One of the neatest data visualization projects I’ve seen lately is Topi Tjukanov’s “Map of Notable People.” It takes a database of birthplaces of notable people and maps them to cities on a global interactive map. Surprisingly addictive to pan around.
Tom Patterson has released a new version of his Equal Earth Map, this time as a physical topographic map style. Beautiful stuff.
This was a cool idea from cartographer Daniel Huffman. He live-streamed a walkthrough taking apart one of his map projects in Illustrator to see how he puts it all together.
I love this idea and am excited to see him do more like this down the road.
In this latest cartography project I’m working on, I’m rediscovering the tedium of searching for appropriate data. I’ll grant that it’s amazing how much high quality data is produced and freely distributed, but given the advances of web technology, it’s frustrating to see how bad many of the web map content management systems are.
Of course the difficulty of finding data depends on the geographic area. I happen to be working on a region that’s pretty sparse, so some data (like rasters) can be harder to find.
Here are a few resources I’ve either found or rediscovered worth sharing:
Another thing this project has prompted is a revivifying of my gazetteer project for working with GeoNames data1. The dataset has evolved in format and been updated since I last touched this tool in ~2013, so I had to make some changes to get it to work again. Since GeoNames is delivered in a raw text format, the goal of this tool is to automate loading the data into PostGIS for easier, faster use in QGIS.
This deserves a full post at some point later. I’ve always had a soft spot for place name data, so more attention on GeoNames and tools for working with it is worth it. ↩
This week is some reading, but some simple admiring. I wanted to highlight the work of two cartographers I follow that is fantastic. We live in a great world that people can still make a living producing such work.
A beautiful, artistic work from David Garcia sorting each island’s landmass by area. My favorite map projects aren’t just eye candy, they also teach you something. I spent half an hour on Wikipedia reading about a few of these islands.
This is a project from cartographer Daniel Huffman using a combination of open datasets, projection twisting, meticulous design, and Illustrator skills. The finished product is really amazing. The attention to detail is stunning. I love the detailed step-by-step walkthrough on how it came together.
A library of vector graphics for cartographic design. Each one has a unique style and could be used in other products, since it’s public domain (awesome). This is another cool thing from Daniel Huffman.
Both of these guys do amazing work. Find more on their websites:
Another great cartographic analysis from Justin O’Beirne, this time on the latest updates to Apple Maps. Their new data and styling covers mostly northern California, and has some gorgeous computer-extracted natural land cover, derived from satellite imagery. The golf courses look particularly impressive:
But even ignoring Apple’s competitiveness with Google, Apple’s inferior place database also impacts its own stated ambitions in augmented reality (AR) and autonomous vehicles (AVs)—both of which heavily rely on accurate and comprehensive place information.
Most of the analysis digs into how they might be deriving this new data: some algorithmically, but some through manual human extraction. The most interesting bits, though, are comparison’s to where Apple sits relative to their competition (Google) on preparedness for the world of automated vehicles. With the changes Apple and Google are making to their products, it seems Google is headed in the direction of human-oriented map data (“where do you want to go?”) versus the type of data required in getting there (“what’s the next turn?”). Of course the latter category will gradually become obsolete as machines take over the process of navigation.
Unless they’re already listed on Yelp, none of the shapes Apple has added appear in its search results or are labeled on its map. And this is a problem for Apple because AR is all about labels—but Apple’s new map is all about shapes.
So is Apple making the right map?
This is Google’s answer to Apple’s recently announced ARKit coming in iOS 11. After years of buzz with little substance, it’s great to see AR coming around to fruition with real commercial potential. The confluence of hardware fast enough for SLAM, mature OS platforms, and the APIs making it simple for developers to drop in and experiment with.
Waymo seems clearly in the lead in vehicle automation. This piece has some stunning figures on what they’re doing not only with their well known Fireflies and minivans, but also in simulated models for teaching the algorithms:
At any time, there are now 25,000 virtual self-driving cars making their way through fully modeled versions of Austin, Mountain View, and Phoenix, as well as test-track scenarios. Waymo might simulate driving down a particularly tricky road hundreds of thousands of times in a single day. Collectively, they now drive 8 million miles per day in the virtual world. In 2016, they logged 2.5 billion virtual miles versus a little over 3 million miles by Google’s IRL self-driving cars that run on public roads. And crucially, the virtual miles focus on what Waymo people invariably call “interesting” miles in which they might learn something new. These are not boring highway commuter miles.
The article mentions a facility where they’ve built real-life replicas of difficult lane configurations and traffic scenarios. I did a little hunting and found the location north of Merced, CA.
While cartographers have developed so many ways to present geographic information, the maps that accompany fantasy novels don’t vary a lot in terms of the information they display. They are about location, distance, and terrain for characters to hike through and for us to follow along. They are rarely political maps. They focus on geography over borders and on movement over status. The scholar Stefan Ekman suggests one reason why that may be: a lot of the borders and boundaries around fantasy realms are dictated by natural or supernatural features and have to do with states of being rather than simple movement in space. The kinds of borders we are familiar with — the result of historic processes or Gertrude Bell-style whim — are mostly banished. Concepts that we have grown distrustful of in our world — border, nation, identity — are magically appropriate in describing elf kingdoms, misty isles, or corsair ports.
I feel the same as the author. Fantasy novels read without their accompanying maps feels wrong for me. Any work that includes maps I prefer reading in hard copy. When I first read the Song of Ice and Fire books on Kindle, I would always have images of maps of Westeros open on another device for continued reference.
An excellent, extremely detailed analysis from Justin O’Bierne on how maps and cartography might evolve if autonomous vehicles negate our need for turn-by-turn navigation.
We can’t apply today’s maps to tomorrow’s cars – but this is exactly what those who think cartography is dying are doing. (It’s not that we’ll no longer be navigating, it’s that we’ll be navigating different things – and we’ll need new kinds of maps to help us.)
Brian Timoney’s done some great writing on this topic over the last few years. In the GIS world, enormous amounts of money are spent by governments to build and host map portals. The goals are typically noble (transparency, openness, providing access to citizens), but the results are mixed. Much of the spend is in making the information interactive. The dirty secret is that people don’t actually interact with these maps. He proposes a number of ideas of how to get the best of both: lower costs to create with the same (or higher) consumer engagement. For example, static maps cost much less to create and could even do better at directing a reader to the right information:
Just because you’re publishing a map to the web, doesn’t mean it has to be a web map. If a user is only going to spend 10-15 seconds with your map without interacting, why spend two weeks wrestling with your Javascript? And the great thing is the focus a static map brings–a single view, a single story: don’t bury the lede.
Jerry Chen from Greylock thinks “systems of intelligence” will be the next business model for software companies to create defensible value. He differentiates “systems of record” and “systems of engagement” as two layers in a stack of software applications that have existed since the dawn of the IT revolution in the 1990s.
These AI-driven systems of intelligence present a huge opportunity for new startups. Successful companies here can build a virtuous cycle of data because the more data you generate and train on with your product, the better your models become and the better your product becomes. Ultimately the product becomes tailored for each customer which creates another moat, high switching costs.
If you’ve ever done much involving symbol sets in mapping (especially web mapping), you know about the nightmare of managing 700 separate PNG files, with different, duplicate versions for slightly different size variations and colors. Even with a small set of a dozen symbols, if you want 3 sizes and 5 colors for each, you’re looking at 180 distinct PNG marker symbols to keep track of. Ugh. SVG format simplifies this in certain ways, but isn’t as universally supported or easy to work with as simple GIFs or PNGs.
With TileMill, I’ve wanted to use marker symbolizers frequently in maps, but I often avoid it because it involves a bit of tweaking and configuration to get all the files in the right place, build out all the styles marker-by-marker, and changing colors isn’t that easy.
To address this issue and make it easier to get dynamic markers on your maps, Zac built Symbola, an icon font constructed by embedding SVG graphics into TTF and OTF files. It uses the open source FontForge library and a python script to grab the set of SVG graphics, convert them to glyphs, and assigns them to unicode characters. There is a process involved to get the unicode data attributes into your data, but if you use PostGIS as much as I do, this is well worth doing. There are instructions in the README on how to insert specific characters into your PostGIS data tables using SQL. It’s a clever way to manage symbology at the database level, rather than creating duplication all over your style files with hundreds of iconography-specific style definitions. Check it out on GitHub, you can even customize it and add your own markers.
A dymaxion projection — a sphere projected on a flattened polyhedron.