Module 4: Data Classification

   

    This week's lab had us utilizing various data classification techniques to display 2010 census data of populations aged 65 and up. We used Natural Breaks, Quantile, Standard Deviation, and Equal interval on both the total population by area of individuals aged 65 and up and population percentage of individuals aged 65 and up. The goal of this lab ultimately was to show us how different data classification techniques, along with different variables within data, produced different maps. We were then asked to essentially pick the method and variable we saw fit best for representation and accuracy.

    Each compilation map displayed the four classification methods: Equal Interval, Quantile, Standard Deviation, and Natural Breaks, with each impacting the visual representation of senior citizen distribution in Miami-Dade County. The Equal Interval method is straightforward but, in my opinion, oversimplifies variations, while Quantile ensures balanced class sizes yet often distorting ranges. Standard Deviation highlighted deviations around the mean, emphasizing population clusters, and Natural Breaks revealed extreme values effectively while maintaining visual balance.

For targeting senior citizens, the Natural Breaks method, to me, best displayed the data as shown above. It minimized differences within classes and avoided overrepresentation of extremes, creating a well-balanced, intuitive map for identifying areas with high senior populations, especially around Miami.

If I were presenting to county commissioners, depicting data as a percentage of the population aged 65 and above seemed to be more effective than using total population by area. Percentages highlight broader distributions beyond downtown Miami, making the data easier to interpret for decision-makers unfamiliar with advanced cartographic techniques. Population counts may misleadingly emphasize urban areas while obscuring senior populations elsewhere.

    Overall, this lab was extraordinarily helpful to grasp the concepts of data classification techniques. It's easier to see how each classification acts on data and reinforces the ability to retain how exactly these techniques work. I'm always very worried that if "I don't use it, I'll lose it" and I hope that I can keep finding opportunities to use what we have done in this lab.