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Using Degree Constrained Gravity Null-Models to understand the structure of journeys' networks in Bicycle Sharing Systems
Abstract : Bicycle Sharing Systems are now ubiquitous in large cities around the world. In most of these systems, journeys' data can be extracted , providing rich information to better understand it. Recent works have used network based-machine learning, and in particular space-corrected node clustering, to analyse such datasets. In this paper, we show that spatial-null models used in previous methods have a systematic bias, and we propose a degree-contrained null-model to improve the results. We finally apply the proposed method on the BSS of a city.
Cited literature [12 references]
https://hal.archives-ouvertes.fr/hal-01500352
Contributor : Lionel Tabourier Connect in order to contact the contributor
Submitted on : Monday, April 3, 2017 - 10:43:30 AM
Last modification on : Thursday, September 9, 2021 - 1:06:01 PM
Long-term archiving on: : Tuesday, July 4, 2017 - 12:40:22 PM
Contributor : Lionel Tabourier Connect in order to contact the contributor
Submitted on : Monday, April 3, 2017 - 10:43:30 AM
Last modification on : Thursday, September 9, 2021 - 1:06:01 PM
Long-term archiving on: : Tuesday, July 4, 2017 - 12:40:22 PM
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nullSpatialModel1.pdf
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