Module 4: Data Classification


This lab session was about understanding data classification. The lecture explained different types of data and how they're shown on maps. It then covered various methods for dividing data into categories for visual representation.

In the Lab portion, we used census data for Miami-Dade County, Florida, specifically looking at the percentage of the population over 65. We created four different maps of this same information, with each map using a different data classification method. Those classifications were Equal Interval, Quantile, Standard Deviation, and Natural Breaks.

We then arranged these four maps on a single layout, including standard map elements. The goal was to visually compare how each classification method displayed the data and what patterns became apparent or less obvious with each approach. Finally, we considered which classification method might be most useful for different purposes, such as identifying areas with a high senior population, and whether using percentages or density (adjusted for area size) would be a better way to represent the data.

For me this was the most interesting module to date, and I look forward to seeing what else we will coverer in the coming weeks.

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