Lightweight, electricity-powered vehicles such as electric bicycles and scooters, known as micromobility, are expanding rapidly in urban areas worldwide. Micromobility holds a promising potential in improving transportation by a number of means, including filling public transportation gaps and reducing dependence upon the private car and thereby internal combustion engine emissions. As a result of the proliferation of micromobility sharing schemes around the world, ridership trajectories can be obtained. Through Ridereport (https://www.ridereport.com), datasets have been acquired from several cities (Santa Monica, California, USA; San Francisco, California, USA; Portland, Oregon, USA; Austin, Texas, USA; Auckland, New Zealand; and Melbourne, Victoria, Australia), this work analyzes the spatial patterns of micromobility which can be obtained from these datasets using the open-sourced exploratory spatial data analysis software, GeoDa. Across all cities, micromobility ridership exhibits positive spatial dependence. Meaning places with high micromobility ridership tend to cluster spatially. This spatial dependence has been explored further using Local Indication of Spatial Association (LISA) coupled with aerial imagery for qualitative assessment. Five themes related to high micromobility ridership spatial clusters were able to be detected (i.e., major thoroughfares, bridges, trails and open spaces, transits, and Hubs). The study contributes methodologically to the field of geographic information systems (GIS) and operationally to the field of transportation
