> Sounds like the bigger issue is that you're able to get "spatiotemporal" data in the first place?
Almost all data is spatiotemporal data, people just aren't used to thinking about it like that. Everything that "happens" is an event with associated times and places.
Tagging of events with spatiotemporal attributes, or with metadata that can be used to infer spatiotemporal attributes, is pervasive. Every system data passes through, even if not the creator of it, observes the event of the data passing through it. Event observation is not trying to track things but it implicitly and necessarily creates the data that makes tracking and spatiotemporal inference possible.
These kinds of analyses rely almost entirely on knowing the events occurred; you could encrypt the contents of the data and it wouldn't matter. Software leaks spatiotemporal event context everywhere across myriad systems, internal and external, that incidentally collect it. There isn't anything nefarious about most of it and much of it is required for reasons of criminal and civil liability.
What people underestimate is that you can analytically stitch together many unrelated sparse data sources with spatiotemporal attributes, many of which are quite crap or seemingly unfit for purpose, to reconstruct a dense high-quality graph. Counter-intuitively, diverse and seemingly irrelevant data sources often produce better data models. It surfaces bias, errors, manipulation, and processing artifacts in individual sources you might otherwise miss.
It is much more difficult to access the obvious first-party data sources than it used to be, mostly because people with that data are far more selective about who they give access. It doesn't really matter, that is a speed bump for the unsophisticated. The exponential growth in the scale and diversity of network-connected telemetry of all types pretty much guarantees these data models will always be constructible.
The historical limiter has always been the absence of data infrastructure platforms that can handle these kinds of analytics at scale.