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Exponential random graph modeling of emergency collaboration networks

Hossain, Liaquat LU ; Hamra, Jafar; Wigand, Rolf T. and Carlsson, Sven LU (2015) In Knowledge-Based Systems 77. p.68-79
Abstract
Effective response to bushfires requires collaboration involving a set of interdependent complex tasks that need to be carried out in a synergistic manner. Improved response to bushfires has been attributed to how effective different emergency management agencies carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among emergency management personnel on the effective outcome in delivering improved bushfire response. There are, however, very few systematic empirical studies with a focus on the effect of collaboration networks among emergency management personnel and bushfire response. Given that collaboration evolves among emergency management personnel when they... (More)
Effective response to bushfires requires collaboration involving a set of interdependent complex tasks that need to be carried out in a synergistic manner. Improved response to bushfires has been attributed to how effective different emergency management agencies carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among emergency management personnel on the effective outcome in delivering improved bushfire response. There are, however, very few systematic empirical studies with a focus on the effect of collaboration networks among emergency management personnel and bushfire response. Given that collaboration evolves among emergency management personnel when they communicate, in this study, we first propose an approach to map the collaboration network among emergency management personnel. Then, we use Exponential Random Graph (ERG) models to explore the micro-level network structures of emergency management networks and their impact on performance. ERG Models are probabilistic models presented by locally determined explanatory variables and that can effectively identify structural properties of networks. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the 2009 Royal Commission Report dataset, we construct and model emergency management response networks. We notice that alternative-k-star, and alternative-k-two-path parameters of ERG have impact on bushfire response. The findings of this study may be utilized by emergency managers or administrators for developing an emergency practice culture to optimize response within an emergency management context. (C) 2015 Elsevier B.V. All rights reserved. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Emergency collaboration network, Exponential random graph, Performance, Bushfire response, Social networks
in
Knowledge-Based Systems
volume
77
pages
68 - 79
publisher
Elsevier
external identifiers
  • wos:000350929200006
  • scopus:84922758016
ISSN
0950-7051
DOI
10.1016/j.knosys.2014.12.029
language
English
LU publication?
yes
id
35ec999a-1872-48a5-bdf3-8f3f5fe818fa (old id 5297214)
date added to LUP
2015-04-24 14:29:17
date last changed
2017-11-19 03:14:26
@article{35ec999a-1872-48a5-bdf3-8f3f5fe818fa,
  abstract     = {Effective response to bushfires requires collaboration involving a set of interdependent complex tasks that need to be carried out in a synergistic manner. Improved response to bushfires has been attributed to how effective different emergency management agencies carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among emergency management personnel on the effective outcome in delivering improved bushfire response. There are, however, very few systematic empirical studies with a focus on the effect of collaboration networks among emergency management personnel and bushfire response. Given that collaboration evolves among emergency management personnel when they communicate, in this study, we first propose an approach to map the collaboration network among emergency management personnel. Then, we use Exponential Random Graph (ERG) models to explore the micro-level network structures of emergency management networks and their impact on performance. ERG Models are probabilistic models presented by locally determined explanatory variables and that can effectively identify structural properties of networks. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the 2009 Royal Commission Report dataset, we construct and model emergency management response networks. We notice that alternative-k-star, and alternative-k-two-path parameters of ERG have impact on bushfire response. The findings of this study may be utilized by emergency managers or administrators for developing an emergency practice culture to optimize response within an emergency management context. (C) 2015 Elsevier B.V. All rights reserved.},
  author       = {Hossain, Liaquat and Hamra, Jafar and Wigand, Rolf T. and Carlsson, Sven},
  issn         = {0950-7051},
  keyword      = {Emergency collaboration network,Exponential random graph,Performance,Bushfire response,Social networks},
  language     = {eng},
  pages        = {68--79},
  publisher    = {Elsevier},
  series       = {Knowledge-Based Systems},
  title        = {Exponential random graph modeling of emergency collaboration networks},
  url          = {http://dx.doi.org/10.1016/j.knosys.2014.12.029},
  volume       = {77},
  year         = {2015},
}