  Micro-mobility services (e.g., e-bikes, e-scooters) are increasingly popular
among urban communities, being a flexible transport option that brings both
opportunities and challenges. As a growing mode of transportation, insights
gained from micro-mobility usage data are valuable in policy formulation and
improving the quality of services. Existing research analyses patterns and
features associated with usage distributions in different localities, and
focuses on either temporal or spatial aspects. In this paper, we employ a
combination of methods that analyse both spatial and temporal characteristics
related to e-scooter trips in a more granular level, enabling observations at
different time frames and local geographical zones that prior analysis wasn't
able to do. The insights obtained from anonymised, restricted data on shared
e-scooter rides show the applicability of the employed method on regulated,
privacy preserving micro-mobility trip data. Our results showed population
density is the topmost important feature, and it associates with e-scooter
usage positively. Population owning motor vehicles is negatively associated
with shared e-scooter trips, suggesting a reduction in e-scooter usage among
motor vehicle owners. Furthermore, we found that the effect of humidity is more
important than precipitation in predicting hourly e-scooter trip count. Buffer
analysis showed, nearly 29% trips were stopped, and 27% trips were started on
the footpath, revealing higher utilisation of footpaths for parking e-scooters
in Melbourne.
