The map above displays four comparison methods of classification using
the percent of population ages 65 and up for census tracts in Miami Dade
County, Florida. The classification methods used are; Natural Breaks, Quantile,
Equal Interval and Standard Deviation. Each method of classification displays
data differently.

Natural Break classification groups data “naturally” into classes by
using an algorithm to find spikes or clusters where the data is
found within the range of data distribution. This type of classification works good for the percentage of age 65 and
up map as it finds breaks in the data where there might be larger groupings and separates the percentages into appropriate views. However,
map readers may not understand the legend very well in determining how the data
is broken up into each class.

The Quantile method of classification divides the total distribution of data
into equal numbers of observations. Each class contains a range value
obtained by determining a midpoint between the highest and lowest value for
each class. I believe the Quantile method is the best method of classification
for displaying the percentage of population ages 65 and up. The data range
shows the highest percent of values placed in the last class. Showing where the highest values exist, map readers can compare against the lower values and easily understand the breakdown of data distribution. Also the legend is easily
understood in how the ranges are broken up.

Equal Interval classification shows the data within equal ranges. Basically
the data is equally split up into breaks or classes. Ranges for the data
are determined by dividing the total data range by the number of classes used.
This type of classification does not display data very well for the percent of
population ages 65 and up map. Since the percent values are equally spread
across the number line and a class of ranges are equally divided, the data on
the map looks very even and is hard to compare neighboring values because the
data is equally dispersed.

The Standard Deviation considers how data
is distributed along the number line. Classes are formed from a mean deviation
and broken up by adding or subtracting values. This classification does not
show the data very well for the percent of population map. It’s very confusing to
understand and hard to portray the data appropriately as the map reader must have
an understanding of statistical analysis.

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