Tools to make analysis accessible to decision makers

The first two articles in this series talked about the complex relationships that influence activities and outcomes in cities, then, how Space Syntax Integrated Urban Models (IUMs) can help understand the built environment’s impact on these. This post explains how the outputs of IUMs can be accessed more easily to support integration across departments, and disciplines, to work towards shared goals.?

Transport, Planning and Public Health departments all have a role to play in increasing levels of walking and cycling, however it can be difficult to coordinate projects across departments. Pressures on resource and capacity, complicated organisational structures, differing disciplinary approaches, specialist tools and data, all contribute to make day-to-day collaboration difficult. One impact is that detailed projects developed in parallel can be less effective than if they had been designed in combination.?

Key to delivering the benefits of active travel is for projects to be coordinated in an integrated approach. Working in this way needs strategic insight into the way towns and cities work, specifically the way the built environment enables or inhibits activities across different neighbourhoods.??

In my last article I explained how IUMs can provide outputs that are easy to understand by non-experts. Making this insight accessible to strategy makers requires the removal of technical barriers, and while models are prepared and analysed in industry standard GIS software, it is still specialist and not everyone can use it (or might be out of practice if they don’t use it regularly).??

Over the last few years’ we’ve been working on projects funded by Innovate UK, and with partners such as Emu Analytics, to make the outputs of our IUMs accessible through intuitive, web-based interfaces. Tools such as LINE, powered by Space Syntax, enable decision makers to quickly map data and visualise analysis. Multiple datasets can be overlayed, and filters applied, to identify areas with specific spatial, socio-economic, demographic and/or environmental characteristics. The power of these tools is that they allow users to visualise overlapping elements of complex systems quickly and easily.??

Using the example of active travel, it’s possible to compare data showing where people actually walk to work, with our Walkability Index which shows where the city makes it possible to walk. This means we can see where levels of active travel are low and start to understand what contributes to this. Areas for intervention can then be prioritised, with projects tailored to each area based on its unique characteristics.??

For example; in the image at the top of this article, the places which are walkable (red, orange or yellow buildings), can be seen at the same time as the areas where few people actually walk (red polygons in the background). These places have an environment where walking is possible but doesn’t happen, and could be suitable areas for nudge-type behaviour change campaigns by Public Health.??

The parts of a town where it’s more difficult to walk (green or blue buildings) can be seen at the same time as the places where few people walk or cycle (red polygons). In these areas walking is more difficult, and change is required to the built environment. Planning and/or Transport departments could propose physical interventions such as cycle infrastructure, public realm improvements or changes of land use. To help shape these interventions further layers of analysis can be shown, in this case the most useful routes for walking and cycling (red, orange or yellow streets) are visualised to identify a wider network which can then be designed considering the movement profile of each street and the different users co-present in them.??

Accessing this analysis through an intuitive tool allows these soft and hard interventions to be combined into coherent city- or town-wide active travel strategies that consider the unique characteristics of place. We’ve used active travel as an example, but the same tool can be used to see where there are more vulnerable communities and if they have access to the facilities they need, or who and what is affected by flooding.??

Combining advanced spatial analysis with demographic or socio-economic data brings multiple benefits; opportunities for enhanced collaboration, the potential to deliver projects which are greater than the sum of their parts, interventions which are more likely to deliver their intended outcomes, and which can help address the big challenges of the 21st century.??