Systems Mapping
In 2019 and then again in 2021, individuals across the Network collaborated to create a 'systems map'. This involved plotting themselves on the map, and then adding detail about how they were connected to other participants involved.
We asked individuals whether they worked together; occasionally, frequently, constantly or not at all.
When comparing the data from both surveys we found that health projects appeared most often on the map and added the most detail when completing the survey.
Unfortunately, many of those who responded to the 2019 event did not fill out the survey map. There was an overlap of only 10 people.
However, we did find that the average degree of connectivity went up in 2021.
2021 Survey
157
people received the survey
44
people responded (30%)
150
people connected
743
occasional, frequent and constant connections plotted
The three maps below show the contrast between occasional connections and frequent ones. As expected, more individuals across the Network are in touch occasionally than constantly.
Occasional contact
Frequent contact
Constant contact
Using Graph Commons, it's possible to also change the sizes of the individuals charted to see various pieces of information more clearly.
For instance, we've been able to see who has the most connections overall and who has the most incoming and outgoing connections.
How it all works
Social Network Analysis (SNA) is a mathematical method which can measure a number of different factors within a network of connecting parts or people.
1. Centrality
Centrality measures the potential of a person to influence a network of others, or their ability to distribute information easily.
2. 'Betweenness' centrality
This tells us who connects the most people from across a Network with ease and therefore has more control over the flow of information.
3. Eigenvector connectivity
This tells us who leaders may be within a network. These are people who are well connected to others who also have many connections.
4. Clusters
This method of mapping can show us where individuals 'cluster' and therefore work together more often and well.