Reproducible methods for network simplification for data visualisation and transport planning

Robin Lovelace, Zhao Wang, Will Deakin, and Josiah Parry (2024). Reproducible methods for network simplification for data visualisation and transport planning. 32nd GISRUK Conference 2024. https://doi.org/10.5281/zenodo.11077553

Abstract

Route network datasets, crucial to transport models, have grown complex, leading to visualization issues and potential misinterpretations. We address this by presenting two methods for simplifying these datasets: image skeletonization and Voronoi diagram-centreline identification. We have developed two packages, the “parenx” Python package (available on pip) and the “rnetmatch” R package (available on GitHub) to implement these methods. The approach has applications in transportation, demonstrated by their use in the publicly available Network Planning Tool funded by Transport for Scotland.

Type: Conference Paper Venue: 32nd GISRUK Conference 2024 Year: 2024

DOI Publisher Link BibTeX

Abstract

Route network datasets, crucial to transport models, have grown complex, leading to visualization issues and potential misinterpretations. We address this by presenting two methods for simplifying these datasets: image skeletonization and Voronoi diagram-centreline identification. We have developed two packages, the “parenx” Python package (available on pip) and the “rnetmatch” R package (available on GitHub) to implement these methods. The approach has applications in transportation, demonstrated by their use in the publicly available Network Planning Tool funded by Transport for Scotland.

Citation

Robin Lovelace, Zhao Wang, Will Deakin, and Josiah Parry (2024). Reproducible methods for network simplification for data visualisation and transport planning. 32nd GISRUK Conference 2024. https://doi.org/10.5281/zenodo.11077553

BibTeX

@inproceedings{lovelace_reproducible_2024,
  title = {Reproducible Methods for Network Simplification for Data Visualisation and Transport Planning},
  booktitle = {32nd {{GISRUK Conference}} 2024},
  author = {Lovelace, Robin and Wang, Zhao and Deakin, Will and Parry, Josiah},
  date = {2024-04-11},
  publisher = {Zenodo},
  location = {Leeds},
  doi = {10.5281/zenodo.11077553},
  url = {https://zenodo.org/records/11077553},
  urldate = {2024-05-02},
  abstract = {Route network datasets, crucial to transport models, have grown complex, leading to visualization issues and potential misinterpretations. We address this by presenting two methods for simplifying these datasets: image skeletonization and Voronoi diagram-centreline identification. We have developed two packages, the ‘parenx’ Python package (available on pip) and the ‘rnetmatch’ R package (available on GitHub) to implement these methods. The approach has applications in transportation, demonstrated by their use in the publicly available Network Planning Tool funded by Transport for Scotland.},
  eventtitle = {{{GISRUK}}},
  file = {/home/robin/Zotero/storage/EZ475BRU/Lovelace et al. - 2024 - Reproducible methods for network simplification fo.pdf}
}

Notes