🚙 Cartography in the Age of Autonomous Vehicles

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.)

🌎 Few Interact With Our Interactive Maps–What Can We Do About It?

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.

💡 The New Moats

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.