My thesis focuses on how international and national actors work with each other to reduce disaster risk— the relationships, governance structures, and power dynamics shaping risk reduction. Concern is my case study and I’m looking at its work in three countries: Kenya, Sierra Leone, and Bangladesh. I’m currently in the process of finalising my dissertation and I wanted to show something that I’m excited about: a few of the maps that I’ve produced for this project.
DRR is multi-sectoral and cross-scalular and multi-hazard, in other words, a rat’s nest of actors, interventions, and hazards. To top it off, actors each have their own interpretation of what DRR actually is, meaning that one person’s DRR is another person’s development or health.
This leads to the practical problem: how do you understand DRR? While some people define DRR a-priori, I’ve turned to regime theory to make sense of this. Regime theory is about figuring out how ‘regimes’ (the confluence of actors around a given issue area) operate and the effect they have on actor behaviour. It’s similar to new institutionalism and polycentric governance, but with a special focus on delimiting the issue area and the actors within it. All this makes it a pretty good fit for my research, and there’s been quite a bit of work on it already so it provides a valuable platform for guiding the study.
I’ve approached understanding DRR interactions as a mapping problem. I’ve collected a bunch of qualitative data (interviews, reports, policies) and quantified it using NVivo, a pretty awesome programme for qualitative analysis. I then plugged it in to Gephi, a tool for social network analysis (SNA), to map the regime. Since SNA is about actors and relationships I’ve had to code those actors and relationships. For example, the sentence local communities work to reduce flooding would be coded as local communities –> flooding; the sentence Concern works with the national government would be coded Concern –> national government. I’ve also categorised each actor by type (public, private, civil society) and level (local, national, international). While this is a meticulous way to analyse data and has involved thousands of codes, I’m pretty happy with the results.
Here are a few of the maps I’ve produced:
The colours in these maps are linked to type: blue is civil society, orange is government, red is private sector, and white is other. The size of the node (circle) and edges (arrow) is dependent on the number of times coded (the more codes, the larger the size) and is an indication of importance. It is important, however, to note that size is relative to each regime and that for size these maps cannot be cross-compared.
From the maps, it is clear that for all three cases the regime, by number of actors, is dominated by public sector with some civil society and very little private sector. That said, in all cases core actors (actors occupying central positions and with a lot of big arrows) are a mix of public and civil society actors. The maps also show the relative scale of the regime, with Sierra Leone (the first one) having the fewest actors, Kenya (middle) a moderate number of actors, and Bangladesh (last) a massive number of actors. This stuff is useful for me because it provides a basic picture of the regime and the interactions, showing that the actors vary in size but are similar in type.
I’ve also simplified down actors to their essence:
From these maps it becomes clear that while DRR is often described as multi-sectoral, the private sector is relatively absent from DRR activities. Interestingly as well, it can be seen that types of relationships and interactions are similar. I’ve also developed maps of how these actor types engage with various hazards, showing the hazards and interventions under the regime.
These maps provide me an overview of what the regime is. After this I need to understand how the regime works— the arrows between the actors. I’m doing this through qualitative data analysis to produce a narrative of actors and their interactions. The narrative is really interesting, particularly the use of mapping, training, and documentation to measure disaster risk and make it actionable (regime construction toward a Weberian bureaucracy).
Both these techniques are letting me figure out how DRR works, which is great, because, even with my years studying DRR, I am still deciding what I think DRR actually is. Results also have policy implications for NGOs and governments working to improve DRR, as they show gaps in the current DRR system as well as how that system operates.