Repulsive curves

Chris Yu, Henrik Schumacher, and Keenan Crane from Carnegie Mellon University are working on repulsive curves, which is a method to efficiently unravel curves so that they don’t overlap:

Curves play a fundamental role across computer graphics, physical simulation, and mathematical visualization, yet most tools for curve design do nothing to prevent crossings or self-intersections. This paper develops efficient algorithms for (self-)repulsion of plane and space curves that are well-suited to problems in computational design. Our starting point is the so-called tangent-point energy, which provides an infinite barrier to self-intersection. In contrast to local collision detection strategies used in, e.g., physical simulation, this energy considers interactions between all pairs of points, and is hence useful for global shape optimization: local minima tend to be aesthetically pleasing, physically valid, and nicely distributed in space.

Be sure to watch the video demo.

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Data visualization crossword puzzle

This is fun. Lisa Charlotte Rost made a data visualization crossword puzzle. Print the PDF and win all eternal glory of your household.

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Best Data Visualization Projects of 2020

It's been a year. We saw a lot of charts. These are the best ones. Read More

Visualization helping us during the pandemic

Hayleigh Moore for the College of Information Studies at the University of Maryland on visualization and the pandemic:

With new updates developing by the hour amidst the evolving COVID-19 pandemic, trying to grapple at the most relevant information can be overwhelming. Data visualization has helped to synthesize this complex phenomena and shape the timeline of the Coronavirus pandemic that has drastically changed how we go about our daily lives. While commonly used to communicate data to the general population, visualization is now having quite a real-world impact in the face of this crisis.

Visualization the field often struggles with real-world examples for how its work plays a role in people’s lives. There should be no questions about that now.

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Best Data Visualization Projects of 2019

As I do every year, I picked my ten favorite visualization projects. Here they are in no particular order. Read More

The Science of Spice by S. Farrimond — in networks


It's feasting time; and any good feast tickles the tongues with flavours unknown and exotic. But not all spices go well with each other. One suggested solution is to "understand flavour connections" in order to "revolutionize your cooking", which is the subtitle of a book by Stuart Farrimond: The Science of Spice (Dorling Kindersley 2018, ISBN: 978-0-2413-0214-9).

In his book, Farrimond categorizes spices into flavour groups characterized by their major and secondary chemical compounds, such as "sweet warming phenols", "fragrant terpenes" and "pungent compounds". He presents a "periodic table of spices" covering 54 spices, and gives a four-step protocol for how to combine spices:
  • Step 1: Choose the main flavour group(s);
  • Step 2: Check the blending science (which is quite elaborate — you have to buy the book);
  • Step 3: Pick your primary spices; and
  • Step 4: Add complexity (something we strongly encourage in general here at the Genealogical World of Phylogenetic Networks).
Farrimond provides five sets of principal data in the various chapters of his book entitled: "Spice science" (an introduction), "World of Spice" (which spices are used in which countries, including a recipe for a local spice blend), and "Spice Profiles" (bit of history, food to spice, blending science). For the 54 spices of the periodic table, they are:
  • chemical composition;
  • geography (uses as "signature", "supporting" and "supplementary spice" in various countries);
  • general characterization, such as "sweet", "pungent", "earthy", "complex";
  • food partners;
  • flavor category.
All of this information can be visualized using our beloved Neighbor-nets. Here, we will show only two: the flavour compounds network (based on information tabulated on pp. 214–217), and a network grouping countries by similarity in spice use. For those interested in the primary data used here; tabulated data, character matrices and raw networks can be found @ figshare.

Spice compounds

Humans are, and have always been, very diverse, and so is their food; and the spices are no exception. They contain numerous flavor-active substances, and Farrimond has picked for his periodic table of spices those that cover a huge range of flavor compounds. Accordingly, the Neighbor-net is star-like, as shown here.

Neighbor-net based on absence/presence of 117 chemical compounds that put spice in spices.

For estimating (Hamming) chemical 'inter-spice' distances, I used ternary ordered characters: "0" – absence; "1" – presence; "2" – flagged as major compound. Most flavor groups are chemically diverse; Mother Nature has many means to tickle our taste buds in a certain fashion. One exception are the spices of the "citrous terpenes" flavor group characterized by citral as the main flavor compound (otherwise only found as accessory compound in wattle, ginger and turmeric) accompanied by linalool (a compound found in many other spices and main compound of coriander).

Geographic patterns

To visualize the geographic differentiation of the spices, I treated the absence/presence of each spice in the local cuisines as an ordered character:
  • "3" – a signature spice, ie. a main spice in the local cuisines;
  • "2" – supporting spice accompanying many dishes;
  • "1" – supplementary spice, ie. a spice to round up or add more particularity;
  • "0" – absent, ie. not mentioned by Ferrimond.
In total, the matrix covers 93 spices for 44 countries/regions. Some spices are relatively ubiquitous, and hence are not informative about geographical variation, such as chili (37 out 44 cuisines, with 26 using it as a signature spice), garlic (25 uses as a signature spice) or ginger (19), while others are rare or geographically quite restricted. For instance turmeric is a signature spice of Indian cuisines and also of South Africa. During the British Empire, many Indians migrated to South Africa, and Indian traditions blended in with African and European; which makes South Africa an interesting place to visit and feast (as I can affirm first hand).

A global network based on the used spices. Colorization refers to the continental regions used by Ferrimond (chapter World of Spice, p. 20ff)

Not unexpectedly, the network shown here reflects geographic vicinity as well as rather ancient historical connections. For example, most aspects of European civilization have their origin in the Middle East, and spices reached medieval Europe via Arab sea-traders and the Silk Route; but there was also influence from elsewhere during the various the colonial epochs.

The Latin American cuisines are spice-wise most similar to those of Spain and Portugal within their regional groups, while Canada and the U.S.A. mix this tradition with that of other European countries such as Italy and France. Great Britain is distinct because His/Her majesties ruled many lands with a great variety of food and spices. In contrast to many other aspects of colonialism, the influence hence goes both ways.

The most unique spice cuisines are Indonesia, the home of many spices (and the reason why both the Portuguese and the Spanish set sail), and (tropical) western and central Africa. That the Horn of Africa graphs within the South Asian group is not surprising as it was for a very long time the sea-trade spice hub between Asia and Europe.

The is also a higher diversity seen in the Southeast Asian compared to the East Asian and South Asian countries and regions.

A bit of an oddball is the placement of the Caribbean cuisine, and especially the Creole kitchen, which is known for its spice mixing — in Farrimond's three-concepts characterization: "Adventurous | Bold | Spicy".

Conclusion

So, in case you want to spice up the coming holiday and festive season, Farrimond's book is an invaluable source for applied science, which has a simple primary use: filling the mouth with taste while filling the belly with ballast.

How Charts Lie

Charts can reveal truths that we never would see otherwise, but they can also be misused to show us something in the data that doesn’t reflect reality. Alberto Cairo’s new book How Charts Lie is a guide on how to better spot the latter. It’s about reading charts more critically and understanding data better, which are necessary skills for everyone these days.

I’m putting this at the top of my queue.

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How practitioners learned data visualization

Visualization is still a relatively young field, so people learn about and how to visualize data in a lot of different ways. For instance, there weren’t any visualization-specific courses when I was in school, so I picked up a lot ad hoc. Alli Torban, looking at responses to the 2018 Data Visualization Survey, shows how others learned. The top three: examples, collaboration with those more skilled, and books.

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What to expect at data visualization job interviews

Krist Wongsuphasawat, who recently interviewed for a healthy helping of visualization jobs, outlines the questions asked and the general flow of things.

[T]here are some sessions that your data visualization skills will play the key roles, but there will be tests for other skills as well. As I have mentioned earlier, data visualization is one of the main skills, but having only that is usually not good enough to land the roles. So do your homework, figure out what are the skills required for the target roles and make sure you can tick all of the checkboxes. If you are choosing the engineering track, there will be lots of expectations for front-end engineering skills.

From there, the tasks presented to you seem to vary a lot depending on what a company is looking for. Sounds stupendous.

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The Tree of Life (April 1)


The so-called Tree of Life is actually an anastomosing plexus rather than a divaricating tree, due to extensive interconnections between the cell and genome lineages during early single-cell evolution. These connections may have been caused by the process known as horizontal gene transfer.

Furthermore, the alleged Last Universal Common Ancestor may not have been a single coherent group, but may have been a mixture of quite different genotypes. After all, this supposed ancestor does not represent the origin of life, but was itself the end-product of an extensive prior evolutionary history.

These two basic points are illustrated in the following figure.


Happy April 1. For previous posts, see: