I'm very new to data science so please be gentle.
I have a dataset that contains record of occurrence of fire for the past 35 years (+-700.000 rows). Each date and time can have more than one occurrence as two fires can happen at the same time in different locations. It's features are approximately as follows:
Date and Time | Borough | County | Area Burned | Type | Cause | etc.
I'm planning to combine this with a new dataset that I'm in the process of constructing. This new dataset would have the following features:
Date and Time | Latitude | Longitude | Av. Temperature | Av. Wind Speed | Av, Humidity | etc.
My idea is to try to predict the likelihood of a fire occurring and it's potential severity from a given set of atmospheric parameters. But I'm a bit unsure what would be the best way to combine them.
If I merge on dates, I'd have multiple repeated rows with fires occurring at the same time in different places, would that be the best way? The problem I see is that the locations where there was no fire would not be represented, so I'd have to add a bunch of blank rows for all locations where there was no fire to balance the set.
Any ideas on the best strategy for this?