As important as to know about layers type, it's important to know how to display layers with symbology.

1. DISPLAYING LAYERS

2. • Overview:
Layer’s appearance could be controlled by changing some of its display properties, such
as the symbol used to draw it. Different symbolization methods and techniques are used
depending on the data source and type of information represented by a layer.
For example, vector data is symbolized differently than raster data. You can also control
other properties related to layer’s appearance, such as making a layer transparent to see
what’s beneath it. You can limit the amount of information displayed on a map by
hiding some of the features in a layer or by specifying which layers are visible at certain
scales.
As all we know that we have types of layers, so vector layers symbology could be
classified into categorical symbology and quantitative symbology.
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3. • Vector layer symbology:
Layers that contain vector data are symbolized with either a single symbol so all features look the
same or with symbols that vary based on the values for one or more attributes. The level of
measurement represented by that attribute determines whether to use categorical or quantitative
symbology.
•Categorical symbology:
With categorical symbology, features are grouped into categories because they have similar text
or numeric attribute values. Each category is then assigned its own symbol using a particular
method(renderer) as mentioned in table 1.0 below(Renderers used for nominal data),
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Level of measurement Method /renderer Example
Nominal:
Values are qualities, not
quantities.
Unique values In a land use layer , each land use type is displayed with a
unique color.
Unique values,
many fields.
In a buildings layer, categories are based on both
ownership(e,g., city, county, private, etc.) and construction
year, and then each category is assigned a symbol.

4. An attribute table may contain numeric attributes that can be used to symbolize a
layer so it represents quantities such as a count, a rank, or a ratio. In quantitative
symbology, features are grouped into classes based on numeric attribute values,
using a classification scheme. Aggregating features into classes allows you to spot
patterns in the data more easily. Each class is assigned a symbol using the
appropriate method (renderer).
•Normalizing attribute values:
When symbolizing a layer with graduated symbology(graduated color, graduated
symbol, and proportional symbol), you can choose another numeric field to
normalize the attribute values. The attribute values are divided by the values in the
Normalization field. The layer symbology will be based on the ratio of two fields.
For example, instead of symbolizing a layer based on absolute population, you can
normalize the population values by area. Then you can symbolize the layer based
on population per square mile or population per square kilometer.
•Quantitative symbology:

5. Table 2.0.
•Quantitative symbology-Cont.:

6. Table 3.0.
•Classification methods:

7. Similar to vector layers, raster layers can be displayed in many different ways depending on the
type of data they contain and which aspect of the raster you want to emphasize. ArcMap chooses
an appropriate display method for a given type of raster, which you can adjust as needed. If the
raster has a predefined color scheme (a color map), ArcMap automatically uses it to display the
raster layer, as shown in table 4.0 below,
•Raster layer symbology: