Depends on the granularity of the data. What's wrong with (say) 100m square blocks as spatial granularity, with ride time to the nearest minute or even 5 minutes.
The city should be able to ensure regulatory compliance, while anonymity is preserved.
The city should be able to ensure regulatory compliance, while anonymity is preserved.
To address privacy issues, data needs to be aggregated, with trip clusters with less than 5-10 trips dropped. Differential privacy strategies also exist, but dropping groups with low number of user contributions is the easiest solution to implement.
Though I grant the general point that making the drop-off location more granular could satisfy both criteria, but you would need some kind of varying size to account for scooter density.
And if you know roughly where rides are going to / from and when, then that helps you integrate your different types of public transport - for example making sure that when the train arrives there are enough scooters there, or understanding how popular scooter ride paths interact with bike lanes, bus lanes, etc.
Mobile phone cell size is generally a pretty good proxy for population density - modern micro-cell sectors can be pretty small, and 5G will make them even small in urban areas.
(I work on this sort of stuff for a major engineering data science and asset management group in the UK).
I understand why a company needs to find a distinct schooter, but why does the city need the ability to find a distinct scooter?