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. 2020 Jan 27;10:1271. doi: 10.1038/s41598-020-57882-2

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Schematic pipeline of the linear model for the two sets of linear models in this study. As shown in the top panels (a), we first produce kernel density estimates (KDE) of every OpenStreetMap (OSM) category and meta-category, which we then compare with the number of traffic disruptions at a given latitude and longitude. The bottom panels (b) show we also aggregate the OSM data points into a total count per ward, which we then compare with the traffic volume going into every ward in Oxfordshire.