Communities are complex ecosystems, so we mapped our insights to reflect this

Through 16 weeks exploring the ‘strengths and needs of the communities’ in Neath, Nelson and Camborne, the team collected almost 2000 quotes and observations (more on how we researched can be found here). This is a huge amount of data which needed to be analysed, grouped and presented in a useful way to identify the right problem area as we designed ideas to test.

It was especially import for us to understand the intricacies and nuances of our insights as we weren’t looking simply for ‘the biggest challenges being faced in the communities’. Our work aims to understand the British Red Cross’s role within a place, and how we can be more led by the communities we work with. This angle means we needed to:

• understand how we can provide value across all 3 communities by addressing identified local challenges,
• whilst finding our role in the community (a.k.a without creating duplication or forcing ourselves into an inappropriate power roles that may negatively effect local dynamics).

Collaborating to analyse the insights.

It took whole lot of team dedication to get from 2000 quotes and observations to 96 findings.

Turning our research into findings

We started our insight analysis whilst still under covid restrictions, and with the team based all over the UK, working through the insights had to be done remotely. We were open to testing a variety of ways of digesting the information:

• Group calls working on our own with background music playing
• Completely individual working with ‘playbacks’ twice a day
• Whole team discussions and reflections

For the majority of the team, this was their first time carrying out a task like this. Pairing up and bouncing thoughts off each other, then regularly coming back together to share progress kept the team energy high and made the process feel more manageable.

This was no small job. We started by affinity mapping on Miro until we had 386 insights. This is a process of grouping common learnings as you can see in the middle image below. Whilst this was a lot more manageable, there were a number of similarities and cross overs, so we synthesised one step further to create more robust findings. This sounds simple but was actually really difficult — as is all synthesis, and in the words of Jo Straw, it. took. ages.

Quotes and observations > Insights > Findings

Whilst Miro can be great in a number of ways, it’s pretty hard to replicate those ‘take a step a back and let’s get some perspective’ moments, online. The debrief at the end of every session held the space for reflection on standout insights and key aspects of our work moving forward. This really helped prioritise our learnings and was our first step in recognising just how interconnected all the learnings in the community are.

Commonalities across communities

We discovered over 70% of the insights gathered were common to all 3 towns. Whilst we were looking for a hyper local solution, we were also looking for something that can be adapted and scaled across the UK. To do this we focused on the commonality rather than unique local insights. The towns varied in size, diversity and location in the UK, but also face similar challenges such as:

• High levels of unemployment due to the decline in industry.
• New transport links bypassing community infrastructure and local commerce, driving people towards nearby cities.
• All 3 communities have been felt to be ‘thriving’ during times of peak industry and residents have experienced and witnessed a decline, which has affected mental health and opportunity.
• High levels of health inequalities.

Mapping the insights to reflect the community

My role as the Service Designer was to take these 96 findings, and present them in a useful way to help us identify the right problem area which we would carry forward to our idea workshops and testing.

At this point, we needed to move off Miro and into the real world (an unexpected luxury). Jessica and I spent 2 days categorising insights, understanding the breadth of what we found.

13 categories emerged — from Behaviours attitudes and beliefs, to Identified health challenges, and Funding and investment.

Theming the findings

Whilst we could have stopped there, I felt the categories were helpful, but oversimplified the insights and put the community attributes into isolated ‘boxes’ instead of reflecting how interconnected each category is. Our research saw community work being often carried out in a silo, not acknowledging the wider picture, meaning the proposed solution didn’t necessarily meet the need. I wanted to make sure our insights didn’t perpetuate this.

The categories also didn’t acknowledge which of these areas have a lot of energy and resource behind them, and which have yet to be resourced — a critical dynamic to avoid resource duplication.

I’ve mentioned this interconnected aspect of the community a few times now — let me give you an example to bring it to life:

When local people highlighted the high levels of antisocial behaviour in the communities, they also discussed the lack of safe spaces for young people to hang out in, the rising level of unemployment in the town and the need for more mental health services. These are all individually important insights and local learnings. They are all also intrinsically linked and impact each other.

I took the categories and started mapping all the links. but as you can see below, whilst it may show the complexity, it’s far from usable or useful. I needed to break down the system into more manageable chunks.

Manipulating and moving the groups of insights around both online and in real life, I found there was 8 clear chunks — or 8 insight and interdependency maps as they became known.

What is an insight and interdependency map?

The insight maps are sections of the wider insights ecosystem which show how our findings interconnect and relate to each other within the community.

We wanted the maps to be useful for our team and others:

• to be our North Star when designing the testing phase and a place to ensure we were considering how our pilot would fit into the wider community ecosystem
• to support other areas in the organisation with qualitative insights as our Strategy2030 looks to understand our role within a place
• to support future work in these communities, and communities of a similar nature
• to understanding areas for partnership and team collaboration.

It took a number of iterations (and a fair bit of disco music) before the Eureika moment came, but it was great when it did! The following maps are a conscious effort to give us a more nuanced perspective, prioritising the relationships and links that must be considered when working at a community level to make positive and lasting impact.

Using the Maps — taking inspiration from the London Tube

Similar to the London tube map, the complete community ecosystem map can be a little overwhelming. Sometimes it can be easier to navigate by looking at one tube line at a time. Our insight maps are like looking at just the northern line, and how the maps relate (shown in the next section) is the whole tube map.

The overarching insights on the 8 maps are:

1. People don’t know where to go for support
2. People want more activities and opportunities to build community relationships
3. People aren’t able to access support locally
4. Wider community action needs a catalyst to build a movement
5. There are a lack of activities and safe spaces for young people
6. Lots of funding has come to the town with limited improvement for residents
7. Negative impacts of mental health are on the rise
8. People help their communities in ways that work for them.

I’ll walk you through an example map below:

Community behaviours, beliefs and attitudes (pink), and Community dynamics (blue)

In the pink and blue boxes along the top, you’ll find 2 of the insight categories:

• Behaviours attitudes and beliefs at community member level
• Community Dynamics at collective community level

When mapping out the interlinks, these 2 categories were embedded throughout. It’s easy to understand why — behaviours at an individual level and collective dynamics effect all aspects of the community in its existing structure and any opportunities for progression and development. The most relevant of each are featured at the top of the insight maps.

Interdependent insights

The main body of the map is a web of insights. Alongside the connecting lines, there is also a traffic light colour scheme which shows how well resourced and supported this aspect of the community is.

This is significant to our work for two reasons:

1. When using a strengths based approach, it’s important to understand where those strengths lie within the community to build out from
2. We wanted to avoid duplication of existing services or resources in what we piloted

As an example, these maps highlight there are a lot of local support and service offerings (green box right hand side of the map) in Neath, Nelson and Camborne. One regional charity leader commented that ‘I have no reason to work in Nelson, so many charities work there, our resources are better placed in the rest of the region’, yet when you speak to people from the community, they said they struggle to get the support they need. So it’s clear commissioning one of our services in the regions would not meet local needs and instead we should explore the rest of the map in order to create an impact in this area.

When looking into the wider map — the picture starts to fill in with understanding that people often go to friends and family before they come to services (and insight echoed in our fires and floods discovery in 2018), or that people who are accessing services aren’t getting access to support because of eligibility criteria and referral loops. This deeper understanding helps deliver outcomes that create longer lasting impact.

How do the maps interact with each other?

Using the same analogy as before, this is the London Tube map. The interdependency maps are numbered 1–8 and common themes across the maps are labelled A-H.

Not only does this illustrate complexity of a community and therefore why we cannot work in a silo, it also helps to navigate which map to focus on. For example when exploring local funding, maps 1, 3, and 6 would be most helpful!

Mapping our insights this way was incredibly useful when working with communities, aligning and agreeing on our co-designed direction for testing. We hope this can be a resource for other teams too, please take some time to explore any themes of interest or that relate to your work through the maps here.

As always, if you have any questions or would like to discuss community approach futher, I’m always open for a chat!:

charlottewilton@redcross.org.uk or on twitter @charjwilton