As part of LOTI’s LDW DataThinks, Nick Lancaster from Neighbourly Lab, explores how complex geo-spatial high street data could be harnessed by qualitative community researchers to understand more about the high streets that mattered to them.

High streets across London face many challenges, from increasing online shopping, the cost of living and the cost of doing business pressures. How should communities decide how to adapt their high streets to thrive? By carefully considering local people’s perspectives or looking at the cold, hard data on where the problems are?

Why not both? At this year’s London Data Week, we explored how to bring together big data and community-led engagement with a coalition of partners: Our Community researcher team, Neighbourly Lab, GLA High Streets Data Service (HSDS), London Councils, Haringey Council, and the LOTI team who made this work possible. It was a unique opportunity to explore how complex geo-spatial high street data housed by the GLA’s HSDS Team could be harnessed by qualitative community researchers to understand more about the high streets that mattered to them. 

The GLA’s HSDS team have rich and granular datasets about places all over London. Their data focuses on spending, footfall, and shop or building vacancies, which can all be broken down by high street or by time of day. This offers a vivid picture of where people like to go, how much money and time they spend in a place, and where there is disused space. For this project, we wanted to explore how the data could be used as a tool for qualitative community research, and how the community research could add additional texture and insight to the data’s portrayal of high streets in London. Above all else, we wanted to develop a proof of concept for how community research can be embedded within analyses of high streets and other places to support local government decision-making. 

Community Research means involving people from the communities which the research aims to explore, in the research process. It is a powerful methodology for generating insight that draws on the expertise, familiarity, and relationships that community researchers bring to their local area, enabling knowledge generation to take place at a hyper–local level. For this project, the high street our community researchers decided to focus on was Green Lanes in the borough of Haringey. 

In a short space of time, this piece of work achieved several outcomes:

• Provided research training to local members of the community, enabling them to create insights at a hyper-local level as people who know and understand the area
• Surfaced new information about life on Green Lanes for residents, passer bys, and shopkeepers that could inform decision-making
• Tested a new research methodology combining complex high street data with qualitative community research, in turn democratising access to civic data
• Proved the ability of multiple partners and organisation-types to work together collaboratively and at pace (Neighbourly Lab, GLA’s HSDS, London Councils, Haringey Council, and LOTI). 

What did this look like in practice? It began with our organisations working in partnership to connect with members of the community, via VCS (voluntary and community sector) organisations in Haringey. Once we had a group formed, we invited members of the community to a participatory session where we explored research techniques, looked at the HSDS team’s high street data, and decided collectively where we’d like to focus our research based on available HSDS data. We landed on Green Lanes because all the community researchers lived nearby this high street, and cared about it. We then made a research plan, including a discussion guide for off-the-cuff intercept conversations with people on Green Lanes. 

A couple weeks later, on the final day of London Data Week, we met as a group in a little cafe towards the top of Green Lanes. We were 11 in total: 6 community researchers, 2 from Neighbourly Lab, 2 from the GLA, and 1 from London Councils. Splitting into smaller groups, we spent the day walking down and up Green Lanes, having more than 50 informal conversations with people and shopkeepers on the high street. 

The insights that surfaced were too wide-ranging to share fully here (we will also be publishing a short report), but it’s worth drawing out a few. On the topic of community and social connection, we heard a nuanced story. Shopkeepers on Green Lanes described the sense of connection they share with one another, communicating regularly and offering each other support. Residents who had lived in the area longer also felt a deep attachment to the high street, and described the many social networks that ran through it. However, a lack of community space was also highlighted, in particular free and available space to use, like community centres, green spaces, or in the words of one resident “a space for connections that is not a pub”. 

Throughout the day, the community researchers showed maps provided by the HSDS team that captured their data on spending, footfall, and shop vacancies, to residents on Green Lanes. The community researchers used these maps as research tools to spark conversations with residents and discuss the stories they told. 

There were also limits to how far the data could go: eg., a ‘monochromatic’ view that needed the colour of local perspectives to be brought to life; some data on individual retailers were out-dated; etc. The data could illustrate the context – for example, xxx nights had lower footfall – but it needed community voices to illustrate what might be driving that: lower footfall due to earlier and earlier closing hours due to perceptions of crime & safety, and lack of transport availability late at night. These relationships could be further evidenced & queried by data – a virtuous cycle.

This complementarity between two very different types of data (HSDS data and qualitative community research) was a continuous thread throughout our project. The community researchers we worked with drove this research forward with their insights and ideas. Their understanding of Green Lanes, their ability to uncover new insights, and their readiness to corroborate and/ or challenge the HSDS data all emphasised the importance of including local people in research processes.

As our high streets and places become an ever growing priority for local government, this is an important learning. It underlines how top-down civic data and bottom-up community research can be brought closer together, and become a valuable tool for generating evidence for local decision-makers. 

This work was made possible through the support of LOTI as part of London Data week, and the incredible coordination efforts of the HSDS team, London Councils, and Haringey Council.