Module 4: Data Classification

 

In this lab, I learned how to apply different data classification methods to a map. The goal was to map the distribution of the population above age 65 in Miami Dade County using four classification methods: Natural breaks, equal interval, quantile and standard deviation. For this lab, I created two map compilations in ArcGIS Pro, one for the percentage of population above 65 and one focused on the total individuals above 65 (map compilation shown above). I provided the map above because I thought it best displayed the data, due to providing a more accurate understanding of where seniors are concentrated geographically, rather than showing percentages that can be misleading in areas of small population. 

My biggest takeaway from this lab is that there is no single correct way to classify data, and each method can serve a different purpose. In this map compilation I found that the natural breaks method was most effective for identifying clusters of high senior populations whereas other methods might have oversimplified the data. Overall, choices in how data is grouped and displayed can influence how a map is interpreted, therefore it is important to be thoughtful about selecting the best method aligned with the intended purpose of the map. 

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