Digital works

#infofails: A busy 2021 kick off

Yup, a new crazy and very busy year have started. So, let’s kick off a new season of #infofails from recent months:

Sand mining projects

To be honest, I never thought about sand before this series of projects. However, sand is the planet’s most mined material, some 50 billion tons are extracted from lakes, riverbeds, coastlines and deltas each year, according to the UNEP. All around this topic is fascinating from a visual journalism perspective, the figures are huge and so scary and sad at the same time. –So, ideal for some good stories right?

Early in February, we did this story on how Chinese dredging ships are swarming Taiwan’s Matsu Islands. If you haven’t see it yet, please have a look first and comeback to this post. [ link here too: https://tmsnrt.rs/39OYbAZ ]

I did some stuff that we didn’t use because we turned into another direction, like the map of Taiwan at the top of this post, I was experimenting a little bit with colour schemes and the way of the relief, below are shown some closer details of Taiwan, Hunan-China, Hong Kong…

I also spent some time trying to understand how these strange dredge boats dig into the seabed, scoop up the sand, and then spit the material through a conveyor somewhere else. It seems that the Chinese used this type of boat often according to what I could see in many photographs of the Taiwanese islands affected by mining. But at the end we keep it simple with just the outside diagram of the boat as you can see in the page. Here’s a little detail of the inside that I didn’t finish completely:

I also got some estimated figures from Taiwan’s Ocean Affairs Council about how much sand the Chinese boats have take over the last five years. In the end we didn’t use it for the story, but the idea was to model piles with the same estimated volume and use the same style as the sand dredger above alongside a 2-meter man to give a better understanding of the amount of sand extracted. .

Some nice data I also was looking into was the GLAD ( Global Surface Water Dynamics ) those data sets are based in Landsat 5, 7, and 8 scenes, and they are so cool but didn’t use for the project. In that DB, you can see how rivers and water bodies in general have changed their shapes since 1999. I was looking at the Mekong river, but the data coverage is global and there are some really nice parts like this section of in Bangladesh:

In fact, I played a lot with these sand projects. From particles to animations, maps, illustrations … I made some fading castles for the top image before it ended up in a city made of sand as shown now. I made sand elephants, particles to see how different the grains are … LOTS of exploration to better understand the subject and I can say that anyone can spend years making visual stories about sand.

Other projects

I’ll keep this short since I already wrote down a lot about sand, let’s consider this tiny section as a bonus track 😆

Also in early January, we saw the story of the Chinese miners trapped inside a deep mine, it may looks like a huge simple illustration, but it has a lot of research behind to make it as much accurate as a breaking news story can be.

Illustrations sometimes can be way much more complicated than dataviz graphics I think. That, because you can’t argue with data, or print your own perceptions into something that it’s already simplified to dots and lines. Let’s make a little parenthesis here:

The illustrated graphics are more humanistic yes, but also complicated because you cannot detach yourself from those same parts that connect you with the information, those that you use to "humanise the information". I mean, you can't control what the people will see there, because it opens a wide range of interpretation. Not like strait forward dataviz isn't? Well, maybe not even in dataviz, everything have exceptions. Just look at [ this ] tweets by Francis Gagnon that sparked a lot of opinions in the visual journalism community about how cold and inhuman are "dots" representing people in a NYT graphic in the print front page. 

Ok, turning back to #infofails, in January also I worked in the Sriwijaya crash story, a straightforward breaking news story. The opening image saw some versions as we tried to tackle a sensitive topic and not give the wrong message to the readers at the very top of the story.

Some times I just want to run away from noisy things, have a look to the most basic and elemental thing in the visual I’m creating. So then I can go and add little clues for the eye. Doing that I think, we can find what’s needed to be highlighted, we can also understand what we know, but maybe not the reader isn’t understanding at the first instance by looking the graphic.

A map of Antarctica’s icebergs, just icebergs. Not land, sea or labels. Based on data from BYU Antarctic Iceberg Database

About #infofails post series:
I keep my beta graphics, those that never go public… Maybe they are tons of versions of a graphic or just a few concepts, part of my creative process. So, where all those things go? well, ends-up in #infofails –a collection of my fails at work.

Did you like #infofails?
Have a look to other #infofails Chapters here:

1: Wildfires
2: Plastic bottles
3: Hong Kong protest
4: The Everest
5: Amazon gold

6: The world on fire

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Digital works, infofails

#Infofails: the world on fire

2020 kicked off with record-breaking wildfires in Australia, the hatching a global pandemic, and later the a new wildfires season turning into ashes thousands of sqkm of the U.S. West Coast, then Trump again… you know all that right?

In the middle of all that craziness, we were juggling to cover the events with visual stories. As usual, I did a little more than necessary in order to explore and get details relevant to our stories… and well, not all of them worked out…

Where there is fire, there is smoke

Some events on our planet reach sufficient dimensions to be easily seen from space, wildfires are one of them.

Mercator projected snapshot of organic carbon data on September 16th, 2020. Data from NASA-GMAO

I had work with GMAO data many times before, it’s a good source to see a model of aerosols and other specific data on a large scale, works very well for continental areas, not too much for a closer zoom like country level.

Anyway, the idea of this. visualisation was very clear, it was about to show the large dimension of the smoke caused by the wildfires in the US. West Coast.

One by one in QGIS, I did a series of renderings like the one shown above with data between June and mid-September (around 100 days), which is probably not too much, but the data is collected every 3 hours so I manually processed about 400 files to get a smooth animation.

Animation test v1.

To get control of the style without coming again to QGIS, I did the series of data only, a layer with the country borders, a layer with labels and so on… I also did one version with the same idea but in a globe.

Style test. v4.

I often try different versions of my graphics, On my team here at Reuters, we often joke that until we get to the twentieth version we won’t be close to finishing … Although in some cases that joke does come true.

The final version ended up looking a bit different. I controlled the final style in Illustrator, Photoshop and After Effects.

Final version. ( v.11 )

There are many more pieces in that story, including a really cool cutaway of the smoke made by my teammate Manas Sharma with data from NASA’s Calipso mission. You can have a look to the full story here: https://tmsnrt.rs/3nkkOkX.

But the wildfires continued to break historical records and turning the city’s skies orange-red. There were many other stories on that tragedy waiting to be told, even though the stories of the covid-19 did not stop harassing us either.

An aircraft swarm

OV-10 “Bronco” sketch test for the Cal Fire aircraft story.

Air attack was one of those stories we worked on in the middle of this year, the main idea was to show the impressive deployment and coordination of planes to deal with the fires in California. Just doing the planes was very enthusiastic, the main issue was how to pick the right ones.

Cal-Fire has on hand an extensive fleet of planes, tankers and helicopters, some in heavy operation, others less so. However, the flight and route logs from FlightRadar24 gave us an outlet to filter the aircraft.

Cal Fire aircraft sketches for the story.

You may have noticed some airplanes in the image above that aren’t in the final story. The AT-802A were used to guide tankers in the old days, they are probably still in use elsewhere, I think you can see them in the Pixar movie “Planes”.

That was one of the “unnecessary resources” that I created, thinking that it might have been nice to show how things have evolved over the years, but it was not the case. You can read the story through the following link: https://tmsnrt.rs/2Iy2K7W

Wine and ashes

There’s one thing you should know about me: I love wine.

I usually work colour at the end. More versions to test and try keeping outlines or light-shadows intact.

Most of the stories I’ve made in the last year or two are sad, some about environmental disasters, people in danger, dying, or losing everything.

Like many, this story came with some mixed feelings. I think it was the first time that I had the opportunity to do something about wine, and it involved the destruction of hundreds of vineyards.

Although I really enjoy to do reporting, create a map, draw a diagram, or write a story draft, those same stories always bring me a strange mix of joy in doing my job and the sadness of understanding the dimensions of a problem or event. I’m not complaining, I keep my job at the office, but it’s curious I guess.

You can have a look to the wine story here: https://tmsnrt.rs/3eZnWQ9

There’s no time enough in the world

Not matter how much time I can have, there’s always one more thing I’ll like to explore. Nice thing is you can save the idea in the bucket for next time. And the fires coverage wasn’t an exception.

Screenshot of the VIRS brightness data over California. Night time Aug. 03, 2020.

Around mid year, I was exploring at VIIRS/NPP data, this data contains 26 data sets including radiance sensors, shortwave IR radiance, earth’s brightness and temperatures etc.

That data can give you a daily quick look of lights and temperatures of the planet’s surface, of course if clouds play nice and go away from your interest area.

After downloading the data for a few days in the area, I noticed some bright areas that turned on and off depending on the day, probably fires that were seen burning from space at night.

The lights were so intense that you can easily mistake them for city lights. Check the white circles bellow:


About #infofails post series:
I keep my beta graphics, those that never go public… Maybe they are tons of versions of a graphic or just a few concepts, part of my creative process. So, where all those things go? well, ends-up in #infofails –a collection of my fails at work.

Did you like #infofails?
Have a look to other #infofails Chapters here:

1: Wildfires
2: Plastic bottles
3: Hong Kong protest
4: The Everest
5: Amazon gold

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Digital works, infofails

#Infofails: Chapter 05, gold rush

I just realised that last time I did one of this chapters was last year, woow. My original idea was for me to publish one of these every three or four weeks … but reality has slapped me in the face: I don’t have time enough between projects.

Anyway, let’s talk about fails then.

That image at the top of this post is a QGIS render of the Amazon Basin made with data from Hydrosheds. It is part of the geographic data from my latest project on Reuters. But you may have never saw that thing anywhere in the project, unless not like that.

The Amazon Gold Project have a mix of styles including 3D, traditional sketching, vector works…

The 3D stage

I did some early versions of the maps using layers rendered in QGIS, a layer for terrain, base colour areas, borders, rivers etc. Then I re-pack all in Cinema 4D to create some side illumination.

Cycles C4D
Terrain layer test imported from QGIS, then rendered alone in C4D / Cycles

The idea was to try some alternative maps, at the end some illustrator labels and the dummy look something like this:

Map dummy with false text annotations

The 3D above was just a quick exercise while I was looking for styles. A bunch of images later, the things turned into flat images

And then to flat vector, probably taking out stuff from the map from crowded to cleaner to whatever…

Maybe I went too far because I really like how this was looking in outlines preview… someone should stop me at that time!

And it happened, in the end I went on to make other pieces putting pieces together on the page, bits of code, illustrations, etc.

If you already saw this project, you know that it has some illustrations, pieces that explain how illegal miners extract gold and contaminate rivers and destroy forests in the process.

Meanwhile I was also processing some satellite images, adding labels and looking for evidence of the miners from the space, then I saw some websites talking about the situation in Peru, so I look up the area in Sentinel and wooow!

I stumbled upon this place near the Peru-Brazil-Bolivia, border. Kilometres and Kilometres of devastation by the gold miners, a strip of more than 30km into the forest and actually I saw it for first time in false color, in near infrared, so the image was shocking to me.

Madre de Dios mining pits. Sentinel Hub, false color, near infrared.
Madre de Dios mining pit. Sentinel Hub, false color, near infrared.

Later we move to true color, trying to match the other satellite images that we had chosen earlier for this project, but still you have the feeling of the immensity of the destruction of these sites. I think that images have an immense power to present the damage dimension

5-6 km long section of mining pits at Madre de Dios. (Sentinel Hub, True color)
Sentinel Hub, true color.
Sentinel Hub, true color.

So, I’ll say, fails in here, YES, well I spend a lot of time looking for alternative styles, experimentation is nice I collected a lot of information, but maybe I went too far styling place holders… Anyway I enjoy a lot this project, but and I’m more than happy that is already over.

About #infofails post series:
I keep my beta graphics, those that never go public… Maybe they are tons of versions of a graphic or just a few concepts, part of my creative process. So, where all those things go? well, ends-up in #infofails –a collection of my fails at work.

Did you like #infofails?
Have a look to other #infofails Chapters here:

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blogging, Digital works, infofails

The last chapter of #infofails… [of 2019]

About #infofails post series: I have a lot of beta graphics that never go public, it can be tons of versions of a graphic or just a few concepts as part of my creative process. So, where all those things go? well, ends-up in #infofails –a collection of my fails at work.

This 2019 is almost gone, big media is doing their “year in graphics” collections, meanwhile I’m in the rush hour trying to fit one more graphic in this year. I’m looking back trough this year, and it has been a crazy one; many unexpected things and lots of changes for me. That’s the case of this project I want to share with you, is one of those unexpected results, or un-result to be accurate.

himalayas

Death rates at the Himalayas peaks

The Mount Everest project (screengrab above) started as a great opportunity for a data narrative, the story behind was the bloom in the climbers amount, many times resulting deathly for them; the whole team was doing pieces to get this story online, if you didn’t saw it, here’s the final result of the project: CLICK HERE. Have a look first, then come back to this story for a better context.

 

 

The fail story

My fails begun when I was trying to get an accurate model of the mountain, I first tried doing some elevation curves map, like the one on top of this entry. The main problem here was to get a good resolution, I was taking as base a 90m DEM produced by NASA, the files are great and works most of the times, but not to the level of detail I was looking for.

himalayas.jpg

90m DEM by SRTM/NASA. This was the starting point.

 

This thing works for a general overview of the whole mountain system of the Himalayas. To me, it was look in a good shape. By exaggerating the elevation, the idea was to add a color range or some other texture to visualize the heights, so then point out the mountains other than the Everest were the climbers usually go.

elevation profile.jpg

Version #1 Himalayas peaks

You maybe noticed that usually I do 1-5 versions of the images to try different ideas, in this case, I didn’t went any further because in the middle of the production some other projects came in. Fortunately my teammates got some other ideas, they took the project from this stage forward. I just jumped in again at the end to collaborate with the finishing touches and adjustments, so I can’t take any credit.

But going back to my fails, I did a few more pieces before the no return point in this project, one of them, a preview of the contours growing-up:

elevation.gif

Everest and surroundings, model based in 90m data by SRTM/NASA.

Also I try some more realistic look using a 30M DEM from the Shuttle Radar Topography Mission. That one was looking better, but I was already out of time:

crop06_0090.jpg

C4D textured model based on 30m DEM data by SRTM/NASA

basic_gshade.pn

Basic shading. C4D model based in 30m DEM by SRTM/NASA

wiref_01

Color ramp by height, Himalayas system. C4D model based in 30m DEM by SRTM/NASA

wiref_04

Mount Everest close-up. C4D model based in 30m DEM by SRTM/NASA

There was also an other idea to show in this graphic. I was thinking that maybe will be nice to show the equipment that modern climbers uses today in comparison with the equipment of explorers from 60 yeas ago when the mountains were the final frontiers of the unknown. Is incredible that teams went there with heavy and basic equipment and yet make it to the mountain (with great help from the Sherpas of course).

50s_sketch.jpg

Climbers equipment detail. Based in documentation of the British expedition of 1950.

Not sure if this graphic of comparisons will be published or not, so I’ll upload just a tiny little part without information or details, but who knows, you maybe see it next year either at Reuters website, or here as another of my fails for your entertainment haha.

It has been a pleasure to have your comments and readings this year, I hope we will read each other soon.

Happy holidays!

________
Did you like #infofails?
Have a look to other #infofails Chapters here:

–Chapter One
–Chapter Two
–Chapter Three 

Stay tuned for the next chapter in 2020! 🥳

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