sDNA is now at the core of adapting Flyvberg‘s Reference Class Forecasting megaproject outcomes appraisal – using explainable AI to appraise new town, CBD, road and rail infrastructures:
A SEM-ANN adaptation of reference class forecasting for the assessment of large-scale urban planning vision, Land Use Policy, Zhou, Y., Zhang, L., and Chiaradia* AJF.
Available here or here.
Key items of interest are:
- the need to appraise early front-end visioning scenarios based on relatively sparse data
- infrastructure projects appraisal needs to be able to incorporate network impacts which are notoriously difficult to identify and model.
sDNA contributes analyses of existing and proposed scenarios of urban rail network (MTR), road network and pedestrian network to a structural equation model (SEM) to determine significant variables which are fed into an artificial neural net (ANN) giving a form of explainable AI. Pedestrian network is here proxied via road network. The case study is a megaproject in Hong Kong HKSAR, China.
Our first foray demonstrating the use of sDNA in economics is now published:
Hossain, MA, Cooper, CHV. Spatial network analysis as a tool for measuring change in accessibility over time: Limits of transport investment as a driver for UK regional development. Growth and Change. 2021; 00: 1– 25. https://doi.org/10.1111/grow.12512
One aspect of wider interest here is our proxy for road travel time, which has correlation (r2) of up to 0.99 with journey times taken from Google maps. All details in the paper so you can reapply the formula in your own network analysis.
In our case this was a national scale network analysis (3 million links) so we used selective sampling of origins to speed it up, though each origin is analysed for quantity and shape characteristics of all destinations within a 1 hour travel time radius.
Finally, one definitive paper on the sDNA software itself: when you next need to cite the software please use
Cooper, C.H.V., Chiaradia, A.J.F., 2020. sDNA: 3-d spatial network analysis for GIS, CAD, Command Line & Python. SoftwareX 12, 100525. https://doi.org/10.1016/j.softx.2020.100525
All published results should cite the software – please help us to help you, as demonstrating impact means we can put more work into sDNA in future. Although focused on sDNA Open, the above paper is suitable for all of the sDNA family including sDNA and sDNA+.
The sDNA Team has been working with Swire Properties HK on a Places Impact Report. Swire Properties is a property developer with investments across Hong Kong, Mainland China, Singapore and the U.S. Using a comparative approach, the Places Impact Report uses metrics based on pedestrian network within a four constructs framework: Vibrancy, Livelihood, Wellbeing and Resilience. The Places Impact Report is part of Swire Properties’ sustainable development Environment Social and Governance (ESG) performance reporting that includes five strategic pillars: Places, People, Partner, Performance (environment and economic).
Thanks to Eric Chan, who completed his MSc dissertation on the sDNA project, we can now produce cycling models in many cases without needing to model motorized traffic first. This saves planners a lot of effort.
Chan, E.Y.C., Cooper, C.H.V. Using road class as a replacement for predicted motorized traffic flow in spatial network models of cycling. Sci Rep 9, 19724 (2019). https://doi.org/10.1038/s41598-019-55669-8
We’re pleased to receive some great feedback from Juan de Dios Ortúzar (co-author of Transport Modelling (4th Edition) with Willumsen, L.G., 2011) on our longitudinal model of the redevelopment of Cardiff 2007-2010:
“I was well impressed by the work done, including – as you well said – the fairly unusual bonus of testing the estimated model in forecasting against observed data in the future. Congratulations.”
Cooper, C.H.V., Harvey, I., Orford, S. & Chiaradia, A.J. F. Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows. Transportation (2019). https://doi.org/10.1007/s11116-019-10072-0
Nobody found a counterexample, so I think this really is the first time a strategic pedestrian model had a proper forecast test: given pedestrian data prior to changes in urban layout, could we correctly predict what would happen to pedestrian flows after the change? Short answer – yes. Much like recent sDNA cycling models, this is based on a multiple hybrid sDNA approach. Although the above paper does not look at mode choice, we expect similar techniques to be applicable (watch this space).
We’ve been saying for years that we should provide more support for transparancy, reproducibility and accessibility of research with an open source release of sDNA, so here it is at last: sDNA Open released under GPL3 on Github. Hooray!
That said, if you’re not reading or editing source code, we’d prefer you to continue to download sDNA from this website. Right now, the functionality of each version is the same, and if you stay here then
- we can continue to monitor how many people actively use sDNA (rather than just download it), which makes it easier to demonstrate its value to sDNA funding councils, who might fund further developments which benefit you, the users.
- if you sometimes use sDNA+, it’s just one installation for both sDNA and sDNA+ so you can switch back and forth easily.
- you can also stay in touch on our mailing list, which provides updates on sDNA and related research about once per year.
Happy network analysing.
It’s time to announce sDNA version 4, or “sDN4″…
For funding we are thankful to Wedderburn Transport Planning, and also Alain Chiaradia and Chris Webster for re-investing their royalties from sDNA+.
sDN4 includes several new features, principally the ability to weight analysis by zones as well as the existing options of links, length and custom weights. All of the above can be combined using custom expressions, and weights from zones easily distributed over each zone according to user defined functions. This is well suited to conducting high resolution sustainable transport analysis based on low resolution census data.
In addition, numerous features previously restricted to those who purchased sDNA+ licenses, have now been moved into standard sDNA:
- Skim matrix
- Network radius
- Destination maps
- Bidirectional betweenness
- Flow bundles (use intermediate link filter for these)
- Other features
- Origin Destination (OD) matrix input
- Banded radii – useful for multivariate analysis with a little less collinearity
- One way links
- Custom spatial tolerances
- sDNA prepare preserving data
- Selected origin/destination and skip functions
- Link disabling functions
- Advanced problem route handling
and Hybrid radius
remain exclusive to sDNA+
sDNA 4 is available on the usual download page.
Crispin has been involved in a large cycling collaboration led by Bendik Manum of Norwegian University of Science and Technology Trondheim, and also including Chalmers University of Technology Sweden and our own collaboration with Arup Cardiff.
The results will be presented at 12th International Space Syntax Symposium, Beijing, 8thJuly.
Bendik Manum; Tobias Nordstöm; Petter Arnesen; Crispin Cooper; Jorge Gil; Erlend Dahl; Ringo Chan; Lillian Rokseth; Sylvia Green: “Using realistic travel-time threshold in accessibility measures of bicycle route networks”.
Catch Ringo Chan (Arup) presenting our latest models at the 2018 European Transport Conference in Dublin this week. Presentation linked below:
C Cooper, Cardiff University; R Chan, Arup, UK Combining spatial network analysis with demographics to study the effect of segregation on cycling mode share.