Some time has passed since the last post in my mini-series featuring energy balances of Latin American countries. Panama was last in July 2019. So today, here is the Balance Energético Nacional (BEN) for Honduras.

This Sankey diagram can be found on page 43 in the report ‘Balance energético: un panorama del actual sistema energético hondureño’ (2018) published by Secretaría de Energía, Gobierno de la República de Honduras.

The figure shows energy origin (left column), energy type (middle column) and consuming sectors (right column). Individual streams are not labelled with quantities, but all the numbers are in the report on the previous pages. Overall final consumption of energy in 2017 in Honduras was 33.376 kbep (kilo barrels of oil equivalents). We understand almost half of the energy was imported.

Some observations that possibly only strike a nitpicking observer like me: Streams sometimes don’t maintain their width when sloping upward or downward (an indication that d3.js sankey has been used to draw this). The flow depicting electricity export goes back to the left (green node ‘Exportación’). Nodes for tiny flow quantities are amplified, so please don’t take the middle column as a stacked bar. Otherwise a fine diagram. I like the icons used for describing the energy types and different uses.

MapProvision is an online service to present data as dynamic layers on an online map. They have now added an Sankey overlay feature.

This screengrab is a from a sample provided by them using World Trade Organization import and export data for 2017.
[See image gallery at www.sankey-diagrams.com]
Here I have selected imports of machinery being imported to Mexico. Flows displayed are based on value of the goods in mio US$ per year. Hovering over the arrows reveals detailed data, a summary of imports for the selected category is shown when hovering over the bracket at the destination.

This comprehensive blog post has more details on how to adapt the Sankey overlay and the different options you have, including color settings and minimum width for Sankey arrows. It also contains an embedded example for fish import to Japan.

Nice work and fun to play around with. Doing Sankey diagrams on a map is particularly challenging in my opinion due to routing issues and because in many cases trade flows between neighbouring countries tend to be high volume, so that the shortest Sankey arrow paths are typically the widest ones (as is the case with the U.S. here).

Another Sankey diagram from a waste water treatment plant (WWTP). This one shows concentrations (of organic matter?) in dissolved and adsorbed phases in grams per day. Actual volume streams are not represented.

Two years ago I posted an early version of the energy balance for the French overseas region La Réunion, an island in the Indian Ocean. Visiting the website of Horizon Réunion (formerly Energies Réunion) now, I am happy to see that they have taken the elaboration of the energy balance much further, and are now publishing this detailed Sankey diagram for energy flows in 2018.

The diagram has been created by Observatoire Énergie Réunion (OER). Flows are in ktoe (ktep, kilo tonne d’équivalent pétrole in French). Overall primary energy for the island was 1441.8 ktep, of which 87% is fossil and 13% from renewable sources. We can see that mobility is the largest chunk of energy use with 64.5%.

You can access the full report with all background data here.

Being forced to work from home now, I launched into cleaning out stuff on my hard disk and dug into long unvisited folders.

There are many Sankey diagrams still sitting there. So, you might eventually see a higher posting frequency again over the next couple of weeks. At least something good…
[See image gallery at www.sankey-diagrams.com]

The study ‘Sustainable biomass and bioenergy in the Netherlands’ was carried out by researchers Goh, Mai-Moulin and Junginger from Utrecht University in the framework of the Netherlands Programmes Sustainable Biomass. It looks at “biomass from the three major categories, i.e. woody biomass, oils and fats and carbohydrates used in different sectors in the Netherlands”.

For each of these three categories a Sankey diagram is presented, like for example this one for oils and fats.
[See image gallery at www.sankey-diagrams.com]
The diagram has a very clear structure. Import streams are from the top and exports leave to the bottom. Domestic Dutch production is from the left, use of oils and fats in the Netherlands is to the right. Flows are in million tons (MT) dry mass. Data is for the year 2014.

See the full report here.

Another sample Sankey diagram in German from the building sector. Swiss architectural firm Moosmann Bitterli has this on their website to showcase their services. Not sure what the unit of flow is, but definitely energy related.
[See image gallery at www.sankey-diagrams.com]

From the museum of Sankey diagrams comes this beautiful visualization of migration flows between Canadian provinces in the years 1956 to 1961. Sorry but I didn’t note the source… still wanted to share it with you.
[See image gallery at www.sankey-diagrams.com]
Unfortunately, flows are not to scale: compare, for example, the exits from the Atlantic provinces in the period (86,078 people) which should have an arrow similar to the inflow of 85,476 to British Columbia. Or look at the exodus from Ontario provice, which seemingly is larger than the influx to the provice (but in fact is smaller).

A comparison of the different pathways that municipal solid waste (MSW) is going in large metropolitan centres such as Toyko, Paris and Mexico City is presented in the article ‘La recogida de basura en Mega-ciudades: En el marco de la sostenibilidad’ by Fabian Tron. Available in Revista INVI v.25 n.70 12/2010 on Scielo in Spanish.
[See image gallery at www.sankey-diagrams.com]
While the data on waste quantities itself is quite old and dates back to 2005, I found the system how household waste is handled differently in these three cities quite interesting. To get an idea of the “weight” of the different pathways, I decided to do a remake of the three figures as Sankey diagrams. Here is the one for Tokyo.

[See image gallery at www.sankey-diagrams.com]
Flows are in 1000 tonnes (kt) per year. There is an informal sector (Economia Informal) where waste streams are not under control of the municipality. The largest part of non-recyclable household waste goes directly to incineration or “reduction”. The remainder is led to treatment plants and eventually most of it ends up in landfill (Vertedero). Note that smaller flow quantities are over-emphasized and are not to scale (otherwise they would hardly be visible in the diagram).

If you want to see the two other Sankey diagrams, please let me know in the comments.

An analysis of energy efficiency and exergy effciency of a cogeneration system in a sugar refinery in São Paulo state in Brazil is presented in the ‘Análisis energético, exergético y económico de un sistema de cogeneración: caso para una planta azucarera de San Pablo’ by Omar R. Llerena of Universidade de São Paulo (Published in: Ingenius no.19 Jan/Feb 2018 under Creative Commons license CC BY-NC-SA 4.0)
[See image gallery at www.sankey-diagrams.com]
Flows are in kW, and even though this diagram appears to be ‘casero’ made from blocks, triangles and curve shapes, the flow widths seem pretty much to scale.
Acronym ‘CC’ is for the combustion chamber and ‘CR’ stands for a heat recovery boiler (caldera de recuperación).

The article also features a Grassman diagram for the exergy analysis. So, if you are interested, please visit the article here.

From the museum of Sankey diagrams, here is a black&white classic. This Sankey process diagram for rapeseed oil production is taken from the year 2002 dissertation ‘Simultane Öl- und Proteingewinnung bei Raps’ (Simultaneous oil and protein production from rapeseed) by Andreas Waesche, Berlin Technical University.
[See image gallery at www.sankey-diagrams.com]
Flows are in kilograms per ton of rapeseed input. Black is the water fraction, dark gray is oil, and light gray is oil free dry matter. Rapeseed husks, filtered matter and liquid rejects branch out on arrows to the right. Extract from the T4 separation stage (‘Extrakt als Ruckfuhrung’) is fed back into the second node although this loop is not shown here.

Another spectacular Sankey diagram from the final report of the project “Resource conservation through material flow-oriented secondary raw materials management” published by German Environment Protection Agency (Umweltbundesamt, UBA). This one is on non-ferrous metals in waste and recycling paths Germany. Flows are in mio. t for the year 2013.
[See image gallery at www.sankey-diagrams.com]
I had presented another Sankey diagam from this report (on paper and cardboard streams) here on the blog back in January. You can access the full report here.

Came across an interesting article by Tei Carpenter, ‘Waste Not, Want More: Zeroing In on Designing Waste’ in the Avery Review 33 (September 2018). It describes a transition to a zero waste scenario for New York in 2030.

This is the Sankey diagram for the waste situation today (that is… 2018). An incredible 12,838 tonnes per year. Of which 75% would theoretically be recyclable. Instead, 80% end up as refuse, while only 20% are “diverted”.
[See image gallery at www.sankey-diagrams.com]
There is also a second Sankey diagram that shows how the city would handle its waste in 2030 with a zero waste strategy. Read the article at Avery Review or download as PDF.

The article ‘Tracing the energy footprints of Indonesian manufacturing industry’ by Yales Vivadinar, Widodo W. Purwanto & Asep H. Saputra from Department of Chemical Engineering, Universitas Indonesia, Depok, Indonesia (published as Open Access in: Energy Science and Engineering 2016; 4(6): 394–405) looks at typical energy usage in different industrial manufacturing sectors in Indonesia.

There are Sankey diagrams representing “energy maps” for basic chemicals, cement, pulp, paper, spinning, weaving and textile finishing. I am showing two of them below. The first one is for the basic chemical industry.

Flows are in kboe (thousand barrels oil equivalent) for the year 2013. Losses are shown as grey arrows. The second one is for textile finishing:
[See image gallery at www.sankey-diagrams.com]
For those of you interested, please read the full paper here.

Just a quick Friday afternoon post in order not to let this week slip past without a post. This is a sample from the latest e!Sankey demo version,

The Sankey diagram represents energy flows in a canning plant with heat being distributed to the various steps of the process. Recovery of energy (red-brown arrow) from two of the steps.
[See image gallery at www.sankey-diagrams.com]

Stephen Cole, Jennifer Hill, Luke Keele, Ilya Shpitser, and Dylan Small write: We wanted to provide an update on our efforts to build the Society for Causal Inference (SCI). As you may recall, we are creating the SCI as a home for causal inference research that will increase support and knowledge sharing both within the […]

Flavio Bartmann writes: Over the last few days, as COVID-19 posed some serious issues for policy makers who, both in the US and elsewhere, have employed statistical models to develop mitigation strategies, a number of non-statisticians have criticized the use of such models as useless or worse. A typical example is this article by Victor […]

Will Marble writes: I’m a Ph.D. student in political science at Stanford. Along with colleagues from the Stanford medical school, law school, and elsewhere, we recently completed a white paper evaluating the evidence for and tradeoffs involved with shelter-in-place policies. To our knowledge, our paper contains the widest review of the relevant covid-19 research. It […]

I’d still go with this article by Ed Yong, which covers biology, epidemiology, medicine, and politics. Here’s one bit: In 2018, when writing about whether the U.S. was ready for the next pandemic, I [Yong] noted that the country was trapped in a cycle of panic and neglect. It rises to meet each new disease, […]

Josh Rushton writes: I’ve been following your blog for a while and checked in today to see if there was a thread on last week’s big-splash Stanford antibody study (the one with the shocking headline that they got 50 positive results in a “random” sample of 3330 antibody tests, suggesting that nearly 2% of the […]

Lauren and Jonah wrote this case study which shows how to do Mister P in R using Stan. It’s a great case study: it’s not just the code for setting up and fitting the multilevel model, it also discusses the poststratification data, graphical exploration of the inferences, and alternative implementations of the model. Adam Haber […]

  This post is by Phil Price, not Andrew. I’m going to say right up front that I’m not going to give sources for everything I say here, or indeed for most of it. If you want to know where I get something, please do a web search. If you can’t find a source quickly, […]

In comments, Joshua Ellinger points to this news article headlined, “Hundreds of thousands in L.A. County may have been infected with coronavirus, study finds,” reporting: The initial results from the first large-scale study tracking the spread of the coronavirus in [Los Angeles] county found that 2.8% to 5.6% of adults have antibodies to the virus […]

Kostya Medvedovsky writes: I wanted to direct your attention to the University of Texas COVID-19 Modeling Consortium’s new projections. They’re very similar to the IMHE model you’ve covered before, and had some calibration issues. However, per the writeup by Spencer Woody et al., they do three things you may be interested in: They fix an […]

Just a reminder that life goes on (thanks to commenter Lemmus), from the British Journal of Social Psychology: Are women more likely to wear red and pink at peak fertility? What about on cold days? Conceptual, close, and extended replications with novel clothing colour measures. Evolutionarily minded researchers have hypothesized that women advertise their ovulatory […]

Uros Seljak writes: You may be interested in our Gaussian Process counterfactual analysis of Italy mortality data that we just posted. Our results are in a strong disagreement with the Stanford seropositive paper that appeared on Friday. Their work was all over the news, but is completely misleading and needs to be countered: they claim […]

The above line is not a joke. It’s from Buzzfeed. Really. Stephanie Lee interviewed a bunch of people, including me, for this Buzzfeed article, “Two Big Studies Say There Are Way More Coronavirus Infections Than We Think. Scientists Think They’re Wrong.” I liked the article. My favorite part is a quote (not from me) that […]

So, there’s this new study doing the rounds. Some economists decided to study the twitter followers of prominent coronavirus skeptics and fearmongers, and it seems that followers of Nassim Taleb were more likely to shelter in place, and less like to die of coronavirus, than followers of Richard Epstein or Cass Sunstein. And the differences […]

I understand the above quote completely. Life would be so much simpler if my work was just reviewed by my personal friends and by people whose careers are tied to mine. Sure, they’d point out problems, but they’d do it in a nice way, quietly. They’d understand that any mistakes I made would never have […]

The following came in the email: I’m a reporter for **, and am looking for comment on the stats Gov Cuomo just released. Would you be available for a 10-minute phone conversation? Please let me know. Thanks so much, and here’s the info: Here is the relevant part: In New York City, about 21 percent, […]

Here are some things people have sent me lately. They are in no particular order, except that I put the last item last so we could end with some humor. After this, I’ll write a few more blog posts, then it’ll be time to do some real work. Table of contents 1. Suspicious coronavirus numbers […]

Jon Baron read the recent article, “Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area,” and sent the following message to one of the authors: I read with interest your article in JAMA. I have been trying to follow this issue closely, if only because my wife […]

Back in the early days of this blog, we had frequent posts about the differences between Republican or Democratic voters and Republican or Democratic areas. This was something that confused lots of political journalists, most notably Michael Barone (see, for example, here) and Tucker Carlson (here), also academics such as psychologist Jonathan Haidt (here) and […]

The OHDSI study-a-thon group has a pre-print An international characterisation of patients hospitalised with COVID-19 and a comparison with those previously hospitalised with influenza. What is encouraging with this one over yesterday’s study, is multiple data sources and almost too many co-authors to count (take that Nature’s editors). So an opportunity to see the variation […]

Steven Wood writes: There now some controversy regarding the effectiveness of Remdesivir for treatment of Covid. With the inadvertent posting of results on the WHO website. https://www.statnews.com/2020/04/23/data-on-gileads-remdesivir-released-by-accident-show-no-benefit-for-coronavirus-patients/ One of the pillars of hope for this treatment is the monkey treatment trial (the paper is here). As an experience clinical trialist I was immediately skeptical of […]

Somebody points me to this by Benjamin Morris. I haven’t read this so I have no idea, but it does seem to have a lot of statistics! The one part I’m suspicious of item 3(c), where he says, “The statistical community over-values Margin of Victory and under-values raw winning percentages.” As I wrote a few […]

Simas Kucinskas writes: I would like to share some recent research (pdf here). In this paper, I develop a new method for estimating R in real time, and apply it to track the dynamics of COVID-19. The method is based on standard epidemiological theory, but the approach itself is heavily inspired by time-series statistics. I […]

Raghu Parthasarathy writes: I’ve been frustrated by Covid-19 pandemic models, for the opposite reason that I’m usually frustrated by models in science—they seem too simple, when the usual problem with models is over-complexity. Instead of doing more useful things, I wrote this up here. In his post, Parthasarathy writes: Perhaps the models we’re seeing are […]

Someone who wishes to remain anonymous writes: Here’s a joke we used to tell about someone in econ grad school, a few decades ago. Two economists were walking down the street. The first one says: “Isn’t that a $20 bill?” The second one says: “Can’t be. If it were, somebody would have picked it up […]

Last week we discussed the Santa Clara county study, in which 1.5% of the people tested positive for coronavirus. The authors of the study performed some statistical adjustments and summarized with a range of 2.5% to 4.2% for infection rates in the county as a whole, leading to an estimated infection fatality rate of 0.12% […]

This post is by Phil Price, not Andrew. There a couple of things that some people who comment here already know, but some do not, leading to lots of discussion in the comments that keeps rehashing these issues. I’m hoping that by just putting these here I can save some effort. 1. The ‘infection fatality […]

Suppose I play EJ in chess—I think his rating is something like 2300 and mine is maybe, I dunno, 1400? Anyway, we play, and my only goal is for the games to last as many moves as possible, and EJ’s goal is to checkmate me in the minimal number of moves. Say I have to […]

The talk will be sometime the morning of Wed 6 May in this seminar. Title: Some statistical issues in the fight against coronavirus. Abstract: To be a good citizen, you sometimes have to be a bit of a scientist. To be a good scientist, you sometimes have to be a bit of a statistician. And […]

The other day I wrote about the challenge in addressing the pandemic—a worldwide science/engineering problem—using our existing science and engineering infrastructure, which is some mix of government labs and regulatory agencies, private mega-companies, smaller companies, university researchers, and media entities and rich people who can direct attention and resources. The current system might be the […]

Seth Flaxman et al. have an updated version of their model of coronavirus progression. Flaxman writes: Countries with successful control strategies (for example, Greece) never got above small numbers thanks to early, drastic action. Or put another way: if we did China and showed % of population infected (or death rate), we’d erroneously conclude that […]

Joseph Candelora in comments pointed to this updated report on the Santa Clara study we discussed last week. The new report is an improvement on the first version. Here’s what I noticed in a quick look: 1. The summary conclusion, “The estimated population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection […]