Abstract
This chapter introduces an alternative perspective to study disaster preparedness and risk reduction (DP/DRR) systems. Study shows that by applying systems thinking and complexity theory we understand better the dynamics and interconnectedness of the DP/DRR. This applies both to interconnected risks (multirisk landscapes) and interconnected actors (multi-actor networks).
These results are part of the broader study commissioned by the Finnish Red Cross (FRC). The aim of the thematic study was to promote institutional learning on DP/DRR project experiences and practices that can benefit better programming in the future. The overall objective of the study was to identify critical issues in designing, implementing and monitoring and evaluation by the FRC and its partnering National Societies (NS).
This chapter consists of two main parts. The first part presents the results of the meta-analysis of the ten countries and 17 projects. The meta-analysis utilises the IFRC evaluation criteria (relevance, impact, effectiveness, efficiency, sustainability and coherence). From this sample, the final case studies were selected. The last part is the case study section introducing the findings and results of the field missions to the Philippines. Case study analysis uses a set of systems methods and tools to better understand the dynamics and interconnection between the risk factors and stakeholders in the field. These results will be presented in Chap. 8.3. The systems approach utilised in the case study provides insights about the dynamics and interconnectedness of risk landscapes and inter-organisational Disaster Management (DM) networks. The study shows that by applying systems methods such as network analysis, the risk components helped local disaster risk management units to better understand the interconnectedness of risk elements and the joint impact of those risks. Also, identifying the relations and connections between the disaster risk agencies and stakeholders helps to explain why certain risk preparedness actions produce better results and effects. The study concludes that the more actors are connected to the network, the more versatile the understanding of the risk preparedness and thus the higher the resilience of preparedness actions.
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Notes
- 1.
- 2.
We use the term disaster preparedness in its broader meaning covering both DP and DRR.
- 3.
Density is the measurement of network cohesion. The density (D) of a network is defined as a ratio of the number of edges (E) to the number of possible edges. We apply valued data so density is defined as the average strength of ties across all possible (not all actual) ties. Where the data are symmetric or undirected, density is calculated relative to the number of unique pairs ((n∗n − 1)/2).
- 4.
The concept of point centrality originates in the sociometric concept of the star. A central point was one which was at the center of a number of connections, a point with a great many direct contacts with other points. The simplest and most straightforward way to measure point centrality, therefore, is by the degrees of the various points in the graph. Tie degree, it will be recalled, is simply the number of other points to which a point is adjacent.
- 5.
With the comparative aspect, other meta-analyses are referred to carry out during the last 3 years. This is a naturally very subjective statement and should be treated as one.
- 6.
Network data was aggregated by coding each actor into a collective actor group. The adjacency matrix was partitioned into submatrices by computing the average scores for each subgroup. This data was thereafter used as N × N network matrix.
- 7.
Degree centrality can be defined as the number of links incident upon a node (i.e. the number of ties that a node has). Betweenness centrality quantifies the number of times a node acts as a bridge along the shortest path between two other nodes. It was introduced as a measure for quantifying the control of a human on the communication between other humans in a social network by Linton Freeman (see more in Freeman 1979 or Johanson et al. 1995).
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Uusikylä, P., Tommila, P., Uusikylä, I. (2020). Disaster Management as a Complex System: Building Resilience with New Systemic Tools of Analysis. In: Lehtimäki, H., Uusikylä, P., Smedlund, A. (eds) Society as an Interaction Space. Translational Systems Sciences, vol 22. Springer, Singapore. https://doi.org/10.1007/978-981-15-0069-5_8
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