Intro to GIS Exercise #5 – Symbology and Map Layout / Design IUP ...

About symbolizing data to represent Quantity
When you want your map to communicate how much of something there is, you need to draw features
using a quantitative measure. This measure might be a count; a ratio, such as a percentage; or a rank, such
as high, medium, or low. There are several methods with which you can represent quantity on a map—
colors, graduated symbols, proportional symbols, dot densities, and charts.
Represent quantity using colors
You can represent quantities on a map by varying colors, as a choropleth map. For example, you might use darker
shades of blue to represent higher rainfall amounts. When you draw features with graduated colors, the
quantitative values are grouped into classes and each class is identified by a particular color. You may want to take
advantage of a color ramp to symbolize your data.
Represent quantity using symbol sizes
You can represent quantities on a map by varying the symbol size you use to draw features. For example,
you might use larger circles to represent cities with larger populations. When you draw features with
graduated symbols, the quantitative values are grouped into classes. Within a class, all features are drawn
with the same symbol. You can't discern the value of individual features; you can only tell that its value is
within a certain range.
Proportional symbols represent data values more precisely. The size of a proportional symbol reflects the
actual data value. For example, you might map earthquakes using proportional circles, where the radius of
the circle is proportional to the magnitude of the quake. The difficulty with proportional symbols arises
when you have too many values; the differences between symbols may become indistinguishable. In
addition, the symbols for high values can become so large as to obscure other symbols.
Represent quantity using dot densities
Another method of representing quantities is with a dot density map. You can use a dot density map to
show the amount of an attribute within an area. Each dot represents a specified number of features, for
example, 1,000 people or 10 burglaries within an area.
Dot density maps show density graphically rather than showing density value. The dots are distributed
randomly within each area; they don't represent actual feature locations. The closer together the dots are,
the higher the density of features in that area. Using a dot density map is similar to symbolizing with
graduated colors, but instead of the quantity being shown with color, it is shown by the density of dots
within an area.
When creating a dot density map, you specify how many features each dot represents and how big the
dots are. You may need to try several combinations of dot value and dot size to see which one best shows
the pattern. In general, you should choose value and size combinations that ensure the dots are not so
close as to form solid areas that obscure the patterns or so far apart as to make the variations in density
hard to see.
In most cases, you'll only map one field using dot density maps. In special cases, you may want to compare
distributions of different types and may choose to map two or three fields. When you do this, you should
use different colors to distinguish between the attributes.