Head/Tail Breaks: A New Classification Scheme for Data with a Heavy-Tailed Distribution
By Bin Jiang. 2012
364 citations recorded by the source corpus.
This article introduces a new classification scheme—head/tail breaks—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 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. I 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. I demonstrate the advantages of the head/tail breaks method over Jenks's natural breaks in capturing the underlying hierarchy of the data. The author would like to thank the three anonymous referees and the editor, Dr. Barney Warf, for their useful comments that led to many improvements. Any shortcomings or inadequacies remain the author's alone.
Subfields: Building and Construction, Food Science, Signal Processing
View Paper: https://www.semanticscholar.org/paper/a31b98a032983cffa9a3ec2fe7156885557510b4
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In Building and Construction
In Signal Processing
Citations in the corpus
- Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (2015, background): “The new definition is in fact developed from head/tail breaks (Jiang 2013) as a classification scheme for data with a heavy tailed distribution.”
- Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity (2014, background): “d, Salingaros and West (1999) formulated a universal rule for city artifacts; there are far more small city artifacts than large ones, due to which the image of the city can be formed in human minds (Jiang 2013b). With the increasing availability of geographic information, the scaling law has been o”