Influence Mapping—the documenting of relationships between people, organizations, and political processes—has roots in sociological, anthropological, and journalistic methods going back a century. The ability to deploy mapping tools in public, on a large scale, and at relatively low cost, however, is new and only now approaching critical mass in early adopter communities—principally among data journalists and civil society groups.
As with most young technologies, the early days of influence mapping have seen a great deal of parallel innovation. Most projects have followed idiosyncratic paths, drawing on diverse technologies and arriving at as many answers to basic questions about data structure, trust, and analytical methods. As the number of projects has grown, this fragmentation—in our view—has begun to limit the effectiveness of influence mapping efforts in general. With a few exceptions, projects don’t connect. There are no metadata or API standards for network mapping efforts, no sharing of visualization or analytical tools, and little coordination around the aggregation of data or sharing of development costs. There is, in short, a great deal of unrealized potential.
Influence Mapping is an effort by this community to take the next step in making mapping a more powerful collaborative enterprise—one that can share data and development costs, bring more analytical power to bear on wider networks, and better engage its key user communities: journalists, researchers, and advocates. The project’s focus is standards development and the improvement of sharable toolsets.
The Tools for Investigative Journalism workshop brought together 30 journalists and developers to advance the state of the art around these tools, including social network mapping and datamining tools. The group met at City University in London, September 21-22nd.
Oligrapher is a social network mapping tool designed to support the work of data journalists and other researchers who need to tell stories based on complex social network data. The Influence Mapping project supported the development of a standalone version that can be downloaded for use with any dataset.