I have fire activity data (i.e. number of fires) and a series of factors (e.g. precipitation, tree cover loss, distance to nearest forest, etc...) that can potentially explain it. I have all this data for different locations for each year between 2002 and 2016.
I am trying to find the factors that can better explain the ocurrence of fires but I am not sure what is the best way to statistically achieve this. My first approach would be to do a (multiple) correlation or maybe even a linear regression. However, how would I approach this situation, taking into account that I have yearly observations for my data?
I guess I could do 15 different regressions (one for each year) but I don't want to interpret the results separately, as my main goal is to identify the factors that better explain fire activity altogether, regardless of the year. I read about multivariate linear regressions and how they can take more than one outcome variable but I am not sure if they would fit my purpose. Should I just merge all of my data and forget about the time dimension?