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Choropleth maps are one of the most popular and commonly used map types out there.
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So, let's have a look at how they work.
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So, the word Choropleth was coined by a cartographer named John Kirtland Wright in 1938.
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He was trying to come up with a word to describe a combination of
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assigning values to different parts of a map or different spaces.
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So, he went to the Greek origins of this,
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which are choros for space and pleth for value.
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So, he combined those to create this new word called choropleth.
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So, that was a word that this guy invented.
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So, why am I telling you this?
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Why have I not mentioned the origin of words in
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a lot of different sections before in other videos?
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Well, because there's a common thing that people really want to do,
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which is to put an 'l' in there and call it a chloropleth map.
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It's just one of those little things that kind of bugs me,
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is that it's not a chloropleth map,
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it's not related to chlorophyll or chloroform
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or chloro this or chloro that, it's choropleth.
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I always think it's important especially when you're starting out and learning
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about these things to get the terminology correct from the beginning, that way,
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you look like you know what you're talking about and you're not making these kind of
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weird mistakes that to any trained GIS cartography type person,
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they'll notice that right away is if you mispronounce it as chloropleth.
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I think I've made my point.
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I just wanted to make sure that was clear and so, you won't make that mistake.
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You'll probably think of this and go, "Yeah,
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I'll never say that now," which is mission accomplished then.
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Okay. So, the whole idea of a choropleth map is that you
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have numbers that are assigned to areas.
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They could be anything,
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but probably the best place to start or one of the most common ways of
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using them or for things that we would call enumeration areas.
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So, think of it,
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like if you've counted the number of people for a census unit,
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something like that in a neighborhood or a word
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or a congressional district or whatever it happens to be,
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you've got a number that you've assigned to a particular area.
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So, here we've got some population counts for different census tracts,
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and so we have one number per area.
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Now, on its own, if you're just looking at that,
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if I quickly asked you to say or I asked you what's the highest value?
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What's the lowest? Is there a pattern going on here?
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Is there a gradation from low to high from east to west? Something like that.
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If you're just looking at the numbers,
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it's really not that easy to see what's going on.
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So, what we do is we tend to,
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this is normally how it would be done,
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is grouped those values together into classes and then
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assign each of those classes either a gradation from black to white,
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like a gray scale or gradation of some kind of color, like I've done here.
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So now, we have five different classes of
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values and if we assign those to the numbers in our dataset,
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we can assign it,
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as I'm saying here, an intensity of color or shade that's proportional to those values,
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then you end up with a choropleth map,
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where it's much easier to visualize those actual numbers.
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I could put labels on there as well if you wanted to,
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but the idea is generally,
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that do you want someone to be able to look at that and very easily be able to
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see which areas have higher population values and which ones have lower values.
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Now, it's not super great to just use population counts,
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it's better to use something like density,
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but I'll get to that a little bit later.
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So, if I apply exactly the same idea to my entire data set here.
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So, these are all the census tracts for Toronto,
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here are all the population counts for each of
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the census tracts and that's exactly the same thing if I said, "So, what's going on here,
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where are the high areas, low areas,
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is there some kind of pattern going on,
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can we see similarities and differences and relationships, things like that."
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Of course, it's really difficult to do that,
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but if we classify the data,
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in this case we can do this in ArcMap with
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the Symbology tab here and I've specified the value as being population.
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I'm going to use five classes.
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So, I've divided up into five, doesn't have to be five.
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It's not some magic number,
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it could be three or seven.
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I'm using a classification method here called quintile.
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We'll explain that a little bit more later and so now,
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I'm using a color ramp here from this lights, what would you call it?
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Kind of a magenta to darker and so that is being used to
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assign a range of that color scheme according to the color ramp to these classes.
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So, that's how this idea of a choropleth map is actually implemented in the software,
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that's exactly how you would set it up if you are
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going in and doing that yourself, you've got a data set,
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you tell it which attribute to use, in other words,
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which column in your table,
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and then you tell how many classes you want,
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you tell it how to divide up the numbers into those classes,
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there's different ways to do that and then you tell it what color scheme to use.
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Boom, it puts it all together and you end up with a choropleth map like this.
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So, now just like I did before,
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I've got my different classes,
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I've got a gradational values that indicate to somebody low to high.
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So, someone looks at this,
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it's very easy for them to be able to look at any part of the map and see
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what areas are lower population versus higher population.
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Now, I have a question for you,
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is this a useful map?
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I want you to think about it for a second and this
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is a common thing with choropleth maps.
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It's something that it's important that I think anybody who is teaching this wants to
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make sure it comes across well is that,
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look at the size of the areas that are being mapped and what's being mapped here.
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So, we're mapping people that live in a city and we're using
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different sized areas to count up how many people are in those areas.
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So, it makes sense or I'm hoping that you're seeing that if you have a big area,
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the odds are pretty good that there's going to be more people in that area,
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and so they'll be a higher value,
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and if you have a smaller area,
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there's less people, the odds are that
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it's likely that there's less people at that location,
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and so you're going to have a lower value.
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So, in other words it's not the most useful way of
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portraying what's going on here because it's bias by area.
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In other words, we want to be able to control for that area or take it out
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or normalize it or somehow count for that,
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so that when we're making a map,
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we can show something that's more true to what's really going on,
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in this case what would be a better way of doing
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this is taking out area or normalizing for it and the way we do
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that is to divide the populations by area to create a population density.
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If I go back to my symbology,
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I have the option of using this thing here called
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normalization and you can select what field you want to use for that.
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So, what's happening here is I've got population,
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let me just try that again.
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I'm going to go, population and I'm going to normalize that by area.
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All that means is,
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that I'm asking the software to take my population column
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divided by my area column and that's going to calculate on the fly,
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so to speak, what the density,
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the population density values are and use those in my choropleth maps.
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So, I'm still using the same color scheme,
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I'm still using five classes,
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I'm still using quantiles.
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But now, I'm going to be representing population density.
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I'm taking out that bias that's being introduced
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by area and see what happens in terms of my result.
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So, here's what we get. We have a map that has
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a very different look to it than the population map.
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This is something that to me makes a lot more sense or it's more useful is that,
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this is downtown Toronto here,
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so yes, the population density is much higher.
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Look at over here we have a much larger census track,
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this is probably one of the biggest ones in the city,
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but it turns out that there's really not that many people
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living there if you account for area.
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So, it actually has a fairly low population density
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and that's probably more useful in a lot of situations when you're trying to
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interpret things that might be related to government policy
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or what politicians might want to use in terms of making decisions.
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Often, the density will be more useful than the count.
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Not always, but it's definitely something that you want to
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take into consideration when you're making a choropleth map.
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Here's a comparison between the two.
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So, we have total population versus population density.
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So, just two different ways of thinking about a variable that you're trying to map
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using a choropleth and what's the most representative or useful way to do that.
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This example might help you understand how
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area can bias results or bias a choropleth map.
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I really like this example.
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It's from a book by [inaudible] it's a great cartography textbook.
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So, the example here is that if you have farmers fields that have been divided up into
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different sized areas and you wanted to make a map or
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choropleth of how much of those fields had been harvested.
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So, notice here that we have 16 acres here,
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16 acres here and this is 64 acres there,
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and so we're measuring the areas in terms of acres.
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If we look at the total acres harvested,
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let's say we're harvesting corn,
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so here we have no acres harvested,
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there was no corn harvested there,
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we have 16 acres harvested here and 64 acres harvested here so all it is,
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is that these had been divided up into different sized fields.
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If we make a choropleth map based on
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those raw counts as opposed to accounting for area of normalizing,
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then this is the choropleth map that we would get.
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So, if we just interpreted this,
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we see a light green so that would mean that there was low or no core harvested,
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we have a medium green so this would be a medium amount
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harvested and this dark green would be a high amount harvested.
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So, that's the way that someone would interpret that choropleth map.
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But if you actually divided by area,
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this is if you added this up 4 times 16 is 64,
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so if you actually look at the same size area,
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the same amount of corn was being harvested but you've got
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two different colors here and it's almost well it is
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misleading or almost lying to
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somebody is that they'll look at these two different colors and say there was
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less corn harvested here and more corn harvested
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there based on these counts which is not accurate.
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It's misleading and it's not a good way of representing your data.
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However, if we divide by area,
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then you can see here that these are now
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the same color because we've normalized for area,
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we've divided by total acres there
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and now we've got something that's more representative when somebody looks
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at that they say oh these are the same color
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that means there's the same amount harvested and that's
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what we want them to see is something that's true and more representative of the data.
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If we apply exactly the same idea to the census tract data,
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let's see what happens there.
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So, what I've done here is I have isolated
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two census tracks that are quite
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different in size and so if we look at the population counts,
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you'll see that this really big census track has 12,909 people in
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it and this smaller census tract has 13,530 people in it.
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So, the population counts are within
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five percent of each other and so you think okay yes.
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So, if we look at that in terms of a choropleth though,
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as a population choropleth these would be the same color
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because they would be very similar values they'd be the same color of red.
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But if we look at the population densities,
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the density of the larger one is 167 people per square kilometer.
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The density of the smaller one is
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3,232 people per square kilometer so way higher density.
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So, the density of one is 19 times higher than the other and
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so which do you think makes more sense in terms of trying to compare things,
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I think you probably see that it makes more sense usually
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to normalize or standardize your data if there's some bias taking
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place with choropleth maps and geography areas
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the most common way that you'd want to do that or that you'd want to account for.
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Just to summarize, we can look at total versus derived values.
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So, total values are things like
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population counts which are not normally used for choropleths unless you have
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a good reason to use them it's not to say that
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it's absolutely forbidden or the software won't let you it's
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nothing like that it's just that you have to be
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conscious of these things and make that decision intentionally.
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So, you wouldn't normally use it for things like
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population percentage tracked as I've just shown you,
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what we do prefer to use for choropleths are things that are derived values.
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So, those are ratios involving area like we were just doing like normalizing.
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So, population density per census tract or ratios that
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are independent of area things like per capita income for a census tracks.
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So, those are things that are not biased by area and so that's perfectly fine.
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As an example of that,
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I just made this map for fun.
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It's a ratio of males to females for
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different parts of the city these are different neighborhoods and so I just put
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the labels on here if you're familiar with Toronto or if you're not and I've used
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this diverging color schemes so where the ratio is almost one or very close to one,
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in other words there's equal numbers of males and females,
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we've got this gray and then I've used a diverging color scheme to show
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increasing amounts of these ratios either higher or lower than one.
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So, as you can see here,
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if you're looking for a lady downtown is the place to be,
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if you're looking for a man then I'd say get to
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West Humbler-Clairville or Wexford/Maryvale,
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I'm just joking around here but the idea is that actually it's
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interesting to see that there is variation in the ratio over the city.
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I have no idea why that is but this is a way of
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showing data that's not biased by area because all we're doing is
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dividing one by the other it's a ratio of one thing to
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another and the size of the census tract will have
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no effect on how many men versus women there would be in a particular location.
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So, appropriate data for chloroplasts are things like
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statistical or political boundaries where people
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have drawn these boundaries to count things like people.
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So, this might be population per census divisions something like that.
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It's usually not used for continuous data.
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So, the reason for this is that the distribution of
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data is not related to the boundaries that are being used to count things.
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So, for example you could do
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this but it really wouldn't make much sense to to make a map of
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average rainfall per census division something like that
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because there's no connection or relationship between those two variables.
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Census divisions were designed to count people for a census,
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that has nothing to do with rainfall and the amount of rainfall
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that falls at a location has nothing to do with these boundaries.
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So, yes if you had boundaries like
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watersheds or something like that that would make sense to you could
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do a choropleth for that but again that's
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an exception it's not the most common way of doing things.
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So, let's just have a look at,
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take this to an extreme just to see how this happens or how this works.
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If you have Toronto neighborhoods,
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so I've got a map here of neighborhoods and here we have elevation data.
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So, this is an example of continuous data.
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If I made a choropleth map of elevation per neighbourhood.
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Yes I can do that it's certainly possible but
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is this meaningful is this really useful not really.
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I had a little fun with this,
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I turned it into an extruded prism map or 3D map just to
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show you conceptually visually how this is actually working,
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is if we took these elevation values for each neighborhood and we extruded
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them this is really what the choropleth is implying.
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Is that you have these perfectly flat areas
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that in terms of elevation and then as you get right to the edge of one of
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these boundaries you fall off a cliff and then you get to
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the next perfectly level area here and then you'd have
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to climb up this cliff to get to this next perfectly level area and so on.
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So, the of course that's ridiculous that's not the way elevation works.
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So, why would you use these boundaries to
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represent a variable that's not related to those boundaries?
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It's different for people. If I actually show it this way for people that's fine
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because these boundaries were designed for people,
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all it's really doing is saying there's
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this many people in this area and this many people in that area.
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There's nothing bad or unrelated about that it makes total sense
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so make a choropleth for that but don't make it for something
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that's not related to the boundaries that are being used.
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This is a better way of showing that data so this is
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continuous data with the neighborhoods draped on top of it.
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This shows you this is quite exaggerated Toronto is not quite
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nearly as dramatic in terms of the terrain but you
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get the idea that you've got a lot of variation in
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any particular neighborhood and it's not really going to be
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representative to show that as one flat choropleth unit.27910
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