This is Lecture 1 in Lesson 2. We're going to talk today about why spatial is special. So geography depends on spatial thinkings and spatial relationships, so that there can be any science of geography at all. And like most sciences geography is governed by at least one law. We have Tobler's First Law of Geography, and this law is pretty simple. It just says that everything is related to everything else. But near things are more related than distant things. That makes a lot of sense, right? I'm hoping that that, to you, is as common sense as it is to me. I sort of can't even believe that that's a law, but it is, he was the first person to say it. But relating stuff is not the same as similarity, right? So, just because two things are near each other doesn't mean that they're necessarily more similar. They can be similar, and in that case, we can measure that similarity by looking at something called Spatial Autocorrelation. Spatial Autocorrelation therefore measures the degree of similarity between observations that are located near each other. You could have Positive Spatial Autocorrelation or Negative Spatial Autocorrelation, it just depends. For example, an example of positive spatial autocorrelation or stuff that's close to each other is more similar. Would be that airports collect business dudes who use Bluetooth headsets and drink gigantic lattes, right? So these more similar, middle age, white guys who all have briefcases and silly things in their ears. they are clustered near each other in space, right? An example of negative spatial auto correlation would be, think about like any car dealer lot. let's say they have a bunch of brand new cars that cost whole lot of money and they've got a used car lot right next door. You might have some cars that are very close to each other in the parking lot but are completely different on every metric, right? You might have a $500 beater parked 20 feet away from a $100,000 E-Class Mercedes, right? That would be an expensive E-Class Mercedes, but whatever. So geography depends on something else too, and that's called spatial thinking. You use spatial thinking all the time, let's say you're making a decision on where to take your next vacation. You're going to weigh all kinds of factors, right, including how far away that place is. How far, how you get there in terms of the conveyance so do you take a plane, do you drive there? Do you take a bus or a train? Do you walk? I guess if you walk to your next vacation you're either very lucky or very unlucky, right? So you're thinking about your place in relation to others and you're comparing those things across space and time, and that's an aspect of spatial thinking. Another place it might come up is determining whether or not you can make it to the next rest area on your next road trip, right? You might have this big argument in your car like we do in our family whether or not another 20 miles is going to work for everybody. Or if we should stop now, everybody take a break, and then inevitable someone else will want to get off at the next stop anyway. That's a good example of spatial thinking too, right? You're going to be thinking about how much can you tolerate between now and then, and how far is that distance really? Each mile is going to seem like a long time by the end, right? So a good way to start exploring this further is by trying to think A-spatially first. So we're going to think without trying to consider space. We're going to try to do that for a minute. We'll see how hard it is. Let's look at some fake data, this is always a fun way to look at a problem I think. So I created some fake data here, this is a table showing the number of Annoying People. The Total Population Average Age, Average Income, the number of sport utility vehicles. And then county and state names. But don't think too much about those yet, because we're trying to think a-spatially. So what could you do with this table right now? You could probably total up stuff, right? You could say well, what's the total number of annoying people on this whole set of areas, right? You could calculate the average age for the entire set of places. You can do the same with Income or the number of SUVs, all that kind of stuff. You could cross tabulate some of that kind of thing. So what's the average income for, for people owning SUVs. You could probably start to tabulate some of that kind of stuff, right? But, don't you really want to see where these things are in relation to each other? So let's take a look at the geography I created here. These are the states of Wholefood and Traderjo. Completely randomly named I'll add and there are little counties in each one of them that have cities by the same name. And I named them a bunch of other totally random Names that have nothing to do with anything in reality at all, I swear. So, these places, nice little neat hexagons, also because in my fake reality I like everything to be nice and orderly, are connected to each other, right. There should be some patterns here we see in this data. So let's look at that data now that we have the ability to make a map out of it This is a graduated circle map. Graduated circles work by having each circle size representative range of values. So in the smallest category here the little city of Hatchback you can see, it's in the smallest category. It has less than 1,000 residents. Where as University Collegeville has more than 50,000 residents. And we use the same circle size here to represent the same categories. So that you can pretty easily compare, let's say, for example, the county of Kingo to the place called Dialupia in the northern part of the map. So, you're starting to see the spatial pattern here, right? There are some places that are near each other that are very different in population. And you wouldn't get that from that table right away, would you? Now you're trying to make sense of this. And, and, making sense of things across space, and time, is a really key aspect of spatial thinking. Here's a Choropleth Map of similar data here. So a Choropleth Map shows areas filled with colors to represent a range of values. Sort of like the Graduated Circles Map uses the circle size to indicate a range of values. You've seen these things a lot. You've already seen them in this class actually in lesson one, with the female head of household map example. But here I've taken my fake data and I've a Choropleth map that shows the percentage of population that owns SUVs. So I've calculated those two things against each other and I've tried to make categories that make sense here zero to 25%, 25 to 50, etcetera, right? And right away you can see that there are some places that are in the lowest category like Pabsto and Hatchback, University Collegeville. And there's only one place in the very highest category that's Bluetooth Village where over 75% of the population also owns an SUV. So, hopefully by now your thinking spacially, right? And if your not, then maybe this between us just isn't going to work out, sorry.
