The multi-source aggregation approach is exactly right for this use case -- the value isn't any single feed, it's the correlation between them. Flight diversions, AIS gaps, and social spikes at the same coordinates at the same time tell a very different story than any one of those signals alone.

Curious whether you're doing any timestamp normalization across feeds. Marine AIS in particular can be spoofed or delayed, and correlated analysis gets messy fast if the time windows aren't aligned.