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Impact of increasing COMPRESSION_FACTOR on central and peripheral hexes.pdf
Recalculating f.xlsx



PROJECT CONCEPTION: Bruce Mitchell, January 2017

METHODOLOGY: Bruce Mitchell, George Tzelepis

SCRIPT AUTHORS: Code core written by George Tzelepis (Γιωργος Τζελέπης).

... restructuring, additional code and annotations by Bruce Mitchell.

ONS Geospatial, June 2019


  • Does your geography comprise polygon sub-units of widely differing sizes?
  • Are the most interesting data to be found in the smaller areas?
  • Alternatively, are your data located at points that are by turns tightly concentrated and widely scattered?

If so, then this is definitely for you.

Our Python tool creates equal area cartograms (EACs) based on hexagonal grids. Its principal purpose is to get around the visual dominance of large areas on maps, and the tendency for smaller areas to be overlooked. Please refer to the report for details on the nature of the problem and setting up and running the tool on your data (64-bit Windows 7 or 10 supported).

The tool has been successfully tested on a wide range of geographies with an extreme range of scales.

The tyranny of large areas

Thematic mapping draws attention to statistical difference across space. Statistical values of interest are often attached to sub-areas of the overall geography, and this may reveal significant spatial variations. However, administrative divisions commonly encompass a range of unit sizes, from small urban districts to large rural expanses.

The eye is drawn to large areas, so when seen together on a map, the larger units dominate and the smaller recede. Think of Canada, Australia and Russia, where huge areas, often relatively sparse in socio-economic statistics, acquire visual dominance at the expense of the smaller and often more populated areas. Those familiar with the geostatistics behind any given map may be able to apply a mental filter to compensate, but the fact remains that the areas where the chief interest of your story resides may be small and hard to see.

Here in the UK it is a similar story. Local Authority Districts (LADs) in the UK range in size from the 290 hectares of the City of London to the 2.6 million hectares of the Highlands of Scotland – but the two are administratively equivalent. On a national map, the City of London is completely invisible. British statistical geographies (Census output areas) are just as vulnerable to the issue as the administrative geographies. They are based on population thresholds, so are made up of units (instances) which are small where the population is dense, and large where it is spread thinly. At the middle level [MSOA (EW), DataZones (Sc) and Small Areas (NI)], they range from just over 1 hectare (ha) to 180,000 ha.

One is usually encouraged to use rate data for choropleths instead (e.g. percentage of one species of tree in the forest) as that were the perfect solution. But the tyranny of large areas persists regardless of your data type – a larger forest is more visible.


Yet it is possible to set aside the visual dominance of large areas altogether.

One can scale the territories not according to their actual geographic area, but according to a value of statistical interest. This is known as a cartogram – a hybrid statistical graph / map, a compromise by which the viewer gains more immediate access to the data but loses some of the geographical context.

Cartograms exist in many forms; they may preserve, or ignore, shape, contiguity and orientation. They are often shown together with a conventional map to aid navigation and interpretation. They powerfully convey general impressions (see Benjamin Henning’s ‘WorldMapper’, but are not intended to convey specific values, and it is next to impossible to assess whether two distorted areas - which are already differently-shaped in their raw forms - represent the same statistical value. Furthermore, a new cartogram must be drawn for each new variable, refreshing the navigational challenge every time.

The Equal Area Cartogram

Our favoured approach is the equal area cartogram (EAC). Here, every district is represented with an identical geometric object of equal size and shape, and therefore, area. No area is inherently (dis)advantaged, as the base geometric representation of all is the same.

Once generated, the EAC provides a single standard layout that can be linked to countless datasets to generate as many thematic maps as desired.

The ONS hexmapping tool

Within this project, we have concentrated upon automatic hexmap generation using Python. Our code runs quickly and can operate on any polygon dataset at any scale.

We have tested the product on a wide range of distinctive and challenging geographies. These include countries with predominantly north-south extent (Chile, Italy, Norway), countries with east-west extent (Russia, USA), irregular shapes (Croatia), exclaves (Russia’s Kaliningrad), doughnuts (Germany - Berlin within Brandenburg), complex archipelagos (Indonesia). It works on all, regardless of projection or scale. Indeed, it works equally well on all the stars within ten parsecs of Sol (our sun) and a tiny detail from an electron microscope image of the brain of a fly.

As your input geography is imported, its projection is noted: it is then turned into pure geometry for the hexmap creation and then re-exported back to its original projection. Two hexmap variants are available. The basic variant overlays the original input geography and the two may be used together. The compressed hexmap is designed to stand alone, without the original geography or a base map. However, because it is in the same projection as the original input geography and the basic hexmap, you may overlay them for testing purposes, to assess the effectiveness of the compression.

The tool works on an inverted gravity model ( - PART FIVE: THE INVERTED GRAVITY MODEL (COMPRESSION)).

Observations are drawn towards the centre of the dataset, defined by the centroid of all the areas’ centroids (Centroid of Centroids (CxC)). Starting out from the CxC, the gravitational force F is negligible, but increases in power with increasing distance from the CxC. However, this produces increasing amounts of white space closer in towards the CxC, because observations towards the centre are subject to increasing friction (less antigravity) and move less and less towards the CxC. I therefore developed the modified inverted gravity model, which adjusts F by reference to the difference between the distance from any observation to the CxC and the MEDIAN distance.

Speed of execution depends upon your PC’s resources and the complexity of the input spatial dataset: for over ¾ of tested geographies, the compressed hexmap is created within ten seconds. Only three took more than one minute – the 1,973 Cantons of France (138s); the 1,385 NUTS3 units of the WEU and EFTA (150 s) and the 7,201 MSOAs of England and Wales (40 minutes).  

USING THE TOOL [see the report for details]




The Compression Algorithm

How the compression works can perhaps most effectively be shown on the example of districts in the Russian Federation. This is on account of its very skewed composition = there are 85 districts of which the smallest (Sevastopol) is 616 The largest (Sakha (Yakutia)) covers over 3 million, while 30 districts are over 100,000 km sq. km. in area, and these are all in the north or east. In addition, Sevastopol + the Crimea (de facto but not de jure administered by Russia) and Kaliningrad are detached from mainland Russia. This is purely for illustrative purposes and in no way a political statement.

As we have already seen, a choropleth map of this geography will be dominated by the large Siberian areas, while the small areas will be all but invisible. However, much socio-economic data is likely to be concentrated in the smaller areas. A hexmap will equalise the visual impact of each district but, without compression, will leave large gaps due to the enormous distances involved.

On the basic hexmap, each hexagon is rooted to the geometric centroid of its district. The geographical distribution is faithfully represented and the hexmap accurately overlays on a basemap. But in the case of Russian districts, the hex size that best achieves this (each hex being 190,000 km top to bottom [they’re pointy-uppy]) means that there’s an enormous amount of empty space, and this results in small hexagons whose labels are hard to read. It would also be hard to interpret any thematic data that was pinned to them (by colour). So, this is a geography for which a compressed hex map would make perfect sense.

We drop the basemap but, for the moment, retain the boundaries for reference. Six peripheral districts are labelled - these will have furthest to travel towards the centroid of centroids (CxC).

As the Compression Factor is adjusted (remember that CF=1,000 is practically no compression, while 1 is extreme compression), the gravitational attraction of the CxC varies. At CF = 3.0, the pattern remains recognisably that of the districts of the Russian Federation – we still have the concentration of districts in western Russia, Kaliningrad Oblast is still separated, the Crimea and Caucasus are distinct, as is the scatter eastwards across the Urals into Asia. Yet now, we can zoom to the hexmap layer – so making the hexagons larger and their labelling clearer.

Complex archipelagos

The program also works with highly complex and regionally differentiated geographies such as Indonesia.

Here, 270 Kabupaten or ‘regencies’ are distributed across a huge number of islands. There is considerable variation in size, generally being large on the islands of Borneo and New Guinea, but tiny on the densely inhabited island of Java. This is a classic example of the shortcomings of choropleth mapping. In terms of mapping socio-economic data at this administrative level, Java will always be disadvantaged.

Producing a simple hexmap will leave large spaces, especially in the less-densely populated Borneo and New Guinea. An uncompressed hexmap (HEXSIZE = 0.6 decimal degrees) will result in the concentration of regencies on Java expanding well beyond the island’s coast, while great spaces exist between the hexes representing the extensive regencies on Borneo and New Guinea. As with Russia, the HEXSIZE required to accommodate this diverse geography means that effective labelling is very challenging.

With a COMPRESSION FACTOR of CF=3 the large gaps are reduced while clearly retaining the overall pattern. One can then zoom into the map and label it far more effectively.

Where clusters of spatial units could cause unreasonable distortions of the overall pattern, these can be extracted to inset maps alongside the general map. Unlike the earlier example, areas not selected for inset mapping are not disadvantaged. Furthermore, generating individual hexmaps for the inset areas results in a better approximation to their actual shape than can be achieved by creating a single overall hexmap. Both basic and compressed hexmap variants (with the inset areas extracted) can more accurately display the areas around the periphery of the inset areas (see around London).

The West Midlands region encompasses some to of the most rural and some of the most urban parts of England, giving rise to a 700-fold size difference between smallest and largest of the 735 MSOAs in the region. It is therefore not practicable to produce an MSOA-level choropleth, graduated or proportional symbol, or basic hexmap of the entire region (scale 1:820,000). On the other hand, a compressed hexmap can succeed, as the compression permits a zoomed-in view, equating to a scale of 1:525,000.

Regional and multiple compression

For some hierarchical geographies (e.g. Local Authorities within regions within countries) with distinct spatial patterns, and where it may be important to retain these, it may be desirable to initially generate regional hexmaps before combining these into a hexmap for the whole geography. Projection, HEXSIZE and other parameters must be common across all regions.

But this will result in the CxC of each regional hexmap drawing the individual hexes inwards, and this will cause gaps to open up between regions. The datasets should then be merged into a single shapefile, and the gaps can then be manually reduced by shifting the hexes of one region towards the other, respecting the common hexagonal mesh.

If excessive space between the individual hexagons on a compressed hexmap disturbs, this may be reduced by applying one or more additional round(s) of compression.

Other scales


The methodology may have applicability well beyond the scales normally used for cartography, and well beyond social geostatistics. Whether the application of this techniques to data at these scales, and which are inherently three-dimensional, is helpful or even appropriate, must be left to the relevant subject experts.

Two datasets have been kindly provided by Dr Benjamin Ciotti of the University of Plymouth’s School of Marine Biology. The first is a set of marine biology sample points in a small part of Plymouth harbour. The x-y location of each sampled point was recorded along with depth and data on the various species found there. The second is a selection of sampled beaches on the western coast of Scotland. In both cases, the dispersal and clustering of the sampling points makes the use of proportional symbols in their actual locations inefficient, with both large gaps and overlaps. In both cases, compressed hexmap are more effective.


Zooming right in, we have generated a (proof-of-concept) compressed hexmap from an electron microscope image of the brain of a fly. In the absence of spatial data on the image and purely as a proof of concept, I converted the image file to vectors and applied a simple Cartesian coordinate system. I then created and mapped a simple (and meaningless) 8-value categorical scale on a small part of the image which, at 1:1200, equates to 0.000025 mm2.


Looking further afield, the technique may also be used at the cosmic scale. The HYG Database is a meta-catalogue containing over 120,000 stars. It includes the x,y,z, Cartesian coordinates of the star (in a system based on the equatorial coordinates as seen from Earth), distance from earth and a range of other fields. Once a suitable buffer is generated for each point to create polygons, a compressed hexmap may easily be generated for any subset of the catalogue. We have generated a compressed hexmap of all the stars within ten parsecs of Sol (our sun).


We have created a tool that can be used to quickly and efficiently create hex-based equal area cartograms from any dataset that includes cartesian or projected coordinates, regardless of the scale. It is most suitable for datasets where the area of polygons, or the distances between points, is highly variable.

The tool can create BASIC or COMPRESSED hexmap from ESRI shapefile polygon input. A point dataset must first be converted to polygon by generation of buffers.

Your polygon input dataset must be placed in the INPUT_POLYGON folder, whose location is defined in script ‘’. It must also be added to ‘’ as GEOGRAPHY_NAME. You also have to select values for HEXSIZE (in units of the projection) , HEXORIENTATION (1 or 2) and COMPRESSION_FACTOR (sensible range 1.2 -> 4).

Note that a value is required for COMPRESSION_FACTOR even if you are creating a basic hexmap. Use ‘2’ as the default. Geometry and image files will be generated and placed in the output folders specified.

Note: if you use a decimal for the COMPRESSION_FACTOR, e.g.2.5, while the folder for the output will be named correctly (e.g. ZAF_adm2_mainland__0.4_1__CF3___C_HXMP), the name of the shapefile within it will be curtailed at the decimal point (e.g. ZAF_adm2_mainland__0). If you try several options (e.g. CF 2.8, 2.6, 2.4) they will all be in separate, correctly named folders, but will have identical filenames.


First: try out some of the geographies supplied with this tool with the parameters as set up in the Python script ‘’ or ‘’.

Then see what happens when you alter HEXSIZE, HEXORIENTATION and COMPRESSION_FACTOR.

Add some of your own data (as ESRI shapefile) to the INPUT_POLYGON_PATH folder and to the appropriate ‘’ script. Experiment with the three parameters.


  • An outline of the original geography would assist orientation. Unfortunately, the compression algorithm operates on hexagons and not the original polygons.
  • Currently, the optimal values for hex size, hex orientation and the compression factor must all be obtained by trial and error. It should be possible to calculate optimal values by reference to the perimeter envelope of the input geography, the number of spatial entities within it and the units of the spatial reference system (projection).
  • The tool should be able to accept as input any spatial file format recognised by Fiona, but it’s currently set up only to accept ESRI shapefiles as input and to write output also only to shapefile. Adding other formats at both ends (e.g. geopackage, geoJSON) – has been experimented with but not implemented.
  • The basic and compressed code began as a single script, remain essentially the same and should really be merged back into each other, incorporating a conditional IF COMPRESSED HEXMAP – ACTIVATE COMPRESSION CODE – ELSE SKIP clause. Otherwise there's double the maintenance. A 'compressed' switch should be added to the parameters.
  • Some of the linear code ought to be rewritten as functions
  • As a cosmetic touch, perhaps a completion bar / hourglass / sound on completion could be added.
  • Under some circumstances, multithreading would help.
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