resilience building and humanitarian networks in disaster...
TRANSCRIPT
Resilience Building and Humanitarian Networks in Disaster Risk ReductionChih-Hui LAI (賴至慧), PhD
Associate Professor National Chiao Tung University, Taiwan
State of Disasters
▪ Global disasters (2017)
▪ 335 natural disasters
▪ Affected over 95.6 million people, killing 9,697 people
▪ Costed US$335 billion
▪ Burden was not distributed equally
▪ Asia as the most vulnerable continent for floods and storms
▪ 44% of all disaster events
▪ 58% of the total deaths
▪ 70% of the total people affected
6/29/2019
Organizational networks in disasters: application of social
media and other technologies
6/29/2019
Organizational networks in disasters: application
of social media and other technologies
Technology use and communication strategies by non-
profit (humanitarian) and public sector
organizations
6/29/2019
Organizational networks in disasters: application
of social media and other technologies
Individuals' social networks and
technology use in disaster risk
reduction
Technology use and communication strategies by non-
profit (humanitarian) and public sector
organizations
6/29/2019
Social network lens
Outline of the Presentation
▪ Basics about social network and resilience
▪ Resilience enacted in interorganizational networks in disasters
▪ Interorganizational networks in four consecutive disasters in Asia-Pacific and Middle America
▪ Resource network after a technological disaster in Kaohsiung, Taiwan
▪ Online and offline disaster response networks after Typhoon Haiyan in the Philippines
6/29/2019
What is a Social Network?
▪ A set of ties linking pairs of nodes of the same type
6/29/2019
Key Components of Network
Node Types (actors)
▪ Persons
▪ Families
▪ Groups
▪ Organizations
▪ Countries
▪ Words
▪ Websites
Relations (ties)
▪ Kinship
▪ Communication
▪ Trust relationship
▪ Business/partnership
▪ Outsources to
▪ Lending drug needles to
▪ Members of same club
▪ Giving information to
6/29/2019
Why is Social Network Analysis “Hot” Today?
▪ Provides answers to explaining regularities of human societies
▪ E.g., why companies with similar organizational culture tend to become partners, why two people facing different situations end up exhibiting similar behavior?
▪ Provides answers to explaining variations across groups/contexts to account for differences in outcomes
▪ E.g., why two project teams differ in their results of negotiating deals with clients?
6/29/2019
In Disaster Risk Reduction, A Social Network Lens Explains….
▪ Why certain individuals respond and recover faster than others in the same community
▪ Why certain communities respond and recover differently than other communities
▪ Why certain organizations respond differently to different types of disasters
▪ Why certain organizations get differently out of different relief efforts
6/29/2019
From Network Resilience to Community Resilience
▪ Activated network resilience
▪ The capacity of a network of organizations to join response actions in a structured yet flexible and adaptable way that helps the affected community to respond to disasters
▪ As a process and an outcome
6/29/2019
6/29/2019
Activated network
resilience
Network resilience
after cyclone and
earthquakes in
Asia-Pacific and
Middle America
Online and offline
disaster response
networks after
typhoon in the
Philippines
Evolution of
resource network
after a tech disaster
in Kaohsiung,
Taiwan
outcome
process
How do humanitarian relief networks exhibit resilience in different types of disasters?
6/29/2019
Redundancy
Resourcefulness Robustness Rapidity
Case Study
6/29/2019
Nepal
earthquake
2015/April
Fiji cyclone
2016/February
Vanuatu
cyclone
2015/March
Ecuador
earthquake
2016/April
6/29/2019
Relief networks (cyclones)
Relief networks (earthquakes)
6/29/2019
Vanuatu
cyclone
Fiji cyclone
Co-
located
network
Co-
cluster
network
Co-
located
network
Co-
cluster
network
6/29/2019
Nepal
earthquake
Ecuador
earthquake
Co-
located
network
Co-
cluster
network
Co-
located
network
Co-
cluster
network
Findings I
▪ Resilience varies by disaster type
6/29/2019
Vanuatu cyclone Fiji cyclone Nepal earthquake Ecuador
earthquake
Redundancy Organizations from the affected region are more active, as well as those others from
North America and Europe
In cyclone disasters, organizations
occupying the central position maintain
advantage in redundant networks
1. In earthquakes, networks are more fluid,
going beyond existing clusters;
2. Central organizations‘ advantage is not
maintained in different redundant networks
Robustness Networks influence each other No distinct structure
Organizations of different types are more likely to join a particular type of network
Organizations from the same region are more likely to join a particular type of network
Organizations of different types are more likely to join a particular type of network
Findings II
▪ Resilience varies by disaster type
6/29/2019
Vanuatu cyclone Fiji cyclone Nepal earthquake Ecuador
earthquake
Redundancy Organizations from the affected region are more active, as well as those others from
North America and Europe
In cyclone disasters, organizations
occupying the central position maintain
advantage in redundant networks
1. In earthquakes, networks are more fluid,
going beyond existing clusters;
2. Central organizations‘ advantage is not
maintained in different redundant networks
Robustness Networks influence each other No distinct structure
Organizations of
different types are
more likely to join a
particular type of
network
Organizations from
the same region are
more likely to join a
particular type of
network
Organizations of
different types are
more likely to join a
particular type of
network
Findings III
▪ Resilience varies by disaster type
6/29/2019
Vanuatu cyclone Fiji cyclone Nepal earthquake Ecuador
earthquake
Resourcefulness Organizations’
participation in prior
disasters (Typhoon
Haiyan) increases
the likelihood of
joining the network
for a new disaster
1.Organizations’
participation in prior
disasters (Vanuatu
cyclone, Nepal
earthquake)
increases the
likelihood of joining
the network for a
new disaster
2. Different types of
experience with
Typhoon Haiyan
facilitate the
formation of a
particular type of
network
1. Organizations’
participation in prior
disasters (Typhoon
Haiyan) increases
the likelihood of
joining the network
for a new disaster
2. Different types of
experience with
Typhoon Haiyan
facilitate the
formation of a
particular type of
network
No distinct
structure
Takeaway 1
▪ Disaster management should take into account organizations, network structure,
and environmental factors in building mechanisms of coordination and
collaboration
▪ Relief organizations take form in different types, whose coexistence helps resource
provision
▪ One type of network is more structured (bureaucratic), making it difficult for new organizations to
enter/creating liability for existing organizations
▪ The other type of network is more flexible
▪ Compared with the other three places, Ecuador’s geopolitical location is more unique, with higher GDP
6/29/2019
Takeaway 2
▪ Disaster-specific networks facilitate redundant resource provision
▪ In disasters with longer period of warning (e.g., cyclones), organizations often comply
with existing structure vs. in sudden disasters (e.g., earthquakes), such structure is likely
to be broken
6/29/2019
From Network Resilience to Community Resilience
▪ Activated network resilience
▪ The capacity of a network of organizations to join response actions in a structured yet flexible and adaptable way that helps the affected community to respond to disasters
▪ As a process and an outcome
6/29/2019
6/29/2019
Voluntary sectors
Societal institutions
Media
Business communities
NGOs
6/29/2019
How does the resource network (resource provision & receipt)
exhibit resilience in the process of evolving over different
phases of disaster?
The Case
▪ The incident of a series of gas explosions in Kaohsiung, Taiwan on July 31, 2014
▪ Affected 2 districts (23 neighborhoods) of the Kaohsiung metropolitan area
▪ 31 deaths and more than 300 injured
6/29/2019
Kaohsiung, Taiwan
6/29/2019
How does the resource network (resource provision & receipt) exhibit
resilience in the process of evolving over different phases of disaster?
Findings I
▪ Relief organizations’ resource provision network
▪ The networks at consecutive time points significantly predict one another
▪ Response organizations’ receipt of resources from other entities helps their relief operations
▪ But not during the last period of observation (two months after)
▪ Particular network patterns immediately and one month after the incident
▪ No clear pattern two months after the incident
6/29/2019
Findings II
▪ Affected communities’ resource receipt network
▪ The networks at earlier time points significantly predict the later ones
▪ Centralization disappears after the 1st four months
6/29/2019
From the Community’s Perspective: 1st Four Months After the Incident
6/29/2019
From the Community’s Perspective: 2nd Four Months After the Incident
6/29/2019
From the Community’s Perspective: 3rd Four Months After the Incident
6/29/2019
Takeaways
▪ Application of disaster management
▪ When to provide affected communities resources, when to curtail resource provision
▪ Pay attention to the social foundation that is built by relief organizations during the disaster →transformed into a latent network of disaster risk reduction
▪ Recognize the social foundation that is built by affected communities before and during the disaster →varied capacity of rebuilding
6/29/2019
From Network Resilience to Community Resilience
▪ Activated network resilience
▪ The capacity of a network of organizations to join response actions in a structured yet flexible and adaptable way that helps the affected community to respond to disasters
▪ As a process and an outcome
6/29/2019
6/29/2019
Resource network of relief orgs on the ground
Online humanitarian networks of relief orgs on Twitter
What are the consequences that result from the sustained collaborative
humanitarian networks, online and offline?
The Case
▪ Typhoon Haiyan – November 8, 2013 in the Philippines
6/29/2019
Resource network of 41 orgs on the ground
Online humanitarian networks of 70 orgs on Twitter
Findings
▪ Resilience as network outcomes
▪ Cross-sectional and cross-geographic collaboration on the ground
▪ Self-sustaining online networks
6/29/2019
6/29/2019
Immediately
after the storm
Within 3 months
following the
storm
6/29/2019
T1 （one month
before Typhoon）
T3 （two weeks
after Typhoon）
T8 （two months
after Typhoon）
Takeaways
▪ Application of disaster management
▪ Cross-sector collaboration（public, non-profit, and private) is an optimal way of helping communities to deal with hazards
▪ Pay attention to emergent, robust ways of communication, which help humanitarian organizations to acquire resources for relief operations→transformed into a latent network of disaster risk reduction
6/29/2019
6/29/2019
Activated network
resilience
Network resilience
after cyclone and
earthquakes in
Asia-Pacific and
Middle America
Online and offline
disaster response
networks after
typhoon in the
Philippines
Evolution of
resource network
after a tech disaster
in Kaohsiung,
Taiwan
outcome
process
