Understanding GIS. David Smith
Читать онлайн книгу.
1:40,000), the labels show up again.
You’ve probably noticed that some parks, such as Elysian Park and Echo Park, are labeled in both the World Boundaries and Places and Parkland layers. You can set the visibility range of the World Boundaries and Places layer reciprocally to the Parkland labels.
5)With the top World Boundaries and Places layer selected, open the Appearance ribbon (there should be no Labeling or Data options).
6)In the Visibility Range group, click in the In Beyond box and type 40001.
Notice that some default options were already set here. These are defaults that are set for this hosted basemap.
By setting this option, the entire top World Boundaries and Places layer turns off when zoomed in at a larger scale than 1:40,001. Most of these labels are actually for places other than parks, but you can accept their loss at large scales.
7)Zoom back and forth across the 1:40,000 scale threshold to test your settings.
At scales of 1:40,000 or larger, the World Boundaries and Places layer appears with a dimmed check box in the Contents pane. This dimmed check box tells you that the layer is turned on but is set not to show at the current map scale.
Visibility ranges
As you’ve just seen, visibility ranges can be set both for a layer’s labels and for the layer itself. In this case, the distinction was blurred because the Reference layer is composed of labels. But layers that are composed of features, such as parks or rivers, can have their visibility ranges set in the same way. When you design maps to be viewed at multiple scales, you normally want layers representing detailed data, such as buildings or utility lines, to be visible only at large (close-up) scales. You may want your map to include multiple copies of a layer, each with a different visibility range and unique symbology. For example, one layer might represent trees with a generic point symbol at medium scales. A second layer might represent the same trees with a more detailed and realistic symbol at large scales.
Dim the basemap
Without doing anything further to the labels, you can emphasize them a little more by dimming the imagery basemap. To do that, you’ll adjust its transparency.
1)With the bottom World Imagery layer selected, open the Appearance ribbon.
2)From the Effects group, adjust the transparency slider to 10%. You can do so by dragging the slider to 10%, or by typing 10 in the box. On the map, notice that the basemap fades slightly.
Add a layer of census tracts
The US Census Bureau gathers socioeconomic data about households and aggregates it by various geographic units. One of these units is a census tract, which is a relatively small subdivision of a county.
1)Open the Catalog pane and browse to ParkSite > SourceData > census.
2)Drag the tracts shapefile to the map.
The tracts layer is added to the Contents pane. The tracts cover everything except the labels.
3)In the Contents pane, right-click the tracts layer and click Zoom To Layer.
The census tract data covers Los Angeles County, including the islands of Catalina and San Clemente.
The tracts layer is made up of contiguous polygons that are reminiscent of a jigsaw puzzle. Because each piece of the puzzle represents a different set of living, breathing human beings, it’s natural that each tract’s attribute values for population, income, age, and so on would be different.
Open the attribute table
Now you can see what attributes the table contains.
1)In the Contents pane, right-click the tracts layer and click Attribute Table.
2)Scroll across the table and look at the field names.
The table has some identification codes, followed by selected population and housing attributes. Some of the field names are fairly easy to interpret, others less so. In lesson 2, you’ll see how to get more details about your information by accessing the metadata.
3)Locate the POPDENS_CY field. CY stands for current year, which for this census data is 2015.
This attribute stores population density (people per square mile) for the year 2015. Because one of your criteria is to locate the park in a densely populated area, this is relevant information. The attribute doesn’t tell you what value should be a threshold for “high density,” but it gives you a way to start making patterns on a map.
4)Right-click on the POPDEN_CY field name and choose Statistics.
A histogram displaying the distribution of the POPDEN_CY data will open next to the table. A Chart Properties pane will also open containing a set of statistics. The lowest value is 0 (at least one must be unpopulated), and the highest is 96,824.5. Most of the values are between 0 and 25,000, with the remaining values spread out in a long tail.
Selecting any of the bars in the histogram will produce a pop-up of information and highlight that data on the Lesson1b map.
5)Close the table, histogram, and Chart Properties pane when you are finished looking at them.
Symbolize census tracts by population density
Symbolizing a layer by an attribute, also called thematic mapping, allows you to see how values are spatially distributed.
1)With the tracts layer selected in the Contents pane, open the Symbology pane by clicking Symbology on the Appearance ribbon.
By default, all features in a layer have a single symbol. That’s why all your census tracts are purple, or whatever color they happen to be.
2)In the Symbology drop-down list, click Graduated Colors. The map updates automatically on the basis of some default values.
3)In the Field drop-down list, click POPDENS_CY. On the map, the tracts are symbolized by population density.
4)Change the Color scheme to Yellow to Red.
You can view the names of the schemes by selecting the Show names check box at the bottom of the list. Additional color schemes are available by selecting the Show all check box.
A lot is going on here if you expand the Symbol chart. (The same information is also available in the Contents pane.) The values for the POPDENS_CY attribute, which range from 0 to 96,824.5, are divided into five classes. The starting and ending values for each class are calculated by a “natural breaks” algorithm that separates clumps in the data. That’s why the range of values is different from class to class and why classes break at seemingly arbitrary numbers. Each class is associated with a symbol in a color ramp and is displayed on the map.
Change