We love maps because we know how to read them, but there are other reasons that many visualisations end up as maps and unfortunately the answer doesn't always have the end user in mind.
Designers love maps because-
- they are easily understood by users, most people can read a map
- they are easy to make - plenty of them online or as vectors to buy and / or trace
- clients want them because they see them, use them and know what they are
- (I also think that on a deeper level, our brains work like maps, with co-located repositories of certain stuff on tap and that we recognise this almost as looking in a mirror when we see one)
I think the main reason is that one of the cleanest and most robust data-sets available these days is map data - location , gis gps etc data. And people have trouble finding good data to build with. So they resort to map data.
When projects at the concept stage discuss data product possibiities, many end products are imagined. But when the investigation of available data (some say materials) happens, it often happens that the reliable data that the project requires is not available.
But there is always some map data and most things are based somewhere so that's that solved. It's a map.
The answer is to put planning into finding data that will fuel your true product aspirations.
The answer is to add data sourcing to the list of data science skills and data engineering aptitude. A data sommelier if you will.
Doing something about health? Don't just do a map because the government data sets are all geo based. Just as Apple finds new raw materials to build thinner laptops, or buys companies who can make 10% better batteries, so should data engineers insist on the right materials to build with.
They may not own enough domain expertise to understand where these data sets lie/ Is it them? Is it an IA job? It's probably one of the new jobs springing up these days.
All I know is "what data shall we use" is a vital pillar of any product concepting session.
People love maps and rightly so but in a hundred years they'll be literate in other useful visual tools - we can't make these without being interested in what they are made of.
We cant make these without getting good at sourcing and making new data.