Data Classification

The purpose of this lab was the classify data representing populations over 65 years old using the following four classification types.

Equal Interval: Divides the data into intervals of equal size, making it straightforward to interpret each range. This method is ideal for highlighting absolute differences in data values, but it can misrepresent the distribution if the data is clustered or skewed, as many values may fall into the same interval

Quantile: Ensures each class contains an equal number of observations, which is useful for evenly distributing data points. This method effectively reveals the relative distribution of data but can hide the actual differences in value between observations within the same class.

Standard Deviation: Groups data by how far each value is from the mean, using standard deviation as a measure. This approach is excellent for identifying outliers and showing deviation from the average, but it assumes data is normally distributed, which may not always be the case.

Natural Break: Aims to minimize variance within classes and maximize it between them, often revealing natural groupings in data. It is particularly effective at showing natural clusters and significant breaks in data continuity, although it may overlook gradual trends.

The following maps were using two separate types of data, percent of the population over 65 and the amount of people over the age of 65 with relation to square miles.

For targeting the senior citizen population, I believe the Natural Breaks classification method is likely the most effective. This method identifies natural groupings and variations within the data, highlighting areas with significantly higher concentrations of seniors, which can help in allocating resources and services more precisely to where they are most needed.

In regards to distribution, using the percentage of the population above 65 is more effective. This method allows for clear comparisons across regions of varying sizes and densities, focusing directly on the proportion of the population that may require age-specific services. Conversely, using the population count normalized by area could be misleading, as it might overestimate needs in densely populated areas where seniors make up a smaller proportion of the total population. The percentage approach is better suited for equitable planning and resource allocation across diverse communities in Miami Dade County.