148 sYMBOLizaTiOn
determined from related maps, fieldwork, and/or from imagery. Related maps might
show where a crop cannot be grown because of soil or slope. Imagery can show the
actual crop in place.
While the size of individual dots doesn’t vary, the choice of dot size and value
is important. A dot that is too small is hard to see and can be mistaken for a flaw in
the paper or an errant pixel on a monitor; dots that are too large stack on top of one
another and obscure patterns. Likewise, if the value of the dot is too large, patterns
can’t
be seen and if it is too small, areas appear too dense (Figure 8.3).
In some cases, two or more phenomena may be represented on the same map,
such as spring wheat and winter wheat or cattle and sheep. In these instances, differ-
ent colored or shaped dots may be used to distinguish the phenomena.
Ironically, the simple dot map, which was commonly used until the advent of
GIS and was one of the easiest maps to construct, is a difficult map to create with a
computer. There is, at this writing, no software to create such maps although they
can be constructed by using imagery as one layer, outlining the areas of occurrence,
and placing the individual dots on another layer. This is a cumbersome task and not
often done. A valuable map type has fallen into disuse as a result.
A more commonly used
type of dot map now is the dot density map (Figure
8.4). On such maps, the dots are placed randomly within the enumeration area. If the
enumeration area is large, such as a state, these maps are somewhat crude and can
be misleading; if the enumeration area is small, such as census tracts or blocks, the
resulting map approaches the quality of a simple dot map. If the data are available
VERMONT
CATTLE & CALVES
1 DOT = 1,000
