As Data Coordinator for the Abundance Project, a key part of my role is to make sure all the information we collect on the project is kept organised and secure, and then, most importantly, to try and make sure it is accessible. Accessibility means that the information is usable and understandable not just to technical experts and academics, but ideally to anyone and everyone to who it could be of use, and particularly for the participants and communities who helped collect the information.
Data collected on the Abundance project
For the Abundance Project, the information we have been collecting has related to three interconnected elements:
- Mental wellbeing,
- Accessibility and use of what we have been calling community, green and cultural assets (CGCAs), and
- The impact ethnicity and heritage have on these first two elements.
This information has been collected both at a local level working with our five Community Research Hubs in Southwest London running walking interviews, focus groups and workshops, as well as at a national level, having recently completed a survey of 1,500 people across the UK on their experiences of CGCAs and their impact on mental wellbeing.
Having collected this enormously rich set of data from thousands of contributors, the challenge now is to try and organise and make sense of this information so that insights can be drawn, providing guidance to practitioners, health professionals, policy makers and communities both locally and across the UK as to how they can better support the mental wellbeing of those from all backgrounds through the use of CGCAs.
Using visualisations of data to increase accessibility
One way to begin making sense of this data is through what is called data visualisation. This is when data, rather than being explained through many words and reports, is instead displayed using graphic design and illustrations. Such an approach can turn what is otherwise an overwhelming list of experiences, views and opinions, into a much more compact image.
This data visualisation approach can generally be described as having two different approaches: explanatory, and exploratory.
Explanatory data visualisations
Explanatory visualisations are an approach that communicates specific findings to inspire specific actions. For example, the visualisation below shows the results of a question from our national survey on whether those answering agreed or disagreed with the statement: “I feel safe walking alone in my local area after dark”.
As we can see, the graph is explanatory in that it picks one specific finding from the survey and focusses on the fact that women were half as likely to feel safe than men in public. This data can therefore be used to inspire actions that aim to improve the safety of women in local communities.
Exploratory data visualisations
The other type of visualisation – my personal favourite – is exploratory. This is where a specific finding in a dataset is not clearly laid out for the viewer, but instead, the viewer is invited to explore the data themselves. This allows the viewer to discover their own insights and patterns, make up their own ideas, and perhaps spot things that a single researcher presenting the results wouldn’t.
For example, below is an exploratory visualisation of conversations that took place during one of our community walking group interviews in Kingston. In this visualisation, topics brought up in conversation are shown as circles. The more frequently a topic was discussed, the larger the circle. If the circle is green, that means that topic was mostly discussed positively. If the circle is red, that means negatively. Yellow, a mixture of both negative and positive associations. Lastly, topics discussed in relation to each other are connected by lines. See if you can spot any insights from the conversation yourself.
The challenges in exploratory data visualisations
There is a challenge in these types of visualisations however, that they can become very complex very quickly. The visualisation you are seeing above is a simplified version of the conversation; the actual conversation had ten times more topics than displayed and therefore can be – as you could imagine – overwhelming to explore.
Therefore, the key to these types of visualisations is often in providing the user better tools to explore the data themselves. For example, below is the same visualisation as above, but where the single topic of conversation ‘access to green spaces’ has been highlighted, allowing us to focus specifically on that particular topic and its other connected topics.
Doing so has made insights related to this topic a little clearer, showing that access to green spaces is very positively connected to mental wellbeing, and is supported by awareness, proximity to home, disabled accessibility, the presence of community, and the size of the park. It has also shown us that access to green spaces is negatively impacted by over-crowding and the visibility of the entrance.
Next steps for data visualisation on the Abundance project
Moving forward, we plan to continue experimenting with these different kinds of data visualisations and exploring ways to make them accessible to all our audiences. This could involve making the visualisations you see above available online, where users can click, explore, and use different tools to discover insights for themselves. We’re also developing other formats, such as interactive maps showing the spatial distribution of our national survey results – like the heritage map below, which reveals the diverse backgrounds of our respondents across the UK.
Ultimately, our goal is to ensure that this collection of community knowledge doesn’t just sit in academic reports, but becomes a resource that communities, practitioners, and policymakers can actively use to create more inclusive and mentally supportive environments for everyone.
This post was written by Jonah Rudlin, Data Support Coordinator for the Abundance Project.