Data Classification

Combined Data Classification Methods

This week we focused on common methods of classifying and presenting data.  Our first project presents four views of the African American population of Escambia County.  Each of the methods used has different advantages and disadvantages, depending on the type of data and the spatial relationship that is being explored.  These differences are easily seen when we apply different methods (quantile, natural breaks, equal interval, and standard deviation) to the same data and map. Since we were comparing methods for the same data and map, I thought it important to use the same color ramp for each map.  This helps us compare the impact of the method better and not just be distracted by different color schemes.

 

Of the four methods used, I thought the quantile method seemed the best fit for the data. The quantile method breaks the total population up into classes (five in this example) of equal population size.  So for a map of population density, each class represents a 20% increase (with five classes) in the subject population. This is an easy to understand distribution and presents a well graded map.