Part 1
Nominal Data: Each unit of data is unique and does
not have a numerical value. These values
are each given names in order to differentiate between them. Some examples could be things like building
type, vegetation type, country, etc. The
colors on the map are somewhat arbitrary and don’t have a clearly organized
scale, rather there is a variety of colors just to recognize the differences
between them. In Figure 1, the colors in
the map are used simply to differentiate between each type of church that is
popular in that area. The colors aren’t
meant to represent some sort of scale, just each unique church type.
Figure 1
Ordinal Data: This type of data places values in a
certain order, ranked from either least to greatest or greatest to least. Often times choropleth maps use ordinal data
because they can easily use a color scale to display values in order. In Figure 2, the author of the map used a color
scale ranging from light to dark to represent places based on completeness of
published architectural work.
Figure 2
Interval Data: Continuous data is used in interval
data classification. This can be used to
show differences between data values, but the interval size between values is
fixed. With interval data, a “zero”
doesn’t really mean anything, it is just an arbitrarily chosen point of reference. An example of this could be the timeline we
use in history. It is currently 2017,
but humans have been around for tens of thousands of years, or more. We chose the year Jesus Christ was born to
start the common era at year 1, and anything before that is “BC” or “BCE”, and
anything after that has the label “AD”.
The year zero wasn’t the first ever known year, it was just chosen as a
reference point. Figure 3 is a good
example of a map using interval data.
Temperature does not have a natural zero, because there can be negative
temperatures.
Figure 3
Ratio Data: This type of data also uses continuous
data, but a natural zero does exist.
This allows magnitude and comparisons to be made with different
values. This data can be mapped in
several ways; choropleth maps and graduated or proportional symbol maps are
common ways to map ration data. Figure 4
shows how a symbolized map can accurately represent ratio data.
Figure 4
Part 2
Classification Methods:
Equal Interval based on Range (MAP 1) - Each class
has an equal range.
Natural Breaks (MAP 2) - An equation is used to break
the classes up by where the largest groups happen to fall in the data.
Quantile (MAP 3) - Each class has the same number of
values within it.
Figure 5
In my opinion, the agricultural consulting company should
use MAP 2 to be presented to potential clients for the purpose of increasing
the number of women as the principal operator of a farm. This map uses the natural breaks
classification method in order to best display where the most female operated
farms are located, as well as where they are not common. MAP 1 which used the equal interval
classification method didn’t display the information in a helpful manner
because it shows almost all of the state being scarcely populated with female
operated farms. Only one county is in
the highest classification, making the map too generalized. MAP 3 does do a nice job of displaying where
female operated farms are most prominent as well as where they are lacking, so
it would be my second choice to show potential clients. However, with the highest and lowest classes
varying so much in range, I feel that a map with classes somewhere in the middle
ground between MAP 1 and MAP 3 would be the best choice. MAP 2 highlights just a handful of counties
as containing the highest number of female operated farms while showing quite a
few more areas that are lacking in female operated farms. The entire northern part of the state could
be targeted for marketing of female operated farms as well as small pockets
around the state that are also lacking.
And even if the agricultural consulting company decided they wanted to
target areas where female operated farms are already more popular, they could
use this map to find the top 5 counties to target for that approach.
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