  This paper introduces a new classification scheme - head/tail breaks - in
order to find groupings or hierarchy for data with a heavy-tailed distribution.
The heavy-tailed distributions are heavily right skewed, with a minority of
large values in the head and a majority of small values in the tail, commonly
characterized by a power law, a lognormal or an exponential function. For
example, a country's population is often distributed in such a heavy-tailed
manner, with a minority of people (e.g., 20 percent) in the countryside and the
vast majority (e.g., 80 percent) in urban areas. This heavy-tailed distribution
is also called scaling, hierarchy or scaling hierarchy. This new classification
scheme partitions all of the data values around the mean into two parts and
continues the process iteratively for the values (above the mean) in the head
until the head part values are no longer heavy-tailed distributed. Thus, the
number of classes and the class intervals are both naturally determined. We
therefore claim that the new classification scheme is more natural than the
natural breaks in finding the groupings or hierarchy for data with a
heavy-tailed distribution. We demonstrate the advantages of the head/tail
breaks method over Jenks' natural breaks in capturing the underlying hierarchy
of the data.
  Keywords: data classification, head/tail division rule, natural breaks,
scaling, and hierarchy
