GIS 5007: Data Classification

This week, we explored various data classification methods to effectively represent spatial data. The objective was to visualize the percentage and distribution of senior citizens (aged 65 and older) across census tracts in Miami-Dade County, Florida. I created two map compilations using four classification methods: Equal Interval, Quantile, Standard Deviation, and Natural Breaks. The first map (Figure 1) displays the percentage of individuals aged 65 and above by census tract, while the second map (Figure 2) normalizes the data to show the number of seniors per square mile.

Figure 1

Figure 2

Data Classification Schemes

• The Natural Breaks (Jenks) method looks for natural groupings in the data. Areas with high or low senior populations are easily distinguished in both maps, making it easy to see potential patterns. The only downside? Since the breaks are based entirely on the data itself, it can be tricky to compare this map with others that use different datasets or classifications. Still, it gave one of the clearest pictures overall.
• The Equal Interval method divides the data into equal-sized ranges, making comparisons across areas easier. It works well when the data is evenly distributed, but in this case, the data on both maps are skewed toward the lower end. This happens because areas with higher or lower senior populations are grouped with more average tracts, making it harder to identify patterns.
• Quantile splits the data so that each class contains the same number of census tracts, which helps maintain a balanced and evenly spread map. However, this method doesn’t necessarily reflect actual differences in the data. It simply divides the total number of values into five classes, which can lead to sharp boundaries that don't accurately represent the real distribution on the ground.
• The Standard Deviation method classifies data based on how far each value is from the average, making it useful for identifying outliers and comparing areas to the average. However, it can be difficult for general audiences to interpret, especially if they're not familiar with statistics. As seen in Figure 2, this method can be misleading, making the senior population appear more uniform across the county than it truly is.

I’ve featured a map using the Natural Breaks (Jenks) classification method, which I believe provides the clearest and most intuitive depiction of senior population concentrations. It effectively highlights areas with high senior populations and is accessible for non-technical viewers. However, when considering areas with densely clustered seniors, using the normalized population count provides a more accurate representation. Percentages give a general sense of demographics but don’t account for tract size, which is crucial for understanding density and prioritizing services.

This exercise highlighted the impact of the Modifiable Areal Unit Problem (MAUP), the way data is aggregated and classified can significantly influence interpretation. Both maps demonstrate how different classification methods and normalization approaches can either highlight or obscure spatial patterns, depending on the scale and method used.