We are pleased to announce that sDNA version 3.4.5 is considered stable!

While sDNA remains very general purpose software, sDNA3 also includes the latest active travel models from our ongoing research projects.

Finally, we have incorporated feedback from our external partners to make the workflow more efficient in day-to-day use.

Enhanced compatibility

• Use Open Source GIS with QGIS Support
• Increased compatibility with network formats (ability to run link-weighted analysis without fixing split links; grade separation and elevation can now be used together)
• Link to other models: export skim matrices from sDNA or use sDNA to assign an external Origin-Destination matrix (sDNA+)
• Easier to install anywhere with offline unlocking and no administrator account required

Enhanced ease of use

• Links can be switched on and off for scenario analysis (sDNA+)
• Do your stats within sDNA, no need for external tools
• Preserve text and numeric data in sDNA Prepare (sDNA+)
• Improved interface to all functions in sDNA, more helpful error messages and it’s less of a CPU hog
• Full user manual – not just for sDNA but guiding you through the theory of spatial network analysis and how to prepare networks

Enhanced analysis

• Produce maps of who uses a selected link (sDNA+)
• Easy to use cycle, vehicle, pedestrian and public transport metrics (sDNA+)
• More realistic behavioural models with hybrid and banded radius, enhanced hybrid metrics for turns (sDNA+)
• Robust multivariate learn and predict tools based on generalized cross-validation
• Geometry outputs are now fully 3d (sDNA+)
• OD Matrix Input and Skim Matrix Output (sDNA+)

Users wishing to use sDNA3 can find it on the downloads page. For enhanced features, see the sDNA+ page.

Part of the sDNA3 development was funded under the ESRC Impact Accelerator scheme, including contributions from Arup, WSP, Sustrans and Tonji University.

To assist with the cycle planning efforts currently underway in Cardiff (Active Travel Integrated Network Map, and the review of Enfys) we are making public our current cycle model outputs.  Cardiff has been sDNA’s primary test case for the development of cycling models.

The report also serves as a demonstration of what sDNA+ can do for cycle modelling.

• Cardiff Cycle Models report
• Accompanying data files

In partnership with Sustrans, Crispin will be delivering two workshops on active travel planning next week at the RTPI Cymru Wales Planning Conference 2016.

Under the Active Travel Act, Welsh Local Authorities are required to produce Integrated Network Maps detailing a forward plan for active travel routes, having regard to the promotion of active travel journeys. Active Travel encompasses both walking and cycling; this session focusses on cycling, however similar techniques are applicable to walking routes. This seminar by Cardiff University will demonstrate how off-the-shelf software can be used to assist active travel plans, by visualising current flows of cyclists, identifying areas with cycling accessibility problems that could be addressed by new infrastructure, and – given a proposed infrastructure scheme – predicting who will use the new route and how. Results based on our latest pilot work using Wales Active Travel mapping data will be shown.

sDNA was used behind the scenes to estimate ecological footprints for numerous Hay Festival alternative transport scenarios.  We found that ecological impacts could be cut most by reducing international air travel and replacing private car journeys with appropriately structured bus and minibus services.  However the success of these would rely heavily on having high uptake, so if that can’t be achieved then car sharing can also cut a good proportion of the transport footprint.

sDNA was used to quickly estimate distances for fastest routes to the festival over the entire UK road network, using angular analysis as a proxy for speed.

A quick nod to our private and third sector partners, who you can approach to commission pedestrian and cycle models with sDNA. That’s if you don’t want to download our software and do the job yourself of course! In alphabetical order:

• Arup global consulting
• Sustrans sustainable transport charity
• WSP Parsons Brinckerhoff professional services

The sDNA team – partnered up with Sustrans – were invited into the Welsh Assembly by AM Jenny Rathbone on Tuesday. Together we are keen to ensure that the Welsh Government follows through on its commitment to Active Travel set out in the Active Travel Act 2013.

Crispin presented recent work on sDNA cycling models using the Welsh Active Travel dataset; slides are available here.

In the soon-to-be-released sDNA3, it will no longer necessary to repair split links.  This looks like a departure from how sDNA used to work, so I’d better explain it.

First, let’s clarify what we’re talking about.  A split link is where a single network link is represented by two or more polylines in the data, rather than a single polyline as is customary.  Here’s an example, in which I’ve placed circles over polyline ends to make them visible.  The polylines shown in red form a split link.  (Of course they look like a single line – we only know it’s a split link because of the node halfway along).

Base data (c) Openstreetmap contributors

The point where the two red links join is often called a pseudonode in the technical literature, so we can also say that split links are the lines joined by pseudonodes.

Two of our aims in making the original sDNA tool were (i) to exploit measures of link density to add information to our models, and (ii) to be compatible with data in the format everybody uses, that is to say, link-node.  For this reason it seemed like a good idea to insist on using whole links as the unit of analysis, so we specified that sDNA must be fed networks without split links, and provided a tool for repairing them.

It turns out that was a bad idea, as it doesn’t offer our users enough flexibility.  In our own work, and also working with our Impact Accelerator partners, we have found several reasons why you might want to leave split links as they are:

• Topology checking.  Accidental intersections between lines in network data cause huge problems for network analysis, as they imply a network model which is wrong.  Unfortunately they are also hard to spot, because on most maps they look just like lines with coincident endpoints.  For this reason, many data providers will split ALL links where they cross, even where they don’t join (e.g. bridges and tunnels – to which grade separation data is added to show what is going on).  Working this way has the advantage that any intersections between lines are definitely an error, so errors are easier to find with a topology checker.
• Representing sub-links with different characteristics.  For example, a one way system, toll charge, major origin or destination, etc, that applies only to part of a link.
• Obtaining output at sub-link level.  If many origins and destinations fall on a link, traffic flow will vary along the length of the link, and you may wish to model this rather than treat the link as a single entity.  Or, you may wish to measure accessibility at sub-link level.
• Sub-link accuracy for routing algorithms.  In some cases, somebody’s starting position on a link will affect the way they choose to leave it.  For most models this does not matter, but if it does in yours, you may want to divide links into small chunks to represent it.
• Testing different network design options.  Ultimately, sDNA exists to help designers plan better networks, so testing out different alternatives is part of the job.  While you could prepare an entire new network for each option you want to test, it’s usually easier to add and remove a small set of links by switching them on or off.  Another new feature of sDNA3 is that it allows you to do this.  However, switching off a link will usually have the effect of creating pseudonodes, and hence split links, at its endpoints.

So, here is how split links are handled in sDNA3:

• sDNA still uses link-node format.
  
• Links can, however, be composed of one of more polylines.  At your choice you can either use one, or more than one polyline to represent each link.  If you choose to use only one polyline per link, you are using sDNA in the way we used to recommend, but you have other options now.
• Each polyline is treated as a single entity for routing decisions, that is to say, all traffic between each pair of polylines will go the same way (unless you use random hybrid metrics and oversampling).
• Three forms of weighting are offered:
  • Polyline weighting:  each polyline receives a weight of 1 (unweighted), or a custom weight which applies directly to the polyline.
  • Length weighting:  each polyline receives a weight equal to its length (unweighted), or if using custom weights, these are taken to represent weight per unit length.
  • Link weighting (the default): each link receives a weight of 1 (unweighted), or if using custom weights, they are taken to represent weight per link.  In the case of split links, the weight is divided over the polylines that form the link, proportional to their length.  Custom weights for parts of split links are scaled down according to the fraction of the link they represent.

Finally, sDNA3 outputs a new measurement called LFrac or Link Fraction.  This shows you what proportion of a link each polyline in your model represents.

Link weighting sounds complicated, but really it’s the same thing we were doing from the beginning.  If all split links are repaired, then polyline weighting is the same as link weighting; which is why in sDNA versions 1 and 2 we achieved link weighting by using polyline weighting and providing you with a tool to repair the split links.

There are, of course, reasons to use the other types of weighting on occasion.  Polyline weighting is useful if you have address point data which you have attached to the network, as it preserves the exact value of each attached weight.  Length weighting is useful for analyses where you think network length is more representative of urban density than link counts.  Both of the latter can also be used along with land use data to provide custom weights per link, or per unit length, for different types of land use.

But go ahead and use split links: leaving them in there won’t affect the way the analysis is weighted by default.  If you use a lot of split links (and I mean a lot – like splitting every link into multiple parts) it will slow down sDNA, but if you only use a few here and there – say for bridges, design options, one way systems, etc – you won’t notice the difference in speed.  If you want higher accuracy only on a small part of your model, you can also split links in that part only to keep compute times down.

Crispin gave an invited talk at Association for Geographic Information’s “GeoBig5: Big Data and You” conference on the links between spatial network analysis and big data.

To summarize,

• Spatial network analysis leverages existing large datasets by extracting novel information from them.  The mere shape of a road layout can tell us volumes about land use, travel demand and preferred routes.
• As every, nobody can quite agree on what Big Data is!  (If computing these days is Big Data, does that make maths Big Sum? Thank you Amanda Turner for this quip).  Almost anything in the GIS field fulfils the “variety” requirement of big data though, and sDNA is no exception as it integrates with GIS to unify diverse data sets.  Examples include our work on community cohesion and use of sDNA in the UK Biobank big data project.
• sDNA is geared towards high computing requirements; e.g. a 300,000 link network contains 90 billion potential interactions, each of which require tracing a route from origin to destination.
• As much of human interaction is mediated by spatial networks, ignore them at your peril!

AGI members can download the presentation from their website.

Crispin will be presenting sDNA models of pedestrian and cyclist travel at the Association of European Schools of Planning Conference, Prague, 14th July 2015.  As ever, get in touch if you would like to meet up.