**Topic 8: Map Analysis**

A map is a form of communication and the process of interpreting map information is the
search for * 'meaning' or 'message'* in the map by understanding
the spatial pattern that is displayed. The

* Analytical Process: *The
process of map interpretation involves analyzing the spatial information that is depicted
and the way in which it is depicted. There are several methods of map analysis:

* Intuitive/Visual*: This is where, without being concious of
it, we are applying a well-known method, or comparing results with prior knowledge.

* Mechanical:* This is where a 'cookbook' application of rules
is being applied.

* Logical:* Thinking through the steps and making reasonable
assumptions.

* Frequency Distribution
Histogram*: There are similarities between the distribution of
values in a population as displayed in a Frequency Distribution Histogram and a spatial
distribution on a map. Three

* Mean:* This is the mathematical average of the all the values
in the population.

* Central Tendency*:
This is similar to the frequency distribution, but based on spatial location. The centre
is the spatial equivalent of the three related mathematical ideas:

* Map Mean:* This is the mathematical average of the all the X
and Y values in the population.

There are two conventional ways of using distance to describe spatial patterns: * Density*
and

**Density****Analysis***
*is the idea of

* Cluster Analysis *is
the way in which spatial features are arranged in a map pattern. A spatial distribution
may be described as

**Clustered Spatial
Distribution**

**Dispersed Spatial
Distribution**

* Variance-Mean Ratio:*
This is a

:Even Dispersion |
Variance-Mean Ratio < 1 |

Random Distribution: |
Variance-Mean Ratio = 1 |

Substantial Clustering: |
Variance-Mean Ratio > 1 |

Directional patterns can be subdivided into those which deal with a * directional
bias* of the pattern as a whole, and

* Directional Bias*:
This is when a map pattern has a greater number of spatial elements (i.e. points) in one
area over another and it is possible to draw a trend line along the approximate boundary.

* Alignment Bias:* is
the measure if directional trends in a spatial pattern. Individuals in an aligned pattern
tend to have neighbors that are much closer.

Line patterns can be analyzed in a similar manner as point patterns, by measuring**
line density **(i.e. lines per unit area)

* Line Pattern Examples:*
There are some line patterns that occur commonly on maps; a few are summarized in
the figure.

* Radial, Braided, and Dendritic Patterns* are found in stream
sytems;

As with point and line patterns, the concepts of density, directional bias, and alignment or orientation also apply to area features. Several area patterns can be observed over a portion of the Chippewa County Land Use Land Cover, Level 1 map:

* Area Density:* wetlands display a higher density over some
areas compared to others;

Area map patterns describing different themes can overlap. This provides the map interpreter with an important problem-solving and analytical tool since it lends itself easily to processing by computers.

* Area Overlay Qualitative Example:*
The addition of two themes results in a third theme with a combination of the original
areas. When combining many themes, this approach is cumbersome, and so a quantitative
method is preferred.

* Area Overlay Quantitative
Example:* By giving a relative value to each of the area features in
each theme based on the importance of that feature, it is possible to create a suitability
map.

By classifying themes in terms of their suitability in meeting specific conditions and
then combining these themes, it is possible to analyse a large number of complex areal
themes. In the following example, * lake sediments* in an area were
analysed for

** **

* Suitability Map for Cu, Zn,
As*: The calculation consists of adding the three separate element
indexes; those with the

One of the reasons we use thematic maps is to compare map patterns. This can be done
visually in a * qualitative sense*, as we have noted earlier, or in
a

* Map Pattern Correlation Example*:
The following map patterns are based on demographic information for the city of Atlanta,
Georgia. The area is subdivided into

* Graphic Correlation*:
Since each

* Regression Lines and
Residual Data*: Residual data points are those that do not fall
within the

* Degree of Correlation*:
Variables may be

**Gershmehl: p. 101 - 144**

**Monmonier: p. 43 - 57
p.
71 - 86
p.
139 - 162**