Q: Tell us about yourself.
I’ve been making maps professionally for over 10 years now. But when I’m not doing that, I could be cooking, messing around in VR (how exactly do you ingest geojson into Unity, anyway?), or running about as fast as the world’s fastest 90 year old. Seriously, I looked it up; his name is Frederico Fischer. My sprinting pace is terrible, but it keeps my legs thicc at least.
Q: Tell us the story behind your map (what inspired you to make it, what did you learn while making it, or any other aspects of the map or its creation you would like people to know).
Oh hey, speaking of running – my boss had the idea for this map while he was training for his first marathon. He came into work on Monday and explained how cool it would be if we could produce a map that showed the bounding boxes of every map our business had ever made. I agreed that it would indeed be cool. Then I promptly forgot about it.
Working on something completely unrelated a couple of months later, which required me to programmatically extract the coordinates at the corners of some map documents, I was reminded of his idea. A bit of Python frankenscripting later – with StackExchange acting as Igor – and I was able to unleash this on our entire corporate directory of map files. Turns out, in ten years of using our current GIS, we’ve collectively authored over eighty thousand maps.
Zooming in to Melbourne (which accounted for 30,000+ maps on its own), I started to play around with layered transparencies to visualise the data. This eventually evolved into a nice glowy blue colour scheme, which reminded me of deep space images of clusters of stars and galaxies, connected by glowing filaments.
This map has no practical use. I’m fine with that. There’s still something really satisfying about it, how it just hints at the tens of thousands of hours of work that went in to making all of those maps, which are reduced down to their most basic representation. It looks nice too (I think). If you got a GeoHipster calendar, I hope you think so too, because you’re stuck with it for this month.
Q: Tell us about the tools, data, etc., you used to make the map.
To scrape the data: A simple, custom Python script, run over a big and messy nested directory structure, full of .mxd files. It extracted the x/y min/max coordinates of every map document, and reconstituted them into a shapefile full of rectangles.
To visualise the data: A mixture of ArcGIS Pro (I love the feature-level transparency), InkScape, and Paint.net.