A detail of a map showing the Afar Triangle in Africa

infofails: how to fail at the 30-day-map-challenge in 2025

Every November map makers from all over the world join the challenge to create maps from a common thematic. The list of prompts is posted few weeks earlier in October I think. The initiative is similar to inktober, same idea but doing illustration which happens (yes, you guessed it) in October.

An image from Inktober2025 — An Illustration from Inktober2025 posted in the *inktober Substack*, [image by **@angelo_dimartino_art**]

A map posted to the *#30daymapchallenge* 2025 [ A map by **@Michael Pincus** ]

Both are things I’ve follow all the time and really enjoy; however, I never had the courage to join either for one simple reason: my obsession with commitments.

I love the work the creators do for both initiatives; I deeply admire people who can take one of these opportunities and successfully made it.

In October I told my family that I was thinking of joining this year’s map challenge. My son and wife saw me and said, “Okay, how many maps do you have already?” to which I enthusiastically replied, “One… maybe”. They know my obsessions so well that no more words were required.

😒

After a laugh, I told them I was worried about starting something like this because if I didn’t finish it, I’d be stressed. The days went on, and when November arrived, I didn’t have many more maps, so my attempt to join the maps celebration turned into one more episode of Infofails. And then here’s how it happened:

Day 1

The excitement was through the roof; my plan was to create a different styles for each map, I could also work on a different region with each day theme… Yeah, what a wonderful plan.

To kicked off, I went with a in minimalistic vector style, maybe places in the middle east? –I thought– So, I downloaded data points using the QGIS geoparquet plugin and set out to funny thematics.

Day 2

Second day theme was lines, so what about visualizing trains network in NYC? Here’s what I ended-up with:

But then, just like in cartoons, that little voice over my shoulder started saying: Don’t you think this wants to experiment a little with animation? Nothing complicated, perhaps one of the things you already have installed like MMQGIS… And so everything began to go off the rails.

Day 2 (take 2)

I turned that static map into a sequence of images using MMQGIS. With not much sense actually, but who cares? It’s all about having fun, isn’t?

Day 1 (take 2)

Then I thought, hey, I also have more points showing other things for that one map of the UAE, what if I revisit day one as well? So instead of having one map for day one and one for day two, I had 20 frames for the NY trains, and 12 maps of the UAE showing different things. Here you have some of those maps showing fun stuff:

Okay, no problem, there’s still plenty of time, I thought. Yes, of course, I’ll keep my anxiety quiet and make it simple with the others.

Day 3

The next challenge was about polygons, so I chose to explore the biodiversity of Central America.

I think learning things is the fun part of doing these maps. To explore biological diversity the Conservation Biology Institute offers a great dataset. I actually learned about a thornscrub area in Guatemala that I didn’t know about before.

^{Detail showing the Motagua Valley thornscrub (in gray)}

After pushing the limits a bit too much in the previous two maps, I kept this one pretty straightforward. Perhaps the #30daymapchallenge is about doing simple things that don’t require you to spend days and days searching for something.

Day 4

Day four was about personal data, this was fun because I learned how to pull out data from my Google Maps app, simple yeah, but I never thought about this before.

I exported my data and grabbed all the locations where I launched google maps. Fun stuff, I selected 5 cities I visited this year and mapped out where I pulled my phone out to see a map and start navigating. Fun and creepy, not sure if you want this data to be publicly out even in an abstract way like in the map below, but it was a fun exercise.

Way too much fun. Again instead of 1 map, I ended up with 5.

Day 5

For this one I wanted to do something in Africa, I started to explore the topography near Somalia in the search for a singularity with data enough to map.

After looking a different sources and things from here and there, I decided to map the Afar Triangle, an extreme corner of the continent in the great rift valley in eastern Africa. There, a triple junction of plates pulls in opposite directions and different speeds, just to mention one extreme thing happening there.

Here’s my map of the Afar triangle:

The northern point of the triangle is below 100m under the sea level, it’s also the hottest place on Earth and it has hypersaline hot lakes. What a place, it deserves more maps than I can do.

The region is also home of the oldest human ancestor, Lucy who died and probably lived its life in the Afar Triangle some 3 million years ago.

^{A detail of the Afar Triangle map showing the Dallol volcano in the picture further up in this post.}

So far so good, I was having so much fun learning new things and procrastinating like a pro, then I looked at the calendar and November was almost here! 😳

Day 6

I was feeling a little more confident I thought I have learn the lesson with distractions. Maybe it’s not a big deal, maybe I take these things too seriously, because the thoughts that I’m running late or failing constantly haunt me. Sometimes I can’t sleep thinking I’m falling behind. Non sense I know.

As November approached, I made day 6.

A map with dimensions to show political violence in Myanmar. Visualizing reports from ACLED to a 10km base grid.

Then I thought about looking at the other side of the coin; that map in red above represents violence perpetrated by state forces, so maybe I needed another map showing the counterpart…

Or perhaps I didn’t need two maps, but a single map that combined both reports. Then I noticed I had mixed up the data and perhaps I should start over with this map…

By this point I knew I‘ll never make it, so I decided to leave this one for a quick review later and bring it on to funtography.

I posted some there already if you want to see them bigger or with a little more context.

Day 7

By day seven I reached the breaking point.

The calendar said November 3rd, and I hadn’t finished anything, I didn’t like that much the first 6 maps I did, and I was walking in circles thinking on how to re-do all of them.

Feeling discouraged and frustrated, I gave free rein to one more idea—something between a poster and a map. I looked for train stations in several cities and finally decided on Berlin just to try a different location with available data.

My idea was to see where I could go within a 250m radius in a wheelchair using trains. I’ve never been to Berlin, so probably my idea is in the wrong approach, but I thought of dividing the city into 250x250m squares.

It’s very likely that wheelchair users travel distances greater than 250 meters (approximately 270 yards) as part of their routine, but it must be a tremendous effort to do so for half a kilometer (just 2 of my squares).

^{Green indicates accessible stations, red inaccessible stations, and gray areas are built-up areas that are out of reach.}

There are some adjacent areas in green which is good, but also many red squares, meaning that if you go there in a wheelchair it’s almost a 50-50 chance that you’ll be able to get out of there where you need to. In short, it seemed to me that if you are limited to one or two squares, in the big picture, it’s more about where you can’t go than where you can go.

Here’s a version with a bit more context, a heart-shaped areas representing blocks with accessible stations, Black circles marked with x are stations without wheelchair access; all other x’s are areas where there are buildings, but without nearby access to a station. I added little dotted lines to the adjacent 250m blocks with accessible stations.

And the whole area with the broader region:

It must also be considered that the demand to access these places is not the same; the areas with more hearts, near the city center, are accessible and perhaps are the areas that people need access to the most.

For all these reasons, I abandoned the idea, got stuck trying to solve it, and ultimately ended up killing the idea of entering the #30daymapchallenge on time for each day.

The lessons I learned from this:
[ 1. ] Keep your ambition in check if you want to achieve something bigger little by little.

[ 2. ] Avoiding procrastination is difficult, especially when you’re passionate but not good on something.

[ 3. ] An finally, I need more free time somehow. 😂

About infofails post series:
I believe that failure is more important than success. No one sets 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… and I fail frecuently:

01: Wildfires
02: Plastic bottles
03: Hong Kong protest
04: The Everest
05: Amazon gold
06: The world on fire
07: A busy 2021 kick off
08: Olympics
09: Floods
10: Doodles for news
11: Random Failed Maps
12: The Mismatch
13: Mapping Taiwanese Food
14: Mapping Damage in Ukraine
15: Mapping destruction across the U.S. in 2024

blogging, Tutorials

Tutorial: How to create voxel-styled maps like this one

In November 2024 I launched a new website to put into a single place all the stuff on which I spent my time at work and in parallel activities like sketching things or mapping for fun. That last point was a fun thing I started to rescue from my external drives files that doesn’t fit into work nor doodles either.

Part of the plan for a new website is to have a space for these things that are lost in space/time. One of the things that makes me happy is making maps: mhinfographics.github.io/maps.html
— Marco Hernández (@mhernandez.bsky.social) 2024-11-08T22:52:21.649Z

Eventually I was able to hang there a few more pieces, all in different styles from personal explorations of data and tools. This tutorial meant to explain how I used QGIS, Blender and little optional touches of Photoshop and Illustrator to create a voxel like map like the one below:

Please note that I’m doing this in good will, I’m not offering any corporate endorsements of the following procedure, software companies or resources, even on open source resources I mention here. Likewise I’m offering this without any warranties or compromise of further assistance.

🎁

Follow me step by step or get the files ready

Here you can find the files ready to use, download the packs and set you render to go, or follow me below to produce the data and render your own. If you are downloading the files, skip to the section titled “Set up the render” and choose either plugin or image input.

Defining the AOI and getting data

I usually start defining the are I want to work on adding first an xyz layer on my blank project on QGIS. If you don’t have that set up, check this link first, and then come back, it’s a quick set-up anyways. Once you have that done, zoom into the area you want to map, create a new scratch layer to define your area of interest (AOI), we will use it as a reference for many things.

This’s the area I choose for the map above, a rectangle of roughly 120×200 km near the Lake Baikal.

Next let’s download some DEM data for the project here https://download.gebco.net/ you will see a panel to the left with some options like in the image below, select a GEBCO data version, enter the coordinates as 52.7 for top; 51.4 for the bottom; 105.11 for left and 108.37 to the right, that would be enough to cover our AOI. I’ll use a ASCII grid, but Geotiff is also ok. Click “Add to the basket” then “View basket” and download the data.

Sampling a grid

Now we need a grid to sample our new base, in QGIS go to vector/research tools/create grid. That would give you a pop-up window, select Rectangle for the option “grid type”, on grid extent use the drop-down menu to select the AOI layer we created earlier:

We want each rectangle to be 1km wide so select “Horizontal spacing” as 1 “kilometers” using the drop down menu, the default is 1 “meters” if you are using a EPSG:3857 projection. Do the same for the “Vertical spacing” option, we want squares of 1×1 kilometer.

Finally, we want some overlapping to prevent gaps in the render, so add “10” meters to the Horizontal and Vertical overlay.

Keep the CRS as the default EPSG:3857 and save the grid as a geojson using the dropdown menu option, your panel should look similar to this:

Once you click “run” after a few seconds an army of squares will fill-out your AOI, if you can see them, go to the menu Vector/Geometry Tools/Centroids to create points that would sample our DEM data. In the input layer choose the 1km-grid file we just created and leave the rest as it’s, that would give us a bunch of dots in the center of each square in a temporary layer.

Re-project the data

To extract the data samples, we will to make the GEBCO layer and the new centroids in the same projection. I’m using a pseudo-mercator projection EPSG:3857. But you can use whatever you prefer, just make sure both layers have the same projection.

GEBCO default projection is CRC84, is you are not sure what you layer projection is double click the layer and selection “source” that would show you the projection. If you need to change it, click the layer name, go to the menu Raster/Projections/Warp and create a temporary layer using EPSG:3857 as target, or what ever your preference is.

Sampling the new grid

We will use a plugin to sample values from our raster into each dot, if you have the Point sampling tool plugin you’re ok, if not go to to the menu “Plugins/Manage and install…” click on ALL in the left panel and type “Point sampling tool” in the search field, install the plugin and close the Manager window.

To run de plugin go to the menu Plugins/Analyses/Point sampling tool in the pop-up window, select the centroids with the dropdown menu, and in the lower field click the GEBCO layer, finally click on Browse and navigate to your project folder and name the result file 1km-grid-zvalues.shp using the shapefile option there.

If you reprojected your raster, this is how the plugin window should look.

Once you executed the plugin, remove all temporary layers, you should have a layer with the new dots named “1km-grid-zvalues.shp” one layer with our 1km-grid, the AOI layer and the xyz google map layer.

Next step is to create a spatial join, basically we will take the value of the dots to be the z value of each polygon in our 1km grid. You can use the QGIS join function from the menu Vector/Data Management Tools/Join Attributes by location, But be aware that would take a little while to process. I recommend to use a little custom python script instead. But to use it, you would need to export our 2 data layers out as geojson files, right click on them and select “export as”, while doing that you can turn off all the fields but the ID in the 1km-grid.

Save them into a new folder, I’ll call mine “intersection” and place this script next to it, name it whatever you like, but use .py so you can run it from your terminal window as ” python intersect.py “

Your folder with the following script and new geojson files.

import geopandas as gpd

# Load the geojson files
zvaluesLayer = gpd.read_file('zvalues.geojson')
grid_layer = gpd.read_file('1km-grid.geojson')

# Ensure both layers have the same CRS
if zvaluesLayer.crs != grid_layer.crs:
    zvaluesLayer = zvaluesLayer.to_crs(grid_layer.crs)

# Check if 'zvalues' field exists in zvaluesLayer
if 'zvalue' not in zvaluesLayer.columns:
    raise ValueError("The 'zvalue' field is not present in zvalues.geojson")

# Perform spatial join
joined = gpd.sjoin(grid_layer, zvaluesLayer[['geometry', 'zvalue']], how='left', predicate='intersects')

# Remove duplicated features
joined = joined.drop_duplicates(subset='geometry')

# Save the result to a new geojson file
joined.to_file('1km-grid-zvalues.geojson', driver='GeoJSON')

Remember you can run this file from the terminal using the commend ” python intersect.py ” while in the folder we just created. Once you run this script a new file would be created next to the script, look for 1km-grid-zvalues.geojson and drag-and-drop it into QGIS.

Style your new grid

You would only need this new files we just created and the AOI layer, remove the rest of the layers if you like to have a clean panel before to continue.

Double click the new 1km-grid layer, go to symbology and select Graduated in the top panel, set it to gray scale, use the mode natural breaks with some 50 classes, in the Value field pick zvalue and click apply. (be sure the negative values are assigned to black, if they are on white invert the ram using the dropdown menu.

We are almost there with data process, here comes the fun part. We need basically 2 layers of data, this black and white layer we just created will serve as base for terrain, create a duplicate of it to make a color layer.

Double click that copy and go again to Symbology, but this time in the color ramp menu select “create a new color ramp, in the pop-up window you will see a few options, change it from gradient to “Catalog: cpt-city” and click ok. Navigate to Topography/bathymetry and select “colombia” that’s a blue-green-brown pallete that would assign a color range to differentiate our terrain from the lake.

You can also create your own gradient and make the value ranges as you like if you want to ink all the water blue for instance, but for the purpose of this exercise all keep the same classes and set up as we did for the BW image. (50 on Natural Breaks). You should have now a black and white layer and a color copy looking something like this:

Set up the render

So, here you have 2 options, one is to export tif images out of QGIS to render the tiles, or export a shapefile to be used with the GIS blender plugin.

Option 1: Using the GIS plugin

If you are using the Blender GIS plugin, click in your 1km-grid-zvalues layer, choose export and select ESRI Shapefile. I have saved a copy of that file in the assets folder, the link is at the top of this page. The follow the instructions here to setup the plugin. If you need a litle more of explanation of how it works, this video has a great walk thru.

Once you are all set up with the plugin, you can import the shape file using the 3D viewport in object mode, use the GIS option there to get the shapefile in, just brow for the file we exported earlier from QGIS:

A little pop-up window emerges usually near the bottom right of you screen, you should select “extrusion from field” use the drop-down menu to find “zvalue” and apply it to the z-axis, leave everything else off, you don’t want to check “separate objects” because we are using a large scale object with a lot of polygons, that option come handy if you are importing smaller data sets to handle objects individually.

Once you are all set, you can create a new material for our terrain, use the z-coordinates to apply a color ramp, you basically need a “texture coordinate” node, a “z” selector, the ramp, which I basically copied color-values from QGIS, and link all that to the default material, here’s a little visual of the wiring:

Once you added a light on your preference angle and intensity, your render should look like the one below, if wyou want to save some time you can use the demo I left for you, I have there 2 cameras, one perspective camera for a close up, and one orthographic camera to get the whole map. Note that there are also 2 lights depending on what you want to render, but that’s just a personal preference.

Option 2: Using images from QGIS

If you opted not to install the plugin and render images, go to the folder named tiff-based. Let’s get those images out of QGIS first:

Enable only your AOI layer in QGIS, then go to the menu Project/New print layout and add an xxx name there, I did “selenga” for mine. You would get a empty layout window, select the “new map” feature in the left panel, click and drag in the canvas to create a preview, set the scale to 1110000 and turn off the background and frame options.

That set-up would give you a full end-to-end raster, turn off your AOI layer and turn on the color layer you have there. Export the layer as a tiff image using the picture icon next to the printer in the print layout. The do the same turning off the color, and turning on the gray scale image. Save both files next to your .blend file, i have called them selenga_color.tif and selenga_terrain.tif

A little trick to prevent weird gaps between the tiles, even if they don’t have gaps the grid effect seems to create lines between the tiles, to prevent that run a buffer clicking the grayscale image, go to the menu Vector/Geoprocessing tools/Buffer add some 10m in the distance field set the caps to flat and miter in a temporary layer:

Leave that layer under your grayscale, copy and paste the styles to the buffered layer, leave that on only for the grayscale image before exporting. I have left both images in the demo folder so you can see the difference on rendering later.

Go to Blender

We are all set, now, go to the blender template. A few things you should be aware first, this blender file uses cycles render engine to get nicer shadows, and its set up to experimental. I’m using v 3.6.4. The print resolution (in the printer icon panel to the right) is the same as the original images we created in QGIS: 4677×3307 pixels. The plane used to load the textures should be scaled to fit those images too, so in the orange icon I have scale X as 4.667 by 3.307 note that’s correspondent to the image physical size. Finally in the camera, I’m using an orthographic camera, click the camera in the collection and then the little green camera icon in the panel below, use the scale field as double the size of the image, you can type there 4.667*2 so your camera resolution matches the plane size as 9.334. Keep that in mind if you want to use another image with different dimension, its all about the original asset.

Navigate to the textures folder and replace the color and displacement terrain accordingly in the shader editor.

Your final render should look like these using the perspective-camera:

Or this if you are using the orthographic-camera:

Keep in mind I used photoshop to tweak a little the color hues in the original map I did in Fun-tography, I also added labels using illustrator. The nice thing about using the orthographic camera is you don’t loose the georeference from QGIS, so if you export labels from there you will get them in the accurate position into illustrator, Inkscape or any other software you use to add vector details on top of the renders.

I hope you can create beautiful maps, go crazy and do taller columns, play around with the color ramps… have fun!

Even if you don’t use this complex approach, you might gain experience familiarizing yourself with super powerful tools like QGIS and Blender.

Happy mapping!

Digital pieces

2023: The year in graphics

As we prepare for the next year, it’s always nice to remember what we spent our time on during the latest Earth’s lap across the orbit. So, here’s a collection of my favorite details at work and other things I have done in 2023.

January: Wild weather

The beginning of the year was full of extreme weather events. We covered some, including the flooding in California, I was immersed in the coverage of the war in Ukraine, but, I did some maps that were not published, among them, a flood map showing the extent of the water using SAR data, and a count of buildings affected by the flood.

January was a month to think about all that I do, and no one sees. I thought that maybe, some of that stuff could be helpful to students or other professionals who are starting their careers. So I made a post showing how to visualize global temperature with data provided by NASA. Quickly I started to receive questions about it, so, I did my best to answer them all. Thanks all of you who reached out.

A map in Atlantis projection showing Surface Temperature Averages at 8am, Jan. 4, 2023

Later I learned some journalists around the world published stories with visualizations using my tutorial. It was very nice to know that I was able to help and I would love to do more of this next year when my other responsibilities allow me.

––––––– :::: 🗓️ ::::: –––––––

February: The next stage of the war in Ukraine

At the beginning of the year we were trying to find clues about where the war in Ukraine was going. Military analysts gave us some opinions as to what the objectives of both sides could be for the spring, although none of the forecasts were accurate, we were able to create a series of maps explaining the aims of both sides.

I always make basic maps and draw things on top of them, like when you use a marker to explain ideas to your colleagues. My editor saw that and convinced me to publish a version of the maps with marker-like styles. You can see the piece at the following link without a paywall: The War’s violent Next St age.

––––––– :::: 🗓️ ::::: –––––––

March: War and Basketball

Earlier this year, all eyes were on Bakhmut. I did some pieces about how the Russians were slowly advancing from the east towards the city while the satellites captured the destruction caused by the fierce battle that the Ukrainians fought trying to maintain their positions.

But small projects also come while we are doing some other long or mid-term stories, like this one I helped with to cover college basketball player Caitlin Clark. The main visuals in the piece are photocompositions showing several of the player’s many shots. The images show the distance and position of each shot as if it were captured in a single photo.

––––––– :::: 🗓️ ::::: –––––––

April: The fierce battles in the Donbas

After months of pouring resources into the war, we analyzed the progress of Russia in Ukraine in 2023, the declarations of taking over the Donbas were not accomplished, and in fact, the progress was reduced to just 4 small settlements. I particularly liked the concept of inking the streets according to whether they were either Russian or Ukrainian held, highlighting the urban areas a little better.

You can read the piece at here: How Russia’s Offensive Ran Aground.

––––––– :::: 🗓️ ::::: –––––––

May: The fall of Bakhmut

In May, the battle for the city of Bakhmut that we had covered for almost a year finally reached a favorable outcome for the Russians when they managed to take the center of the city. We made a series of maps showing how the Russian forces slowly moved to engulf the city.

––––––– :::: 🗓️ ::::: –––––––

June: A busy month of publications

Since I joined the Times team, on average, every month I have published two or three stories. But June of 2023 was a pretty intense month, I reported on breaking news like the train crash in India, the Kakhovka dam breach and the smoke from the Canadian wildfires. I also did other mid-term stories like the tragedy of the submersible that imploded in the Atlantic while trying to visit the Titanic wreckage, I also reported on the sudden rebellion of the Wagner forces marching towards Moscow, and another story about people using little figurines to protest against war in Russia through Instagram.

Almost at the end of June, we published an analysis of the challenges that Ukrainians face in their counter-offensive in the south of their country. The story shows some of the obstacles along a strategic 21-mile stretch that the Ukrainians must navigate if they wish to make their counteroffensive effective.

––––––– :::: 🗓️ ::::: –––––––

July: Paper editions

The print edition always comes after we publish our stories online. And since the story of those 21 miles of obstacles was published near the end of June, it wasn’t until July that the paper version was published with the help of the print editors.

July was also a special month, I also received my copy of Nightingale magazine, where I contributed with an article about my reflections on things I have learned in these twenty years making graphics in different media/continents.

It was an honor to be part of #NightingaleInPrint 3rd ed. Thanks @DataVizSociety for inviting me. pic.twitter.com/E9CGTrb36Y
— Marco Hernandez (@TmarcoH) July 14, 2023

––––––– :::: 🗓️ ::::: –––––––

August: Incognito mode

August was a month completely dedicated to a long-term project I’ve been working on for over a year now. As I write this post at the end of November, my hopes are that we will be able to publish very soon. I think once we publish I’ll be able to make a long #infofails post with all the stuff we won’t use including things like this:

But since I still can’t share much about it, I leave you an image of something else from August:

^{_{One of August’s entries of my weekend hobby at}_{sundaysketchbook.art}}

––––––– :::: 🗓️ ::::: –––––––

September: Natural Disasters

In the first week of September, an intense earthquake hit a mountainous region south of Marrakech, causing extensive damage to villages and leaving a high number of deaths and missing persons. During that weekend we made a series of maps to report the situation with various updates as we received updates.

Just a few days later, a storm system over the Mediterranean Sea wreaked havoc in Libya, especially over the city of Derna where shocking images showed how the water swept away the city. The rains were so intense that huge temporary lakes formed in the desert, some big enough to be seen from space.

––––––– :::: 🗓️ ::::: –––––––

October: NY + Amsterdam

Reporting and preparing projects on Ukraine continued throughout this month. However, I was able to take made a quick pause to participate of Information Design Conference in Amsterdam for a few days. I shared a little about the work we do at the New York Times and my personal interests while I’m away from work.

But I have to say that the best part was the wonderful people I had the opportunity to meet there, including some well-known names in the data visualization field based in the Netherlands.

––––––– :::: 🗓️ ::::: –––––––

November: The war in Gaza

A conflict that has been active for many years intensified at the end of the 2023. It is a really complex and delicate topic, so it’s not wise to provide opinions behind it. However, in November we took the initiative to gather information to report on the structures that are under Gaza and that have been documented by various sources including both sides of this conflict.

From the perspective of military experts, the piece shows the particularities of the tunnels in Gaza.

November also brought a very special moment for me as Ball State University invited me to their campus in Muncie, Indiana to receive the great honor of the Majeri Award.

Even though my trip began by forgetting my phone in a taxi in New York before entering the airport, the conferences with the students and every moment of that intense week were memorable.

––––––– :::: 🗓️ ::::: –––––––

December: A chance for a snowy xmas

I’m not sure how intense this winter will be, I wrote this post at the end of November and we already saw a few snowflakes this week in New York City. Last year we barely saw snow in New York, the winter was very mild here, but maybe there is a chance for a white Christmas after all this 2023. I collected data from the US National Ice Center (usicecenter.gov) to make the map below showing the extent of the snow. The purple spots are population density from the SEDAC (sedac.ciesin.columbia.edu).

I promise I’ll make a tutorial to create snow visualizations soon. I did those maps above to see how the snow is already dancing around the north pole, but maybe we can run a story on snow if this winter turns out to be extreme (hopefully not).

Snow map November 30, 2023. US National Ice Center

Anyway, that was my 2023 in graphics.

Once again we say goodbye to the year, but before I switch into holidays mode, I want to thank all my colleagues at the Times, this was a great year, I couldn’t be more grateful to be able to share my days with all of you.

And to you all my www-friends, I wish you the best in this new beginning. Happy New Year!

See you all in 2024, Merry Christmas!

Some of my old collections

mhinfographics: 2022: My Year in Graphics.
mhinfographics: 2021: My fav little details of 2021 on Reuters.
mhinfographics: 2020 in graphics: My favourite little details.
mhinfographics: 2019: My sincere gratitude to the South China Morning Post.
The New York Times: 2022: The Year in Visual Stories and Graphics.
Reuters: 2020 – The year in gr a phics.
South China Morning Post: 2018 highlights.

blogging, Tutorials

Tutorial: Visualizing global temperature step-by-step

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:

Visualizing organic carbon in near real time. –A step-by-step guide

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.

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”
Open your terminal window
Type cd in the terminal window followed by an space
Drag and drop the folder you created inside the terminal window:

Then copy+paste the following command line in your terminal window and hit enter:

curl https://portal.nccs.nasa.gov/datashare/gmao/geos-fp/das/Y2023/M01/D04/GEOS.fp.asm.inst1_2d_lfo_Nx.20230104_0000.V01.nc4 -o 20230104_0000.nc4

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.nc4
01:00 AM >> 20230104_0100.V01.nc4
02:00 AM >> 20230104_0200.V01.nc4
03:00 AM >> 20230104_0300.V01.nc4
...and so on...
08:00 PM >> 20230104_2000.V01.nc4
09:00 PM >> 20230104_2100.V01.nc4
10:00 PM >> 20230104_2200.V01.nc4
11:00 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.

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:

# QGIS Generated Color Map Export File
INTERPOLATION:INTERPOLATED
224.0615386962890625,14,17,21,255,224
250.69161088155439643,80,122,146,255,251
266.87675076104915206,235,238,217,255,267
275.3270921245858176,225,213,143,255,275
285.49591601205395364,214,155,59,255,285
293.66160637639046627,187,80,30,255,294
298.05635745871836662,170,33,23,255,298
308.53047691588960788,58,14,11,255,309

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.

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:

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:

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.

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.

Your animation should be smooth and nice, something similar to this great story from NASA’s Earth Observatory

2022 ended with a shiver, but the start of 2023 broke a sweat. 🥶🥵

This weather whiplash may have been caused by changes in the polar vortex, the band of strong westerly winds over the Arctic. Details: https://t.co/gnOAqDOncS pic.twitter.com/4QDmgsNsE5
— NASA Earth (@NASAEarth) January 5, 2023

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 #DataVisualization pic.twitter.com/62CO1lMVdH
— Marco Hernandez (@TmarcoH) January 6, 2023

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 or Mastodon I 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:

gdal_translate -of netCDF -co WRITE_BOTTOMUP=YES NETCDF:"/Users/marco/Desktop/input-original.nc4":TLML your/directory/output-file-name.nc4

***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.

blogging, Tutorials

Visualizing organic carbon in near real time. –A step-by-step guide

Long time ago someone on twitter ask me to do an explainer on how I did the “smoke” animations for this 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.

Let’s talk about this wonderful data first

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
Open your terminal window
Type cd in the terminal window
Drag the folder you created inside the window

A “*cd folder”* example stolen from the web.

Then run a short command like the following, you would get a file named 20220619_0130.nc4 in you data folder:

curl https://portal.nccs.nasa.gov/datashare/gmao/geos-fp/das/Y2022/M06/D19/GEOS.fp.asm.tavg3_2d_aer_Nx.20220619_0130.V01.nc4 -o 20220619_0130.nc4

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.nc4
04:30 AM >> 20220619_0430.V01.nc4
07:30 AM >> 20220619_0730.V01.nc4
10:30 AM >> 20220619_1030.V01.nc4
01:30 PM >> 20220619_1330.V01.nc4
04:30 PM >> 20220619_1630.V01.nc4
07:30 PM >> 20220619_1930.V01.nc4
10:30 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:

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:

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:

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

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:

Click the paper icon, then click the folder icon and select the python script you dowloaded above. Just be careful with the filePath option.

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.

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.

Happy mapping!

blogging, infofails

Random Failed Map Details

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.

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 ]

Roads network around Mariupol and Berdyansk

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.

Close up of rivers, lakes, canals and streams in Ukraine

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.

As of the date of publication of this entry, I have worked on about 15 pieces with some kind of map of some region of Ukraine analyzing all kinds of approaches, such as the strategy to isolate Ukrainian forces in the east, aerial bombardment, the damage in the port city of Mariupol, and fighting reports all in a day cycle. More recently we have focused on deeper stories like the battle in the Donbas region that we just published where most of this entry’s images came from. (There are some more map stories coming soon).

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:

Doodles for news.

Airplanes, weapons, vehicles and other military equipment are part of the tenth entry of #infofails. Some pieces stored under my rug https://t.co/m7IL4gmO3c #Doodles pic.twitter.com/hFzTJ1d4hk
— Marco Hernandez (@TmarcoH) October 15, 2021

blogging, infofails

🎵 I can see clearly now the rain is gone…

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.

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 👇

1: Wildfires
2: Plastic bottles
3: Hong Kong protest
4: The Everest
5: Amazon gold
6: The world on fire
7: A busy 2021 kick off
8:Olympics

I accumulated about 7GB of files to create the Olympics project we published this week… more than a few illos there 😁 #infofails a little of the backstage here: https://t.co/1FfwN62qyO pic.twitter.com/BhK2dUX1jE
— Marco Hernandez (@TmarcoH) July 2, 2021

Digital pieces

Nerd facts: Asian American population

US elections are coming and here in Hong Kong we want to see it from an Asian perspective. Some weeks ago I start this project to collect the data of Asians in US and visualize it.

The map in first instance is nothing more than a choropleth map, I start from a black map as the one in the top, then one by one I manually add a grey value in five clusters, white for 0% and so on to +25% as the dark grey.

The US Census Bureau has a very specific data for Asians in US, but I use data also from the APIA and AAJC Asian organizations in US as well, the research phase takes me about a week, producing the graphics and coding it was about four days, and about an other week making edits and corroborating each small graphic and each word in the special feature. I must highlight that this graphic was not just a map, each state in the US has a detail of text and graphics so it was not ease to check, edit and revise.

Early version of the country over view. As you may note, beyond color the elevation is different, my first data set was for total population instead of percent of the population

The map was generated with Cinema 4D, based on a grey scale county map, then styled with Illustrator and photoshop and coded with Brackets io trough SVG, Jquery, JS…

This slideshow requires JavaScript.

The color code is very important because the position of camera will change in perspective, so the color make easier to visually determinate the height of each area no matter the position. In the following image the camera position affect the way we perceive the height of the counties, just because the matter of point of view, but when color is on… well, see your self:

Brown means Asian population over 25% in the specific county (as well the height), I call that to perceive data in multidimensional ways, but keep it understandable

The position of cameras will change for each state, as for the videos and other sections using the specific portions of the map to tell different stories.

Following the 3D idea, the complementary graphics was also generated in C4D and the layout phase begin.

Some graphics display scroll-trigged video on desktop versions, then for mobile switch for static renders because IOS don’t allow autoplay videos or video out side the native player

This is one of the city spinners, all of them uses an image like this, is positioned by the mouse drag final position and makes the 3d effect

Hope you as reader enjoy the project much as I enjoy to make it, please visit the special feature here http://multimedia.scmp.com/news/world/AsianAmerican/

Digital pieces

Pokemon invasion

The Pokemon fever runs out the whole world, here in Hong Kong the people also was infected with the Pokemon global madness, so we in the SCMP graphics want’s to offer something different to our readers, and that was a detailed map of the Hong Kong Pokemon spots and a plus graphic about the Skills and weaknesses of each class of Pokemon.

The map was a hard work, to get all this data the folder of Pokemon project reach about 12 Gigabytes of my computer, but off course the final application on line is just about 500KB, I catch this information from an other game in which the creators was based to create the Pokemon virtual world, the whole process takes me 5 days including the new design and the development phase.

When I finish the data catch phase and clean it, the points shows a really nice pattern, following the urban areas but also reveals by him self the Islands shapes.

10% map sized of the original data. In the bottom right corner the most concentrated area of Pokemon spots, corresponds with the Hong Kong Island districts and the Kowloon territory crossing the bay area.

My first design was a purple points for the spots over a grey map, to give it a high contrast, but, then turns green and blue to keep it closer to the actual game interface. Usually I wrote my own text, but as you may know my english is not the best so, the layouts and the final product changes a bit on that also.

Illustrator layouts for Pokemon project. Version 2.2

The bottom section has a graphic about some characteristics of each class of Pokemon, the dropdown menu allows readers to switch the class and display different graphics, I have to say that I don’t like the Pokemon at all, even when I was younger, but I love to create data visualizations and offer to the people an other way of stories… visual stories; so Yes, I enjoy this project much as the Pokemon lovers has enjoy it also.

All the small graphics for Pokemon skills. Red for attack, blue for defence.

If you go inside a region of the map, the graphics will show you how dense are the spots in that area, how many spots are there and how big is in square km, ho! and also will allow you to zoom in just hovering the mouse in the map.

Area in detail, you can access by clicking areas in the main map, clicking the half circles of the top graphic, or choosing an specific region in the drop down menu.

You can find and enjoy the infographic in this link http://bit.ly/2aFU7W8

Digital pieces

The Hobbit: The Battle of the five armies

The gate of Erebor. 3D Model by Edgar Jiménez; Art direction by Marco Hernández and Manuel Canales.

In the eve of the final movie of the Hobbit trilogy, we decide to give to our readers at nacion.com a new perspective of the scenary. Edgar Jiménez an terrific 3D artist, Alexander Sánchez the journalist of films and myself works together to develop this special site of the Hobbit’s Lonely Mountain, the field of the Battle of the five armies.

This its part of the promotional pieces:

nacion.com post for social media

This site its a scroll controlled experience that allows to explore the Lonely Mountain and the city of Dwarves, Erebor. There you will find, besides, the 3d model, short texts hidden about key points of the movie. Hope you will enjoy it!
Here’s the link: http://goo.gl/8TMVKw

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Day 1

Day 2

Day 2 (take 2)

Day 1 (take 2)

Day 3

Day 4

Day 5

Day 6

Day 7

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🎁

Follow me step by step or get the files ready

Defining the AOI and getting data

Sampling a grid

Re-project the data

Sampling the new grid

Style your new grid

Set up the render

Option 1: Using the GIS plugin

Option 2: Using images from QGIS

Go to Blender

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January: Wild weather

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February: The next stage of the war in Ukraine

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March: War and Basketball

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April: The fierce battles in the Donbas

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May: The fall of Bakhmut

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June: A busy month of publications

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July: Paper editions

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August: Incognito mode

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September: Natural Disasters

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October: NY + Amsterdam

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November: The war in Gaza

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December: A chance for a snowy xmas

Some of my old collections

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About the data set

SURFACE TEMPERATURE

Where’s the data? and How it’s named?

Step 1. Get the data

Step 2. Loading the data into QGIS

Step 3. Styling your map

Step 4. Preparing to export your map

Step 5. Export your map

Step 6. Color key

Step 7. Setup and export your animation

Update

Using gdal to convert data to 180-180

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Let’s talk about this wonderful data first

ORGANIC CARBON

Step 1. Get the data

Step 2. Loading the data

Step 3. Styling your map

Step 4. Preparing to export your map

Step 5. Export your map

Step 6. Export your animation

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Basic vectors

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The testing continued

Extremes

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