This might be a long post, it was a pretty challenging project.
I wrote last week about my Amazing Animals in Crisis map of 50 endangered species, I got some bad news on Friday that my usage of the IUCN polygon data was in breach of their licencing conditions and a request that I take the map down, which I promptly did. This was a big disappointment as I was just about to expand the map to almost 400 species and now I had no data.
Learning – even if data is apparently free and open, carefully read the license conditions before you use it. Obvious, I know, but I had to learn the hard way.
Along with the request to take down the map, there was a glimmer of good news that I could use the assessment data that I had requested for the Amazing Species as long as I correctly cited all the authors of the individual species assessments. So I had a lot of text about each species but no spatial data, how could I make a map? I briefly considered extracting the country names from the range info for each species and building a country level choropleth but that would have been very misleading as some species are only found in a small part of a country and I could not think of a way to portray the coverage of marine species.
Then I remembered that when I had been researching data sources for the original map that Claude had identified the Global Biodiversity Information Facility (GBIF) as a possible source. GBIF has hundreds of millions of point observations of species, both human observations and automated tracking data from satellite and GPS tags, they include coordinates, lots of useful metadata and in many cases links to photographs. Most importantly the GBIF data is open and licenced as CC0, CC BY or CC BY-NC and the data was accessible through an API.
It’s important to understand the difference between the GBIF occurrences data and the scientifically researched species boundaries available from IUCN and I hope the explanation in the info panel on the map explains this:
- “This is a map of where people have found and recorded the species, not a map of the species’ true range or population size — a well-visited national park can look “busier” than a remote area the species is actually just as common in, simply because more people are there to record it. Birds and fish can also turn up well outside their usual range — a storm-blown vagrant, a fish following a current — so an isolated sighting far from a species’ other hexagons isn’t necessarily a mistake.“
With several investigations of the data and tests in a simple map, Claude worked out what data to download, how to filter and process it and few hours later I had almost 750,000 species points across 388 species including 159,000 points with a photo link covering the last 10 years. That’s a colossal data set. Through numerous iterations we worked out how to process this data into something meaningful and usable by using hex bins to represent the coverage and colouring them by point density for each individual species (low, medium, high) and storing that data before dropping all of the points with no photos and then aggressively thinning the photo rich species while retaining the broad geographic coverage, that took a few tries as my data had 7,500 pictures of lions and large numbers for several other species. The final result was 45,000 points with photos.
What made a massive difference was getting Claude to spin up a browser based test rig that could test different hex bin settings and approaches to thinning the photo points until I got to something I was happy with.
The map has a species card that has a hero image, a summary of the IUNC assessment data, the countries where the animals were observed, the citations for each author and a credit for each hero image – 388 times!
I wanted to curate the hero images to pick attractive images that were a manageable size, I had spent several hours finding images for the original 50 species, I needed to find a better way to select 338 more. Claude built me a tool that read my list of species, searched Wikipedia Commons, filtered the search by file size, dimensions and licencing terms to present a selection of images, once an image was chosen, the tool added the image url, author and license type to my species data. With that tool I whizzed through the remaining 338 species in about as long as it had taken me manual searching for the initial 50.
We tried to auto-gather the citation data from the IUCN web site but that didn’t work so I got Claude to build me a tool along the lines of the image tool to speed up gathering the citation data.
Learning – using Claude you can quickly build tools to automate or semi automate repetitive tasks, each of those tools took about 10 minutes and a couple of iterations to build and saved hours of my time.
Reading through this, it sounds quite easy but in fact it took a lot of thought and trial and error to find the solutions to the challenges in building the map. Claude is patient and never gets pissed off if you decide to retry or change direction, at some points I needed Claude’s advice to help me reach decisions. In code terms Claude did everything, but in design and direction I was in the driving seat – this map was my idea of how to present the massive data set in a useful and performative way (the first try had some performance problems until I came up with a strategy to preprocess and thin the data).
I am pleased with the finished map, no doubt there will be a few tweaks over the next week. There are several sites that present species data for an academic/conservation audience (search for “maps of endangered species”) but none of them are easy to use and understand for the public, I think Amazing Animals on the IUCN Red List hits the spot. Let me know what you think
<p>The post Amazing Animals on the IUCN Red List – a massive project that I couldn’t have considered without Claude Code first appeared on KnowWhere.</p>
Período: 31/08 a 01/10/2026
Horário: das 19h às 22h
Carga horária: 48 horas – 16 aulas
As duas primeiras aulas serão gravadas. A partir de 02/09, os encontros serão online e ao vivo.
Historic map of Belém used in the QGIS 4.2 splash screen. Source: 
MobiML architecture overview. Photo by Michael Szell. Source: 
Imagen de la plataforma ciudadana Observadores del Mar
Event/occurrence model (Fuente: Biodiversity Information Standards (TDWG), licensed under a 
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