Hong Kong Water Flows

Sometimes I get a little nostalgic… Here is a Sankey diagram of water flows in Hong Kong. My guess is that it pre-dates 1997, so this would be the former British colony Hong Kong. Originally published in Worldbank’s Eco2 Cities book (Hiroaki Suzuki, Arish Dastur, Sebastian Moffatt, Nanae Yabuki and Hinako Maruyama. Eco2 Cities: Ecological Cities as Economic Cities. 2010), it is pictured in this guide on page 41.


Flows of water are shown in 1.000.000 m³ of water (difficult to see, but I read this as 10 to the power of 6). Obviously hand drawn, so flows are not fully to scale.

Hongkong receives an average 2.000 Mm³ of precipitation (per year?) on a land area of 1.046 km² (interesting: todays area is 1.108 km²). Most of the water directly evaporates, and a large chunk goes into the sea.

This is considered an early example of a material flow analysis (MFA) visualization, and also of an urban metabolism study.

WEEE in Midi-Pyrénées

From what I know, France’s approach to tackling energy and waste issues is to break the topic down to the regional level, and to involve local stakeholders.

Here is an article on ‘Métabolisme territorial et filières de récupération-recyclage: le cas des déchets d’équipements électriques et électroniques (DEEE) en Midi-Pyrénées’ by Jean-Baptiste Bahers that was published in the journal Développement Durable et Territoires. Vol. 5, n°1 in February 2014.

It discusses the ‘Territorial metabolism and recovery-recycling chain: the example of Waste Electrical and Electronic Equipment (WEEE) in the “Midi-Pyrénées” region and has the following Sankey diagram figure.


Licensed under CC BY-NC 4.0

WEEE waste streams are in kilo tonnes (kt) in the year 2008. Additionally, recovered energy from waste treatment is shown (in MWh) with orange arrows. The red line delimits the region, so apparently the electronics waste recycling and disposal (élimination) takes place outside the Midi-Pyrénées region. Some flows are labeled with a range (e.g. 6-14 kt), which is obviously difficult to draw as Sankey arrow. My guess is that the median value was used to determine the actual width of the affected arrows. A nice feature are the per capita values (e.g. 2-4 kg/hab), which makes it much easier to grasp and to relate to for the indivdual person living in Midi-Pyrénées.

World Oil Flows Map

Did a clean up some of my hard disks and came across a number of gems I had saved. Unfortunately I hadn’t noted the sources for all of them.

Here is one of these. A photo of two facing pages in a book depicting world oil streams. You can find more Sankey diagrams on maps here on the blog if you search for the tag ‘map’. This one is different though, as it uses a special map projection (probably Goode homolosine) with a cut along the Atlantic and Hawai’i as an inset.


Unfortunately I do not know from which book that was taken. Neither do I know the year of reference or the unit of measure for the flows. We can see the oil shipments mainly starting from the Middle East and Venezuela with Europe and the U.S. as main destination markets. Additionally, areas where coal, natural gas and petroleum are extracted are marked on the land areas.

In the botton left corner the legend reads for “Movement of petroleum”: Width of flow lines is proportional to tonnage of petroleum (crude and products). The flow lines do not necessarily indicate exact routes of movement’

UK Resource Flow 2014

This Sankey diagrams for the resource flows in the UK in 2014 can be found in the Digest of Waste and Resource Statistics – 2018 Edition. The report is published annually by UK’s Department for Environment, Food & Rural Affairs (DEFRA). This is for all mass flows nationwide, but excluding fossil fuels and energy carriers. Unit of flows is megatonnes (mt) per year.

That figure didn’t quite convince me. Apart from the the pixelated arrow segment borders where arrows don’t run vertically (see red arrow), I found that some flow quantities were missing. Further, I didn’t like that the blue recycling back flow was exaggerated (read: not to scale) and the 91 mt arrow as wide as the green 143 mt biomass flow (at least in some segments). To be fair, the footnote for the diagram warns “that the ‘pipes’ are not all to scale”, but my impression was that this effect was mainly used to emphasize the thinner arrows.

I did a quick redo of this resource flow diagram only to find out that it was impossible to determine some of the missing flow quantities. I could find some of them in the report, but was unsuccessful for others. So I had to estimate them (which is indicated with an asterisk in my version).


Warning: Some values based on estimates, please do not use the data from this figure.

As you can see the recycling stream is less wide in my remake. Didn’t fully hit the right color codes, but tried to stick to the original layout as much as possible.

sankey-diagrams.com – New Look

I had some downtime due to an unexpected issue with the blog, but everything is fine again now. Big shout-out to Chris who helped me with backup and recover.

The old theme didn’t work any more with the latest WordPress release, so I ended up with a new theme too. Still tweaking some of the font sizes and colors, but here we go.

After 12 years (since the first post in 2007) with the same look a refresh was probably justified.

Wood Flows in Auvergne Rhône-Alpes

Following up to my post on energy flows from biomass in the Auvergne Rhône-Alpes region, here is another great Sankey diagram on wood flows by Auvergne-Rhône-Alpes Énergie Environnement (AUR-EE) agency.


Flows are in 1,000 m³ (f) wood fibre equivalents (read more about this unit here). The annual average of the years 2009 to 2013 in the region is visualized.

Wood is used for energy generation (top part), wood products (middle section), and pulp & paper (bottom part). Imports of wood or wood-based products come in from the grey box at the top, and export streams leave to the bottom grey box. Import and export in this case is from/to the area outside the Auvergne Rhône-Alpes region.

The green box on the left represents the existing standing wood stock (estimated at 704,700 thousand m³ wood fibre equivalents), which has an annual increase of 23,066 m³ (f) on average. Only 8,144 m³ (f) are removed from the forests per year.

The color coding relates to the data reliability (fiabilité des données): Green flows are based on reliable data, while yellow is for medium reliability and red for less reliable.

Losses in Fruit Production

Food loss or wastage has been a topic a previous posts here on the Sankey diagrams blog before (see here or here).

Here is another Sankey diagram from the dissertation ‘Environmental assessment of Catalan fruit production focused on carbon and water footprint’ by Elisabet Vinyes i Guix (p. 73). It visualizes losses in the production chain for apples and peaches in Catalunya.


For each kg of fruit arriving on the market (or at the point of sales), some 1.21 kgs of fruit are being cultivated. Losses occur in the farming process itself as well as along the retail system. Of the 1 kg fruit purchased by the consumer, only 83% is actually eaten. 17% turns into waste.

Spelling Variants Distribution

Interesting and fun example of data analysis and visualization of distributions using tree-like Sankey diagrams: The Pudding (“a digital publication that explains ideas debated in culture with visual essays”) has a post on the Gyllenhaal Experiment.

How do people spell the more or less difficult names of celebrities? Using occurrences of these names on reddit, they counted the spelling variants, then displayed them in this Sankey-style distribution diagram.

Here is one for the actor Zach Galifianakis (via TNW):


You can read this figure like branching pathways for alternative spellings. The blue path is the correct spelling. All people get the first three letters ‘Gal-‘ correct, but then they go on and spell it differently. Very quickly you get a great number of variations (which I am sure Galifianakis who has Greek roots is used to…).

Only the two main options (‘Galifinakis’ and the correct one, Galifianakis) show the absolute values, and the widths of the bands are apparently to scale. The other options with less hits only show two default widths. I also noted that the further downstream on a path, spelling variants, which would cause further branches, have been ignored.

I wanted to do one myself, and checked the underlying data. Turns out there are 2.632 spelling variants (many of which only have one count), so showing all of them in a tree-like diagram does not make much sense. One would have to either choose the 10 or 20 most common misspellings, and then decide to sum up the rest under “other” or drop them for good.


And here is what it looks like based on the top 10 spelling variants (n=15848). Flows are to scale and absolute numbers have been added. Less than 16% from this group spell the author’s name correctly, the most popular spelling variant collects more than 40% (click image to enlarge)


Maybe time for Zach to consider a name change 😉