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We've seen how we can alter the look of our map by specifying in appropriate symbology for each feature. And we've also seen how we can call attention to important details or attributes about each feature through labeling. There's another way to bring meaning into your map by changing the symbology dynamically based on the specific attributes of a feature. You've probably seen lots of maps that do this, for instance, those red-blue election maps, where states or counties are colored based on the political party that they elected. To see what I mean, let's start by creating what's commonly called a coraplif map, where colors indicate relative values of a feature.
To do this I'm going to double click on the county layer here. That will bring up its properties. And I'll make sure that I'm on the symbology tab. And the symbology we've been working with is the single symbol option. And we can see the representation here. If I click on that, that will bring up the symbol selector that we've been working with. Let's go ahead and say okay, look at another option. Over on the left, we have the option to symbolize based off of quantities. I'm going to choose the Graduated Color option here and now I can specify a value from one of my attributes to symbolize on. From the Value drop down list, I'm going to choose Pop 10. This is the population in the 2010 Census.
Now the symbolization here is classified into five natural break classes. And I can see the ranges of data here. When I press Apply, the colors of data, my counties back in the background. And we can see that the light color indicates the population of between 2,266 up to 60,699. The darker color counties indicate a population up to 1,931,000. And if I move this out of the way we can see which counties are light and which counties are dark. The city of Seattle is in King County. And it's the most populus county in the state.
But what we're looking at right now is just overall populations. Without regard to the size of the county. What we can do is normalize our data based off the size of the county to get a better look at population density. Back in the layer properties, I'm going to take a look at this normalization here. In the drop down list, we can choose an area to normalize by. In this case, in our data, it's A land mi. For the area of the land features, in square miles. Now our data is going to display a population of people per square mile. And I can see that my least dense counties go from three people per square mile, up to a maximum of 912 people per square mile.
If we want to change how the labels appear over here on the table of contents, what I can do is click on this label and change its format. Now I can specify how many decimal places will appear and I'll change this down to two decimal places. And we'll see a cleaner look at our data here. Let's go ahead and press apply and we'll see that change update on our map. Now we're looking at density instead of population. And we can see that Kitsap, King, and Clark Country are all highly dense counties. Incidentally, Clark County is exactly opposite the river from Portland, Oregon. So it's considered part of the Portland Metro area.
And that accounts for part of its density. Now, right now, in Eastern Washington, there's not much visual difference between these lesser populated counties. I can increase the differences between them by changing the number of classes here from five up to maybe ten. We'll go ahead and say Apply, and now we can see some of the subtle differences in these counties over here. Lastly, I can change the color wrap that's been applied. Back in the Layer Properties window, I've got this color wrap option here, and I can scroll through the list to see all my different options. I'm going to choose this green option at the very bottom here. We'll go ahead and say apply.
and take one last look at our map. Alright, that's looking pretty good for our counties. Let's go ahead and take a look at one more. I'll go ahead and say okay, and I can see that the counties are updated over here in my legend. Now I'm going to symbolize the state of Washington. I'll double click on it to open the properties and in the symbology tab I'm going to go to categories. Here we can specify different categories from the attribute table. And if you remember back to the movie on meta data we explored this cartosymbology field here. I'm going to go ahead and say add all values from the cartosymbology field. And that adds the values of 1, 4, and 9.
Now back in the metadata we saw that the category of 1 indicated land features, 4 indicated water features, and 9 indicated features outside of the state. And we can use that to symbolize the different areas of our map. I'll double click on this first one here for the value of 1, to open its symbology, and I'm going to choose this green color here, and actually maybe I'll change it back to that apple dust color here. Go ahead and say okay for that. The next one down is 4. Those are our water features. So, I'll double click on that. And, I'm going to choose this lake symbology. Go ahead and say okay. And the last one, 9.
That indicates areas outside of the state. I'll double click on that. And, I'm going to scroll down and choose this gray option here. We'll go ahead and say okay. And we'll apply that to our map. Go ahead and say okay one more time to dismiss the properties. And now we could see that we have color symbolized for the outside of the state areas, the water features, and if we turn our counties off we could see the State of Washington as that apple sage color. So changing the symbology of your map features based on their attributes can help visualize yet another level of information in your overall map. Making appropriate use of qualitative, quantitative, or custom class data in those visualizations can often clarify the story that your data is telling.
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