Module 4 – Data Classification

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Module 4 assignment for the Cartography class was to compare four different classification methods using the Miami-Dade County 2010 Census data. The assignment was to create 2 separate maps utilizing the same data and classification methods, with the second map normalizing the data based on area. ArcGIS Pro was used to create the maps using the provided 2010 US Census data for Miami-Dade County in a polygon shapefile. The following fields were utilized for creating the maps:

·        PCT_65BV – Percent of population age 65 and older for each census tract

·        AGE_65_UP – Count of population age 65 and older for each census tract

·        SQ_MI – Square miles of each census tract

·        Name10 – Name of each census tract

Map 1 utilized the four different data classification methods using the percentage of the population age 65 and older without normalizing the data.


Map 2 utilized the four different data classification methods using the population age 65 and older for each tract normalized on square area of the tract.

Equal Interval Classification

Equal interval classification divides the dataset into classes where there is an equal range of values in each class. Class ranges are created by subtracting the maximum value by the minimum value and dividing it by the number of preferred classes. This classification method is best used with data that is evenly distributed. Equal interval classification can be useful in revealing outliers in a dataset.

Quantile Classification

Quantile classification divides the data into classes that contain an equal number of features in each class. The classes are created by sorting the data from lowest to highest and then dividing by the number of preferred classes. This classification method will have no empty classes or classes with too few or too many values. The quantile classification is ideal for ranked data. The disadvantage of this method is that similar values can be placed in different classes and values that are very different can be placed in the same class.

Standard Deviation

Standard deviation classification shows how the data values are related to the mean of the data. Class breaks occur at equal intervals from the mean usually at one, one-half, one-third, or one-fourth the standard deviation. This method does not work well when the data is skewed by very high or low values. This method is used when you are interested in how the data relates to the mean.

Natural Breaks

Natural Break classification divides the data into classes where there are natural breaks in the data values. Class breaks are created using the Jenks algorithm that attempts to keep like values together and maximize the differences between classes. This method is useful when data values are not evenly distributed and considers outliers in the data.

My Findings

The Natural Breaks classification using the field percentage aged 65 and above would be the best choice if you are looking to target the senior citizen population. Equal interval and standard deviation classification methods do not work well when there are outliers in the data. Tract 90.40 has 79% senior citizens population, which is a very high value compared to the other tracts. The quantile classification method considers the outliers but also includes tracts that are very different.

Normalizing the population counts by the area of each tract would be the best way to depict the distribution of population for Miami-Dade County. Normalizing the data based on the area gives a better representation of where the senior citizens are spread across the entire area of the Miami-data county. The data is normalized by dividing the total population of senior citizens in the tract by the area of the tract. The Quantile classification method looks to be the best option since the class breaks are evenly filled where each class has the same number of features. The quantile method is generally good for rank data.








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