# Maps revisited

I've been thinking about maps a lot lately.  How can we visualize feels like weather in an intuitive way?  

I've landed on using something like Uber's [Hexagonal Hierarchical Spatial Index (H3)](https://eng.uber.com/h3/) to aggregate weather data and then classify each hexagon as feeling freezing, cold, cool, perfect, warm, hot, swampy, or scorching - for the whole globe - based on your "personalized feels like" profile.

At a high level, I'm thinking:
- according to the user's current bounding box / zoom level (what you see on the map) call out to a service to individually get near real-time temp, wind speed, humidity, etc intensity grids
- aggregate these grids to intermediate output H3 cells
- apply an algorithm to the H3 intermediate output cells to calculate "personalized feels like" values for each cell, outputting the final feels like cells

In other words, raw weather grids -> transformed into aggregated hexagons -> matrix math across each weather factor (temp, wind, etc) for each location = final feels like weather hexagons.

Not sure how I'm going to do that in real-time from a technical standpoint.  But that's the fun in this :D