Module 4: Data Classification - Population Distribution of Individuals Aged 65+ Within Miami Dade County, Florida

Module 4 introduced the topic of various data classification methods. The objective was to display the same data set using 4 different classification methods and compare the differences that each has on the final map output. Population census data for Miami Dade was provided from the Florida Geographic Data Library. ArcCatalog software was used to preview the data and data table to determine the census tract with the highest number of individuals aged 65 years or older as well as the census tract with the highest percentage of the population aged 65 years and older. By sorting the table based on the appropriate fields, the data shows that census tract 90.40 has the highest percentage of people above 65, at 79.17%, while census tract 58.02 has the highest amount of seniors at 2372 individuals. The same data was then classified using four different classification methods, natural breaks, quantile, standard deviation, and equal interval, based on using the percent of the population aged 65+ data for each tract. The results were then mapped using ArcGIS Pro software. A second map used the same classification methods and the same dataset, but the total population of individuals was used and normalized based on the area of the tract, rather than the percent of the population data. Both maps comparing the four classification methods are shown below: 

Tract symbology ranged from low (white) to high (dark blue) percentage or number of individuals. The tracts with the highest densities of individuals over 65 years are mostly located in the northeast portion of Miami Dade based on this data. The most accurate classification method based on the population percentage and the total population methods seems to be quantile. The ranking from least to most accurate classification method for this particular dataset would be equal interval, standard deviation, natural breaks, and quantile. When comparing the total, normalized population method and the population percentage method, the normalized quantile portrayed the data more accurately and efficiently, so that there are obvious areas of high and low senior densities. This exercise showed that the same data can be mapped differently based on how the map creator chooses to group the data. Therefore, it is very important for the map creator to choose the most accurate method of data classification for the data they are trying to visualize.