Guest blog: Where is the 15 minute-city concept currently failing?

This blog was writen by PhD candidates at the Centre for Doctoral Training (CDT) in Geospatial Systems, Clara Peiret-Garcia, Chris Larkin, Rachael Sanderson, Sophie Mann and Sam Christelow.

In late April, we collaborated with Diagonal as part of an event organised in conjunction with our PhD program, the Centre for Doctoral Training (CDT) in Geospatial Systems at the Universities of Newcastle and Nottingham. The event, known as Challenge Week, allowed us to work on a challenge proposed by a company over the course of a week. This year, we were fortunate to have Diagonal lead the challenge, an experience that proved to be extremely edifying. The challenge required us to answer the following question: “Where is the 15-minute-city concept currently failing?” employing geospatial data science techniques.

The 15-minute-city is an urban planning concept aimed at making cities more sustainable. The 15-minute-city envisions urban areas where residents can meet their daily needs within a 15-minute walk or bike ride from their homes. It promotes the idea of compact, mixed-use neighbourhoods that offer a diverse range of amenities, such as shops, schools, parks, healthcare facilities, and workplaces, all within proximity. The idea is to make walking and cycling to all these amenities more convenient in order to reduce our dependence on cars, improving people’s health and reducing pollution.

The 15-minute-city concept was not new to us. Some of our team members’ research focuses on using geospatial data science to explore the implementation of new urban planning models, such as low-traffic neighbourhoods, aimed at making our cities more sustainable and our communities more liveable. In this respect, the use of geospatial techniques plays a crucial role in achieving these goals. From urban planning to transport and mobility, geodata science can assist in making more informed decisions about zoning, resource allocation, or developing more efficient mobility solutions. This challenge was a great example of what geodata science can offer to policy makers in this respect.

From the very beginning, our group envisioned this challenge as an outstanding opportunity to learn from one another and from Diagonal. The company presented their software Bedrock (b6) to us, as one potential avenue we could use to solve the challenge. b6 is a tool that allows users to, amongst many other things, retrieve data from OpenStreetMap (OSM) and perform a wide range of analyses, including routing and accessibility analyses. Following initial discussions, we all agreed on testing b6 and taking it to the limits by basing all our analysis on this tool. And we were not disappointed. b6 proved to be an exceptional tool for fetching data, with a speed that surprised us all, particularly those of us who had used other tools to retrieve OSM data. Its performance was so impressive that we eventually ran our analysis at a higher spatial resolution than initially intended.

For our analysis, we chose the city of Liverpool. Initially, our aim was to generate an accessibility index based on the categories of urban amenities that could be accessed from each neighbourhood. In a nutshell, we retrieved the limits of the different neighbourhoods in the city and the amenities we were interested in; schools, supermarkets, pharmacies, parks, entertainment, gyms, and ATMs, and calculated how many of them were within a 15-minute walk from the neighbourhoods’ centre. All these tasks we did using b6. We were particularly surprised at how little it took for us to understand the logic behind the tool. It is written in Python, which makes it intuitive and accessible to those familiar with this programming language. Surprised by b6’s performance, we decided on pushing its limits a bit more and moving the analysis down to building level, which, to our surprise, worked fantastically well.

Our outputs can be accessed here and here.

The rapidness with which we obtained our results allowed us to expand our analysis. As a second phase of the study, we incorporated geodemographic data, extracted from the Census 2021, to better characterise our study areas. Overall, the experience was extremely rewarding, and insightful, as it allowed us to further our knowledge both on the technical side of the analysis but also on the socio economic reality of Liverpool.

Collaborating with Diagonal proved to be an exceptional experience. The team’s unwavering commitment to the challenge was truly impressive, as they maintained constant communication with us through a dedicated Slack channel. Their prompt responsiveness greatly enhanced our productivity, which was particularly valuable given the time limitations we faced during the challenge.

Furthermore, Diagonal’s willingness to incorporate participant suggestions and swiftly update the tool on-the-go demonstrated its agile approach to work. By the end of the week, we all felt fully supported throughout the challenge and recognised the immense benefits that resulted from our collaboration with Diagonal.

Overall, we will definitely reflect on this year’s Challenge Week as one to remember, and b6 as a tool to look forward to. We are particularly excited about what Diagonal will develop in the future, so, as geodata scientists, we will keep an eye on them!