In this week’s lab, we focused on data classification by using four different methods: natural breaks, equal interval, quantile, and standard deviation. We created maps using each of these methods and compared them using two types of data: total population per square mile and percent of the population over 65 years old.
Each method groups the data in its own way, which changes how the patterns look on the map. I found that using natural breaks with the percent of population over 65 gave the most helpful results. It groups similar values together and highlights meaningful trends, which makes it easier to understand where seniors live. The downside is that it can be harder to compare maps since the class sizes are not always even.
Looking at percent over 65 helps show which areas have more seniors compared to the rest of the population. This is useful because it points out places that might need more resources or support for older adults. If we only look at total population, large cities might stand out just because they have more people—not necessarily more seniors. That could make rural areas with a high percentage of older residents seem less important, even though they might actually need more attention.
Overall, this lab helped me understand how the choice of classification method and data type can really affect how information is shown and interpreted on a map
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