Another really important thing that's fundamental to Geography is the concept of scale. And there are two major key concepts of scale that I want you to understand for this class. One is the concept of scale that probably popped into your mind immediately. That you're just thinking over off the top of your head and that's map scale. That's the ratio of the distance on the map to the real distance on the Earth. So that could be one inch on the map equals 1,000 miles in the actual planet, right? And we get into some problems here with the language, because cartographers, geographers talk about large scale, and small scale in a way that probably differs than, than how you might refer to it yourself. You might think of large scale meaning a continent or a country and small scale meaning a little town. That's how most people kind of refer to it in normal terms. But in fact, large scale would be something like one to 1,000. Where the fraction itself is bigger than small scale, which would be one to ten million. And a one to 1,000 map would be something like a neighborhood, so zoomed way in on the Earth, right? And a small scale map would be one to ten million, zoomed way out. It might be a whole continent, like showing South America, for example. That's one kind of scale. The other kind of scale is a scale of analysis. The specific geographic context used to understand a problem is what I'm talking about here. So that could be that you're looking at an analytical problem at the neighborhood, the county, the state, the country, the continental level. The planetary level, right, if you're looking at stuff in space, in general. Space, outside space. So, the scale of analysis really has more to do with what you need to know in order to understand a problem, than it does with anything to do with the map scale itself. You could have lots of different map scales used to explore a problem that impacts the scale of analysis at a state or a country, right? So those are the two major kinds of scale I want you to understand. Another really important thing to consider is that scale matters. Depending on the scale at which you analyze things, you might be able to derive completely different results and show people totally different maps. In Geography we call this the Modifiable Areal Unit Problem or MAUP. Which is also the, also the toughest Pictionary prompt ever and the sound a cat makes before launching a hairball. Let's look at an example. This is a map showing solar potential, so, how possible would it be to and sustainable would it be to put solar panels on your house, for example, and recoup, recoup the cost. This is from, from the National Renewable Energy Lab in the United States. This shows state level data here, so we've got Texas all being excellent and Oklahoma being excellent. And even Florida is very good, which is kind of amazing. because Florida is usually not very good at anything. So this map's fine, right? We can make a decision with it. If I live in Nevada, I will just go ahead and buy some solar panels. And if I live in let's say Washington state or Michigan, I might as well just quit. Well let's look at it at a county level. It's the same data. This is the same data that was used to make the state stuff. I've got a totally different picture here, right? Here's some counties for example, in South Carolina that looked pretty good. There's one county that looks decent in in Montana. Some of the counties in New Mexico are not in the perfect category, so that's interesting. And even Eastern Texas is only good. So, what do we do with this stuff? Well, let's look at another level. This is what the original data was, this is just gridded data that was developed by Enbrel in the first place from which everything else in these maps have been derived. And what you can see here is that there's places in Montana and Southeastern Montana, for example, look pretty darn good. But they get washed out when you try to average everything out by counties, and then furthermore, when you try to average it all, all out by state. So this is a good example about how scale matters in terms of how you can make decisions in geography.