Unemployment
in Alabama
Unemployment is something that ever graduating college student fears.
It is something that many people hope never happens to them.
The following maps provide a look at unemployment by county for the great
state of Alabama. The maps are
different only in classification and design.
The first map (figure 1) displays unemployment in standard deviation.
The second map (figure 2) shows unemployment in quantile. The third classification of unemployment (figure 3) is found
using natural breaks classification. The
final classification method (figure 4) takes a look at equal interval.
The purpose of this paper is to take a look at each classification to
determine similarities and differences for each classification.
The first classification that this paper focuses on is standard deviation
(figure 1). According to Slocum
(1999), “standard deviation is a method of data classification in which the
mean and standard deviation of the data are used to define classes”(p. 268).
This classification is good because classes are broken at even points away from
the mean. The mean value for this
data set is approximately 6.4%, with the standard deviation of 2.8%.
This means that at one standard deviation the data will fall between
approximately at 3.6% and 9.8%. A
major disadvantage for standard deviation is that it requires that the data be
normally distributed for the analyses to be valid.
This data set is not normally distributed.
The data does follow somewhat of a normal pattern, but there are too many
peaks and valleys in the data set for me to consider it normally distributed
(Figure 5). This map shows that
unemployment is lowest in and around Birmingham and Huntsville.
The highest unemployment rates are found in the black belt area of the
state particularly in Sumter County with an unemployment rate of 13.2%!
The second classification used the quantile (figure 3) method to classify
the data. According to Slocum
(1999), “quantiles are a method of data classification in which an equal
number of observations is placed in each class” (p. 270).
The quantile method produced a larger break in the data.
The highest unemployment range was from 9.1% to 13.2%.
This would be an excellent classification because it shows a pattern of
high unemployment that extends on the western part of the state along the state
line. It extends east throughout
the Black Belt. It makes almost a L-shape design. The lowest incidences of unemployment are once again centered
in the counties surrounding Birmingham and Huntsville.
The third classification used was natural breaks (Figure 3). According to
Slocum (1999), “natural breaks are defined as a method of data classification
in which a graphical plot of the data is examined to determine natural groupings
of data” (p. 269). This classification is great because it considers what the
data looks like and breaks the data accordingly.
However, the class limits can be very subjective.
Like all the other maps, the highest unemployment is found on the
southwestern part of the state. Using
this classification, low unemployment is centered on the big cities.
There is also low unemployment in DeKalb, Cleburne, and Lee Counties.
These counties all border the Georgia state line.
The fourth and final classification used was the equal interval data
classification (Figure 4). According
to Slocum (1999), “equal intervals are a method of classification in which
each class occupies an equal portion of the number line” (p. 266). This map
did not vary much from the natural breaks classification. In
fact, the top two classes of high unemployment did not change from one map
classification to the other. The
difference was in the bottom three classes.
For these three classes, equal interval was a little narrower than
natural breaks. For example, the
lowest unemployment range for equal interval was 1.6% to 2.6%, while the
unemployment rate for natural breaks was 1.6% to 3.7%.
The advantage of equal interval is that the legend is gap free. A common disadvantage is that the data is not considered when
the classes are drawn. It goes out
an equal distance and puts a class. This
more confined class is useful to see where the most unemployment is located.
Shelby County is far and away the best area for unemployment.
I admit that it is not surprising to me given that Shelby County is the
fastest growing area in the state.
All four of these maps show the same phenomenon.
The highest areas of unemployment is found in the south and west part of
the state. Sumter County has the
highest percent of unemployment with 13.2%.
Shelby County has the lowest percent unemployment with an outstanding
1.6%. The lowest unemployment tended to center around the bigger
cities of Alabama. Huntsville,
Montgomery, and Birmingham have low unemployment that extends to the surrounding
areas. As mention earlier, the
highest unemployment tend to follow the old Black Belt region of the state.
There is one county that appears to be a little out of place.
Etowah County has a higher unemployment rate than any of their
surrounding counties. It is
probable that some large employer must have closed their doors at the time that
this data was collected resulting in the higher unemployment rate.
The unemployment rate can tell a lot about a county.
The unemployment rate can tell if your area is growing or lagging far
behind. As you can see, the
unemployment rate is a very useful subject to map!
Works
Cited
Slocum,
Terry A. 1999. Thematic
Cartography and Visualization.
New Jersey: Prentice Hall.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
