I have an assignment where I am trying to find correlations between Lightning Strikes and Telecommunication damage. The two datasets consist of many columns (especially the human-recorded Telecommunication damage one), but let's assume it was something like this :
Telecommunication_Damage_df.columns = (timestamp, geolocation(lat, lon), type_of_damage) and
Lightning_Strikes.columns = (timestamp, geolocation(lat, lon))
I have done some EDA and cleaned the data, also assigned each lightning strike and each telecommunication damage row to a particular location(cities/areas), but now I am confused as to how I should proceed. Every other Data Science / Machine Learning project I have been involved in was much more direct and usually had one training/testing dataset whereas with this one I am stuck as to how I should proceed, is there a model that could help? Is there a methodology I am unfamiliar with?
I tried following this crime/geolocation tutorial (https://www.kdnuggets.com/2020/02/introduction-geographical-time-series-crime-r-sql-tableau.html) but it's not exactly the same, because there is one dataset being used, crimes, if a crime occurred then that's it, whereas here if a lightning strike occurred that doesn't necessarily mean a telecommunication problem was found and vice versa.
I know it's a bit vague, but I've been stuck for quite a while, and I was hoping that someone could guide me to any direction, because at the moment I am idle.