This data is analized through AI, and Big Data. Go look up a tutorial on python for how to do this. The tutorials will speak about means, correlations, and patterns. This is a very complex field, so to be short, I will give you its applications instead of how it is done. Also, to be frank, I don't understand half of it myself.
The best example is weather prediction models, because of how open the whole affair is: Firstly, data is gathered by hundreds of weather stations, many maintained by hobbyists, and students at universities. These gather humidity, air pressure, wind direction and speed data, and many others. This data can be visualized, for wind data in spain: (spain has a lot of wind activity and very interesting as well.) https://www.xcweather.co.uk/ES/observations Wind data can be correlated with seasons, elevation, and the other factors such as pressure. It could also have to do with how much greenery exists in an area. Then along comes a Big Data analyst. with a computer, takes all of this data, and spends weeks trying to find correlations. Such correlations as temperature and presence of trees. Trees keep the area slightly cooler. So from this correlation we can derive that cities should plant trees on walkways, this is better for people!
