Head/tail breaks is a clustering algorithm scheme for data with a heavy-tailed distribution such as power laws and lognormal distributions. The heavy-tailed distribution can be simply referred to the scaling pattern of far more small things than large ones, or alternatively numerous smallest, a very few largest, and some in between the smallest and largest. The classification is done through dividing things into large (or called the head) and small (or called the tail) things around the arithmetic mean or average, and then recursively going on for the division process for the large things or the head until the notion of far more small things than large ones is no longer valid, or with more or less similar things left only.[1] Head/tail breaks is not just for classification, but also for visualization of big data by keeping the head, since the head is self-similar to the whole. Head/tail breaks can be applied not only to vector data such as points, lines and polygons, but also to raster data like digital elevation model (DEM).

## Motivation

The head/tail breaks is mainly motivated by inability of conventional classification methods such as equal intervals, quantiles, geometric progressions, standard deviation, and natural breaks - commonly known as Jenks natural breaks optimization or k-means clustering for revealing the underlying scaling pattern of far more small things than large ones. Note that the notion of far more small things than large one is not only referred to geometric property, but also to topological and semantic properties. In this connection, the notion should be interpreted as far more unpopular (or less-connected) things than popular (or well-connected) ones, or far more meaningless things than meaningful ones.

## Method

Given some variable X that demonstrates a heavy-tailed distribution, there are far more small x than large ones. Take the average of all xi, and obtain the first mean m1. Then calculate the second mean for those xi greater than m1, and obtain m2. In the same recursive way, we can get m3 depending on whether the ending condition of no longer far more small x than large ones is met. For simplicity, we assume there are three means, m1, m2, and m3. This classification leads to four classes: [minimum, m1], (m1, m2], (m2, m3], (m3, maximum]. In general, it can be represented as a recursive function as follows:

```    Recursive function Head/tail Breaks:
Rank the input data values from the biggest to the smallest;
Compute the mean value of the data
Break the data (around the mean) into the head and the tail;
// the head for data values greater the mean
// the tail for data values less the mean
End Function```

The resulting number of classes is referred to as ht-index, an alternative index to fractal dimension for characterizing complexity of fractals or geographic features: the higher the ht-index, the more complex the fractals.[2]

### Rank-size plot and RA index

A good tool to display the scaling pattern, or the heavy-tailed distribution, is the rank-size plot, which is a scatter plot to display a set of values according to their ranks. With this tool, a new index [5] termed as the ratio of areas (RA) in a rank-size plot was defined to characterize the scaling pattern. The RA index has been successfully used in the estimation of traffic conditions. However, the RA index can only be used as a complementary method to the ht-index, because it is ineffective to capture the scaling structure of geographic features.

### Other Indices based on the head/tail breaks

In addition to the ht-index, the following indices are also derived with the head/tail breaks.

• CRG-index. It is developed as a more sensitive ht-index to capture the slight changes of geographic features.[6] In contrast to the ht-index, which is an integer, CRG-index is a real number.
• Unified metrics. Two unified metrics (UM1 and UM2) were proposed in an AAAG paper [7] for characterizing the fractal nature of geographic features. One can be used to answer the question of “I know there are far more small things than large ones, but how small (or large) are these small (or large) things?”, whereas the other one to answer “I know there are far more small things than large ones, but how many more?”
• Fht-index: It is a fractional ht-index, which is able to capture fractional hierarchy.[8] The fht-index might be of help for creating an intermediate scale between two consecutive map scales, leading to so called continuous map scales.

## Applications

Instead of more or less similar things, there are far more small things than large ones surrounding us. Given the ubiquity of the scaling pattern, head/tail breaks is found to be of use to statistical mapping, map generalization, cognitive mapping and even perception of beauty .[3][9][10] It helps visualize big data, since big data are likely to show the scaling property of far more small things than large ones. The visualization strategy is to recursively drop out the tail parts until the head parts are clear or visible enough.[11] In addition, it helps delineate cities or natural cities to be more precise from various geographic information such as street networks, social media geolocation data, and nighttime images.

### Characterizing the imbalance

As the head/tail breaks method can be used iteratively to obtain head parts of a data set, this method actually captures the underlying hierarchy of the data set. For example, if we divide the array (19, 8, 7, 6, 2, 1, 1, 1, 0) with the head/tail breaks method, we can get two head parts, i.e., the first head part (19, 8, 7, 6) and the second head part (19). These two head parts as well as the original array form a three-level hierarchy:

the 1st level (19),

the 2nd level (19, 8, 7, 6), and

the 3rd level (19, 8, 7, 6, 2, 1, 1, 1, 0).

The number of levels of the above-mentioned hierarchy is actually a characterization of the imbalance of the example array, and this number of levels has been termed as the ht-index.[2] With the ht-index, we are able to compare degrees of imbalance of two data sets. For example, the ht-index of the example array (19, 8, 7, 6, 2, 1, 1, 1, 0) is 3, and the ht-index of another array (19, 8, 8, 8, 8, 8, 8, 8, 8) is 2. Therefore, the degree of imbalance of the former array is higher than that of the latter array.

### Delineating natural cities

The term ‘natural cities’ refers to the human settlements or human activities in general on Earth’s surface that are naturally or objectively defined and delineated from massive geographic information based on head/tail division rule, a non-recursive form of head/tail breaks.[12][13] Such geographic information could be from various sources, such as massive street junctions [13] and street ends, a massive number of street blocks, nighttime imagery and social media users’ locations etc. Distinctive from conventional cities, the adjective ‘natural’ could be explained not only by the sources of natural cities, but also by the approach to derive them. Natural cities are derived from a meaningful cutoff averaged from a massive number of units extracted from geographic information.[11] Those units vary according to different kinds of geographic information, for example the units could be area units for the street blocks and pixel values for the nighttime images. A natural cities model has been created using ArcGIS model builder,[14] it follows the same process of deriving natural cities from location-based social media,[12] namely, building up huge triangular irregular network (TIN) based on the point features (street nodes in this case) and regarding the triangles which are smaller than a mean value as the natural cities.

### Color rendering DEM

Current color renderings for DEM or density map are essentially based on conventional classifications such as natural breaks or equal intervals, so they disproportionately exaggerate high elevations or high densities. As a matter of fact, there are not so many high elevations or high-density locations.[15] It was found that coloring based head/tail breaks is more favorable than those by other classifications [16]

## Software implementations

The following implementations are available under Free/Open Source Software licenses.

• HT calculator: a winform application for obtaining related metrics of head/tail breaks applying on a single data array.
• HT in JavaScript: a JavaScript implementation for applying head/tail breaks on a single data array.
• HT Mapping tool: a function in the free plug-in Axwoman 6.3 to ArcMap 10.2 that conducts geo-data symbolization automatically based on the head/tail breaks classification.
• HT in Python: Python and JavaScript code for the head/tail breaks algorithm. It works great for choropleth map coloring.
• pysal.esda.mapclassify: Python classification schemes for choropleth mapping, including head/tail breaks map classification.
• smoomapy 0.1.9: Brings smoothed maps through python.
• Ht-index calculator: A PostgreSQL function for calculating ht-index (also see [17]).
• RA calculator: Software for calculating the ratio of areas (RA) in a rank-size plot (also see [5]).

## References

1. Jiang, Bin (2013). "Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution", The Professional Geographer, 65 (3), 482 – 494.
2. Jiang, Bin and Yin Junjun (2014). "Ht-index for quantifying the fractal or scaling structure of geographic features", Annals of the Association of American Geographers, 104(3), 530–541.
3. Jiang, Bin, Liu, Xintao and Jia, Tao (2013). "Scaling of geographic space as a universal rule for map generalization", Annals of the Association of American Geographers, 103(4), 844 – 855.
4. Jiang, B. (2019). A recursive definition of goodness of space for bridging the concepts of space and place for sustainability. Sustainability, 11(15), 4091. https://arxiv.org/ftp/arxiv/papers/1909/1909.01073.pdf
5. Gao, Peichao; Liu, Zhao; Tian, Kun; Liu, Gang (2016-03-10). "Characterizing Traffic Conditions from the Perspective of Spatial-Temporal Heterogeneity". ISPRS International Journal of Geo-Information. 5 (3): 34. Bibcode:2016IJGI....5...34G. doi:10.3390/ijgi5030034.
6. Gao, Peichao; Liu, Zhao; Xie, Meihui; Tian, Kun; Liu, Gang (2016-10-01). "CRG Index: A More Sensitive Ht-Index for Enabling Dynamic Views of Geographic Features". The Professional Geographer. 68 (4): 533–545. doi:10.1080/00330124.2015.1099448. ISSN 0033-0124.
7. Gao, Peichao; Liu, Zhao; Liu, Gang; Zhao, Hongrui; Xie, Xiaoxiao (2017-06-02). "Unified Metrics for Characterizing the Fractal Nature of Geographic Features". Annals of the American Association of Geographers. 0 (6): 1315–1331. doi:10.1080/24694452.2017.1310022. ISSN 2469-4452.
8. Jiang, Bin; Ma, Ding (2017). "How complex is a fractal? Head/tail breaks and fractional hierarchy". Journal of Geovisualization and Spatial Analysis. 2: xx–xx. Preprint. arXiv:1703.00814. doi:10.1007/s41651-017-0009-z.
9. Jiang, Bin (2013b). "The image of the city out of the underlying scaling of city artifacts or locations", Annals of the Association of American Geographers, 103(6), 1552-1566.
10. Jiang, Bin and Sui, Daniel (2014). "A new kind of beauty out of the underlying scaling of geographic space", The Professional Geographer, 66(4), 676–686
11. Jiang, Bin (2015). "Head/tail breaks for visualization of city structure and dynamics", Cities, 43, 69 - 77.
12. Jiang, Bin and Miao, Yufan (2015). "The evolution of natural cities from the perspective of location-based social media", The Professional Geographer, 67(2), 295 - 306.
13. Long, Ying (2016). "Redefining Chinese city system with emerging new data", Applied Geography, 75, 36 - 48.
14. Ren, Zheng (2016). "Natural cities model in ArcGIS", http://www.arcgis.com/home/item.html?id=47b1d6fdd1984a6fae916af389cdc57d.
15. Jiang, Bin (2015). "Geospatial analysis requires a different way of thinking: The problem of spatial heterogeneity", GeoJournal, 80(1), 1-13.
16. Wu, Jou-Hsuan (2015). "Examining the new kind of beauty using the human being as a measuring instrument", http://www.diva-portal.org/smash/get/diva2:805296/FULLTEXT01.pdf.
17. Tian, Kun; Peichao Gao (2015). "A PostgreSQL function for calculating the ht-index (PDF Download Available)". ResearchGate. doi:10.13140/rg.2.1.3041.0324. Retrieved 2017-08-08.