 Welcome to lesson two, Juint Data. Human activity leaves signs in the landscape. Measures of the land and signs of human activity become Juint Data. Let's talk about the lesson objective for three in this lesson. First, I want you to be able to list examples of the major categories of Juint Data, and I'll cover those in this lecture. Second, we will discuss the concept of technique and its importance to Juint Data. We'll actually do that a little later. We'll also describe the role of Juint Data in sensemaking. Juint Data generally begins as information. Data beginning as information. Sounds a little funny, doesn't it? To understand this statement though, let me explore a few of the terms. Data are facts and statistics collected about something on the earth. They are raw numbers in a lot of cases. We'll talk about an example one later on. Information is what is conveyed or represented by a particular arrangement or sequence of things. That could be data. Insights are an intuitive understanding of what a person sees. Oftentimes, we use data, which is converting information to help us give insights. Satellite imagery is one of the key tools of Juint. Some people say it's the heart of Juint. Actually, I think there's much more to it than that. Satellite imagery in itself is information that contains data. That sounds a little funny, doesn't it? Satellite imagery is information that contains data. Well, if you break that satellite imagery down, you'll find that it's comprised of pixels, which stands for picture element. Every little one of those picture elements represents a portion on the earth's surface. Consider that the raw data fed from the satellite would be useful to the analyst. It would be a string of numbers. That data then has to be processed a range and something done to it is actually processed, organized, structured, and presented so as to give the analyst a useful satellite image. Importantly, this data or this image can be used to create other data. So what I'm saying is the satellite imagery is information that can be used to create other data. For example, we'll take the satellite image, which is information, and we'll extract from that the height of a structure, maybe like the Eiffel Tower. Another example is we may use a satellite image to denote a particular building. We maybe want to find what the use of that building is from that image, we'll then locate the building and then determine the use based on where it is. This cycle of data to information, which then becomes a basis for additional data information, is really nothing new. Map makers and users have done that for thousands of years. If you think about the first map, it was really data that someone had collected by walking around that they represented in a form of information on a map. So maps in imagery, like satellite imagery, are really information representations of the Earth's surface and not the Earth itself. We're looking at a representation of it. The key we have to do is take that information, convert it to data, and put it in a form that we can use it in a GIS. And a lot of time says to what we do with something called digitizing. Until we started using remote sensing to get information about the Earth's surface, we had to actually visit a spot to measure it. If I'm trying to find the height of the Eiffel Tower before we had remote sensing, a couple of ways I could have done it, I could have written somebody perhaps and they could have gone out and measured it, or I could have visited Paris and measured it. The key is someone would have had to measure it. Remote sensing has allowed us to find the height of the building without actually visiting a location. And in some cases, maybe we can't visit the location. Remote sensing helps us to find information about an area that we're denied access of. Information can be shared. That's one of the key aspects of information. If you think about it, a satellite image, we store it digitally, we can put it on hard drives, we can put it on some sort of device you can move around, we can put it on the internet or a network, that's the key thing about information. It allows us to share information easily. The other thing information does is allows us to store it so we can look at changes over time. The key thing about information is that we're not actually seeing the object, it is a representation. Let's talk about a couple other concepts in data. One is primary data. Primary data is raw information collected about a specific purpose. If I look into the height of the Eiffel Tower and I actually go out and measure it, that's primary data. It's a direct measure of something like my building height. Secondary data is obtained by studying information developed for others to extract data from. For example, I'm taking my satellite imagery to extract the height of the Eiffel Tower. I'm not actually visiting it, I'm using other information that may have been collected for other purposes in order to extract data from it. Not unusual, a lot of reasons to do this. Let's explore those for a second. The advantage of primary data, it's an opportunity to tailor the data to what your needs is. And I know the data, we know the data, we've collected it. We know the faults of the data, we know the strengths of the data. This advantage of primary data is it's costly and time consuming to collect. Maybe joyful to go travel to Paris to measure the height of the Eiffel Tower, but it would be extremely costly. Some cases though, we actually can't visit those spots too. The main advantage of secondary data is it's quicker and often less costly. We can examine other information, such as imagery or text, other things collected over a period of time to obtain this information. It also allows us to look at things as they change through time. However, information may be outdated. It may not have been collected well, it may be inaccurate, it oftentimes may be too vague and we may have to make some major leaps in order to collect the information we want. The key thing here is we need to know the secondary data so we know what we're using or what we're extracting from it. The below diagram shows this data information cycle. Actually, it shows the data information, the insight cycle. Very common to loop back and forth, we do it all the time. Let's talk about GON data in terms of its content and organization. The table to my right illustrates the general types of data and relationship between them. GON data is typically in response to identified need within a cycle. We call this the intelligent cycle. We'll talk much more about this intelligent cycle in lesson three. The data then is organized for use in some sort of geographic technology, geographic information systems. In this form of data, it's structured. Typically, in two forms, we'll talk about a little later called raster and vector format. We'll talk much more about those. However, there are also unstructured data types. It may be email, text, document, videos, photos, web pages. Things that are not easy to get into computers. Just because they're unstructured does not mean they're not useful. In fact, some say that 90% of the data that an organization uses is unstructured. It is some of the most important data. As I said, GON data can also be divided into two other major types or categories called physical and human. Physical data focuses on entities such as landforms, vegetation, climate and weather. It's the things we can touch and feel. You could also say things that adhere to the laws of nature. For example, water flows downhill. They interact with that. Human geography is a record imprint of human activity on the earth. It can be called the cultural landscape. Examples include cities, buildings, roads, power lines, utility, anything the human puts on the surface of the earth. It's the human imprint on the surface of the earth. Culture has a great impact on that. Next lecture, we'll talk much more about GON data as symbols and samples. We'll also discuss GON's data in terms of its wisdom we need when we combine this data back together. That's what I call the technique. And finally, we'll talk about how this data is used in sense making. Thank you, see you in the next lecture.