We often tend to create laws and regulations by blindly hoping that we will get them right, but in reality there are precise ways to measure the invisible processes under the surface that give rise to these laws.
The hidden dynamics embedded in our communities, like values, perceptions, trust, influence, local knowledge, etc., seem like subjective categories, but imagine being able to measure them. Imagine visualizing how they cluster, where and by whom, and how the carriers connect to the other actors and the other groups.
Social Network Analysis (SNA) can create such maps.
SNA can help decision-makers see how people, organizations, and ideas relate, and can provide a way to measure, intervene, and navigate those connections.
The iceberg of governance
How to use SNA to our advantage
I got introduced to urban governance topics in my Ph.D. program. My exposure to the work of Christakis and Granovetter on Social Network Analysis came at roughly the same period in my life. I was in my second year and in need of massive changes to my scientific mindset to continue. Within the first pages of “The Strength of Weak Ties”, I was hooked. Understanding the power of SNA was comparable to the moment when Neo gets the Matrix.
SNA allows us to comprehend what and why something is happening in a system, organization, or governance network, and exactly how we should intervene in the network to respond and react to make the best decisions possible.
Ever since I discovered SNA, I have been using this tool for making green infrastructure implementation more effective.
For example, I flew to Tuscon, Arizona to work on a research project dealing with urban green infrastructure (UGI) implementation, aiming to create the methods for more effective and efficient project implementations. I focused on local governance network mapping and analysis and using them as tools for steering planning processes. More particularly, I produced the visualizations and analysis of environmentally knowledgable, influential, trusted, central, “in-between”, and marginalized actors - and their relationships. These are some examples of those networks:
SNA graphs: The nodes represent actors in the governance network. Different shapes and colors represent sectors, institutions, and departments. The links show with whom these actors communicate and collaborate, and the size of the node is how knowledgable (graph above) or influential (below) they are.
The fascinating thing about SNA is that quantitative analysis of no more than just these two graphs provides rich and reliable data to “map” dispersal of knowledge, influence, and centralities, and to direct interventions and actions to deal with the most common issues in UGI implementation, such as:
- Shaping attention to UGI and maintaining public interest: SNA identifies influential actors and relationships that can be used or advanced to shape attention and public interest to the UGI problem.
- Surviving political environment: SNA sheds light on informal relationships that influence planning processes and outcomes. SNA makes these structures visible and helps identify communication gaps and related problems in the implementation process.
- Coordinating multiple levels of organizations - SNA offers tools to visualize and analyze complex relationships within multiple political jurisdictions across the local, regional, state, and even global levels, and non-governmental actors. SNA can identify key players, cliques, and marginalized groups and their qualities to create bridging and mediation strategies.
- Achieving better coordination, cooperation, and participation: Understanding the distribution of influence and environmental knowledge in governance provides insights into which actors or connections it would be wise to engage or empower for better implementation of UGI related issues. Such an analysis can also help assess institutional heterogeneity and key institutions in the network and help create valid participative processes.
- Utilizing environmental knowledge: SNA recognizes knowledge carriers, their distribution, and their juxtaposition with perceived influence. With this information, we can build optimal links and empower relevant actors to increase knowledge throughout the governance network.