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Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa

Plans for smart mobility through cycling are often hampered by lack of information on cycling patterns and trends, particularly in cities of the developing world such as Johannesburg. Similarly, traditional methods of data collection such as bicycle counts are often expensive, cover a limited spatia...

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Detalles Bibliográficos
Autores principales: Musakwa, Walter, Selala, Kadibetso M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109266/
https://www.ncbi.nlm.nih.gov/pubmed/27872887
http://dx.doi.org/10.1016/j.dib.2016.11.002
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author Musakwa, Walter
Selala, Kadibetso M.
author_facet Musakwa, Walter
Selala, Kadibetso M.
author_sort Musakwa, Walter
collection PubMed
description Plans for smart mobility through cycling are often hampered by lack of information on cycling patterns and trends, particularly in cities of the developing world such as Johannesburg. Similarly, traditional methods of data collection such as bicycle counts are often expensive, cover a limited spatial extent and not up-to-date. Consequently, the dataset presented in this paper illustrates the spatial and temporal coverage of cycling patterns and trends in Johannesburg for the year 2014 derived from the geolocation based mobile application Strava. To the best knowledge of the authors, there is little or no comprehensive dataset that describes cycling patterns in Johannesburg. Perhaps this dataset is a tool that will support evidence based transportation planning and smart mobility.
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spelling pubmed-51092662016-11-21 Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa Musakwa, Walter Selala, Kadibetso M. Data Brief Data Article Plans for smart mobility through cycling are often hampered by lack of information on cycling patterns and trends, particularly in cities of the developing world such as Johannesburg. Similarly, traditional methods of data collection such as bicycle counts are often expensive, cover a limited spatial extent and not up-to-date. Consequently, the dataset presented in this paper illustrates the spatial and temporal coverage of cycling patterns and trends in Johannesburg for the year 2014 derived from the geolocation based mobile application Strava. To the best knowledge of the authors, there is little or no comprehensive dataset that describes cycling patterns in Johannesburg. Perhaps this dataset is a tool that will support evidence based transportation planning and smart mobility. Elsevier 2016-11-09 /pmc/articles/PMC5109266/ /pubmed/27872887 http://dx.doi.org/10.1016/j.dib.2016.11.002 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Musakwa, Walter
Selala, Kadibetso M.
Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa
title Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa
title_full Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa
title_fullStr Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa
title_full_unstemmed Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa
title_short Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa
title_sort mapping cycling patterns and trends using strava metro data in the city of johannesburg, south africa
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109266/
https://www.ncbi.nlm.nih.gov/pubmed/27872887
http://dx.doi.org/10.1016/j.dib.2016.11.002
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