poster-cnday-tom-russell.pdf (5.32 MB)
snkit: a spatial networks data cleaning toolkit
snkit [1] is an open-source python package that helps with data cleaning and preparation for spatial networks analysis.
A typical geographical spatial network dataset may contain nodes (points in space), edges (lines in space) or both. It may have less than full information about the topological connectivity (node ids, edge ids, from/to node ids for each edge).
snkit has methods to:
- add endpoints to each edge
- connect nodes to nearest edges
- split edges at connecting points
- create node and edge ids
- add from_id and to_id to each edge
In particular, snkit aims to take care of the details arising from approximate point-to-line snapping and intersection.
snkit has been (or is being) used in several projects to prepare spatial networks data for further analysis: transport networks in Argentina for a multi-modal flood risk analysis [2]; fixed digital communications networks in the UK to look at fibre-to-the-x rollout costs and benefits [3]; and connected electricity, water and wastewater networks in Greater London for a multi-commodity flow simulation model to assess infrastructure policy interventions across sectors [4].
[1] Russell and Koks (2019) https://doi.org/10.5281/zenodo.3269519 or https://github.com/tomalrussell/snkit/
[2] Pant et al (2019) https://argentina-transport-risk-analysis.readthedocs.io/en/latest/
[3] Oughton et al (2019) https://doi.org/10.5281/zenodo.1468786
[4] Majid et al (2019) https://doi.org/10.13140/RG.2.2.23512.96008
Captions:
Simplifying local connection for national-scale modelling. Electricity transmission and distribution networks, from OpenStreetMap, National Grid and multiple operating companies.
mid right
Creating dependencies between networks for flow distribution and rerouting. New Zealand electricity and road transport networks, from asset owner/operator datasets.
Routing along road network to connect electricity water and waste water networks, from OpenStreetMap, Google Locations API, local water resource management plans and European Wastewater Treatment Directive database.
Connecting rural, provincial and national roads in Argentina for flow routing and intersection with flood hazards, from Argentina DNV and MoT and FATHOM Global flood hazard.
A typical geographical spatial network dataset may contain nodes (points in space), edges (lines in space) or both. It may have less than full information about the topological connectivity (node ids, edge ids, from/to node ids for each edge).
snkit has methods to:
- add endpoints to each edge
- connect nodes to nearest edges
- split edges at connecting points
- create node and edge ids
- add from_id and to_id to each edge
In particular, snkit aims to take care of the details arising from approximate point-to-line snapping and intersection.
snkit has been (or is being) used in several projects to prepare spatial networks data for further analysis: transport networks in Argentina for a multi-modal flood risk analysis [2]; fixed digital communications networks in the UK to look at fibre-to-the-x rollout costs and benefits [3]; and connected electricity, water and wastewater networks in Greater London for a multi-commodity flow simulation model to assess infrastructure policy interventions across sectors [4].
[1] Russell and Koks (2019) https://doi.org/10.5281/zenodo.3269519 or https://github.com/tomalrussell/snkit/
[2] Pant et al (2019) https://argentina-transport-risk-analysis.readthedocs.io/en/latest/
[3] Oughton et al (2019) https://doi.org/10.5281/zenodo.1468786
[4] Majid et al (2019) https://doi.org/10.13140/RG.2.2.23512.96008
Captions:
top right
mid right
Creating dependencies between networks for flow distribution and rerouting. New Zealand electricity and road transport networks, from asset owner/operator datasets.
bottom right
bottom left
Funding
MISTRAL: Multi-scale Infrastructure Systems Analytics
Engineering and Physical Sciences Research Council
Find out more...