 Hi, my name is Todd Bacostow. Welcome to the second lecture of Lesson 2. We're going to talk about joint data, geospatial data, and particularly geospatial data as symbols. Data are symbols that represent measurements of phenomena. Number of ways we could do that, we could do it as pictures, we could do it as words, we could do it as numbers. These measurements are characteristics at a location. They represent something that's going on. Why do we use symbols and make data? That's a good question. Because it allows us to do things easier. We can store it once we have it in a symbol, but also we can then begin to process that. We can do other things with it if we want to study it. It allows us to look at certain characteristics in certain ways at certain times. How accurate the data are actually dependent on how we collect the data, when we collect the data, where we collect the data, and what aspects we collect. Sort of important things. Data can be viewed as a commodity today. Big data is the buzzword you hear all the time. It's being collected all the time by entities, Google's collecting data on you. The value of data is not necessarily lost, however, when it's used. For that matter, being old, it still has value. In fact, in some cases it may have more value because you can see how things would change if you collect current data. We mentioned before, though, that data can also be transformed in information, and then that information can be transformed, again, back into data. So data is sort of an interesting thing. Data are samples. What do we mean by that? The earth is just too big and detailed to collect all the information, all the data. So we go out with a scheme in order to sample the earth. It allows us to represent the earth, we hope, in an accurate way, with a lesser number of points. There are two primary schemes for sampling the earth. Vector and raster are the two schemes. The vector approach or strategy involves sampling along lines. It may be along the length of a road. It could also be around the perimeter of a building. We collect information at points or locations, and we connect these points together with a line which will give us a linear feature, a road. If we close the line, it will give us a polygon which could represent a building. Vector data model is how surveyors survey a property boundary. The vector strategy is well suited for mapping entities that have clearly defined edges, highways, parcel boundaries, things that we know where the edge is. There's another alternative way of collecting this information. It's called raster. The raster approach involves sampling attributes at a fixed interval. Each sample represents one cell in a checkerboard-like fashion. The raster strategy is a good choice for representing phenomena that lack clear boundaries, like elevation. Where's the edge of an elevation? The important thing about raster is digital airborne and satellite imagery today are largely raster data collected by running a scanner across the earth's surface. This is a pixel by pixel, row by row collection. What's the bottom line? Both raster and vector approaches accomplish the same thing. We are collecting samples about the earth's surface. They allow us to record and to characterize the earth's features with a limit number of samples, since we can't sample at all. How do we distinguish between these two? Let me give you two analogies. The vector approach is like a stained glass window where we have lots of shards of glass that are brought together to cover the entire surface. The raster approach is like a tile floor. We have very regular pieces of tile and a uniform pattern to cover the entire area. However, if you think about what we've done with both the vector and raster approaches, we've sampled. We have looked at the earth's surface and we've disassembled it. In order to use this data, we need to reassemble it the way we want to. In the process of disassembling the earth, we've also simplified. We have to put some understanding back into the data. So data without understanding really just gives us meaningless symbols. We do collect metadata about our data, but that metadata by and large is technical details of the data, file size, file structure, etc. What is really necessary when you're in the joint realm is to put knowledge back in there. We term this technique. It is the human craft of bringing knowledge back into the data. It is the process of adding understanding back into the data when we fit data into a frame. And I'll talk much more about this frame later on. Technique is a term derived from Greek. It means craftsmanship, craft, or an art. It could be also described as data know-how, but really it's much more than that. It's wisdom. It's the quality of having experienced knowledge and good judgment about the data. Much of this wisdom is acquired through experience. The analyst uses the wisdom when handling the data in process to form judgments about a place. Data wisdom is difficult to communicate. It's very difficult to write your wisdom down in words. This is often termed tacit knowledge. Tacit knowledge is learned through experience. If you think about a stone mason, you can read a book about stone masonry, but you really will never become a mason until you apprentice with someone that shows you the art. It's a craftsmanship. It's gained through observation, imitation, and a lot of practice. What I'm suggesting is this tacit knowledge with G-end data, when we put it back together, that knowledge is gained through observation, imitation, and practice. As we discussed earlier, the analyst's craft of fitting the G-end data to a frame includes technique. It is that wisdom that brings meaning to the symbols of a place. However, there are typical understandings of a place we may have. There are a lot of conceptual models about how places are organized. This will help us build a container or a skeleton that we can put the data in, so we can give it some understanding, give it some meaning. Knowing the typical geospatial organization of a place combined with the data will allow us to frame something. These frames allow us to make judgments or educated guesses about some qualities of the area. If you think about it, we do it every day. If I'm driving my car and I'm on a certain road, and I see a certain situation in terms of both the physical arrangement of cars in front of me and maybe a sense that I have that an accident is about to happen, I'll potentially take some evasive action. That's from experience. It's from the experience of driving my car. Theories often form as basis for a spatial organization that allows us to frame things. These theories help us build mental models and build relationships between things we might not recognize without the theory behind it. Geography has a number of great theories, just to name a very few. Examples of important theories that help us build models are the gravity model, central place theory, Weber's model of industrial location, von Tuna's agriculture model, and the corp periphery model. There are many more. They help us organize what we see. They give us a framework. They give us a skeleton. I'm showing you a picture of the central place theory. If you've ever flown across the central plains of Germany, if you looked out the window of your aircraft, you might see the cities and towns configured somewhere to that. So a frame provides us a cognitive skeleton. It helps us to arrange the data in a meaningful way. A frame can take a number of different forms. It can be a narrative. That narrative can be a chronology of events and cause relationships between them. A frame can also be a map. If you think about the map, it has distances, directions, connections. It helps us organize things. It explains. It gives us a story of what's happening. Frames can be thought of as models, like a map's thought of as a model. It can also be thought of as generalizations of our world, like a map. Frames, however, are not theories. But a frame may be influenced by a theory. For example, the central place theory may influence how we frame something. Framing is normal. It's an everyday activity essential for geographic problem solving. In fact, maps are our frame. Juant data and frames help us make sense of what we see. Let me define sense-making. Sense-making is the ability to understand ambiguous situations. Three primary outcomes of this understanding. Identify patterns, describe patterns, and predict future patterns. In sense-making, juant data are a central part of a mental dialogue to explain what the data shows in the context of how we frame it. Here, a juant sense-making involves continuous, cognitive work to understand the relationship between the data and the place and events in our frame. Let me summarize what we learned in this lecture. Juant data represents a place while conveying a sense of physical and human qualities. In addition, juant data requires an understanding about a place, a spatial organization, to make sense of what we see. Knowing the spatial organization allows us to frame human activities to understand the processes which create these patterns. The depiction of this juant data with respect to a known model is a frame. The frame guides the search for additional data. The frame ultimately provides us a story or a map to account for what we see. Thank you very much. I appreciate you joining me.