Regional comparison of socio-demographic variation in urban E-scooter usage

Bibliographic Data

ID21248096
AuthorsPriyanka Verma (0000-0001-5803-6899, McGill University, corresponding author), Gemma Mckenzie (0000-0003-3247-2777, McGill University), Grant McKenzie (McGill University)
Year2024
Volume51
Issue7
Pages1548-1562
Publication date2024-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning B Urban Analytics and City Science (JOURNAL)
Journal identifiersISSN: 2399-8083 • E-ISSN: 2399-8091
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/23998083241240195
OpenAlexW4393191291
LanguageEN
References cited32

In recent years we have witnessed explosive growth in the shared, free-floating, electric scooter industry. While still controversial in many North American cities, a number of large e-scooter operators have managed to carve out a piece of the urban transportation landscape. As these vehicles shift from novelty services to increasingly reliable modes of short personal travel, the discussion has turned to investigating who exactly benefits from these micromobility services and who are being left behind. Though population surveys have been administered to identify the socio-demographic characteristics of e-scooter riders in the past, little work has linked these characteristics through trips, or investigated the regional variation in these demographic factors. In this work we explore the variability and similarities in e-scooter rider characteristics across three major U.S. cities. To accomplish this, we apply a Moran’s Eigenvector Spatial Filtering linear regression model and compare our results to more commonly used spatial regression approaches. Our results indicate that the spatial filtering approach outperforms other methods in identifying socio-demographic characteristics of e-scooter users, across multiple regions. We find that many socio-demographics associated with e-scooter usage are regionally variant, despite younger users making up the core user base in all cities. There are variations in usage based on gender, income, and race across cities with Black and Hispanic populations remaining underserved. The implications of these findings are discussed

Advertising · Business · Economic geography · Geography · Regional science · Regional variation · Human Mobility and Location-Based Analysis · Smart Parking Systems Research · Urban Transport and Accessibility

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