kansas_sar — Kansas search and rescue communication (1981)
- Description
Communication among the 20 organizations involved in a search and rescue (SAR) operation after a small tornado flipped a pleasure boat on Lake Pomona, in Kansas (USA), from the study of multiorganizational emergency responses of T. E. Drabek and collaborators. The organizations include law enforcement agencies, fire departments, underwater rescue teams, general emergency agencies, and county, state and federal agencies. The County Sheriff took control of the operation, and a command post was created in a Highway Patrol facility. A directed edge from one organization to another indicates communication between them, as in the rows and columns of the source matrix. The documentation describes the original data as valued (communication intensity from 1, about once a day, to 4, continuous), but the "valued" matrix distributed is identical to the binary one, so the network is unweighted. The data were distributed on the Pajek datasets page of the book Generalized Blockmodeling (Doreian, Batagelj and Ferligoj), from which this version was obtained.
The network has the following properties:
- Nodes:
name, the short name of the organization;code, its letter code (A to T) as used in the publications;long_name, its full name.
- Nodes:
- Tags
- Social Communication Unweighted Metadata
- Citation
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- T. E. Drabek, H. L. Tamminga, T. S. Kilijanek, C. R. Adams, "Managing Multiorganizational Emergency Responses: Emergent Search and Rescue Networks in Natural Disaster and Remote Area Settings." Institute of Behavioral Science, University of Colorado, Boulder (1981) web.archive.org
- P. Doreian, V. Batagelj, A. Ferligoj, "Generalized Blockmodeling." Cambridge University Press (2004) 10.1017/CBO9780511584176 Sci-Hub
- Upstream URL OK
- https://web.archive.org/web/20130507093932/http://vlado.fmf.uni-lj.si/pub/networks/data/GBM/kansas.htm
- Networks
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Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
- Ridiculograms*
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