There are numerous ways of classifying data, and there are more or less suitable classification schemes for different types of data and data distribution. 

A head/tail break distribution describes data with a "head" and a long "tail". The head contains a small number of high values, and the tail contains a large number of low values. 

To visualize this distribution in GIS, a head/tail break classification scheme can be calculated by dividing the data into a head and a tail, where the head contains data with values that are greater than the mean, and the remaining values are in the tail. The head data can be further divided in this matter, until the notion of far more low values than high values is violated. In this lab, head/tail break classification was practiced using different types of geographic data. 

In this raster elevation data, there are far more pixels with low height values than high height values. As the natural breaks classification fails to visualize this, a head/tail break classification was applied.

The maps beneath represent the population density in Kansas city. The left map was classified with natural breaks, and the right one with head/tail breaks. The right one comes across as more logical and provides a better picture of how the population density is distributed.

For this part of the lab, data was partially created using random numbers in order to further visualize the difference between natural breaks and head/tail breaks. The left map is classified with head/tail breaks and the right one with natural breaks. The left one is more city-like and more living, in a sense. It is structurally more beautiful than the right one, which has an unnatural looking distribution.