It’s not just about location, you can figure out usage habits this way:

These response times vary depending on whether a phone is active, idle, offline, connected to WiFi, or using mobile data.

Stable and fast responses can suggest that a device is actively used at home, while slower or inconsistent timings may indicate movement or weaker connectivity.

Over extended periods, these patterns can reveal daily routines, sleep schedules, and travel behavior without accessing message content or contact lists.

With a baseline of your normal usage behavior, I can start to build prediction patterns for what you’ll do and when, and then start analyzing deviations from your normal usage. If I do this for an entire service network I can then start to link up people with similar behavior patterns and build relationship webs.

That kind of information would be relatively easy to sell to advertising businesses. For example, if I’m pushing ad notifications on personal devices (Amazon) then I might want to know what times of day a user is most likely to view and interact with my ad notification. That might be information I’d be willing to buy from a service provider.

The potential uses for such information get darker from there - things like government agencies tracking the behavior of critics and progressives and building relationship profiles for them.

Given the usage patterns and location tracking and credit card and banking records for a given individual, I can pretty much understand their entire life.