These numbers suggest a distribution with very high variance; the number of events at most points are small, but some get very, very large. In fact, we might be dealing with a power law. Work with social-scientific or complex data for any length of time, and you'll start seeing power laws everywhere. There are some good reasons for this, but we don't need to worry about them in order to make a cool map.

So why do we care about the distribution right now? Because we need to know how to plot the points. Make the smallest ones too small, and we won't be able to see them; make the largest ones too large, and it will overwhelm the rest of the map. A log-scale is probably a good call here; a point with 10x as many events will be twice the size on our map.

Putting data on maps is quite literally more art than science. It took me some tweaking to find something that looked good. I ended up settling on taking the log of the event count for each point + 1 (since $log(1) = 0$ ), and multiplying it by 2 to get a size in points. Take my word for it, or play around with it to make it better.