What comes first: The destination or the footfall? On the one hand, a great destination might help increase the number of feet on the street. On the other hand, a high footfall location can help sustain more exciting destinations. With our town centres under pressure to deliver both, maybe we need better tools to balance our decisions.
On February 3rd 2023, the Oval Partnership and Loughborough University hosted a PX Forum at Hassell’s London studio, marking the beginning of a Knowledge Transfer Partnership created to push the boundaries of pedestrian forecasting tools for urban regeneration. The forum brought together regeneration consultants, developers, GLA officials, and local council officers to discuss what needs could be met, and what challenges might be overcome, by applying advances in data science to footfall planning.
Setting the scene
Retail-centric town centres and high streets have experienced a continued period of decline in recent years, exacerbated and accelerated by the shift towards online retail during the COVID-19 pandemic. As the needs of communities change, it is becoming increasingly apparent that a diversity of land use and a distinctive sense of place are crucial in creating a high street that is a ‘destination’, generating consistent footfall across an entire day or week. Therefore, understanding the needs of a community is essential to ensure that the appropriate amenities are provided in an inviting layout.
Pedestrian footfall data allows for a greater understanding of these needs. For example, if you know the number of people passing through a train station at rush hour, this information can be used to determine the size of platforms and terminals needed when that station is redeveloped.
Modern footfall data can be collected from a range of sources: sensory data collected by physical sensors that recognise passing pedestrians, location data generated from mobile devices (aggregated and anonymised to protect privacy), location data generated through social media (albeit non-representative of a broad user group), and purchase data generated from transactions in shops, restaurants, cafés, etc.
The aim of this Knowledge Transfer Partnership is to ultimately harness these increasingly rich data sources to develop a tool that can be used to predict future footfall and to understand how interventions might affect the distribution of this footfall.
To achieve this, stakeholders were gathered to determine what the industry demands for footfall data might be. This summary sets out some of the key takeaways from their discussion.
- 1. The data exists but our use of it lags
As outlined above, there is a diverse range of rich data sources at our fingertips. Data science has been applied in other industries to great effect – healthcare, advertising, and more. To date, there has been a lack of vision in using these resources in the fields of planning and urban design. Stronger decision-making is needed by those in authority, industry-wide. We need to make sure we harness every resource available.
“For property decision-making, we almost have access to too much data as an industry. It is how we use it that sometimes lags behind.”Tom Atkinson, TfL Property
- 2. Data has limitations
Footfall data collected from mobile phone data and card transactions have substantial blind spots in that they do not account for people without phones and bank accounts, such as children or the economically deprived. Similarly, variations in the level of data coverage can result in incomplete, inconsistent, or misleading datasets. There is a risk that the use of quantitative data only in decision-making could have negative consequences and disrupt local economic ecosystems, creating ‘Damage through Data’.
“We need to recognise the limitations of data. If we’re now looking at the high street as an experience, it’s important to understand that transactional data no longer defines the use and value of our high streets.”Stephanie Edwards, Urban Symbiotics
- 3. Combining the qualitative and the quantitative
Data-driven insights are only useful when that data is relevant to the community in question. Different communities behaving in similar ways might be doing so for completely different reasons. Quantitative data (footfall) is useful for telling us what is happening at a large scale, although it is limited in capturing underlying social structures and infrastructures. This is where qualitative data comes in, letting us better understand why things happen.
A forum participant shared this example: footfall data might tell us that young men in a neighbourhood visit a barbershop regularly; meanwhile, qualitative data might tell us that they visit that barbershop not just for haircuts, but for companionship and to socialise. Footfall data alone can cause us to miss ‘invisible’ social infrastructure of this nature.
“It might be that the guidelines for how to use these tools will be as important as the tools themselves, to ensure that people aren’t blinded by the quantitative aspects.”Dr Zoe Marshall-Jones, Loughborough University
- 4. Many pieces make a whole
Built environment professionals working with the spatial and physical fabric of high streets cannot of themselves conjure cohesive social structures, but they can strive to create the right conditions for these to thrive. High streets that layer uses and users are generally more resilient and vibrant than monofunctional spaces. Similarly, as great as any single data set might be, the magic of modelling is in the combination of multiple diverse variables.
“You need data to measure the effect of combinations. It’s how the whole thing comes together that counts.”Simon Bishop, Islington Council
- 5. Changing needs or driving needs?
Future high streets must be tailored to future needs. There has been an overarching shift towards online retail during the pandemic but this is especially true for Gen Z and millennials. Any tool that predicts footfall should be able to account for how the needs of a community will shift over time. An advanced footfall tool could give us insight into what is attracting people to a place – the popular attractions and key drivers of footfall – and might be able to predict how these attractions and drivers will evolve over time.
“Predictive modelling is always challenging. Any predictive analysis is trained on past data, and therefore it may be more misleading than accurate.”Craig Campbell, London High Streets Data Service
- 6. Morality of data
Data has been used in regeneration projects to design places for commercial profit. For example, designing places that extract the maximum number of transactions out of people who use them, or designing places to maximise the amount of rent that a landlord can collect from their tenants. Data could similarly be used to design places that exclude specific groups of people or intentionally dissolve social infrastructure. How do we ensure data isn’t used nefariously, and who will hold the people to account?
“Our focus should be on local communities. We have a responsibility to remember the human side of data.”Cristina Gaidos, Croydon Council
We look forward to sharing further updates from this project as the research progresses.
About the initiative
Knowledge Transfer Partnerships (KTPs) aim to help businesses improve their competitiveness and productivity through the better use of knowledge, technology, and skills within the UK knowledge base. This KTP project was funded by UKRI through Innovate UK.
- Martin Wedderburn, Wedderburn Transport Planning
- Dr Zoe Marshall-Jones, Loughborough University
- Jonathan Pile, The Oval Partnership
- Olumide Odetunde, The Oval Partnership
- Tom Atkinson, Transport for London
- Simon Bishop, London Borough of Islington
- Craig Campbell, GLA
- Stephanie Edwards, Urban Symbiotics
- Prof. Marcus Enoch, Loughborough University
- Cristina Gaidos, Croydon Council
- Isabelle Hease, Visitor Insights
- Richard James, Hassell
- Ahmad Merii, Inner City Consulting
- James Parkinson, GLA
- Aishni Rao, GLA
- Victoria Smyth, Avison Young
Edited by Fergal McGinley & Camilla Siggaard Andersen