The update rate for a global map data model, all of which are still woefully incomplete in many contexts, is surprisingly high. The territory underlying the map

The update rate for a global map data model, all of which are still woefully incomplete in many contexts, is surprisingly high. The territory underlying the map is a lot less static than people assume. Also, local reality is often much less “regular” than people assume such that a person really can’t figure it out reliably. Currently there are literally thousands of people tasked with incorporating these changes because it has proven to be resistant to automation thus far due to the pervasiveness of edge cases. For your basic global map data model, these are the edge cases that are left after several thousand heuristic and empirically derived rules have been applied. It is a deeply complex data model that changes millions of times a day in unpredictable ways. Unfortunately, many applications are very sensitive to the local accuracy of the model, which is much higher variance than average accuracy. Only trying to be “good enough” in an 80/20 rule sense is the same as “broken”. The updates are also noisy and often contain errors, so the process has to be resilient to those errors. The resistance of the problem to automation and the high rate of change has made it extremely expensive to asymptotically converge on model with consistently acceptable accuracy for the vast majority of applications.