Finding Correlations in two Datasets
About the type of problem: apparently here the goal is not to do any kind of supervised learning, it seems to be more a kind of descriptive task.
The first thing I would try to do is to align the two datasets, i.e. merge them based on events in one occuring at the same time and place as in the other one. In order to do that you need to be able to compute for any two events whether they appear in the same area around the same time. I guess you'll need a window, for example defining two events as related if they occur within 1h of each other and are distant by less than 10km. Be careful about noise in the data: the time of a telecom incident might be the time it's reported, not the time it actually happens.
Once you have linked potential related events, you can merge the two datasets into one. You should probably preserve the events which have no match in the other dataset for statistical purposes, so use an outer join.
At this stage you have an exploitable dataset. I would start by doing plots such as overall probability distribution of related events, probability of related event by location. Probability of related events across time.