Using Ridership Profile Clustering and Geographically Weighted Regression to Investigate Spatio-Temporal Patterns of Shared E-scooter Ridership in Christchurch, New Zealand

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Main Authors:	Leung, Wai Chi Goldie, Sila-Nowicka, Katarzyna, Conrow, Lindsey, McKenzie, Grant, Kingham, Simon, da Silva Brum Bastos, Vanessa
Format:	Recurso digital
Published:	Zenodo 2025
Online Access:	Acceder al recurso
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_version_	1866901620833386496
author	Leung, Wai Chi Goldie Sila-Nowicka, Katarzyna Conrow, Lindsey McKenzie, Grant Kingham, Simon da Silva Brum Bastos, Vanessa
author_facet	Leung, Wai Chi Goldie Sila-Nowicka, Katarzyna Conrow, Lindsey McKenzie, Grant Kingham, Simon da Silva Brum Bastos, Vanessa
contents	<p>This study presents the street-segment analysis of shared e-scooter ridership using real trip count data, combining Dynamic Time Warping clustering and Geographically Weighted Poisson Regression to reveal distinct patterns and spatial influencing factors of ridership in Christchurch, New Zealand.</p>
format	Recurso digital
id	zenodo_https___doi_org_10_5281_zenodo_16868798
institution	Zenodo
language
publishDate	2025
publisher	Zenodo
record_format	zenodo
spellingShingle	Using Ridership Profile Clustering and Geographically Weighted Regression to Investigate Spatio-Temporal Patterns of Shared E-scooter Ridership in Christchurch, New Zealand Leung, Wai Chi Goldie Sila-Nowicka, Katarzyna Conrow, Lindsey McKenzie, Grant Kingham, Simon da Silva Brum Bastos, Vanessa <p>This study presents the street-segment analysis of shared e-scooter ridership using real trip count data, combining Dynamic Time Warping clustering and Geographically Weighted Poisson Regression to reveal distinct patterns and spatial influencing factors of ridership in Christchurch, New Zealand.</p>
title	Using Ridership Profile Clustering and Geographically Weighted Regression to Investigate Spatio-Temporal Patterns of Shared E-scooter Ridership in Christchurch, New Zealand
url	https://doi.org/10.5281/zenodo.16868798