These are maps, where areas are shaded according to a
prearranged key, each shading or colour type representing a range of values.
Population density information, expressed as 'per kmē,' is appropriately
represented using a choropleth map. Choropleth maps are also appropriate for
indicating differences in land use, like the amount of recreational land or type
of forest cover.
An example from the Czech Republic is shown below.
Choropleth Map with Proportional Symbols
from ARCDATA PRAGUE, in GIS: Our Common Language, ESRI Map Book, Volume
Disadvantages of Choropleth Maps
Although choropleths give a good visual impression of change
over space there are certain disadvantages to using them:
They give a false impression of abrupt change at the boundaries of shaded
Choropleths are often not suitable for showing total values. Proportional
symbols overlays (included on the choropleth map above) are one solution to this
It can be difficult to distinguish between different shades.
Variations within map units are hidden, and for this reason smaller units
are better than large ones.
Isopleth maps differ from choropleth maps in that the data is
not grouped to a pre-defined unit like a city district. These maps can take two
Lines of equal value are drawn such that all values on one side are higher
than the "isoline" value and all values on the other side are lower,
Ranges of similar value are filled with similar colours or patterns.
This type of map is ideal for showing gradual change over space
and avoids the abrupt changes which boundary lines produce on choropleth maps.
Temperature, for example, is a phenomenon that should be mapped using
isoplething, since temperature exists at every point (is continuous), yet does
not change abruptly at any point (like population density may do as you cross
into another census zone). Relief maps should always be in isopleth form for
Isopleth example: precipitation 10th June 2000 (mm)
The disadvantage of isopleths are that they are unsuitable for
showing discontinuous or 'patchy' distributions and a large amount of data is
required for accurate drawing.
Proportional Symbol Maps
As the name implies, symbols (most often circles) are drawn
proportional in size to the size of the variable (e.g. employment change) being
represented. Proportional symbol maps are not dependent on the size of the area
associated with the variable. In other words, on a proportional symbol map of
Europe, tiny Liechtenstein would have the same visual importance as Spain if
their unemployment values were the same. This would not be the case with a
An example of proportional circles is shown on the Czech
Republic Voting Register map (above).
Scaling proportional symbols. Much research has gone
into the optimal scaling for proportional symbols. As a general rule, make sure
that the area, rather than linear proportions like radius or length of a side,
is the scaled parameter. For example, if there are four times as many gentrified
businesses in El Raval Site 1 than in Site 3, the area of the symbol should be
four times greater for Site 1. If the symbol choice is a circle, the radius of
the Site 1 symbol should thus be only twice as great (since area scales with the
square of the radius).
Used to show the distribution of phenomena where values and
location are known. Dot maps create a visual impression of density by placing a
dot or some other symbol in the approximate location of the variable being
mapped. Dot maps should be used only for raw data, not for prearranged data or
percentages. Appropriate themes for dot maps include the distribution of dairy
farms, and population distribution in a region.
Their limitations include the difficulty of counting large
numbers of dots in order to get a precise value and the need to have a large
amount of initial information before drawing the map.
Dot map parameters. When constructing a dot map, two
parameters must be considered: the graphical size of each dot and the value
associated with each dot. For example, you might stipulate that each dot be 2
pixels in diameter, and each represent 100 persons. In general, many small dots,
each representing relatively few instances of the attribute, is more effective
than a few large dots, but is more tedious to construct.