I’ve been trying to generate a snapshot of the thousands of stories we’ve collected. Below are community NGO maps generated for several communities in Kenya and Uganda. Two ngos are connected in this map when a scribe collects stories about both organizations. The network algorithm tries to order all organizations relative to how interconnected they are. Only the “central core” of each map is shown, for simplicity.
This is a slum in Nairobi with about 150,000 people (although certain NGOs have inflated this population estimate to 750,000). Many NGOs work here, but the core NGOs are shown below. (394 organizations)
A bigger city than Kibera slum, but with relatively fewer NGOs. (135 organizations)
This city has too many NGOs in it to generate a useful map. (514 organizations)
Alt version, with sub-clusters colored:
A part of Uganda with a plethora of scribes and NGOs. The yellow cluster appears to be large international NGOs, and the blue cluster are local NGOs. The medical NGOs like Kitovu Mobile and Uganda Cares form the bridge between these two.
Note the center of this “NGO community map” is not an NGO at all, but a bank. (60 organizations)
In the above maps, I have removed stories that are attributed to broad categories instead of NGOs. When you include these labels as connectors, the community maps become much more complex. In fact, there are two distinct clusters here. The yellow one links back to WEWASAFO, the organization that is coordinating story collection in Kakamega. The green cluster is a distinct group.
A map very similar to Masaka (57 organizations)
These maps, like the hand-drawn versions I created previously, should help people at organizations do this stuff:
- Identify potential collaborators in the same community.
- Learn whether people associate one organization with another, indirectly, based on working with the same people.
- Provide leads for cross-organization evaluations. If you serve the same people as another organization, you should be sharing data.
Perhaps these community maps alone are not useful yet. I am looking forward to mixing in other story data, such as the percent of success vs failure stories told about that organization, or quality (measured in diversity of sources) of data about that organization in the storytelling collection.