 Hi, my name's Todd Bacchastot. Welcome to Geospatial Intelligence and the Geospatial Evolution. This is lesson four, the second lecture. This lecture focuses on the human aspects of the Juin tradecraft. As I said before, analysis or problem-solving is core to the tradecraft. The Juin tradecraft might be described as problem-solving chaos. I have some tools that may help this. However, it can be said broadly that it consists of three stages and organized in two repetitive parts. The stages are analytic stage one concerns problem-solving initiation. This stage broadly outlines a general analytic question which you should go back and investigate the geospatial components of. Analytics stage two concerns information foraging. Here the analyst collects data and also refines the question. As you collect data you may reinterpret your question, go back and enhance it. Analytics stage three, this stage is about sense-making. It results in the development of a detailed analytic assessment. There are two major repetitive parts of this and that is foraging and sense-making. Foraging recognizes that analysts tend to look at a broad body of data and then narrow it down as they proceed. Much do and work never departs the foraging stage and actually consists of extracting information and repackaging it. The second repetitive action is sense-making. This is the ability to make sense of ambiguous information. When geospatial analysis departs the foraging and completes its sense-making process, it yields an analytic product. The process of interpretation begins with data which is generally context-free. Information is derived from data by the human craft and implying a geospatial context. This is what we discussed before. It is place. The craft is a concept of technique which we discussed in lesson two. For example, if we have a crime at a location in a time it's really just data. However the crime to have meaning we need to put it into a context. For example, we had an armed robbery in daylight. The geospatial context is critical because the space and time determines the interpretation of the data. There are three general geospatial contexts within which we put data in order to have the data information translation. These include behavioral space, physical space, and cognitive space. Behavioral space is thinking about the relationship between the human and objects in the physical environment. This is place. It's exemplified by the image I have the United States at night. It shows where the humans live. We try to build models of this to explain these patterns. We have some success but it is difficult. Physical space focuses on the scientific understanding of nature. This is reasoning about the physical aspects of place. Thinking about this involves understanding and models of way nature works. Cognitive space involves how one thinks about place in an abstract. An example is how one prefers one place over another. It's difficult to build models about this. It's in someone's mind. It's very difficult to do. An example I have here is actually a student drawing supposed to draw Penn State. What this individual did was drew a map of Pennsylvania with the University Park on it where I am today. It's interesting because it's a window onto the student's mind. If you look off to the right hand side, you'll see X's which would be the state of New Jersey. I think they have them labeled as a wasteland. This shows what this individual thought of space, how they considered it. Let's briefly discuss confidence and analytic judgments. As you may recall, intelligence is not truth. It's an approximation of truth with some level of confidence. Confidence in a judgment is based on three factors. The number of assumptions, credibility and diversity of our sources, and the strength of our argument. Each factor should be assessed independently and then in concert with the other factors to determine the overall confidence level. Multiple levels are stated as low, moderate, or high. An interesting point here is that oftentimes people don't do this final assessment correctly. Let me give you an example. If I had a scenario of three events, each with a probability of 70% certainty occurring. Most individuals simply average the probabilities together to have an overall assessment of 70% certainty. However, statistically, the correct probability would be 0.7 times 0.7 times 0.7 or 34%. A major difference between 70% and 34% probability. When we state our judgments, we state them as we judge or we assess. These are used to call attention to the confidence we have in any assessment. The figure shows the guidelines for likingless terms and the confidence levels with which each correspond. Let's discuss bias. We all have them. It's knowing your bias is the important thing. Much of the difficulty analysts have in their analysis is cognitive and not related to the tools. In other words, GIS. Oftentimes we know how to use the tools. We don't know how to use our brain. Unfortunately, every analyst might see the same piece of information and interpret it differently. In essence, one perceptions are morphed by a variety of factors that are completely out of the control of the individual. These cognitive patterns are potentially good and potentially bad. On the positive side, the models we use these patterns tend to simplify information for comprehension, but they can also bias and interpretation. The key risks are that analysts perceive what they expect to perceive. Opinions what's formed are resistant to change. New information is simulated, sometimes erroneously, into existing mental models. Conflicting information is often dismissed or ignored. Quite honestly, I do it all the time. It's understanding that you do this is the important thing. Let me give you a few simple visual examples. What do we see here? Is it a chalice or is it two faces? Actually it's both. One person will see one thing and another person will see nothing and oftentimes I flip between them. What's this image? Is it a young woman or is it an old woman? It's actually both. When you study for a while you can actually flip between. Since people observe the same information with inherently different biases, we have a few safeguards. These are structured analytic techniques. Let me introduce these to you. They are aimed at overcoming biases. They're also known as SATs. SATs are toolbox. They are toolbox to help the analyst mitigate their cognitive biases and their limitations. SATs taken alone do not. SATs taken alone do not constitute an analytic method for solving geospatial analytic problems. They are simply tools that help you overcome some biases. Most distinctive characteristic of an SAT is it helps one decompose their thinking in a matter that enables someone to review it, to document it, and potentially for other people to redo it, see if they get the same thing. It's actually a publication that explains 12 of these that are frequently used. This is a tradecraft primer for structured analytic techniques for improving intelligence analysis. It highlights a few SATs used in the private sector, academia, and the intelligence profession. The document can be found at the web at this address. Structured thinking is at variance with the way the human naturally thinks. Most people solve geospatial problems intuitively by trial and error, or as I like to call it, see of the pants. Structured analytic techniques can help you at least approach it in a much more methodical way. Structured analytic techniques help the mind think more rigorously. They don't solve the problem for you. In the geospatial realm, they ensure that our key geospatial assumptions, biases, and cognitive patterns are well considered. The use of techniques helps us document analysis and identify our causes of error. Thank you. This completes my second lecture, lesson four. Over the two lectures, I've defined the geoint tradecraft, described the relationship between GIOScience, Geographic Information Technologies, and the tradecraft, and discussed a few of the tools of the tradecraft. Thank you again.