At some point in 2022, I was collecting information about Taiwan and its national identity. Among many, I read some articles like this one and documented records about their increasing drive to identify themselves as Taiwanese and progressively distance themselves from the Chinese. The subject is very deep and tangled, I’m not going to pretend to understand it, but in short, Taiwan’s heritage is deeply tied to the mainland (PRC), and blends with many other influences from its diverse past. In such a way, its particular conditions have created some interesting things that have taken root in the simplest things of everyday life.
One of those things that any one can see on the streets is food. Not only its dishes that have evolved and conquered the world like the yummy Bubble Tea, but the simplest things like how the business are tagging their restaurants in food delivery platforms.
Uber Eats and Foodpanda use labels to make it easier to find what you’re looking for, just like any other platform you might be familiar with. In those “categories” you can find Japanese, American, Chinese, Thai, Taiwanese… I’m sure you know how it works, but just in case here’s a screenshot of what I mean:
I scrapped that data just to see how popular the Taiwanese tagging versus Chinese tagging. The gray squares on the map below are restaurants listed on Foodpanda and Uber Eats in Taipei:
It was really interesting to see how numerous the places with Taiwanese tag were. Look at the same map, but with yellow circles for Taiwanese restaurants.
A massive difference with those showing Chinese tags on its categorization. Same map but red circles for Chinese tags.
In fact, American tagging for restaurants is way more popular than the Chinese label in Taiwan. Green circles show restaurants with American tags:
I ran the same script for all of the listed cities in Taiwan for those food delivery services, and the story was similar no matter where you looked along the island. FoodPanda displayed about 4,000 restaurants across Taiwan, 36% of those were tagged as Taiwanese and less than 3% Chinese. Uber Eats followed the same trend, I pulled data for +600 restaurants and 6 of every 10 were Taiwanese, while only 1 or none was listed as Chinese.
I understand some restaurants use more than one tag, but looking at how many of them prefer to be labeled Taiwanese rather than Chinese says something about customer preferences.
They ideas never flourished, I was completely dedicated to Ukraine stories and the data just got older and older. Basically it lost momentum to gain a spot on the news, this happens very often actually, it seems that time is never enough to do all the stories you want to do.
Anyway it was a fun exercise pulling this data and see the trends.
About the data
I used a python script to pull data from Uber Eats and Foodpanda, I’m sure there’s a smarter way of collecting this data… I’m not a developer. But if you want to try your self like I did, you will need to collect all the urls from these companies, often offered by city, then add them into something like this:
from email.headerregistry import Address
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.options import Options
import pandas as pd
import csv
restaurantList = []
driver = webdriver.Chrome('/usr/local/bin/chromedriver')
driver.get("https://www.ubereats.com/tw-en/city/hsinchu-hsq")
name = [ e.text for e in driver.find_elements(By.XPATH, "//*[@id='main-content']/div[6]/div/div/div/a/h3")]
category = [ e.text for e in driver.find_elements(By.XPATH, "//*[@id='main-content']/div[6]/div/div/div/div/div/div[2]/div[2]")]
location = [ e.text for e in driver.find_elements(By.XPATH, "//*[@id='main-content']/div[6]/div/div/div/div/div/div[2]/div[4]")]
dtable = {'Name_ZH': name,'Category': category, 'Address': location}
df = pd.DataFrame(dtable)
df.to_csv('../data/uberEats-hsinchu.csv')
driver.quit()
Note that you may need to install a few dependencies to run this code, but eventually it will spit a lovely .csv file with a column for the restaurant name, a col for address and one more for category listed in Uber Eats. Food Panda uses a different structure, but the code is pretty much the same except by the urls and the targeting of fields.
If you are working on something similar, I’ll love to see the outcome, reach me out on Twitter.
About infofails post series: I believe that failure is more important than success. One doesn’t try to fail as a goal, but by embracing failure I have learned a lot in my quest to do something different. My infofails are a compendium of graphics that are never formally published by any media. These are perhaps many versions of a single graphic or some floating ideas that never landed.
In short, infofails are the result of my creative process and extensive failures at work.
Are you liking infofails?, have a look to previous ones:
This is what happens when you are out of sync. I just published one more entry of #infofails. If you’re not familiar with it, infofails is a summary of my creative process and extensive failures at work. Check it out here:https://t.co/jjWL8ydUPgpic.twitter.com/6VLu2XJDfo
This is a follow up to my previous tutorial for visualizing organic carbon. The process is more or less the same, but it uses a different dataset, which has some extra considerations. You can revisit it below:
Before continuing, to follow my guide and visualize global temperatures, you should be able to use your Terminal window, QGIS and optional Adobe After Effects or Photoshop.
About the data set
NASA’s Global Modeling and Assimilation Office Research Site (GMAO) provides a number of models from different data sets, this is basically a collection of data from many different services processed for historical records or forecast models. This data works well for a global picture or continent level even, but maybe isn’t a good idea to use this data for a country level analysis, for those uses you may want to check other sources of the data instead of GMAO models, like MODIS for instance if you you are looking for similar data.
Global Surface Temperature average Jan. 4, 2023, 8am. || Data by GMAO / NASA.
SURFACE TEMPERATURE
There are a lot of different sets of products available at GMAO. For purposes of this tutorial, I’ll be focusing in the Surface Temperature which is stored into the inst1_2d_lfo_Nx set. That’s a GEOS5 time-averaged reading, which includes surface air temperature in Kelvin degrees in the 5th band of the files, there is some documentation available in this pdf. ( No worries if is this sounds too technical stay with me and keep going. )
These files are generated hourly, so a day of observations accounts for 24 files. This is great for animation because it would look smooth (even smother than the one we did for Organic Carbon before).
Where’s the data? and How it’s named?
The data is stored into this url. You can go into the folders and get all 24 files for each day manually if you like or get them with a command line using wget or curl into the terminal, I’ll recommend you the command line since it’s easier. Here’s how each file is named and stored:
Step 1. Get the data
Create a folder to store your files with some name like “data”
Once it reaches 100%, you would get a file named 20230104_0000.nc4 in you “data” folder: Note that I have renamed the output ( -o ) with a shorter name. The file will go to your folder ready to use into GQIS. Of course you will need a few more files to run an animation. Remember that this data is available for every hour every day, so you need to set the url and name for something like this:
00:00 MN >> 20230104_0000.V01.nc401:00 AM >> 20230104_0100.V01.nc402:00 AM >> 20230104_0200.V01.nc403:00 AM >> 20230104_0300.V01.nc4
...and so on...
08:00 PM >> 20230104_2000.V01.nc409:00 PM >> 20230104_2100.V01.nc410:00 PM >> 20230104_2200.V01.nc411:00 PM >> 20230104_2300.V01.nc4
Just create a text file listing all the urls you need and run the script into the terminal window with the same process:
curl-O [URL1] -O [URL2]
Each file is usually about 10MB, if there’s something wrong with the data the file will be created anyway but would be an empty file of just a few KB. Remember a full day accounts for 24 files but it starts from zero not 1.
Step 2. Loading the data into QGIS
Once you have a nice folder with all the files you want, you can just drag and drop the .nc4 files into QGIS. We are looking for the 5th Band, TLML which is our Surface air temperature:
QGIS prompt window when you drop one of the file in.
Once you have the data loaded, you want to set the data projection to WGS 84, this will enable the data layers to be re-projected later on. To do that, select all you data layers, right click on them, and select Layer CRS > Set Layer CRS > 4326. Be sure of selecting all the layers at once so you do this only one time. Otherwise you will need to doing over and over.
Data layers projection to WGS 84.
Since this is a good global data set, you may want to load a globe for reference, you can use your own custom projection, or use a plugin like globe builder:
Access Globe Builder from the plugins menu > Manage and Install > type: Globe.
Once installed, just run it from the little globe icon, or in the menu plugins > Globe builder > Build globe view. You have a few options there, play around with the center point lat/long. You can always return here and adjust the center by entering new numbers and clicking the button “Center”.
Step 3. Styling your map
The color ramp is important, you want to have a data layer and maybe a outline base map for countries, QGIS has some pre-built ramps for temperatures, you can check them out by clicking the ramp dropdown menu, select Create New Color Ramp and then select Catalog cpt-city.
Once you have your ideal color ramp for one layer, right click on that layer, go to Styles > Copy style. Then select all you temperature data layers at once, right click on them and select Styles > Paste Style.
I have created a ramp to fit better my data ranges and style a little the colors. If you not are using the optional ramp below, and want to proceed with the pre-built ramps skip this to step 4.
To use my ramp, copy and paste the following to a plain .txt file:
To apply the ramp to your layers, doble click one of the .nc4 files, and select Symbology in the options panel. Under render type, select Singleband pseudocolor, the look for the folder icon, click it and load your .txt file.
QGIS prompt to load a custom style.
Step 4. Preparing to export your map
You are almost done, by this point you can see how each data layer creates nice swirls, maybe some evolution of it too just by toggling the layers visibility. I like to have all the layers well organized so you can quick check the data. I’m maybe a little too obsessive but I usually rename all layers and groups to something like the image below, however this is just for me to know which files are on which day:
QGIS layers panel.
The name change works if you are using an automatic export of all layers, the script in the next step takes the name of the layer to name file output. But there are alternative ways to do this if you’re not as crazy as I’m and don’t want to spend time manually renaming.
Step 5. Export your map
There are many ways of doing this, you can set up the time for each layer by using the temporal controller, there’s a good guide here. That way you can get a mp4 video right away from QGIS, but you need to set up each data layer time manually.
You can also use a little code to export each layer into an image, which you can then import into After Effects. To do that, the first step of course, is to get the script. Download the files from my google drive HERE.
Now, go to the plugins menu at the top, there, you will see the Python console, go and click that, you will see this window popping-up:
Python console in QGIS.
Click the paper icon, then click the folder icon and select the python script you dowloaded above. Just be careful with the filePath option.
If you are on a mac, right click your output folder and hold the option key, that will allow you to copy the absolute path of you folder, paste that to replace the filePath field value (the green text in the image below). If you are on Windows, just make sure to get the absolute path and not a relative one.
I left some annotations on the script to better understand what each part is, it’s based on a script someone did with Vietnamese annotations, source and credit are in the drive link too.
Now just click the play button in the python console, seat back and look all the frames of your animation loading in the output folder you selected. You should see a file for each of your layers when the script finishes.
Step 6. Color key
The temperature in this set is provided in Kelvin degrees. The range of the data depends on your date / file set up. But if you are using the ramp I have provided above with data for Jan. 4, there’s a svg file named “scale.svg” in the drive folder within this range. I have nudge a little the color and ranges matching the map with nice round numbers.
For January 4, the data rages are about 224°K to 308°K, you can use google to covert that to Celsius or Fahrenheit depending on your needs. But basically you can take your Kelvins and subtract 273.15 to get Celsius. The min. Temperature would be ~ -49°C (224°K) and Max. ~34°C (308°K). If you are into Fahrenheit, I’m sorry the math would be a little more complex for you… go ahead and use google.
Step 7. Setup and export your animation
On my previous tutorial to visualize Organic Carbon, I used Adobe After effects to add the dates, you can use the same principle here, or using any other alternatives. For example, once you have the output files you can drop them all into photoshop. By going to the menu Window / Timeline you can add a frame animation, simply click the + icon in the timeline panel followed by turning one layer on at the time.
Adobe Photoshop frame animation.
If you are using Photoshop, pay attention to the order of the files, it should match the data dates from newest at the top to oldest at the bottom. Once you have you sequence ready, in the timeline panel menu, you will find a render option to export your animation as video, or you can create a gif animated by using the top menu File / Export / Save for Web or command + option + shift + s if you are on a mac.
Or something like this, if you have used the same data and ramp from this tutorial:
I love GMAO's data. This is yesterday's global temperature hourly averages. Note how the while the sun rises the surface temperatures create a "wave" from Brazil to North America #DataVisualizationpic.twitter.com/62CO1lMVdH
If any of this doesn’t make sense to you, or if you’re having trouble with a step, feel free to reach out to me on Twitteror MastodonI will be happy to hear from you.
Happy mapping!
Update
Using gdal to convert data to 180-180
Someone contacted me about this tutorial because they were having problems with the projection of the temperature data.
For some reason if your files are in 0-360 format instead of 180-180 you will usually see the globe aligned with the vector layers but not with the temperature rasters, which usually appears to the side in QGIS
If that’s happening to you, you may need to convert your data before dropping it into QGIS. Here’s a quick tip on how to fix that:
From your terminal window cd your folder like you did before, look for the directory where your temperature data is.
Type gdalinfo add an space and paste the file name it should look like this:
You will find the subdatasets. We are looking for TLML (temperatures) that highlight on blue above.
Gdal would help you to convert the data so you can use it, the command line looks like this:
***Note your file path will be different copy that from your terminal window (the blue highlight)
That will give you a new file in the directory of your choice (your/directory/output-filename.nc4) in this example there is a folder called directory inside a folder called your in which is the file called output-filename.nc4. Be careful when renaming files the dates are important to the animation process.
Looking back at what was the year has become a tradition. Each year has brought a great diversity of projects and 2022 also meant a complete adjustment of life. In the midst of changing media, countries, schools, etc., these were my favorite details from the graphics that were born during my first year at the New York Times.
January: A new year, a new purpose
January was a tough month of transition, I spent a lot of time doing paperwork, looking for a place to live, and settling in at The Times. The learning curve on the internal tools and ways of producing it turned out to be longer than expected, but in my spare time I had some space to try out some new terrain processing stuff to start what would become almost a year in maps.
One of the things I enjoyed the most was playing with the elevation data to render images like the one above with Blender. It’s a bit of a fiddly setup, but once you’ve got it, it can spit out some really nice base maps.
I have use #cinema4d for such a long time that makes the learning curve of #blender longer + tedious |🐢🐢🐢🐢🐢| I hope to soon learn to use this properly, or at least a little more efficiently. I'm testing my patience with this map. pic.twitter.com/4mW5FARJ7g
A month later, I was little more confided about the environment and the first graphics started to bloom. I enjoyed being part of the team covering the Winter Olympics. I learned a lot about disciplines that until this point I completely ignored.
Some of the pieces included photometrics, 3D transitions, and basic vector graphics as well. The process was very effective, even though the competitions were at crazy hours for New York (like starting work at 3 am) the pieces were ready in a matter of hours, just in time for you to enjoy over breakfast. That was possible because of the collaboration of the team, many of us working together for each key competency, but also because these things were so much easier to achieve with the internal tools that the team has produced.
Learn more about this story
If you haven’t seen it, or want to refresh your memory, you can enjoy one of these pieces here: https://nyti.ms/3sn959n
In my opinion, some of the coolest things were these almost-real-time pieces our team produced for social showing the performance of the skaters:
By the end of February, the war in the Ukraine had arrived. However, in my case, March was the starting point of a coverage that has kept me busy all year. Hundreds of maps, 3D models, diagrams, illustrations and more have been the tools to inform our readers about the unfortunate stories that this ruthless war has spewed.
I’ve worked on dozens of updates to our breaking news maps page. This page is a quick response to events happening in Ukraine due to war or related issues, each entry delivered in a “small capsule” format that is published in no more than a few hours in a single shift.
Perhaps the most complex aspect of this page, in addition to the short times to create the entries, is the collection and verification of the information, which is often an arduous task. With the passing of the months, posting have become less frequent, but not because we left the coverage, but because many topics were deepened on a separate page since a small informative capsule was not enough.
Working in the news exposes you to know the worst of humanity. It’s true that in the coverage of a war you do not expect to receive images of fields in bloom, but sometimes it can take you to visit the lowest points of the human kind. The map below is part of a dark history of the bodies of Ukrainians murdered in the streets of Bucha, a small city north of the Ukrainian capital. These were civilians, who in addition to being killed in their own town, could not find respect and peace until the Russians left.
As snow was melting away in Ukraine, the Russians were also forced to move away from many regions in Ukraine. That was the main story I worked on in April.
A way to show evidence of their shrinking ambition was to look at the fighting reports we had collected over months from official Russian and Ukrainian statements and other sources. That gave way to those little maps that open the story.
May was also a good time to share a bit of behind-the-scenes work from the perspective of failure, of course. I collected failed maps from the first 5 months of 2022. Some were ideas I wanted to try while working on mapping the same areas of Ukraine over and over again, and other times just observations that caught my eye.
Just because this has been the year of maps for me, I found some free time to continue exploring with terrain processing, this time adding unusual colored textures to the base-maps of my beloved Costa Rica.
One of the things I enjoy the most of my work is the chance to diversify the things I do. 2022 was a lot a bout mapping, but sometimes like in June, I had the chance to use something else to communicate, in this case Cinema 4D to create models of Russian equipment, including this terrible weapon that breaks into small fragments and mini-bombs that probably still lie dormant waiting to detonate in many places in the Ukraine.
What Hundreds of Photos of Weapons Reveal About Russia’s Brutal War Strategy: https://nyti.ms/3tO4xui
A New York Times analysis of more than 1,000 photos found that Russia has used hundreds of weapons in Ukraine that are widely banned by international treaties. Russia's attacks made widespread use of weapons that kill, maim and destroy indiscriminately. https://t.co/zq5KNscNwL
Having access to so many satellites is awesome. Those things flying over us all the time are a great tool to provide evidence for our stories. By July, I was working in a piece about the Azovstal Steel Plant in the city of Mariupol. That factory was a stronghold for the Ukrainians. But the development of circumstances led that industrial complex to become a horrible trap for civilians who ended up trapped with no way out for months.
I used a large image of the plant to point out key locations, playing around with color and contrast of the things we really wanted to focus on first. But maybe the most interesting part of that piece was the radar data. I have use this data many times, there are plenty of ways to take advantage of the Sentinel missions data. The map below is the variance in the readings over a range of weeks, once processed it can show you where the structures of a city have being physically changing, in this case revealing evidence of damage by the war.
August brought Taiwan to the main focus. China decided to show its muscles encircling the island with military exercises so we presented a visual analysis of the particular conditions of Taiwan.
To do that I have the opportunity to work with my friend the super-talented Pablo Robles.I have worked with Pablo in different countries/media and I only can say he has an exquisite sense for design and graphics in general. We worked together for the first time at the NYT to produce these series of maps and graphics showing how China may choke the island in order to push it for an outcome similar as they did with Hong Kong.
Seven months of war had brought a lot of stories and hundreds of maps. September was also a turning point where the Russians have no option but back down its war. Ukraine managed to conduct some effective offensives taking advantage of some geographic conditions and Russian weakness.
It's also interesting to see how VIIRS fire spots (Aug29 – Sep8) align with the area of fighting. Almost all of these detections in the red circle near Shevchenkove occurred on Sep-8. No official confirmation, but perhaps related to fighting. (personal raw file not published) pic.twitter.com/p5m5ta1uVU
I always keep an eye on satellites data when a key development happens, in this case the thermal readings onboard of VIIRS satellites showed fire spots matching the advance of the Ukrainians.
Towards the end of the month, a group of colleagues was working on coverage of Hurricane Ian. They put together the map below showing the intensity of the flooding caused by the hurricane. I collaborated with a very, very small part, but the work they did seemed simply impressive due to the magnitude of what it communicated.
The Hurricane Ian caused a lot of damage in Florida, I worked on a piece precisely about that checking the before and after after the storm wiped out dozens of buildings.
Our coverage included realtime maps maintained by our team, I did my self some of that. Some maps were easier to update, and some other pieces included a little more of customization.
NASA's Black Marble captured preliminary images of Florida after Hurricane Ian. It's amazing how many lights are gone, especially on the devastated west side https://t.co/dHnZSc4IJy ( level 1 data not calibrated ) pic.twitter.com/kD3teNjSsy
In early October the bridge connecting Russia to the illegal annexed Crimean peninsula got hit by an explosion. The structure wasn’t just a Russian symbol in Ukrainian soil, but a key supply line so its relevance was enough to trigger a large retaliation over Ukrainian structures in the following weeks to the explosion.
After analyst reviewed the evidence, we prepared a piece showing how it was very a difficult operation to plan, if it was planned that way by the Ukrainians. I thought an illustrated piece could do the main explanation well.
On Dec. 1st, I have this idea to do a new entry for the Ukraine maps page about trenches, short story is the piece grow with so many things I found, that we move it to its own page instead. I think every little thing lined-up to do analysis, I found a lot of evidence in radar data, great HD images from satellites with very few clouds, the military experts also give me great material from a few interviews a found a lot of good references for illustrations… all was set for a nice piece to say good bye to the 2022 with a final report on Ukraine.
December also bring me a nice memory from the past. I did a small collaboration before I left Reuters a year ago (times flies wow) this December I saw it published by the mates of Reuters, it was so nice to see this published after so much time. So many great memories came back to me.
This project was full with amazing illustrations by my friend the talented Catherine Tai. Hipper realistic illos of beautiful creatures.
What I learned after drawing 150+ bugs is they're amazing and complicated! Thanks for the opportunity @SimonScarr . Incredible experience. Sharing some of my fav in thread. @ReutersGraphicshttps://t.co/toZWOOWjhg
There are only a few more days left in 2022, looking back at what this year has been like, there are so many sad stories. I sincerely hope that 2023 brings us all happier things to read. My thoughts are with all the victims of the war, and I hope that it ends soon for the good of the world.
Please consider visiting the links above, this is just a glimpse of what’s in these stories. For practical reasons, I have omitted many details and perhaps a broader perspective is necessary.
Here we are again saying goodbye for another year. I’m very grateful to all my teammates at The Times for the patience they all had with me in helping me through this transition year. To you all my www-friends, I wish you the best in this new beginning.
Earlier this year I spent some time learning about the world of phenology. After reading some scientific papers and doing some interviews with researchers, I just found myself getting more and more curious about it.
If you google Phenology it will return something like “Phenology is the study of periodic events in biological life cycles and how these are influenced by seasonal and inter-annual variations in climate, as well as habitat factors.”
Since we live in a single network, studying the effects of climate on species brings us closer to what will inevitably also affect us, but it’s also a way to connects us a little more with all those other living beings with whom we share this space.
“The love for all living creatures is the most noble attribute of man.”
Charles Darwin
Darwin was right, after talking to a lot of people and understanding their passion for plants and animals, it is easy to understand the concern about the changes that some species are facing.
But moving on, if you have visited this blog before you may know where this is heading to… yup, this is another #infofails story. Here’s how all went wrong:
An unfinished illo for a blooming/ecological mismatch project I tried to run.
The embarrassment
The most embarrassing part of my failures is not facing your editor with a dumb idea, the hard part is getting excited about the information from sources and interviews and then watching time go by without you being able to develop the story you had in mind, especially if the people who spoke to you were super collaborative.
My first source in this endeavor (with whom I’m still embarrassed) was an Ecologist with the USGS. She shared with me some info from studies in the Gulf of Maine where she studies seasonal disturbances in marine life. In fact, it was she who explained to me what Phenology is. –Explained by a scientist who works on it.
My embarrassment also is with Richard B. Primack. He’s a Biology Professor at Boston University, I had a great conversation with him, he shared tons of great data.
You see, Prof. Primack has been studying and documenting the ecological mismatch for years, in 2016 he published a study where he explained how some birds arrived late to forage because spring is starting earlier. He show this example comparing the spring in 1850 describing the natural flow: first birds arrive, then leafs come, then insects appear, and finally flowers pop. Here’s a quick draft I did based on his publication:
Sketches of the spring flow in 1850. Based on Prof. Primack’s paper published in American Scientist Magazine, 2016.
Makes sense doesn’t it? the observations show that these birds have continued to arrive on similar dates, but now spring is coming earlier. In 2010, for example, the leaves arrived earlier, so the insects also appeared earlier and spoiled the entire cycle for other species.
Staying with that same example from 2010, birds were observed arriving around the same date to find flowers when the insects should be just showing up. In other words, these days, for some species the natural flow looks something like this:
Sketches of the spring flow in 2010. Based on Prof. Primack’s paper published in American Scientist Magazine, 2016.
Prof. Primack along with many others researchers used Henry Thoreau’s observations to reconstruct the past of seasonal changes, that alone was a big story for me. So I went on and on, making more questions and asking for more data. And kindly they send me over tons of papers and tabular data.
Some of that data Prof. Primack shared with me included detailed records of plants and animals where he spotted those changes in spring and the struggling birds.
A data sketch I did with part of the data collected by Prof. Primack and a team of researchers merged with Thoreau’s records.
When I have a dataset that looks this interesting, I’m inevitably driven by ideas of how to show this in a story, it’s like a need of sketching data. At that point I need to somehow present this to my editors to push it forward and turn it into a story. Sometimes I spend time developing my ideas into sketches just to explain to editors what I’ve found interesting, but it’s not always as obvious to them as it is to me, so it’s necessary to write some paragraphs and accompany them with those images.
Some of the tree species that sprout leaves earlier. The steeper the slope of the red line, the earlier the leaves sprouted on average.
Just the right timing
That same process that I follow sometimes takes too long to put together a draft for my editors. When I came up with the proposal for this story, it was almost spring and it was hard to move a story past that window. That was just one of the things that spoiled the initiative I think.
It’s important to note that for those types of stories, I’m not developing the drafts over my daily work, but rather in free moments, which lengthens the process even more. But anyway, the lesson of this part was to keep an eye on your post window and not let your inner child distract you with what you find and diverge, maybe you’ll get the idea to the editors in time, it would be more easy for this to happen, who knows…
Adding more, more, more…
Certainly I was fascinated with the data and all the potential for a story, I was finding more and more data related to the same issue of animals struggling with the climate changes, the only problem was the this data was a little old already. Like this fascinating 2018 paper by Prof. Marketa Zimova + describing molting conditions in furry animals and how they struggle to survive when there is little snow and you are still covered in white fur. You may noticed the illustration at the top with a white hare on brown background which is kind of what they look to predators when there’s no snow around. Really sad the reality that these animals are going through, you know how it ends if you’re a white prey animal on a brown background.
A diagram based on the research data by Prof. Marketa from the University of Montana.
My second problem turned out to be that I was following the white rabbit into the world of tangencies. There is so much information on this that I started to integrate other studies and data, maps and things that led me to create a monster draft. A lot to digest from a news perspective maybe.
Earth temperature anomaly in April 2007. Based on NASA NEO. This event caused heavy damage to fruit tree crops during the spring of 2007.
A lesson from this would be to narrow the focus, crunching the idea down to its essentials can help early in the process. My mistake here was probably in choosing and editing the story I intended to show my editors. I added a thousandthings on it, including interesting but a bit old data, maybe not the best selection for a news story.
While not everything should be breaking news, at least the focus of the story should be less scattered and consequently better defined.
Don’t follow the white rabbit. They tend to show you things that lead to a spiral of tangencies. –A silly and perhaps inappropriate joke, sorry. I hope you get the idea anyway.
We are experiencing climate change in many ways. In fact it’s easy to find news and research papers on early blooming and animal habitats threatened by seasons arriving earlier or later than they used to be and so many other changes that every species on this planet (including us) must endure.
If you’re in to news, I encourage you to talk more about this topic, worst case scenario don’t publish your story, but at least you’ll meet amazing people along the way and learn a little more about the fascinating world between us.
About #infofails post series: I truly believe that failure is more important than success. One doesn’t try to fail as a goal, but by embracing failure I have learned a lot in my quest to do something different, or maybe it is because I have had few successes… it depends on how you look at it. Anyway, these posts are a compendium of graphics that are never formally published by any media. Those are maybe tons of versions of a single graphic or some floating concepts and ideas, all part of my creative process.
In short, #infofails are a summary of my creative process and extensive failures at work.
Are you liking #infofails?, have a look to previous ones:
I’m not as consistent as I wish but I hope you keep enjoying #infofails this time dedicated to #maps ‘Random Failed Map Details’ https://t.co/TxDcUTuYat
Long time ago someone on twitter ask me to do an explainer on how I did the “smoke” animations forthis Reuters piece. It has been a while since then, but maybe it would be useful for someone out there, even if that mean learning how NOT to do things.
Before continuing, to follow my guide and visualize organic carbon, you should be able to use your terminal window, QGIS and optional Adobe After Effects.
Organic carbon released into the atmosphere during the wildfires season in California in 2020
Let’s talk about this wonderful data first
NASA’s Global Modeling and Assimilation Office Research Site (GMAO) provides a number of models from different data sets, this is basically a collection of data from many different services processed for historical records or forecast models. This data works well for a global picture or continent level even, but maybe isn’t a good idea to use this data for a country level analysis, for those uses you may want to check other sources of the data instead of GMAO models, like MODIS for instance if you you are looking for similar data.
ORGANIC CARBON
There are a lot of different sets of products available at the GMAO servers, you can check details here, here and here. However for purposes of this practical guide, I’ll be focusing in the emissions of Organic Carbon which is stored into the tavg3_2d_aer_Nx set. That’s a GEOS5 FP 2d time-averaged primary aerosol diagnostics, which includes Organic Carbon Column mass density in the 38th band, there is some documentation available in this pdf. ( No worries if is this sounds too technical stay with me and keep going. )
A day of observations accounts for 8 files since this data is processed every 3 hours. This is great for animation because it would look smooth. Knowing that, let’s move to our guide.
Step 1. Get the data
The data is stored into this url. You can go into the folders and get all 8 files for each day manually if you like or get them with a command line using wget or curl into the terminal. You just need to know a little of the url structure:
GMAO organic carbon files and url structure
Create a folder to store your files with some name like data
Note that I have renamed the output ( -o ) with a shorter name. The file will go to your folder ready to use into GQIS. Of course you will need a few more files to run an animation. Remember that this data is available for every 3 hours daily, so you need to set the url and name for something like this:
01:30 AM >> 20220619_0130.V01.nc404:30 AM >> 20220619_0430.V01.nc407:30 AM >> 20220619_0730.V01.nc410:30 AM >> 20220619_1030.V01.nc401:30 PM >> 20220619_1330.V01.nc404:30 PM >> 20220619_1630.V01.nc407:30 PM >> 20220619_1930.V01.nc410:30 PM >> 20220619_2230.V01.nc4
Just create a text list with all the urls you need and run the script into the terminal window with the same process:
curl-O [URL1] -O [URL2]
Each file is usually about 120MB, if there’s something wrong with the data the file will be created anyway but would be an empty file of just a few KB. Do a day or two first and check, that’s 8-16 files, check them, if all looks good load a few more if you like.
Step 2. Loading the data
Once you have a nice folder with all the files you want, you can just drag and drop the .nc4 files into QGIS. We are looking for the 38th Band, OCCMASS which is our Organic Carbon Column mass:
QGIS prompt window when you drop one of the file in.
Once you have the data loaded, you want to set the data projection to WGS 84, this will enable the data layers to be re-projected later on. To do that, select all you data layers, right click on them, and select Layer CRS > Set Layer CRS > 4326. Be sure of selecting all the layers at once so you do this only one time. Otherwise you will need to doing over and over.
Data layers projection to WGS 84.
Since this is a good global data set, you may want to load a globe for reference, you can use your own custom projection, or use a plugin like globe builder:
Access Globe Builder from the plugins menu > Manage and Install > type: Globe.
Once installed, just run it from the little globe icon, or in the menu plugins > Globe builder > Build globe view. You have a few options there, play around with the center point lat/long. You will see that this data sets always have large concentrations of emissions in Africa, maybe that’s a great place to start. I’ll do a similar view to the California story for now.
Step 3. Styling your map
The color ramp is important, you want to have a data layer that can be overlayed in the base map, so you want to have white/black for the lower values and high contrast in the other end of the data, since we are working on white background I’m using white to black with yellow and brown stops. Check what are the highest values in your data set the style for on layer to something like this:
Number in the min/max will change depending on the highest values of your data and the style you want. This image is set for OCCMASS from June 19th, 2022, 4:30 pm.
Once you have the ideal color ramp for one layer, right click on that layer, go to Styles > Copy style. Then select all you carbon data layers, right click on them and select Styles > Paste Style.
Step 4. Preparing to export your map
You are almost done, by this point you can see how each data layer creates swirls in the atmosphere, maybe some evolution of it too just by toggling the layers visibility. I like to have all the layers well organized so you can quick check the data. I’m maybe a little too obsessive but I usually rename all layers and groups to something like this:
QGIS layers panel.
The name change works if you are using an automatic export of all layers, the script takes the name of the layer to save each file. But there are alternative ways to do this if you’re not as crazy as I am and don’t want to spend time manually renaming.
Step 5. Export your map
There are many ways of doing this, you can set up the time for each layer by using the temporal controller, there’s a good guide here. That way you can get a mp4 video right away from QGIS, but you need to set up each data layer time manually.
You can also use a little code to export each layer into an image, which you can then import into After Effects. To do that, the first step of course, is to get the script. Download the files HERE.
Now, go to the plugins menu at the top, there, you will see the Python console, go and click that, you will see this window popping-up:
Python console in QGIS.
Click the paper icon, then click the folder icon and select the python script you dowloaded above. Just be careful with the filePath option.
If you are on a mac, right click your output folder and hold the option key, that will allow you to copy the absolute path of you folder, paste that to replace the filePath field value (the green text in the image below). If you are on Windows, just make sure to get the absolute path and not a relative one.
I left some annotations on the script to better understand what each part is, it’s based on a script someone did with Vietnamese annotations, source and credit are in the drive link too.
Now just click the play button in the python console, seat back and look all the frames of your animation loading in the output folder you selected. You should see a file for each of your layers when the script finishes.
Step 6. Export your animation
Take all the files this into After Effects. First, add your carbon data as sequence (0001.png, 0002.png, 0003.png…), keep that in a sub-composition and use a multiply blend mode to overlay the layers, then add the countries/land and the optional halo.
Finally, in the drive folder you will see a .aep file, that’s a simple number animation to control dates, copy the text layer into your composition. You know when the data starts and when it ends, in the example is just 3 days 19-21, “June” is a different text layer, so add those numeric values to the keyframes into the text layer you have copied, and leave it at the very top:
Once you are all set, just export to media encoder to get you mp4 animation.
If any of this doesn’t make sense to you, or if you’re having trouble with a step, feel free to reach out to me on Twitter. I will be happy to hear from you.
Recently I have been working on maps, maps and more maps. I really like the world of cartography, although I’m not a cartographer a lot of my work includes trying to make maps for news. –My apologies to my carto-friends who actually do this properly, I’m just an enthusiastic fan with perilous initiative. 🤣
Since I moved to the NYT, I have been in a process of rebooting, adjusting myself to the new environment learning new stuff and understanding how things work in this side of the world. But as usual, while I’m executing random ideas I have left behind a bunch of un published visuals like the screengrab at the top of this entry which is a DEM of an area of eastern Ukraine.
For nerdy purposes, the image at the top and the following are SRTM elevation and Open Street Maps data processed with QGIS with a little color retouch in Photoshop.
A failed map of eastern Ukraine.
Of course these detailed images doesn’t work well for the purposes of the news story I was working on. If you have seen our Ukraine maps coverage, you’ll notice that while our maps have evolved, they also keep consistency somehow. To be honest, I made those alternate versions because I couldn’t stop thinking about how this would look in another style. You can see what I mean below, these are the same area in eastern Ukraine rendered for different purposes:
Alternative terrain section of eastern Ukraine including part of the Sea of Azov at the bottom
Screenshot of the piece published by the New York Times
Alternative terrain section of eastern Ukraine including part of the Sea of Azov at the bottom
Here are some closer shots of that map above, the geography of this region of Ukraine is marvelous.
There are so many of these maps, I have literally spent months looking at the progress of the war with maps, many different approaches and a heavy editing process of what takes place until the final version of the story. It is a strenuous process but super interesting at the same time. I feel very grateful to be able to see all this and be part of the search for the truth to inform the readers of the NYT.
Basic vectors
There’s something with the base layers, is amazing how you can see the population density of a place just by plotting roads. Some areas with certain road layers look like leaves or some kind of vein system. [ Click on the images to see a larger single image ]
The same thing happens looking at water features, some times you are able to see canals making geometric patterns in contrast to the organic river beds.
Since Ukraine has vast tracts of land dedicated to agriculture, those patterns are clearer in some regions, however the rivers and lakes are still fascinating as well.
About #infofails post series: I truly believe that failure is more important than success. One doesn’t try to fail as a goal, but by embracing failure I have learned a lot in my quest to do something different, or maybe it is because I have had few successes… it depends on how you look at it. Anyway, these posts are a compendium of graphics that are never formally published. Those are maybe tons of versions of a single graphic or some floating concepts and ideas, all part of my creative process.
In short, #infofails are a summary of my creative process and extensive failures at work.
Are you liking #infofails?, have a look to previous ones:
That time of year has come once again, the best of the year in my opinion. All us is doing the list of the best of the year to give a glimpse of what 2021 was like and, of course, to give a final push to their stories as well. So, like last year, I want to do a quick rundown of my favourite details of the 2021 projects. Keep in mind the pieces in this entry are out of context and you may want to take a look into the full story for better understanding.
January: The amazing Amazon rainforest
The 2021 kicked off strongly, during the first month of the year I worked various projects including some breaking news. My favourite details of January was a small graphic part of the project titled “Jungle Lab”. The graphic itself isn’t a super complex visualisation, actually it’s just a simple illustration, but the message behind it is very powerful. It makes you realise the relevance of the virgin rainforest right away. I truly believe that our work on infographics is not about fancy effects but powerful messages to our readers.
Researchers soaked in sweat tore trees limb from limb, drilled into the soil and sprayed paint across tree trunks. It’s all in the name of science https://t.co/X4c6pfUUjJ via @jakespring
As I said before, January was a busy month. Here are some other details that I also enjoyed working on, mostly breaking news.
You may remember the story of miners who were trapped in a mine after an explosion in Northeast China [link here]. There’s a small graphic showing dimensions of the rescue shafts dug by rescuers, that’s something really difficult to imagine without a familiar reference.
Aside from the miners, you may also remember the tragic accident of the Indonesian flight SJ182 [link here]. I recon working with those bathymetric maps helped to explain why recovering the black boxes was a difficult operation. Also kind of shocking to see a few incidents of airplanes around the same area.
February: Sand.
After a tight January, news continued to pop up everywhere, lots of stories with great potential for a visual project. I have the opportunity to do some experimentation with 3D assets using amazing high resolution images courtesy of Planet Labs. We created a detailed story of the massive landslide in India [link here]. Here’s also a short recording of the piece running in C4D: [Drive video].
However, a much larger project was published in February. For a long time we worked on a series of projects on a topic that impressed me. To be honest, I never thought about it before: Sand mining.
Sand mining and trade is a whole world itself, this commodity is unnoticed present in our daily lives. It have a dark side of illegal trafficking and mafias too and even it have sparked diplomatic issues for some countries.
But one key thing that came to mind when I started working on this was this: Why we don’t use desert sand to feed our huge demand? There’s plenty of it!
Well… the explanation is a little more complex, but in short, desert sand grains are too small and rounded. That is why we are dredging rivers, digging abysses in mountains and making beaches disappear.
Sand is the planet’s most mined material, with some 50 billion tons extracted from lakes, riverbeds, coastlines and deltas each year, according to the United Nations Environment Programme. Per person, that’s more than an elephant’s weight in sand. (2/7) pic.twitter.com/ykk9nBqDAe
2021 broke some records with extreme weather events, in fact I did an entry here about a 2021 failed project on floods. You probably remember the floods in Germany and China, but there were many more events like that throughout the year.
In March, eastern Australia suffered what the Australian government called the worst flooding in 60 years. That week I was working in a daily-graphics shift, so I did a quick small map to visualise the event, here’s a small part of the graphic:
Also in March, I did a small collaboration on the nice project “Bats and the Origin of Outbreaks”. I really enjoyed working on that piece in every aspect, from the story angle to the opportunity to work with a custom style. You may also want to take a look at that piece:
According to the latest report from the WHO's Joint Mission, the intermediate host which passed the novel #coronavirus from 🦇 bats to🧍humans is still to be identified. Here we explain zoonotic viruses and why bats carry so many of them. #COVID19https://t.co/b4Sc2fauOU
April surprised us with a breaking news story, the Le Soufriere volcano violently covered St. Vicent island in ash, devastating the island infrastructure and prompting a sea and land evacuation of thousands of residents. It also released emissions at spectacular heights into the atmosphere. It was like the omen of a year full of massive volcanic eruptions around the world.
I collaborated in the story with different pieces, but my favourite piece was Simon’s map showing buildings, shelters and risk areas among others. Here is a small detail of that map:
Explaining the series of eruptions from #LaSoufrière which have devastated St Vincent's infrastructure and prompted a sea and land evacuation of thousands of residents. #StVincenthttps://t.co/AbMEFswvGw
One other frequent topic on my daily work this year was space exploration. I did a good amount of small pieces on telescopes, comets, asteroids and spacecraft.
May saw the landing of the Martian explorer ‘Tianwen-1’ develop and successfully landed in the red planet by the Chinese. Here is a little detail of that graphic:
June: Olympics.
My favourite from June was the singular Olympic sports story. Can’t imagine the adrenaline rush of a 200m obstacle swimming competition. Jumping over boats and diving again to be the fastest hurdle swimmer of all times, they sure had a lot of fun there.
Here’s a small diagram of the course of the Paris games of the year 1900:
There are many disciplines and events long forgotten by today’s generation of Olympic spectators. Here's a look at some of the most interesting and obscure events that did not stand the test of time and no longer feature in the #Olympicshttps://t.co/U0k69vLirDpic.twitter.com/E7OvMBtml0
By mid year the news put me back thinking in the outer space. The Hubble Telescope was literally an eye-opening for the scientific community and for all of us in general. Even more so if you stop to think that this magnificent achievement of science was designed with technology from the 80’s. I can’t believe how the old computer from the 80’s still worked there. Here’s a small detail from a daily basis graphic that explains where the telescope problem was. ( The 80’s computer SIC&DH )
This 2021, I spent a little more time making small single-day graphics. I enjoyed the small break from big projects, and the quick and intensive research part you have to do to get it done in a single day. However, I think the best part was going back to the long-medium term projects that were almost done to finish them, with my mind clear and fresh.
August: Wildfires & aircraft data
August was a very busy month too. One of my favourite pieces was this sad new record: For the first time in the records, smoke from the fires reached the north pole. Check out the graphics thread below. There’s a third graphic in that thread, click on it if you want to see some temperature records too:
The second part of the month was infused with the chaos surrounding the US departure from Afghanistan. We did a few pieces on this, overall my favourite was the spaghetti drawn by the aircraft around the airport. It’s really cool how when you are digging into the data many stories pop up, and often many questions more.
When Afghanistan’s government collapsed and the Taliban began to roll into Kabul, a huge multinational evacuation began. Here is a look at what is flying and what they might be up to:https://t.co/pLKk5XPVSj
Like what happened to me this year with the sand, there are things that you never expect to be so interesting. Ice is more than frozen water, ice cores are cool stuff. These things can help to retrieve ancient records, they are like windows to the past of our planet. On September we published this story about that.
The sad part is that we are loosing those records due global warming, and it’s not slowly loosing them, it happens at freaking vertiginous fast speed. The graphic above shows the average of how much ice melts in the world EVERY DAY! The amount is equivalent of placing a gigantic 273m-high ice cube in NY’s Central Park.
Ice provides historical records about climate cycles, and as #glaciers melt, scientists are working to collect ice cores from them before it's too late. Learn more @Reuters: https://t.co/bLtLzpClBo.
The forests stories returned in October. This project took many days of 3D scene testing, hundreds of calculations to put thousands upon thousands of trees in place, modelling objects and illuminating leaves to show how quickly we are removing the things that are keeping us alive. We are nuts isn’t?
Not just ice, but we are also losing trees on an incredible rate. On average, 67,000sqm of rainforest is lost EVERY MINUTE. The following video begins at ground level, right in front of 3 people and a logging tractor, all to scale. In order to see the portion of the forest that we lose, you see people and tractors as ants.
There were also other quick projects on this month, one of them was the story of the Chinese incursions on the Taiwanese ADIZ. Since then, many more events had happened and tensions only continued to escalate between both sides. Thinking about the region that has been my home for so many years, I hope this doesn’t go any further. Unfortunately, there are always chances that this will get out of control.
India is a very particular place. During the last months of each year, northern cities are suffocated by pollution trapped at the foot of the Himalayas. There are many reasons behind this such as seasonal crop fires, fireworks celebrations, and many others. However, southern cities avoid polluted skies. That was the subject of a one-day quick map, here’s a detail of it:
December: spin, spin and throw it.
Back to outer space news! …or kind of.
Near the end of last month (Nov.) I was working in one more of those small pieces for the daily basis. This time my mind blowed up with this idea of launching things into space without rockets.
The idea is to spin a projectile in a vacuum chamber, gain momentum, and propel it into orbit around the Earth. I imagine something like throwing a hammer at the Olympics, but on an enormous scale. Here is a small detail of the graphic in mention:
My 2021 list of graphics
Just a few days more of the 2021 are left, so many stories have crossed under my Wacom and keyboard, it was a great year. Hope you enjoyed this sneak pick of all the stuff I worked on over the year. I hope you also considered visiting the stories mentioned, just hit the link at the end of each month’s entry to get a better context of each of the details highlighted here.
2021 was a great year, I’m very grateful to Reuters for all the good things, also to my teammates, for everything we did together and how much I learned. There is no better way to say goodbye to this year.
August 2021 saw a tsunami of stories from Afghanistan in the news. Apart from a bunch of small graphics, I participated with “Chaos in Kabul“ and “Flights over Kabul“ both stories related to the Afghan skies and the complex situation lived there after the US troops withdraw. At Reuters, we did a good long list of potential stories, even I did a few kickoffs of some, but a packed agenda of long term projects for this year left most of them out.
illos published at the story “flights over Kabul” Aug. 2021 | Reuters graphics.
Have you ever felt that you need a few copies of your self to materialise all the stuff in your mind? no? well maybe just me weirdo but I do. I would have loved to have some copies of me working on a few more stories from Afghanistan to get them in time to publish back then.
Those aircraft illos above are from stories that do made it, but you know I love to share pieces from under the rug, so… here you go:
For a day or two my desktop was full of guns blueprints, aircraft dossiers, technical documents of military equipment, tons of field photographs from our news feed, news articles… The home screen of photoshop and illustrator slowly turned red.
I usually start these things in photoshop, drawing outlines, then a base colour layer, a layer shadows and one more of lights on top. All always to scale and 2.5x the size I’ll need in the final version.
The cool stuff related to my job. ❤
The night vision googles were my favourites, weird device actually. I learn some of them are like a video game with this cool display of augmented reality. The visuals looks really nice, check this story from The Washington Post [ here ] or just google “US army augmented reality night vision goggles” you will see.
I love to learn new things, that’s one of the best things of my job actually. You never know what’s waiting for you tomorrow, what new curiosities are waiting there for you. And even if they don’t get published for sure you will learn something new.
The grey area of working in news
Beyond the research for the illustrations and the drawing itself, I made some maps and videos on a demo page with the proposal. Maybe all of this was pointing too high in the little time we had at the time since all the rest of the work has to be considered to complete the story.
Sometimes news are a bit cruel, you must do everything quickly before it’s no longer news. The tricky part of it is that most of us in this industry think quickly of the same things. It’s like a race against the clock, so, unless you can distinguish your story from others that have been already published, things are doomed, and may end here on my blog… which is fine, but of course it’s a sad thing to bring potential stories to the graveyard.
The summary of this failed idea contains a fair amount of learning, a lot of cool nerd stuff is on my head now. Although unfortunately none of this was published, it was not a complete waste of time either as I did not work on this exclusively. There are many other interesting things in the pipeline, some almost done, so enthusiasm always remains high.
About #infofails post series: Graphics that are never formally published. Those are maybe tons of versions of a single graphic or some floating concepts and ideas, all part of my creative process. All wrapped up in #infofails, a compilation of my creative process and failures at work.
Did you like #infofails? Have a look to other #infofails
Last July was a crazy month full of flood news all over the world. I remember seeing impressive videos and images of the floods in China and Germany, and digging a little deeper I found many more reports about it from around the world. I tried to put some things together, but time and other projects played a trick and the project became material for #infofails.
Some times taking notes of things isn’t enough for me. One or two illustrator artboards with basic ideas have become the new “office whiteboard sessions” since we started remote work. Quick sketches and some data samples usually help me to organize myself better.
Sampling flood reports and daily precipitation data.
I collected some data from NASA including the PPS and MERRA-2 to visualize precipitation. It was so cool when I saw the data of total rainfall in a month over the planet. Is curious to see how dynamic our planet is isn’t?
July’s total precipitation. Data by NASA’s Precipitation Processing System (PPS)
Whenever I have a global data set, I always look at how things are for my family and friends in Costa Rica. I remember that in July I had seen videos of flooded areas in Turrialba, a region in the Atlantic region of the country. And yes, the accumulated data showed that intense blue layer near the border with Panama.
Detail of the precipitation data. NASA PPS.
Of course, there were other much worse areas that saw terrifying amounts of precipitation causing dozens of deaths, western India for example was one of those areas. I continued to explore a bit more on the map and checking against the flood reports I found to find points of interest and to highlight later in the story.
Detail of the precipitation data. NASA PPS.
The testing continued
One aspect to consider was how to visualize the data in the end. There was even a 3D spinning globe in the process… As you can imagine it was chaos displaying flood reports, animated rain data, and 3D navigation all at the same time.
However, one of my favourite pieces was not the maps. There were some small graphics to condense powerful messages had something interesting too. Within them was this simple stacked bar chart where each block showed the total precipitation each month in Zhengzhou, just by putting the amount of water they received on July 20 next to it was really impressive. This is real evidence of how extreme our planet’s climate is becoming.
BTW, there’s also a great graphic from the South China Morning Post friends explaining the huge amount of water that Zhengzhou received over the downpours [ check that story here ]
Extremes
A few years ago I was working on a graphic about extreme temperatures of the earth, it was happening the 2019 polar vortex in the US and at the same time Australia was on 40° C on the other side. In my head, the perfect title was “Earth’s Goldilocks Climate.” It sounds crazy but it is actually very common, our planet is full of those strange contrasts all the time.
In July China was having its own ‘goldilocks’ event, or kind of, because wasn’t temperature. As enormous amount of water flooded train stations and caused chaos in Henan, south of there a nine-month drought hit Fujian province.
July total precipitation in China. Data by NASA PPS
Similar situations occurred in the Middle East, in Afghanistan a long drought was worsening the already difficult situation of the Afghans. Ironically, extreme rains in the border areas also caused flash flooding, while the country as a whole has not seen any rain for months.
July total precipitation in the Middle East. Data by NASA PPS
NASA’s MODIS/Terra offers also daily and monthly averages of surface temperature. This was some other stuff I was considering for this story. It’s incredible to see how high the temperatures go in the region. There’s also an other cool data set of monthly temp. anomalies here in case you want to explore the world too.
Temperature anomaly for Feb. 2021. Red areas show were the temp. was higher in comparison with the averages of 10 years ago. Afghanistan was about 12C warmer in average according to NASA Earth Observations data. LPDAAC and MODIS.
Anyway, none of these charts, maps or data made it into a true story on Reuters, but it was fun collecting, preparing and sketching ideas for it. And of course, in the end it became an average #infofails story here. Maybe later we will take back again this story, unfortunately extreme weather events are becoming more and more frequent
About #infofails post series: Graphics that are never formally published. Those are maybe tons of versions of a single graphic or some floating concepts and ideas, all part of my creative process. All wrapped up in #infofails, a compilation of my creative process and failures at work.
Did you like #infofails? Have a look to other #infofails 👇
If you’ve seen my #infofails before, you might already know that it’s about the things I create for news and some of the mess in between. Here’s the link [ The Forgotten Olympic Events ] of the project related to this post if you want to have a look first and then come back here to get a better sense of what’s this about.
It’s a bit strange to say that we are about to see the start of the Tokyo 2020 Olympics in the summer of 2021 (if Covid-19 allows the games to go on anyway) but whatever the case, this is one of the most popular events in the world and we couldn’t let it go without a special story.
The idea of doing a story on unusual sports came first in April from my editor Simon Scarr. Later, I started reading old Olympics reports, books and websites to get good references of events that seem a bit unusual in a list of sports/discipline/events from the International Olympic Committee.
Some of the reports and books consulted for the project.
Once we had a good list of curiosities, I started doing some tests for the illustration style. It is not usual to find a solution quickly as I like to try alternatives. However on this particular project the style quickly established, and a few days later I had a lot of illustrations with many of those sporting events to display in the opening of each section.
Top images are part of the concept illustrations, I choose to go with colour as in the top right. The 2 bottom images are part of events the were left out of the story.
I can say that part was relatively easy, the difficult thing was to obtain visual references of the events where you could see how things were prepared for each event, or at least, a detailed written description. I probably spent more time looking for those things rather than writing or sketching.
A reference of the firing range setup for the running deer shooting event
A detail of the rifle shooting section in the project
This project lasted about three months, however I didn’t dedicate myself exclusively to it, but I did spend a few days here and there trying to obtain reliable sources that would explain these singular Olympic events and their rules.
Some references were harder to find than others. For example, the way of placing the wooden birds for the archery events was one of those difficult ones.
I did a drawing of Hubert Van Innis, an Olympic archer who won six medals in the 1900 and 1920 games, but then I found mixed references on how arrows were shoot in the moving bird events, probably my illustration had the wrong pose. I think that’s part of the process, many times I corroborate the references and sources and all makes sense, then something else comes along and the piece becomes a wrong interpretation. So it’s important to check not once but as many times as you can.
Cherry picking
Apart from little difficulties like that one of the moving bird event in archery, the main problem turned out to be too many nice things. It was necessary to take a decision to prevent a never ending story so we took off some events in the list giving priority to the ones with good references and “colourful facts”.
Some events sound very interesting by name, so we chose them for the first list of curious possibilities. A good example of this is the two-handed discus throw, digging a bit further I found that it wasn’t a very creative way of holding the record with both hands as it sounds, but two discus throws, first using the left hand and then the right hand… 😦
Some of the events that I considered within the initial list of Olympic curiosities, but that didn’t make it to the final version. Among them, a 12-hour bike race and army pistol shooting.
It was a very fun story to do, a really enjoyed to read the reports and references. I filled out my mind for a few days of images of how the people saw the games and how different are today.
Here’s a crazy collage with some of the drawings of the project.
Some of the drawings of the project.
A funny project that looks like a huge illustration, but in fact, it has a lot of data behind, a lot of text/data documents, hundreds of old pictures and references of poses developed in 3D. One nice tool that help me a lot to create diverse poses was Magic Poser, this is open web-based 3D environment where you can set a model to any position you need. Here’s the link in case you get in trouble with anatomy or just need some help with pose models.
The folder with the production files of this project. I usually only have 4 or 5 projects on my computer. I guess otherwise it might not move at all 😆
About #infofails post series: Graphics that are never formally published. Those are maybe tons of versions of a single graphic or some floating concepts and ideas, all part of my creative process. All wrapped up in #infofails, a compilation of my creative process and failures at work.
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Marco Hernandez 