job. The model is very nice and tractable. And I think this is a great tool for other researchers to build upon. So as I was saying, the paper has been around for quite a while. I think a lot has been said already about the paper. Whatever I'm gonna say, I'm sure Mariam and Laura have thought about it and they have good reasons not to have included it. But anyway, just for the sake of the discussion, let me go over some of my comments. So I would say one thing that is striking about the paper when you read it, is that for a phenomenon that is very empirical, that is all about data, there's not much data in the paper. So this is a very theory-driven paper.  We should be allowed to write pure theory papers. But it's true that in this paper, I think the tiny tension is that a lot of the predictions are very, very model dependent. And so it would be nice perhaps to give some supporting evidence for all these modeling choices. Now, I'm saying that, but of course, this is one of the first papers in the literature. You have to make choices. I think the choices that Mariam and Laura have made are very natural, especially coming from people that work on information. But let me give you a few examples of the things that of course we're led to question. One is, for instance, so why is data something that affects mostly the level of TFP and not the growth of TFP? So Mariam talked a little bit about that, but the long-run implications, whether or not you can generate growth, are absolutely linked to this. People in the literature, John Zentonetti, have likened data to ideas. So therefore, ideas is something maybe that could be an input to R&D innovation. So naturally, we can question that. Another one is the fact that the paper puts a lot of emphasis on this feedback from information, and this idea that transactions generate information. So I think it's a very interesting idea. Is it true in practice? Is the data that firms collect really something that scales up with their activity? I'm going to try to show you some pictures later. Perhaps this is not always the case, so we may be led to question that maybe this feedback might be weaker in practice. Another one is also the strengths of the diminishing returns here. So here, naturally, we think that there are diminishing returns in the long-run because data is something that, as I was saying, in the long-run you know everything, so data does not really reduce uncertainty anymore. Well, that actually depends a lot on functional form assumptions as well. So here, the authors are choosing a Gaussian model of learning. Of course, this is great. This is super tractable. You have one state variable. But if we were using a fat tail model, and if the information you were getting were about where the lower threshold is, actually, the variance may not decrease, and you might still actually have endogenous growth through a different channel in this model. So I'm not saying that I dislike any of those choices, but just that the predictions depend on them. And ultimately, what we'd want to do is that these choices should be guided by empirics. Now, so I'm going to discuss a little bit the empirics then. I'm going to try to do that in practice. So it would be nice to know who, which firms use big data, which sectors, from what sources, what is the kind of data that they collect, and also how they use this data. So I'm going to try to go a little bit over that. Now, I was surprised to discover that despite this being a very important phenomenon that is all about data and firms having too much data, it seems that these firms do not share this data with us. Most surveys are still actually quite vague and quite loose. So I'm just going to show you what we have. What I'm going to show you actually is from one of our students at UPF. So one of the great impacts of the paper is that it has triggered a lot of new research. And our students are working on Mariam's paper. So my student, Alejandro Rabano Suarez, has kindly allowed me to show you some of his pictures. So let's go over that. So the sources are going to come from two surveys, one from France, the other one from the US. And these are surveys that are going to try to cover a broad enough spectrum of firms, trying to ask about their use of big data. Big data, of course, is something we need to define. It's a loose concept. The way it is done in most of these surveys is the following. So big data, we're usually going to think about the use of massive data sets. Are you usually going to have a huge volume of data, a huge flow with continuous updating of this data and also a complex structure? All these things that make it hard to use standard tools to analyze the data and require specific technique. So that's kind of the broad idea what we're going to define as big data. I'm not sure you can see very well, but this is data from France, so that tries to cover across different sectors the adoption of big data and also the sources of big data. And what's interesting is to see that actually in France, at least the main sector that uses big data is transport. And the source of the data that they use is actually geolocalization. So think about Uber really using geolocalization for its customers and also its drivers, but also think about delivery firms that are actually tracking their drivers to optimize the delivery process. Already there, we can be led to question whether or not the amount of information necessarily scales up with the number of customers if it's just about tracking your own drivers. That's not necessarily the same. The next sector is information and communication, not too surprising. What's interesting is that for them, then the main source of information is going to be social networks. So the main things to take away from this kind of picture is the following, that big data is clearly something that is big, 25% of adoption in the transport sector, but it's also a very varied thing. Different sectors may or may not use big data and they may also use very different kinds of data. This is another figure that goes along the lines of Mariam and Laura's paper. This is about the use of big data as a function of firm size. And it seems to be that, indeed, small firms do not use big data. Large firms do. So that's something that seems to be in line with the predictions of the model. Also something that could be in line with the use of fixed costs. Perhaps sending up a big data department in the firm also requires huge setup costs, lots of storage facilities, and the right skills to study that. Finally, about the use of big data. So it turns out that it's been very hard to find also good evidence on how exactly is this data used by firms. So this data here is from the US. This is not about big data. This is about the use of AI, which is still somewhere along the lines, AI being one of the main techniques that I used to analyze big data. And so this is what firms have reported in the survey. So for the users of big data, how they use big data. Some of them replied that they use it to expand their businesses, orders to automate. So going back to Luca, orders to upgrade, or maybe the quality channel that they are proposing. So I think trying to connect back these different uses to what people have been discussing, I think it's fair to say that we can think perhaps about maybe these three different uses of big data. One big use of big data that seems to be true in the data is that a big part of it is might be used for marketing and advertising. So perhaps the firms that have responded that they expand their businesses. So perhaps a good example is the targeted advertising through social media. And so perhaps we can think about big data as improving matching between people. This is something that they touch about a little bit in the paper as a possibility, but I think indeed is an interesting avenue for research. I think about a model of search frictions with imperfect information. You have more data, perhaps you obtain a better matching between firms and its customers. Perhaps this could be the only model of product awareness as in JCPerla's John Marquet paper models of advertising. Another one is about improving the production process. So thinking about firms are just producing one good. It's still the same good, but you're just going to become more productive at doing that. So for instance, delivery firms using geolocalizations for their drivers to optimize the delivery process. So this is probably something that we want to model a big data as R&D, something that leads to innovation and a growth effect in JCP. And finally, I think as we see in this survey, 80% of the firms mentioned the quality. So of course, the answers are a bit loose, but that might be exactly along the lines that I think Laura and Maryam are trying to go. The idea that there is some customer taste that is volatile, that firms are trying to track, and that they're really using a lot of this data to offer the best product to fit this taste. And here I have a quote from Netflix and the Cheesecake Factory. There's a lot of anecdotal evidence that firms are doing that actively, and this is what this paper is about. So overall, what we see is that the adoption of big data is large, seems to affect businesses importantly, but the phenomenon is quite varied, and probably could be modeled in different ways. And I think it's important to think about these different ways, because the welfare impact and if you want to think about regulation is going to depend a lot on this. Another thing is that not all data is necessarily linked to past transaction. We saw that there are many different sources, social media, connected objects, et cetera. So perhaps the feedback from data that is put in the model might be weaker in practice. So in the end, so I think whenever we talk about big data, I think it's very easy to be quite loose about big data. It's a very abstract thing, and it's hard to measure. I think a strength of this paper is that it does not do that. It embraces a very particular way that what data can do it is about forecasting. Now, I think the paper still is a bit loose on the product market side, because in the end, the model tries to capture this idea of quality, but the quality is never fully measured. We have perfect competition. Everything is loaded on the TFP function, this function G. And it would be nice to know what is G. Where is it coming from? How do you discipline it empirically? Or perhaps use micro foundations to do that. I don't have much time. Perhaps I'll have some nerdy comments in the end, but maybe I see a minute off time. So I'll keep it for Mariam. In the end, just great paper. I think it's a very inspiring paper to lay the grounds for the debate. It's already one of the supersighted paper that has opened the entire agenda. Of course, when we see the paper, we want to introduce more forces. And I'm listening here, some of those forces. Thanks a lot, Mariam, for the paper, and yeah. Why didn't I give you the floor back for in case you have some feedback? I just want to say thanks so much for the very kind comments. And I cannot agree more with all the stuff that you said. In particular, the fact that there is a lot of lack of data about data. I, in fact, with two colleagues, we have fought very hard to finally buy a data set about the supply chain of digital services from a startup in California. So hopefully we can talk about these three classifications. We, in fact, ask them to specifically classify their technologies that are in these like advertisement, quality improvement, blah, blah, blah. So you're like, at least you're exactly on board. I don't know how we did it because I haven't told him about it. The only part that I think I would probably, my way of thinking is slightly different from you is this notion of data is a byproduct of economic transactions. Let's, let me just mention what you said about like the geolocalization in like Uber or transportation. That is in, I mean, I would call that economic transaction because if somebody would not order an Uber, a driver would not go there. But that's the way that I think about. So probably you're right that, I mean, we have to be more precise about what I'm including in economic transactions. But, but, but that's, that's, I guess, that's the only thing that I would say. The language that I'm using is different from the one that I, but everything else I'm like 100% on board. So I think it's a great paper and I agree with the discussion that it's very inspiring. So I wanted to ask about the following. So I think it would be, it would be great in the future to have a version with imperfect competition. So I really like this idea of thinking as data, as used for forecasting and as being gathered as part of economic activity. But then if I think of a firm that's gathering data as part of its own economic activity, it's probably gonna give this firm some informational advantage over the others. It'll make it possible for the firm to charge a markup. And then if I think about the mark, how the market for information would work, then it would actually be, so you, at some point you said, you know, if we think about physical capital, then either I have it or you have it. Well, with data you can, you could sell your data to me and then we both have it, right? So actually your IOTA might be zero, but what I gain by buying your data is I may be gaining the ability to steal some of your customers. I'm forecasting better myself now. So maybe I'll end up reducing your markup. And I don't know actually, so this extension about with business stealing may be going some way toward this. Maybe you could say a few more words about that. Okay, so I think what you raised is like actually great. So in fact, I would like to think about IOTA exactly as you mentioned.  I'm 100% with you that the next step would be to incorporate imperfect competition and that's actually not even the only margin. The margin of entry is very important in digital markets because when you think about digital platforms, one of their points of selling is that we're providing a platform for new entrants to actually use computing power and big data using the fixed cost of somebody else like AWS or Microsoft Azure. So in a sense, these are incumbents that are making money out of the new entrants. So they might not have the right incentives to share the best possible data with them. And that's a slightly different margin but again, imperfect competition. In particular, the business stealing because we don't have that notion of information advantage in fact goes in some sense to the other direction in the sense that as a firm, when I sell my data because everybody else is small, I don't incorporate the fact that my data is being used against me in that I'm basically, I have to be better than them but that helps them. So I oversell my data in a sense and the planner, a social planner would like to a little bit attenuate the activity in that data market. Okay, that goes the other direction but that what you mentioned is really, really important. Yeah, very nice paper. I was wondering a bit about like the interaction with R&D and discoveries of new products or technologies, whether there might not sort of be a trade-off here that firms may like. I think also about John's talk yesterday about platforms and maybe you put more investment into sort of milking that data rather than inventing goods. So would there be sort of a long-run trade-off there? So that is an extremely good point. So one thing that you can think about in this framework and there are other papers who think about this in other frameworks is that okay, data can be used for a level shift in quality, the key part of the paper and there is downward return to scale. At some points firms are better off actually doing a jump, a ladder, but they might, but if that's kind of investment is costly, they might go too long for milking the data as you mentioned. So that's a trade-off that would be amazing to think about. We have not thought about the ability of the firm to switch between these different two types but that's a great idea. Luke. Thanks. I wanted to push you on regulation of data markets. I mean to the extent you can do it with the model that you have because most of it is, things are efficient, right? But in this extension with data stealing that Bartosz also asked about, would you like us to think about regulation? Regulation of what? Because first of us thinking maybe the depreciation schedule. We could just have different accounting rules or whatever. But then you also highlighted the fact that most of these economic transactions are not even incorporated in the way we measure outputs. So could you help me a little bit to think about what we would regulate? Okay, so let me say two things. One of them is unfortunately we took it out of this paper because the paper was too large. Is that this notion of negative bid-ask spreads that I mentioned makes that even if you start with perfectly identical firms in the steady state, the data market would not die because firms would like to trade, okay? And then the model is too simple, but if there is the other business stealing, if you want like force, then there is too much trade and firms become too dispersed from each other while. In terms of the regulation, there is... I'm sorry that I'm not aware of what's happening in Europe, but in the US there is this kind of newer industry called data brokers. What they do is that they basically collect either other firms' information, like the firm that we bought their data from, or they collect customer information and they sell it to other firms or competitors of the original firms and want to know what their competitors are doing or sell it to firms who want to advertise to these customers. And for instance in California and I think Vermont, these data brokers have to at least subscribe to a list so that people know this. So this notion of at least, I don't know, providing some broad knowledge that you have to at least say that you are this or maybe trying to tax them in some shape or form because they sell these data packages and I've called like 40 of them and I was like, can I get like anonymized quantity price of your transactions and they're like, no way because that's their business model and because that's so unknown, I have to ask you as a key regulator, how do you think it's possible to get access to the data of these guys? I don't see further questions from the floor, but there is one here. In the linear normal framework, more information reduces variance. Could trading on it introduce noise to counter decreasing returns to scale in forecasting? So there are different ways of decreasing returns to scale to forecasting.  Well, that's one way. The other way, which is slight in some sense, simpler but it has its own difficulties is if the variance of the noise of the AR1, which is the variance of innovation, is increasing over time, then the effect of downward return to scale kind of falls because you're discounting the same information more. So that becomes then more and more important. When basically the point of this variance is that your prior information gives you a lot of information so the new data is less useful. If you discount that more, then new information is more useful. So the decreasing return kind of dies down. That's a very good point. That is again, as everybody mentioned, is very context dependent in some markets. That's the most relevant one. In some markets it's less relevant. So this brings us exactly at the end to the end of the session. Great paper, very good discussion. Thank you and great session in general. So many thanks. There is a coffee break now. A hard commitment to come back at 11.45. Thank you.


Awesome, let's get started. Hi everyone, I am Rasagya. I am here to talk about maps, data and how to get data on maps or in maps. Just to kind of wake everybody up a bit and see whether everybody is actually listening to me. Quick raise of hands, how many of you used a map to come to MLR convention? Great, so I think people are there in the last seat and here I mean people from Israel. Cool, second question, how many of you used a map to come to this auditorium? Not so many, like few of us probably using the Haskig app to figure out how the venue is, but few of us already built a map in our head. So we had like a mental map of how this place is, some of us are Haskig regulars, so they already know this place very well. The third question, which is obviously the start of the talk is how many of you have used data and put them on a map to show in a visualization or in a project or an attachment? Awesome, okay great. So that means that a lot of you have already seen some of this stuff which is great, a lot of you have been trying to do this stuff which is also great and some of you probably would be scared by some jargon which hopefully by the end of the talk you would not be scared of. So to quickly start off, I work at AppOps. It's a platform for developers and designers to create basically different kind of stuff with maps. You would want to make visualizations, you would want to make a dashboard, you would want to make a tool which shows how delivery is happening or the people here are using Snapchat, you want to use Snapchat and see where plays a different people's photos. You would use Mapbox or a tool like Mapbox. So the way we like to call it it's like a Lego building pieces, take different blocks, put them together and make what you want to make. So this is roughly what Mapbox is. And my work at Mapbox, I work as a designer. So my work is a lot in making custom maps. So we sit and refine details and details of what shows up on a map and sometimes it leads to really good results. So if you would see for example the Fifth Elephant website, the map looks beautiful because it feels part of the whole website has this nice scale color. So custom maps is fun but this talk isn't so much about custom maps, it's much more on visualization. So the other kind of stuff that I tend to do is to see how you could use data and put that on a map and try and see if that gives a more insightful view than say making conventional charts like R charts or 5 charts if you are the one behind. And I also try to play around with using maps as art. That's a separate topic. I spoke about it a while back with the elephant but people used to catch me that's the kind of stuff you like to talk about as well. So let's get started. With all of the stuff that I keep doing, I tend to get two questions most often. The first question is what? Like, hey, I have this data, what can I do? Which essentially means what are the different types of charts or visualizations that I can make with the kind of data that I have. Now the common charts that we already know work very well. There's so much written about it. There's also like a very seminal paper called A Tour of the Visualizations Zoo. I'm hoping some of you have read it or heard about it. That's partially where the talk title is taken from, a voyage into visualizations. It's made from Jeffrey Hayes' seminal paper. But yeah, we all know about conventional charts. But some of us are not so familiar with what to do with a map, right? Or how do you approach that? So what can we do? And the second part is you've already figured out how you want to show your data is to think about how do I do it well? Or maybe if I'm making say a choreograph or a cartogram or do I keep in mind so that I can do this well. And that's roughly the two things that I'm going to talk about. A very quick list of things that I want to pick. So I don't want a jargon drop, but I want to use this as an itinerary for this talk today. So going to go and keep checking one, one, one thing of a list and see how we're doing it. So quickly start off, talk a bit about maps, about things that are there that you would want to put on a map and then move forward. Alright, so let's get started. This is a very typical map visualization. This shows basically different bus stops that are there in Mumbai. You probably wouldn't recognize the shape. What I want you to think about is the layers that are happening here. So currently that you can see there are two major there. There is this data of these nice firefly like dots that have been shown on a map. And then there is the underlined data that's actually the mapping, right? There's a shape of Mumbai that you all can recognize. There is a label which tells you that it is Mumbai. There are these small airport symbols and there are roads etc etc. So for any visualization that I'm making, I need to think about two things. I need to think about the geo aspect. And by geo I mean what is the map? What does the map look like? Where will the data go on top of? And second is the actual data. That is your own data. You have like a CSV, a topo JSON, a geo JSON, a different sort of data sets that you already have and how do I get that onto a map? So the reason I'm talking about this and that's a very small segue that I want to do is just get everybody on the same page. And also like break these very obvious terms that people generally talk about in geo visualization which I think is very easy for all of us to know. So let's just quickly think about it. First, there are three types of things that you can make on a map or you can show on a map. They can be nodes, ways, relations, which are just fancy terms for saying dots, lines or like squares or molecules, right? So you would have saved places. For example, how many different MLR convention centers are there in Bangalore? Each will be represented as a top and that works very well. You would probably have lines for example, what is the path to one MLR to another MLR? How is it traffic on top of it? Which one is an alternate route? Or are those are lines? And then you would have polygons. For example, how big is the MLR convention? How much of that MLR convention is an auditorium? How much of that is a top? And that would require you to use polygons. So roughly in this case you can see that the dot on the top or the dot on counterculture is a node. The distance from the MLR convention to the nice Phoenix mall is basically a route. So it's a line and then you have an area of the mall that's a map that's a polygons. And that's roughly the technical so that you would want to know if you're doing anything on maps. The second thing that you want to do, which you were just talking about, is to pick a map to show data on, right? We generally, by default, use something like this, which is a very typical bright colors, night's icons, lots of little typical map, right? So this, if you're using map box, this map box streets is similar sort of style that you would get in other tools that you're using as well. But it's always interesting to try and change this map itself. So if I'm going to show data on top of this map, I don't want to see the highway speak to 75 stand out so much. Maybe I don't even want things to be in different colors so that I can use those colors on my map. So suddenly we could do this by choosing a different base map, right? And this is a very small sort of segue which I'm going to do to just get that point across that at times you would want to compare and see how does the underlying map look like and does it help show my data well. So just to kind of throw some points on it and see the difference that on the first map style, which is the streets map style you can start seeing that the data is visible but kind of conflicts there are so many other things that are catching for your attention. In other new map styles, we certainly start focusing your attention on the data that we want and in a sense in any code visualization, your first and primary goal is to make sure that the data is easily visible. So that was the introduction. Just to pick recap you need to think about your base map and about the data before you get stumped in. A quick segue also here on what could be there or what exactly is a map and a visualization and where do they kind of fuse in. So while we don't conventionally call it a visualization, seeing traffic on a map is itself a visualization and that's something that we're already using very regularly but doesn't really feel the same as say conventional visualizations because it just feels part of the map. Suddenly the roads that you already know have a color attached to them and that color shows whether there's high traffic or low traffic. And that's an interesting sort of way to visualize data where it doesn't really stand out so much in your face as something that's been put on top. Another very cool example that I love which might not be very visible here is to see Google Maps and in Google Maps you can see some areas that get highlighted which have a lot of popular places in. So let's just try and see if I can get this here. If you notice this is the 100 feet road and you can start noticing that very subtle yellowish cringe that's been added to the map that makes you realize that the part around the road here has a lot of interesting places whereas the rest of it doesn't. These are very subtle sort of visualization technique that people do to add onto a map which doesn't feel suddenly like oh there's a big circle on a map, there's a big shape for that. And these are super interesting to kind of see. So yeah, this technique is super subtle. You're basically tweaking the layers of a map. This is also a very niche technique and for most of us sitting here we don't really want to do this much. So we want to try to make something very very basic and by basic I mean let's start with the most basic thing. I have a bunch of dots which represent something I want to put them on a map. So let's get started. I'll select a base map in this case I've selected this very light sort of map so it doesn't really stand out too much. And I've added a bunch of things here. These are all the bus stops that are marked on open street map for bandwidth. And you can start seeing this it looks okay. There's too many props. There are places where they're getting overlap and while we're getting all the attention to the data I think it's getting too much of attention. So quickly changing things and playing around with some visual properties like capacity. And this slightly gets a bit funny but also starts highlighting some interesting parts of the data. So you can start seeing some clusters around a particular set of roads which start making this more interesting. Let's take this further. We could use color to try and show some other property of data. So in this case I might want to say out of the bus stops that are marked on open street map which of these seem to have some sort of a wheelchair attitude are the wheelchair friendly are they not friendly is there any data. Suddenly I can start using color to make some of these dots above. And next I can start using size as well. So now suddenly I can add a few more properties and start changing dots to start looking bigger or smaller based on which one is important or which one is the data that I want to stand out the most. And in essence what we just did for folks who've already been doing visualization for a while or have been reading some really good text is essentially just picking visual encoding different variables and applying them in the map style. So for those who aren't used to this, there's a lot of literature in it about what are the different ways in which I could take a particular sort of form on a map and just let data denote different things on it. So you could use color, you could change size you could change direction etc. So feel free to kind of look this up, there are a lot of different people who have given different explanations to it but before we move forward I want to do this one small sort of mention here about how this is separate. So when we have our data for those who already have been using a lot of data sets there's a very clear difference between the data that is continuous so it might be numbers or data that is categorical which could be discrete values. So for example in this case you can see that if your data is say how many buses go on each bus stop that's a number that changes from 0 to maybe 10 or 100 and you might want to use a gradual scale whereas if you have something that is each point being each quality of data or each type of data for example what are the different types of let's say we want to represent different types of facilities available at different bus stops. So there isn't an inherent numerical order in them and there's no inherent quantitative order in them and that's the place where you would want to use the first section where you identify them with colors maybe which have different views and stuff like that. So just like I want to get some of these very basic stuff out of the window so that now we all feel on the same page to get started talking about some real map visualizations so here are some examples one is a fun map there's a bunch of folks who went around asking bunch of folks in America here's a map can you tell me where Ukraine is and I'm not sure how many of you I probably shouldn't have told this before but I wonder how many of us could also mark where Ukraine is so in this case every dot is a guess they've used color to kind of show how close your guess is to actual Ukraine location and they've used opacity to suddenly highlight where people you know where people think Ukraine is versus where it actually is. So it's a fun way to get started with something as simple as putting dots on here's another really interesting case in this case they're showing earthquake data over the years they're using very subtle techniques of time as well as opacity and also this is fun simple thrown in so suddenly there is a triangle which represents volcanic eruptions and then there are these two different colors up which have been used and suddenly they start seeing some really interesting use of something as basic as putting dots on a map and this gets really interesting you can make this interactive and you can start doing some really cool stuff with it you can also create some really controversial pieces of this for example this is a racial segregation in New York and neighborhood areas where each dot is colored based on what is the common sort of race that stays there and these become really interesting to show the detail in the data that you have on a map that we all are so familiar with but there are times when you would want to highlight insights and it's time like these where you would want to use say the size a lot in this case this is a visualization over time where you're showing how many jobs were lost or gained the color is denoting that and the total number is showing the impact you can start seeing in the initial part the bubble burst the dot com bubble that burst and then the recession that came in and this is like a graduated proportional simple map if you enter the technical names but there are these techniques and they very quickly hit a sort of a ball and this is like a very fun thing this is essentially all the workflows that are there in New Jersey and while the intention is really great it doesn't really communicate much and what's happening is that we start plotting individual points on a map there's only so many points that we can put and after a while we really hit a limit as to what data can be shown and like this is a cue for all the folks who are big data or like huge monodata are saying yes this doesn't work maps are useless let's throw them out and let's use big data for something else but let's not get ahead of ourselves so a very easy way of the first obvious solution to this problem is to cluster right so we still have the dots we still show individual dots on a map but if there are too many of them nearby we cluster them and make a bigger dot right and we can change the clustering based on the size the type also add some sample effects like over here you have some color that we added to your highlight mode and this is basically the boom in March that happened a few months back not trying to be political here but this is a great example of what you could do with this so essentially with dots it's really interesting if you keep in mind the different visual encoding principles that you have and it's super important for us to make sure we control the density so suddenly if there are too many of these we want to figure out something else and apart from clustering second something else that we are all very familiar with is coro blitz often called chloro web but it has nothing to do with chloro in it but what we are going to do here is something very simple instead of showing individual dots let's count how many dots appear in an area that we are all very familiar so different states of us you all know how they look like let's count how many are in one and just show the data right so suddenly now it becomes very easy to show how many of the data that you have in shapes that would have been nice so this is one example where you are using light to dark color you cannot denote the sort of density or how much amount is there this is a pretty good chart by Quartz whereas this is another example by microstock where you are using two things very suddenly changing the hue a little bit and mostly changing the brightness right and this is like a third example and in this example versus in this example you can start seeing that there is a change in how the story is being done this is going from light to dark so the darkness really stands out whereas in this case both the edge points really stand out so this is a good example of how you want to use color in a coro plasma and try to see if you want to make a diverging scale whereas there is something that is showing the positive or the negative part of your data and highlighting that much more than the average data or you would want to just use a single sort of scale that shows the edges or the maximum of the data set right so just quickly running through this part but that is essentially what coro place would be right they would give you your familiarity but the only problem here is that the data is always based on the area right so for example in this case or let's take the first case each state is being colored but we still don't know where exactly or how much of that state actually has humans to begin with right and this leads to these really fun visualizations where people say oh the places which tend to have a lot of McDonald's also tend to have a lot of car accidents so you should shut McDonald's so that there are less car accidents but the truth there is that more places with high population will have more McDonald's and more McDonald's have nothing to do with more car accidents it's the same thing population and population so a similar sort of question always comes up when you do coro place right and a quick way to change that is to split the whole map into small things that you would want to call bits so I am going to quickly move through some of these examples just to get the point across about how diverse the map visualizations scene is and where you would want to pick which one to get started with so here is an example of X bit so essentially breaking the map into hexagonal shapes and then counting the number or the point of data or highlighting the data that you want to highlight so this is no longer to dense with too many points it's no longer too abstract or very very high level like coro place it's somewhere in the middle each X bit is equal sized so it gives you a better perspective of how the data is distributed and next thing is really interesting so there is a really lovely journalism piece on scene on Germany and how Germany even though the wall between East and West Germany broke down still very segregated in different sort of attributes but what's really interesting is that they are using these very nice circle pins break Germany into small pieces all of equal size count the data that you want to show and then show it using color and this becomes really interesting to see this like there are segregation sort of battle that they show up so in a sense if you are using pins it's a great idea because it's the best of coro plates as well as clustering or not plots and becomes really interesting to use it to also get some sort of insightful data but let's move forward and go into these really wacky map visualization as well and these are the parts where things get really interesting especially if you are doing data which has some real big impact this is the map of the world and that's interesting because it feels familiar but it has some really weird skewing going on and that's roughly what a cartogram is. A cartogram is going to take a familiar geographical area and going to skew it, move it, tweak it around based on a property that more truly reflects what you want to see so in this case you see population being used as a data to skew the country so no longer do you see Europe standing out as much as it does but India suddenly and China become these huge mammoth blobs on the map here's another sort of example which I really like because it shows this interactively and really gets a point across so if you see the top I'm just switching between different data fields and the map is slowly moving to highlight the sort of view of the world based on that data and this is a very impressive technique it really breaks the sort of biases that you might have which are coming in because of how a map looks like so suddenly that's because there is a huge state doesn't mean that it will have as many people and density of people or your own data properties starts becoming much more useful and easier to reflect and here's another technique so in this technique what we are doing we are taking a geographical area that we are used to seeing and tweaking it around. The second technique on the other hand makes a shape for each of the geographical area represents them in a form that we are very comfortable with and then moves it around so it becomes slightly more abstract this is called the dotlings cartogram this is an example for my course notes protovis time but it's super interesting because it illustrates two things it illustrates how obesity has increased over time in USA and the color kind of gives the density or like how much not density but the percentage of people in each state and how much obese they are so this is going a bit on the abstract side suddenly we are not using the map as we see the map but we are using an abstract representation of the map to start visualizing data and there is another sort of very interesting piece and if you are interested in cartograms in general the best idea is to look at a lot of journalism websites because they deal with these sort of issues the most where you are seeing a shape of a country in some state which you are very used to seeing but you are changing the data to actually reflect the story that you are showing so here is a square cartogram which changes the size based on how many policies are there and it uses color to kind of identify is this is this the state that has said yes is this the state that is thinking about it or is this the state that has said no and here is like another interactive sort of switch between making like a traditional coroplet to making a cartogram and I think this is a good example to highlight so this is UK and you can see London which is way below gets highlighted more prominently and some parts of the map which are generally less populous are reduced in size so this is a fun technique to try out I would love to have conversations later on how to make these or what exactly has been a problem that you have been trying to solve with this but there is another technique and I am just here to talk about different techniques so that we all have different photos to think about the next time we are doing something and this technique is called tile equipments so in the previous one we were changing the shapes or the sizes of different parts of the map based on the data in this one we represent each and every shape in a single equally sized and this becomes more interesting if your data set is something that is equally important for each state so it does not depend on population it definitely does not depend on geographical area but it just depends on the number of each state so if we suddenly start talking for example how much percentage of budget goes in each state in different countries like USA or India you do not care whether the state is huge or not you are first interested in seeing the percentage maybe you want to do a per capita and it removes the population at all and gives you a better picture so in case like this a tile grid map becomes really interesting and becomes even more interesting if you use something called small multiples so folks are very used to data visualizations small multiples is a very familiar term make a chart repeat it and change the data to show seasonality or see the change over time and so tile grids because you have suddenly become very abstract and easy to show in a very small space make for great contenders to do small multiples so this is a great example of showing the similar data of the basicity but now showing it in a single view instead of using a timeline to move through it so that was cartogram cartogram slightly complicated to create you can use tools which are say GIS specific or you can write a bit of code and then try and create this but they are much more honest they are much more closer to the data that you want to show and the last few that I want to quickly talk about and then maybe I know some people are also getting hungry and there have been three data whiskers which have been heating up all your brain so I would quickly go through these not take too much time on this so heat maps very familiar we have all seen say weathered being shown as heat maps or different sort of variables so I am going to show like a I am going to show the snapchat example some of you have snapchat seen this but if you have not seen this what this is essentially doing is finding clusters where people are all posting photographs of snapchat publicly and showing a heat map so suddenly a heat map is no longer just a data visualization technique it's also a great technique to nudge people explore different parts of the data and this becomes really interesting in some daily interactive use cases one of them is snapchat so essentially if you are making heat maps it's going to be great if you are going to do like a high level overview and the only caveat there is to choose your colors wisely so in this case you can notice that the color palette goes from a light color palette to a dark color palette even though it's still very rain bush and that's just something that we can again talk a bit more about in the OTR session if you want to talk about or maybe catch me up on how do you pick colors well because colors itself even though we think red, green and blue feel like a natural progression have different hue and different sort of brightness and saturation that goes around the same as to see how you would want to use this and last two very easy examples also to talk about isoplets and it's a very scary name and they have like many more variations like exocrone and isopart etc etc but they're essentially using lines to mark data as boundaries right and here is like a very easy to see example so from MLR connection in Bangalore where can I go to different parts of Bangalore over different amount of time right so while we're very used to seeing point to point data temporarily saying from place A to place B the more time it will take suddenly we start putting temporal or like time related data on top of a map and start seeing huh this is interesting there's some part of an infrastructure that's really limiting people to go into one direction and maybe that's a great example for somebody in infrastructure to understand or maybe it's just easier for us to understand hey next time we are hosting fifth elephant what is the most accessible place which reaches to most people in Bangalore so isocrones are super interesting slightly complicated to make because they're essentially going to use a lot of data in this case you would want to use some sort of a direction or navigation data set that's going to tell you okay this to this oops so this to this is going to be this much time but it's super interesting to get the insights out and I know now is going to be like a better trigger for people to slowly move out but before that let's quickly finish two more things so flow maps have been one of the more commonly used map techniques back in the days before you had interactivity before you had all of these fancy tools that we have and this is one of the many interesting flow maps that you can see these become much more interesting when you have connectivity or connections in your data this today is slightly more complex to represent and you need to mold your data to get easily like makes us maps they're also very interesting to tell stories so some of my favorite stories just seen in journalism have come out with people using flow or showing flow from one place to another to tell inside so in this case for example if you can see the arrows this shows where people of as students from each state in USA go to another state just attend public schools and that makes this very interesting sort of conversation that none of the other sort of map visualization techniques would be able to convey so successfully or easily so to like wrap this up flow maps essentially connections they're really useful to do story telling and build narratives around and last and not the least because this is something that people love and hate is 3D so gonna just wrap this up by talking about two very interesting examples of what you would do when you have 3D stuff on a map so this is the first example this is Vancouver and the government there is trying to see how is our area basically divided in different zone based on different types of usage of the building area but it's interesting here to see the data not just in a 2D view but also in a 3D view because you get like a better perspective of okay this is actually how city is also these areas have these really small really low height buildings whereas that's the main downtown area and the downtown area has been dedicated to be kind of zone in to have these sort of types of buildings right so this is an interesting use case where you would want to use 3D you want to show the real world as close to possible and then you would want to use a common techniques in this case essentially a sort of thorough plan you may but you just use a 3D element to make it interesting another reason to use 3D is to actually make good use of the third dimension so suddenly instead of just showing a two dimension map which shows one data property you can start using two dimension on the map so there will be say color in this case there is also height and there is also volume right so you suddenly start using 3D as a nice a venue show more sort of insightful data queries that you might not be able to make if you are seeing a 2D map so in this case the bar's height shows the population density in each of the small building but because it's 3D representation you can also see how much volume is there which essentially is the number of people staying there right so suddenly you can start making a difference between okay this part is really thin but really tall and that means that a lot of people are trying to stay in this really small area and that stands out a lot if you notice some parts in slightly south of San Francisco you'll notice these are anomalies and these are some of the good examples or use cases where you would want to try and use 3D in all other cases 3D is generally a gimmick and as many other database guys would say before you are using 3D think you really need to use 3D right so that's I know this is like a very heavy load of suddenly like a textbookish or like oh my god 10 different ways to visualize data on a map but the idea or the goal being hey this other things that you could do and before we wrap up I just want to mention very quickly the tools that you would want to use I am a designer so I don't write as much code as I would like to but I know a lot of people here love to do that so this is something that we can have a longer discussion in the OTR session but just to very quickly summarize there are interactive tools there are tools that you would not want to write code in so there's tabloo which is really great drag and drop and you get your data loaded and it's up and ready for you to start visualizing there is mapbox studio which is what I have been using to make all these custom maps will give you much more control over each and every element of the map and then you can overlay data on top of it and then there's also other tools like carto which also do a lot of interesting sort of data visualization by drag drop or very seamlessly making map styles then there are these slightly more things maybe things that I think we are more familiar with so familiar name being B3 mapbox has a web gil that's graphical library mapbox gil is also uber the guys are outside so you should probably chat with them and there's deck gil and then there are these GIS tools so so I want to wrap up with this very interesting and like slightly tongue in cheek code which says that nothing is certain in this life but access and request for geographic data to be put on a map so with that that's me the Sagar thank you so much so we have time for questions so we could take some questions now or otherwise you can all come over for the OTR session where Anand, Amit and I will be hanging around as well any questions or do we all just follow the crowd everybody wants a tea break we'll be back in here at 11.50 and at noon we have an OTR on financial data analysis


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Going on everyone Sam here with late and sports cards doing the random for 23 puny mosaic football hobby for box break number 5 Random team style to look again at your random results as always can find those on the website under the results tab as well as the link in chat I've been trying to get his chain in the stadium.  He was talking to him about the game 14 to go on left side seal numbers as well everybody 14 to go 14 to go To go Look at the rest of the stuff filled up tonight to get to it personals as well, everybody B John That is a Paris Johnson jr. Nice one there. We're at auto for the Cardinals To one ninety-nine crystal lava Jamari Connor the kid I can't I suppose 14 spots remaining everybody that is gonna be points random to all at the end 400 of them Here he writes Carbon copy gonna do a random for this or dice roll I should say one two three left four five six right if it's a gold or lower It will be a separate random at the end It's gonna be a Four go into the right that'll go with Ramsey and whatever team is on the right On the carbon copies Oh, yeah, this guy's still on cloud nine. We'll love us orange to Bonte Adams to Um one ninety-nine. I love that they added the orange this year, dude Marvin Mims orange Mike Evans to one ninety-nine silver CJ Stroud and a white audible Trevor Lawrence to 25 10 of 25 He lost the flowers Jonathan Taylor silver Garrett Wilson Hi, Chris Godwin red Alan page Odell Beckham jr. Splash. I have a drive alive for this game The late sleep Billy white shoes Johnson and 99 I might I think I do CJ Stroud Sydney Brown mosaic group Tell you right after this OBJ moments time right after these two packs. I will tell you Josh Downs Felix and wiki Uzoma Darnell Wright for the Bears 299 and a mark audible Will less Wilson silver Jair Brown and more base All right Let us see here. Can imagine this is gonna be very good right now Let's see one live entry now three live entries. Yeah. Oh One of them is really not good 11 1124 out of like 1400 and then the one right now. I'm an 8800 of like 10,000 or 14,000. So yeah, not great.  Yeah, I know You guys are the one no problem He went nothing to do with it. Have a go on Shane. Hey, we'll see you Shane blue West welker 249 99 excuse me price young intros Yeah, pick up the Eagles go Jeff Thank you Jeff Nnt hand in ochre That is will Anderson blue cam makers tonight and I DK and center stage. Mr. Putt. Herty Appreciate you Jeff. That is awesome and the Saints. Just got the Eagles and the Saints. Thank you Jeff for two teams gone now in Come on, dude Echler Michael Parkinson's Roku on Smith Jalen hurts silver kiddle And 10 10 to go an NT everybody Otis Anderson How do we go into the Giants? Tank Bigsby, but I love how you started the saluting emoji It's awesome, dude It did as it should have as a Rams auto Byron young to one ninety nine rats on that LA painting Manning CJ 2k carbon copy Really Sean I'm picking up a 10 box spot. Thank you Sean Appreciate you buddy There's orange Eddie George to one ninety nine That one time of fanatics like we got a tessellation Salation in Cooper cup. Nice. There you go Rams to 15 10 of 15 on the National Bride silver Jordan Addison Quentin Johnson and a blue shroud on the introduction to 99 Kyle you're in fifth place in the pick-and-lead Last night probably in fourth place. I shouldn't pick the box. I was gonna say that was a Homer pick right there Definition of a Homer pick Jamo Jk and Tyler Alger. I'm taking it now one guy's like seven games ahead of me He's gonna have to have a bad week Go ham He's a beast Roshan Johnson mosaic rookie Lamar red and silver audible It wasn't the greatest pick Sean, you know, I mostly did it because I picked it right in front of Donnie. I had to go I just stick with it 14 to go flawless left side everybody 14 to go Floutis Jinway with a spot in contender's optic. Thank you, buddy That is a Tyler Lacy J. Caner that is a their call Ryan Tana. Hell red and her big herbie I don't think one draw for me to regret that decision. True. That's been ridder blue trailance to 299 with Muzzgrave Portland Sutton purple to 49. I've worked jaker Mayfield. Good. Yonte banks.  That's actually pretty cool. Congrats Giants Damn, we got the time out on the course do Tyree kill gonna be a redemption of Rony of Jackson Smith and jigba in focus signatures read to 199 for the Seahawks Congrats Seattle Gonna send you the video See y'all. I have an ingrown. Can you hear I'm like squealing Tomorrow Williams for the Saints nice one there new Orleans CPAT and killer trav young Orange is jacorian Bennett And a blue Joe Burrow There's uh, Christian Kurt to 199 and a couple of ace as well Will Anderson that's an orange Larry Johnson to 199 Rangers Clemson got postseason tickets on sale. Wow. Yeah, I must have done Nope, that's just a mosaic will Anderson orange christen mccaffrey And audible mosaic of awesome action That is a upside down Derek Henry and yonte banks Joe burrow mosaic montage That is trade talker. That's not an auto that's a uh Nice one there. Hey, thank you pa. Have a good one, sir Let's pull me rich. It's an auto. Let's go. That's awesome Sean. Hell. Yeah, man Hey, have a good one paul Hey, let's go Sean. Hell. Yeah, Sean Uh to 49 cordel Stuart purple auto nice one there for the Steelers Yeah, Sean picking up a 10 box spot Thank you starbuck with a couple of pyt spots over in mosaic tomorrow Yonte banks rookie red mile or silver miles murphy and silver b-john rookie Raven was Yeah with the 84 Eric Osmer The one that you're forced to use bj ojolari Red rookie auto congrats cardinals to 199 Tua my home's red The 84 Eric Osmer Yeah, it doesn't sound like an movie to show come on Yeah CJ Stroud That is a tim brown orange ventral miller to 199 Bryce young debut orange courtland Sutton to 199 Jim Kelly silver in a blue dany dime straight fire Sean actually says that uh, he's the best quarterback ever named Dan It's daniel jones There's a lot of good dan quarterbacks one that played for the gulfines. No matter fact Sean still you can't insist That daniel jones is the best daniel quarterback. You can't legend daniel oski. Oh, yeah Yeah, red chevrolet Lawrence Cooper cup j caner that is a diran pane mosaic red breeze hall CJ stroud and deandre swift silver. All right quick points recap or points random everybody The points recap Points recap All right, everybody gonna roll the dice whatever team's on top after eight times. We'll get the points One Two three four five six seven and eight bucks patriots the bucks patriots combo Get in the points. Congrats on that um That spot Recap we had jamal williams jackson smith and jigba odis anderson Paris johnson bj ojolary firing young and cordel steward I'll do a break everybody. Thanks again for the film up next number


Thousands of people have been ordered to leave their homes on Monday, February 28, that was after heavy rains smashed Australia's east coast, submerging thousands and stranding citizens and residents on Rupetov. Authorities were warning of further life-threatening floods. The debt toll rose to eight on Monday after a man was swept away, trying to cross a flooded road according to emergency service. And this is a very sad incident where the government is trying its best to evacuate homes around 15,000 who have been impacted by the rising waters.


Let's go BrewCruits the Pirates versus the Brewers tonight at 7 10 p.m. at American Family Field Let's brew it up Milwaukee Brewers Nation go Brewers


What's going on guys welcome back to the channel critical overload here We're gonna be talking about Chucky season three in this video here again today This is gonna be a rather quick video just like how a lot of my Chucky videos haven't become they're becoming rather short and quick we're gonna be mostly be talking about Jennifer Tilly and a major scene it would appear she has coming up I'll just say rumored until proven otherwise, but the evidence was all sent to me Jennifer Tilly AKA Tiffany we know was sentenced to death by lethal injection during the first half of season three and It looks like something regarding the lethal injection will indeed be showcased during the second half of Part of this season Now the other thing we unfortunately will need to address is the exciting news We got about when we can expect Chucky season three part two to air on Sci-Fi USA has not been scrubbed from the internet source Which again directly came from one of the two networks, which has me kind of skeptical But I have I have my speculation on why it was scrubbed, but The second half of tea of season three We know before I get into this topic of this release the second half of season three We know from the promo teases some sort of chaos unfolding inside of the prison Which must be linked to Tiffany escaping in some capacity Don Mancini has also Stated and teased that we will be seeing a seance of some sort during the second half and as I've stated this seance Likely will tie into Collins who recently saw his dead son during episode four and that sparks a seance moment Since it's referencing the Lincoln administration and the lost Lincoln apparently endured during his time in office Something in many of you listening most of you I would say know nothing about or have no idea about Besides what you can find on the internet because we were non-existent Anyway The lethal injection going back to Tiffany the lethal injection scene is on the way as well now Allegedly, I'll just again say allegedly and rumored you're gonna meet a character named the executioner They're gonna talk to Tiffany It's gonna be the standard procedural They're gonna ask Tiffany if she has any last words while they're prepping her for the lethal injection That's basically a scene that you can expect during the second half of Season three My prediction is that this is where Tiffany's escape plans will be accelerated So you now know that the executioner again will be a character that shares a scene with Tiffany during season three But my guess is that she's done something to the injection that will render it useless and then chaos ensues or something happens That doesn't lead to the injection process even going ahead because of something else. She's prepped Leading up to her inevitable escape. My prediction is that we'll see her prepped for the injection And then something will go wrong And that's the scene we'll see prior to all of the chaos unfolding. I could be wrong But again, there's a character named the executioner going to be introduced They're gonna be walking Tiffany through the process of killing her that 9 to 10 is not gonna be successful I do not see Tiffany dying during the second half of this season She definitely won't be dying in that prison if anything she's gonna if she's gonna die She's gonna die somewhere else. She won't be dying in that prison.  Tiffany will die there But the next thing we're gonna we're gonna talk about is the fact that It would appear and it's very evident in the article the same article sci-fi has removed the May 2024 Release mention from the article that was related to Chucky and that new Ted show it explicitly stated Chucky Return in May now that's been scrubbed It just says that Chucky season 3 will air on or the second half of Chucky season season 3 will air on the USA and sci-fi networks What happened to me? It's way to me for my best guess is this the person who published the article They let something out that they shouldn't have so now they had to have to retract it And they're just gonna try to treat it as though it didn't happen But people already have their screenshots people already posted it online But sci-fi when whoever wrote the article they just jumped the gun and it wasn't supposed to be listed there that's my best guess or maybe it genuinely was a Error on sci-fi is in and it's not coming out in May or it was supposed to come out of May and it's now shifted If it shifted, let's hope it did not shift into the fall I've stated before it if you can help it if the networks will allow Why oh why must we wait for the fall for what reason? Just drop it in the summer drop it in the spring My prediction is that may 2024 will still be the date But it's been removed from the direct source that again was one of the network sci-fi It's gone from the sci-fi article My only reasoning for it is because it wasn't supposed to be there And they had to remove it after their higher-ups told them to take it out of the article or whatever one of the editors But let me know just think about this down the comment section below What do you think about now knowing that there's a rumored scene for Tiffany to be involved with an executioner Who was prepping her for the lethal injection a sequence? I think will be the prelude to her escape. Let me know about that And what do you think about may being removed? Do you think that was just because it wasn't supposed to be out there? Do you genuinely now think it won't drop it mate? Let me know all that down in the comment section below if you haven't already course make sure you subscribe turn on post notification There is a video in the description. I have links on social media accounts I'm on Facebook Twitter and Instagram you can message me there Of course, let me know if there are any movies news or reviews you have me to cover in the future And with all that in mind guys, I will see you in the next video