Google Earth User Guide - Manual (Page 107)

maximum height display for the entire set. It then maps all data within the set in a way that best corresponds to each
individual field. In the example, the Square_footage field is used to map height values, with the minimum value of 2000 and a
maximum value of 6234. Each of the 9 elements in the set is displayed at a slightly different height value that most accurately
displays its relationship to the other points. This particular method is useful in smaller data sets where individual distinctions
between points or shapes are easily visualized.
With the continuous mapping method, you can use the Scaling factor slider and the Height units selector as described above
in
Height Values for Text Fields . Additionally, you can enter the desired height you want for the beginning and ending ranges
of your data. For example, you might want houses starting at 2000 feet to be displayed in the 3D viewer at a height of 100
meters, and houses at the ending range to be displayed at a height of 5000 meters.
In the 3D viewer, the visual distinction between individual elements using this method is affected by both the height range you
set and by the number of elements in the data. For example, a range of 100 - 5000 meters for a set containing only 9
elements creates a visually distinct height for each point.
If you decrease the range or increase the number of elements in the set, the distinction between each element diminishes.
Splitting Values into Buckets
Use the Split into buckets mapping method to create up to 8 height groupings for your data. This method works well for large
data sets where continuously mapped heights are not easily visualized in the 3D viewer. For example, if your data set
contains over 1000 housing listings clustered around a small region, it might be difficult to see the actual difference between
houses in different height categories. By using the Split into buckets option, you can create more meaningful categories and
define visually distinct gaps in their display. For example, you might have all listings between 2000 - 3000 square feet display
at a height of 500 meters, all listings between 3000 - 4000 square feet display at a height of 1500 meters, and so on. While
this method will not distinguish a house at 3000 square feet from one at 3200 square feet, it will allow a more immediate
visual grasp of the categories you have defined.
As with color and icon styles, the maximum value for each bucket is automatically computed, but can be adjusted manually.
Use the Scaling factor slider and the Height units selector for this method as described above in
Height Values for Text
Fields . As you set the number of buckets and define the maximum value for each bucket, the Style Template wizard displays
the count of items for each bucket. For more details, see Using Buckets for Field Data .
Using Style Settings to Modify Point Display
The color values you set for point data are applied to the icon that you map to points as well as to the line that is extruded
from the point on the earth for the height of the line, as shown in the real estate listing example above. However, in some
cases it might not be easy to visualize a single-pixel line in the 3D viewer against the earth imagery.
In that case, you can edit the style settings for each point in order to modify the line thickness.
1. Right-click (CTRL click on the Mac) on the point you want to modify and select Properties from the pop-up menu.
2. In the Edit Placemark dialog box, in the Style, Color tab, modify the point's appearance as appropriate.
3. Click OK.
Since this process is not practical for large data sets, you might consider applying changes to entire folders or subfolders.
Beware that if you do this, any individually defined styles will be lost. In this case, use the sub-folder feature of the Style
Template wizard to group similarly styled data into subfolders. Make sure that each folder created has similar color and icon
data. Then, apply the height value to your data and save the style template. Later, use the steps above to create shared
styles for each subfolder you set up. As long as all of the data within each folder has the same color value and the same icon
value, changes to the line thickness will not impact those settings.
Using Buckets for Field Data
When using color, icon, or height mapping for specific fields in your data set, you typically define a number of buckets, or
containers, to distinguish different ranges of data. The sections that follow describe how different field types are interpreted by
style templates, as well as how you can adjust the range of values when mapping numeric data.
Choosing Field Types for Style Mapping
You can choose two basic types of fields from your data when mapping color, icon, or height values:
●
Text (string) fields - If the field that you map to color or other style contains non-numeric data (i.e., text and other
characters), the application looks for the first 8 unique text fields, and maps those fields to the style. If there are fewer
than 8 values in your data, each unique value is paired to a different color, icon, or height. If there are more than 8
values, the first 8 unique values are mapped to a style, and the rest of the values are grouped together and mapped to
a ninth style. For this reason, it typically is most useful to apply a style to text fields that contain small unique sets.
For example, in the real estate example described in
A Style Template Example , there is a field in the data called
School_district. This field defines the school district ratings for each listed house. Because there are only three
districts: AA, AAA, and AAAA, it makes sense to use a style to distinguish this type of text field. You might, for
instance, decide to map a height to this field, so that users viewing your data see the highest points as those belonging
to houses in the highest-rated districts, and so on.