Mesh Density Benchmarking

What is it?

One of the most re-tweeted posts of last year was the simple screen grab from Open Street Maps showing the cycle networks as reported in the Netherlands compared to the UK. In this screen shot, the Netherlands appears pink because the cycle network density is so high, compared to the UK and France where cycle networks appear far sparser. From this we saw that we could combine OSM data with simple mesh density analysis to create ‘Mesh Density Benchmarks’.

Traditional mesh density analysis divides a region into grid squares and provides a simple calculation of the network length within each of those squares. This is basically the cycle network length per unit area. Below we present the average mesh density calculated for three city regions – Exeter, Cambridge and Amsterdam. From this it is clear that the average cycle network density in Amsterdam is almost double that of Cambridge.

Figure 3 shows what a traditional Mesh Density Analysis of Exeter looks like with no target applied. It doesn’t really tell you much. However, by applying the benchmarks to our heatmaps we learn a lot more about how a city is really doing comparatively to others. Figure 4 shows the same analysis, but using Cambridge as a benchmark and Figure 5 shows Exeter’s cycle network benchmarked against Amsterdam.

How can it help?

By looking at cycling in this way, we can very quickly see how our city compares at an aggregate level or at the level of the chosen zoning system (in this case grids). This type of analysis can enable cities to set targets, in particular for certain zones such as ‘Mini Hollands’. By choosing a data source which is frequently updated, such as OSM or OS Paths, cities can regularly track their progress against specific targets. For example, Exeter would need approximately 294km of extra cycle path to equal Amsterdam’s mesh density.

They can also be used to compare network density with trip rate data to determine where infrastructure is not translating into behaviour, creating the potential for targeted interventions.

Establishing key connectivity corridors

This technique can be used in conjunction with the identification of key cycle routes or cycling superhighways. The idea here is that the network around these routes should be as connected as possible in order to encourage their use. Simply by looking at the benchmarked mesh density along these routes, cities can understand whether there are sufficient links to their superhighways or in key residential (origin) or destination zones.

Combining with quality data

Finally, similar extensions of these approaches can be applied when path quality data is added – for example the development of a simple quality metric with the data collected via audit or crowdsourcing. This then enables cities to quickly identify potential bottlenecks and relevant network improvements.

Porosity Analysis

What is it?

Porosity analysis can be thought of as an analysis of the integration between naturally developing zones within a city. It assesses the accessibility into and out of these zones and can be observed for walking, cycling or other networks. The zones are created by looking at natural, physical or human barriers such as major roads, rivers, walls buildings etc. The crossing points between these zones are then mapped. For example, under this analysis a bridge over a river or motorway would be identified as an entry / exit point between 2 zones. The porosity analysis counts the number of entry / exit points, providing another measure of the relative accessibility of each zone. In the Exeter example below, it can be seen that the cycle-way to the north of the River Exe has a number of access points. This is the pedestrian- and cycle- friendly area known to residents as Exeter Quay. However, this analysis shows many zones where OSM tagged paths do not provide as much accessibility. This indicates the existence of barriers that are likely to act as a deterrent to walking and cycling within the network.

How can it help?

The key use is to ensure that key zones are joined up and interventions to reduce major barriers are understood in more detail. For example, a city may wish – having decided on its core walking zones – to understand how interconnected they are. This analysis can be used to ensure that moving between these zones – usually pedestrian- or cycle- friendly areas – is as easy as possible. Similarly, links to major transport hubs should also be considered – for example, a closer look at the section of Exeter between Exeter Quay and the major railway hub (Exeter St Davids), shows far fewer access points and hence a weaker zonal integration. Finally, in order to encourage walking and cycling, it is also important to enhance accessibility from residential zones. Using this analysis, zones can be categorised by type, giving planners the opportunity to set strategies for different elements of land use. Interventions can then be further prioritised using different, more detailed metrics.

Analysis for Linked Journeys

What is it?

To design sustainable travel into the fabric of a city, a range of techniques focusing on specific modes can be used. However, the linked nature of journeys is often forgotten. The propensity to make a sustainable journey tails off as the distance of that trip increases (Figure 7). A linked journey however is one that uses multiple modes – for example walking to the bus stop, getting a bus and then walking a short distance. Linked journeys are absolutely essential in the promotion of sustainable longer distance trips. Linked journeys can be understood through a range of lenses, such as accessibility, cost, equity or capacity. In the visualisations below, we introduce some simple accessibility analyses for linked journeys.

The visualisations below show two examples of isochrones that combine rail + cycling and rail + walking respectively. These effectively show how far you can get from a particular site in 30 minutes of travel time. To create these we generate an isochrone for each public transport stop including the cost of getting to the stop added. The example below shows how far you can travel from Exmouth station in half an hour on a weekday.

How can it help?

These linked journey isochrones provide an important analysis based on the distances commuters and other travellers are likely to travel if living in sub-urban areas within large cities where the distances themselves become large. Linked journey analysis can be used in the following ways:

1. Single Site Analysis

The above examples (Figure 8 & 9) focus on two separate sites within Devon. This single site analysis is useful for travel plans and transport assessments, and can inform much about the viability of public transport options from new developments. Very often we see travel plans that don’t include clear maps to this effect.

2. Strategic Level

It is possible to use this technique to create comprehensive metrics of linked-journey accessibility across an entire city. This approach, at a strategic level, can enable policy makers / transport planners to identify areas (or demographics) with poor accessibility to jobs, education, healthcare or leisure facilities and target interventions / public transport improvements appropriately.

3. Mode Competition Analysis

It is also important to consider the competition between modes. When making a choice to use public transport, citizens are also considering the alternative modes available to them. By overlaying two or more isochrones, visualisations can identify (and statistics quantify) areas where public transport options are not competitive with private car, and, as a result non-sustainable modes are preferred. This can help prioritise interventions to increase the uptake of walking and cycling.

Author

Alex Dawn MSci MCIHT MTPS, Transport Modeller.

Special thanks to Greg Howell, Summer Intern 2017 for his help developing these tools.

Part 2 of 2 – Part 1 covered cycling accessibility analysis and the LCWIP walking zone