CDM WORKING PAPER SERIES
Networks That Matter: Planning and practice in the complex disaster environment
following the September 2009 Padang, Indonesia Earthquake
Leonard J. Huggins and Jian Cui
Working Paper: 1103
http://www.cdm.pitt.edu/AboutCDM/CDMWPS/tabid/1346/Default.aspx
CENTER FOR DISASTER MANAGEMENT
Graduate School of Public and International Affairs
University of Pittsburgh
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Pittsburgh, PA 15260
November 3, 2011
The views expressed herein are those of the authors and do not necessarily reflect the views of
the Center for Disaster Management and the University of Pittsburgh.
© University of Pittsburgh, 2011. By Leonard J. Huggins and Jian Cui. All rights reserved.
Working Paper: Not for citation without consent of the authors
Networks That Matter: Planning and practice in the complex disaster environment
following the September 2009 Padang, Indonesia Earthquake
Leonard J. Huggins and Jian Cui
CDM Working Paper No. 1103
APPAM Paper No.: PAPER2306
November 2011
ABSTRACT
Following the catastrophic tsunami of December 26, 2004, the Government of Indonesia
embarked on a program to reform its disaster management structure with key emphasis on
preparedness. While the new disaster management plan was adopted at the national level before
September 2009, the province of West Sumatra had not yet implemented the new system.
However, the City of Padang, within the province of West Sumatra, was one of the few localities
that actively participated in the reform process and actually implemented the plan prior to
September 30, 2009. On September 30th
, 2009, a 7.8 Magnitude earthquake struck 38 miles off
the shore of Padang and sharply tested the effectiveness of the newly adopted disaster plan. This
paper identifies the networks of communication and coordination that emerged among the actors
engaged in response to the earthquake. While the social network analysis show significant
interaction among national public agencies within the new system of disaster management,
serious gaps remain in promoting coordination among subnational agencies as well as between
private and nonprofit organizations, key sectors essential to maintain a resilient community.
Managers could use this information to reframe the policy issues and guide implementation of
disaster risk reduction strategies for future threats, especially during periods of transition from
one system of disaster management to another.
Leonard J. Huggins
Graduate School for Public and International Affairs
Center for Disaster Management
University of Pittsburgh
Pittsburgh, PA 15260
Jian Cui
Graduate School for Public and International Affairs
Center for Disaster Management
University of Pittsburgh
Pittsburgh, PA 15260
1
2011 Association for Public Policy Analysis and Management (APPAM)
Fall Research Conference, November 3-5, Washington DC
Networks That Matter: Planning and practice in the complex disaster environment
following the September 2009 Padang, Indonesia Earthquake
Leonard J. Huggins and Jian Cui
Introduction
Poor information flow among networked organizations is among the principal sources of failure
in the preparation for, and management of, disasters. On September 30, 2009, a magnitude 7.8
earthquake struck West Sumatra Indonesia collapsing over 3000 buildings and apartments and
leaving over 1115 people dead, despite six months of aggressive disaster preparedness through
the implementation of a new disaster management governance structure in West Sumatra and
other parts of Indonesia (BNPB, 2009). The sobering fact is that such events are no longer
unexpected in this region, but residents prepared for a tsunami that eventually did not occur, and
some sadly lost their life to the Earthquake instead. Disaster managers prepared the community
to anticipate the sequence of interdependent events that may occur in a disaster and to plan
actions to save lives and property. Yet, during the stressful and urgent environment of the actual
disaster, poor information flow, uncertainty and the complexity of the event overwhelmed the
community and disaster managers.
Failures in the interorganizational network further exacerbated delays in communication.
There were gaps in implementation as some organizations still functioned under the older system
of disaster management based on traditional responsibilities while others adopted the new
approach. Such gaps exposed a key policy problem in the timely adaptation and implementation
of public policy. Successful reduction of risk requires not only informed public policy and
systematic education in all phases of disaster management, but also the sustaining of an
organizational network within the disaster response and emergency management environment
(Comfort, 1999).
In 2008, all exercises and training activities were placed under the newly established
BNPB. The exercise held in Padang in February, 2009 served as an organizing framework for
response operations in the October 30, 2009 earthquake. First, public officers with key
2
responsibilities for emergency operations immediately contacted one another by radio, and the
Mayor of Padang City activated the emergency plan within five minutes of the earthquake.
Second, when residents felt strong shaking from the earthquake, they followed evacuation signs
posted on the streets (prior to the earthquake), which guided them to higher ground. Third, the
emergency plan activation enabled key officials to mobilize response operations immediately
with continued electrical power.
This paper aims to exam the effectiveness of the new system of disaster response and
preparedness in Indonesia in the aftermath of the October 30, 2009 earthquake. It also identifies
any possible conflict between the co-existing systems. More specifically, we ask the following
questions: 1) how effectively does the de facto system work in terms of interaction and
communication, taking organizational overlapping and conflict into account? 2) to what extent
does the new system supersede the old system while yet being challenged by it? By conducting
network analysis on data from local newspapers for three weeks following the event, the paper
critically examines the role of response networks during times of organizational transformation
from a network perspective. It examines the gaps in the emerging disaster response network
following the earthquake (practice) for consistency with the organizational changes implemented
under the new Indonesian disaster management law (planning).
Background: City of Padang and Indonesia’s Disaster Preparedness Framework
Following the devastation of the December 26, 2004 Indian Ocean Tsunami, public leadership in
Indonesia embarked on an intensive initiative in disaster reduction. The initiative sought to
overhaul existing disaster management legislation and public administration structures to
improve planning and preparedness for disasters. Padang, Indonesia was one of six cities in
Indonesia selected for focused investment in disaster reduction.
Padang was a critical site as almost 75 percent of its 900,000 residents live or work in
low-lying areas prone to inundation from flooding associated with tsunamis (Antara, 2009). This
high degree of vulnerability emphasizes the need to build national and local preparedness as part
of establishing any early warning system. According to the UNESCO Intergovernmental
Oceanographic Commission for the International Early Warning Programme (IEWP), “securing
3
the downstream flow of information from the warning centres to populations and communities at
risk” poses the most difficult challenge in early warning systems (UNESCO, 2006).
Organizational transformation
Under the new national disaster management law (Law 24 of 2007), the Government of
Indonesia planned and conducted training and evacuation drills for the communities and public
institutions and leaders. One of the major changes under this new law was the broadening of the
focus to a holistic-approach to disaster management. Such a change required the establishment of
a new national agency, the BNPB (Badan Koordinasi Nasional Penanggulangan Bencana), which
now has greater autonomy than the previous agency. Also, under the new structure, the agency
executive director reports directly to the president, avoiding much of the bottleneck associated
with more hierarchical reporting structures.
The system also transformed the old administrative system of regional ad-hoc agencies
(SATKORLAKs) and municipal councils to local and provincial Disaster Management agencies
(BPBDs) that sought to assess risk, train emergency responders, and educate local people
regarding disaster risk reduction. This process began in 2007, and while the national agency was
well established by the end of 2008, several provinces lagged behind in their commissioning of
the provincial BPBDs.
Figure 1 provides a glimpse of the subset of this new system at the provincial level (West
Sumatra). By 2008, the City of Padang had adopted the action plan and later developed standard
operating procedures for disaster response with responsible public agencies. The City of Padang
commissioned its local BPBD in February 2009, but several localities and provinces such as
West Sumatra did not do so until late 2009 or in 2010. Prior to the earthquake of September 30,
2009, the City of Padang had implemented the new disaster plan through an intensive training
program and field exercise for disaster risk reduction in the City. Notably, while the City of
Padang was prepared to utilize the new plan on September 30, 2009, the Province of West
Sumatra was not fully prepared as it had not fully implemented the new system. Such
unevenness in the transformation and transition to the new structure presents key gaps which
may cause fragmentation in the operating and responding environment, and ultimately
vulnerabilities in information “downflow” as well as decision making.
4
Figure 1. Planned Disaster Management Organizational Structure for the Province of West
Sumatra, Indonesia (SOURCE: Disaster Management Plan West Sumatra Province 2008-2012)
The Test
On September, 2009, the 7.8 Magnitude earthquake that struck 38 miles off the shore of
Padang sharply tested the effectiveness of the newly adopted disaster plan. While the City of
Padang was planning and conducting preparedness activities for a tsunami, the earthquake
caused loss of 1,115 lives and severe damage to its infrastructure. How did the City of Padang
and Indonesia perform, in terms of interaction and communication, amidst the transition to a new
disaster management structure? To what extent was the new system taking hold over the old, yet
coexisting, system of disaster management? These are questions that we examine from the
perspective of organizational networks following the disaster event.
Complexity, Organizational Transformation and the Relevance of Networks of Action
The planned transformation of a system of management across many agencies is, even
under the best of circumstances, characterized by inconsistencies and inadequate information that
need to be overcome to mobilize effective action. Working under urgent time constraints in the
complex environment following a major earthquake increases the stress on decision makers who
are responsible for the lives, property, and continuity of operations of their community. Decision
5
makers cope with urgent demands by relying on other actors who share their responsibilities, but
adapt their actions reciprocally through a unity of effort (Flin, 1996). Common training and
coordination may enhance decision making among these actors, but it is the relationships among
them, the exchange of information, and the communication achieved among members of the
team that provides the basis for effective decision making and coordination. This shared
perspective creates a ‘knowledge commons’ among members of an emerging network of actors
that can act collectively under extreme conditions. Establishing integrated patterns of
communication to mobilize action, resources, knowledge and personnel during a disaster event is
therefore critical to reducing risks and saving lives in the uncertain disaster environment.
Holland (1999) grapples with the complexity and interactive exchange of information
among agents and the external environment, where the interacting agents define, follow, and
redefine their working rules through the three fundamental mechanisms of tagging, internal
models, and building blocks. Here, tagging refers to a process that is informed by aggregation,
the first structural property that each agent begins their individual learning and decision making.
Through this process, agents learn through interaction and feedback from other agents and the
environment. During this process, the system aggregates diverse forms of information and
performs nonlinearly. Such process finally forms up the function-based flexible structure of the
system, which lead to the hierarchical but diverse adaptation attribute of the whole system.
Holland’s theory provides a streamlined framework of four structural properties and three
mechanisms to understand the complex adaptive system (figure 2), which facilitates the
application of network analysis. In this theory, complex systems are modeled by structure,
process, and their aggregating hierarchy from micro level to meso and macro levels.
6
Figure 2. Theoretical framework of Complex Adaptive System (Holland, 1999)
Disaster preparedness not only requires communication and training, but organizational
coordination mechanism building. In fact, both preparedness and response revolve around action
networks that are inter-organizational, multi-jurisdictional, and interdisciplinary. These
networks depend highly on collaboration among organizations as well as the ability of its
participants to generate valid information, facilitate informed choice and foster timely
commitment to action (Comfort et al, 2011a). Networking during the disaster response provides
a promising representation of the working dynamic system, where effective information and
interaction take place. Preparedness is not only local or national, but also global due to the
increasingly trend in global response to disaster events. As such, disaster preparedness and
disaster management networks undertake a much broader scope than local organizations can
often manage, especially in terms of actor heterogeneity and their roles to adapt to the response
system in general. Further, awareness of changes in local disaster management structure in the
global community can facilitate more coordinated efforts to save lives and properties in the
actual disaster event. However, a prior presumption that every actor would envision the new
working system and thus take advantage of the resources would be farfetched, and would
possibly lead to ineffectiveness, if not to systematic dysfunction and organizational conflict. This
paper examines the local, national and international networks that emerged following the
September 30, 2009 earthquake to further assess how any gaps in collaboration, particularly
associated with the organizational transformation and preparedness, impacted the response.
Disaster preparedness also shows growth in organizational learning. Since the
catastrophic disaster event of December 26, 2004, Indonesia had embarked on substantive
7
training and awareness among both communities and organizations. This approach supports the
finding that the key to early warning systems and disaster preparedness and response is effective
networks of action that relay information to decision makers, vulnerable populations and
responders in a timely manner (Comfort, 2011b). According to Andreou et al, 2010, designing
an information process to support learning relies heavily on the underlying information
infrastructure including the networks that undertake action or facilitate information sharing.
Multiple organizations, multiple information sources and multiple recipients must engage in a
common information system to harness the full reach and effectiveness of the information
exchange to reduce risks and vulnerabilities (Axelrod and Cohen, 1999). The conscious design
of organizational networks of actors is key in the complex disaster environment which relies on
the interconnectedness of both the social and technical networks to provide early and effective
warning to communities (Boulas et al, 2011). Yet, there remain discrepancies between
information generation and dissemination as well as preparedness and practice. This framework
of networks provides an avenue to assess how the City of Padang was able to execute its newly
adopted disaster response plan following the September 2009 earthquake.
Methods and Analysis: Assessing the September 2009 Earthquake Response Networks
We use the techniques and methods of Social Network Analysis (SNA) to identify the networks
of communication and coordination that emerged among the actors engaged in response to the
earthquake. Following a content analysis of local newspapers for three weeks after the event, a
network dataset was developed to provide formal and strict methods to measure interaction and
interdependence among interacting units (Wasserman and Faust, 1994). Within the framework of
SNA, actors and their actions are viewed as interdependent and responsive, rather than
autonomous and isolated. Relational ties here serve as channels to transfer resources and
information, while the de facto structure of the working system that includes both the old and
new management systems serves to buttress the networking flows.
Selection of Articles and Data Coding:
The articles for this study were selected from Antara, the leading newswire feed for
assorted newspapers across West Sumatra and Indonesia, through a search of the Nexus database
for the first three weeks following the September 30, 2009 earthquake. All articles were returned
8
based on variations of the search term “Padang earthquake.” In total, 129 articles were obtained
for week 1 (September 30 to October 6, 2009) while 153 articles were obtained for weeks 2 and
3 combined. The data were then coded for interactions based on explicit transactions that
occurred. In each case, an initiating organization was recorded and if there was a corresponding
responding organization, it was also recorded. In addition, any organization that carried multiple
names or abbreviations was classified as a single organization. Each organization was further
classified by funding status (public, private or nonprofit) and by jurisdiction (local, provincial,
national or international). This process of coding was conducted by three different individuals
and then reviewed by a “Socioteam” at the Center for Disaster Management at the University of
Pittsburgh (i.e. 7 additional doctoral and postdoctoral researchers) to validate systematic social
network coding and to minimize threats to validity.
Analysis:
Once the network data were authenticated, it was imported into UCINET software as well
as ORA where static and dynamic network analyses were performed. Network analysis allows
the researcher to determine the strength of ties among actors in a network of organizations
(Wasserman and Faust, 1994; Borgatti et al, 2002). Both UCINET and ORA were used to
compare consistency of the centrality statistics provided for the network. ORA was also used to
perform the dynamic network analysis over time. The analysis represents network performance
from the global network perspective as well as the local/provincial (Padang/West Sumatra)
network perspective. The findings from the network analysis allow managers to identify gaps in
the response network and use this information to reframe the policy issues and guide investment
for disaster risk reduction of future threats.
Limitations:
Limited by second-hand data, it is nearly impossible to exclude alternative factors leading
to a more effective response to the September 2009 Earthquake. Such factors, such as the local
transportation reform of Padang City before the earthquake as well as specific community
programs to improve the individual capacity at the local level, are not analyzed in this study.
Meanwhile, in their paper “Planning Matters,” Comfort at el. (2009) pointed out three factors
contributing to reducing disaster risk in Padang and West Sumatra from the perspective of
9
network and its effect. They are 1) annual training exercises for tsunami warning and evacuation
organized by national agencies since 2004, 2) high-level public awareness of tsunami risk and
immediate self-evacuation of Padang citizens, 3) back-up generators at key facilities - radio
station, hospitals, fire station, and mayor’s residence to maintain a resilient leadership and
information flow. Though they carry a network epistemology, the understanding of how this
emerging network works in practice still remains less measured and insufficient. The reform of
BNPB system captures these three perspectives. This paper emphasizes the structure of
networks, which creates constraints and opportunities for individual actions. And such structure
is regarded as a lasting pattern of relations among all actors involved in the response period.
Findings: Networks that Matter
Since the catastrophic tsunami event of December 26, 2004, Indonesia has committed resources
to reduce vulnerabilities in communities through disaster preparedness planning, training and
education. The change in disaster management structure to shift to a holistic approach is one of
the key commitments. Network analysis was used to understand the performance of the response
networks following the September 2009 earthquake in the following discussion.
Transforming Policy to Practice: What worked and didn’t work?
Initially and as expected for a disaster with international focus, the Indonesian Office of
the President (IoOOP) was the central actor very early in the response (Figure 3 and 4).
However, after the initial response (3 days) when the focus turned primarily to the disaster
response operations on the ground, the Indonesia BNPB (the new national disaster management
coordinating arm) assumed key centrality for disaster operations (Figure 4). See appendix 3 for
detailed network statistics. Several national ministries and the Government of Indonesia
(GoIndo) also increased their degree of centrality within this phase of the response. Nine of the
top 10 actors with highest level of in-degree centrality were among national ministries
(Appendix 3), where they were pivotal in responding to communications from other
organizations. This showed that at the national level of the new system of managing disasters
was taking hold.
10
Figure 3. Most central organization (IoOOP) in the disaster response network.
Noticeably, however, the old SATKLORLAK (SATKOR) remained among the key
actors with a total degree centrality of 0.100 compared to 0.330 for the most central actor in the
Indonesian Office of the President (Figure 4). This presents an area of conflict. This conflict
arose at two levels: (1) at the national level where several international organizations were not
fully aware of the institutional changes within Indonesia (Figure 4), and (2) at the provincial
level where the province still functioned under the old system of disaster management while the
City of Padang and the national level organizations operated under the new system. International
relief and rescue organizations (except the UN agencies) appeared to coordinate with familiar
faces within the national ministries who at that time did not have central responsibility for
coordination and management of disaster response operations. On the plus side, since the only
coordination with the Satkorlak on the national level involved international agencies, it indicates
that the process of transition to the new system among national organizations was taking hold.
In both cases however, poor information flow caused unevenness in the response operations.
While the province and the City of Padang had the same set of responsibilities working in the
11
already complex and changing disaster environment, they were not operating under the same
system of management or rules. Operating under different management structures created an
obstacle to the information flow process, especially at the provincial and local levels.
A common operating picture is fundamental to improve decision making in the disaster
environment (Comfort et al, 2006). Research has shown that information should be extracted
from known domains prior to the disaster and integrated with information generated from the
disasters for response to be highly effective (Comfort, 2011a, von Lutz et al, 2008). However,
actionable knowledge, as such combination is referred to, depends on compatible networks of
action that facilitate the timely exchange and sharing of information, including both information
generation and dissemination. Yet, the uneven transition to the new disaster management
system was not ideally implemented to minimize such inefficiencies. The patterns of
preparedness in West Sumatra were largely dominated by the national organizations. According
to Comfort et al, 2006, a knowledge commons provides an avenue to alleviate such discrepancies
before and during the disaster, as organizations would be operating with the same pool of
information to make better informed decisions in the uncertain disaster environment.
Figure 4. Total degree centrality for top 8 networked organizations
up to 3 weeks following the earthquake
12
Figure 5. Degree centrality among organizations up to 3 weeks
following the earthquake using UCINET
13
The networks that emerged from the September 30, 2009 event were different than those
expected based on the post disaster plan. First, we draw reference to the degree of betweenness
among organizations. Betweenness centrality measures the location of actors in the network
relative to other actors. In essence, an organization with high betweenness has high influence
over what flows in the overall network. By definition of the traditional disasters law, it is
expected that the national BNPBs and the provincial/local BPBD should have high betweenness
centrality and this relationship should be sustained throughout the response phase. However, this
was not the case (Figure 6). The national BNPB accounted for the initial spike in betweenness
centrality on October 1, 2009 while the Padang Office of the Mayor (PadOOM) and West
Sumatra Office of the Governor (WSOOG), not the BPBD, accounted for spikes near the end of
the three week response period under examination. This pattern was not sustained throughout
the period and in fact was much lower than the out and in degree centralities for the same period.
This indicates that while organizations may have been connected, they did not rely primarily on
the BPBD or BNPB for information and activities during most of the response phase. At the
local level, the political administration drove the early operations. Such process further suggests
that information was not appropriately validated by the designated organization, further causing
fragmentation in the information flow.
Figure 6. Comparison of degree centrality among organizations up to
3 weeks following the earthquake
14
One notable observation on the national scale, from Figure 6 above, is the sustained
spikes in in-degree and out degree centrality among organizations. In examining this further, we
realized that several organizations have relatively high eigenvector centrality. This is
outstanding for the new system because it suggests that there is no single point of failure should
one of these organizations be removed from the network. Such networks are more resilient and
adaptive than ones that are dominated by a single organization. It is difficult to determine if this
observation would persist once the full transformation to the new disaster structure is complete.
However, all the organizations with high eigenvector centrality are public (black color). Thus
though no single organization represent a point of failure, a single type of organization may in
this case. No nonprofit is among the organizations that orchestrate the flow of information
during the response phase. This is a key point of failure as was demonstrated in the case of the
Haitian earthquake in January 2010, when the entire government structure collapsed leaving
nonprofit and international organizations to assume response leadership in a fragmented
collaborative framework (Comfort et al, 2011b). This gap is not directly addressed with the new
law, though it calls for a collaborative framework for action.
To further examine the effectiveness of the implementation of the law in the City of
Padang, we move from the global disaster and national level to the sub-national (i.e. regional,
provincial, district and local) level of operation. This level presents different challenges as
several provinces had not fully adopted and implemented the law at the time of the September
30, 2009 earthquake. In essence, fragmentation was expected in the system at the sub-national
level. In the global perspective in Figures 7 and 8, the provincial structure for handling disaster
events was very evident with the West Sumatra Governor playing a prominent role in
coordination whereas the West Sumatra BPBD was almost nonexistent, which reflected its
underdeveloped state in this context. It also shows the relevance of key political figures in the
response and recovery process.
15
Figure 7. Eigenvector centrality among organizations by funding type (shape)
and jurisdiction (color) using UCINET
16
Figure 8. Betweenness centrality among organizations by funding type (shape) and jurisdiction (color) using UCINET
17
Also, in figure 9, while the national level showed several dense networks and cliques for
coordination with the national BNBP is central, the coordination among sub-national
organizations remained largely isolated. In fact, the West Sumatra BPBD (WSBPBD) did not
emerge as a central actor in the sub-national network (Figure 10). This is a major gap in
coordination at the sub-national and local level. This is consistent with the view that the
preparedness training was largely orchestrated and dominated by the national BNBP and that the
local focus was somewhat overlooked in this process.
Figure 9. National and subnational networks among Indonesian organizations
by jurisdiction (color)
Key: Jurisdiction
National Provincial District Local City
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Figure 10. Sub-national networks among Indonesian organizations by jurisdiction (color)
Conclusion – Networks that Matter
Consistent with the level of preparedness, it was evident that sub-national coordination
was overshadowed by national coordination efforts. While the national structure was clearly
defined and active in this event, the subnational level of coordination remain fragmented because
agencies were operating under different sets of rules. The province acted under the old Satkorlak
system while the City of Padang operated under the new BPBD system. On one hand,
overlapping rules and actors who follow these rules caused inefficient resource allocation due
either to information asymmetry or path dependence. On the other hand, the limited time for
information exchange made decision making ill-informed. The co-existence of both the old and
new systems created an ambiguous environment among active actors on the ground, which
caused confusion during the response as well as the regular system of public management. To
facilitate more effective collective action in the already dynamic and unstable disaster
environment, the design and implementation of a shared knowledge base at the subnational level
may alleviate the inefficiencies caused by the uneven transition.
Key: Jurisdiction
Provincial District Local City
19
Several implications can be drawn from this paper. First, it is essential for designers and
disaster managers to identify the weakness of the changing system and the need for players from
both the old and new systems to interact, in order to smooth the organizational adaptation in the
systemic transition. Reflective adaptation of capabilities to the changing demand and
complexities of new situations and transitions are therefore essential to close the gaps that exist
between policy and practice (Comfort, 1999). Second, there is a need for a knowledge commons
among disaster management agencies in the new BNBD /BPBD system in Indonesia. Despite the
valiant efforts by the City of Padang to implement the new disaster management law, the
organizational networks for communication of risks and actions remained somewhat fragmented
at the broader provincial due to unevenness in transition. However, with a knowledge commons,
the learning triggered by this event should increase the accuracy, relevance and timeliness of
information and the effectiveness of organizational networks for future events. Third, while a
better-designed and highly-integrated new system has been established through jurisdiction, it is
not necessarily working well, especially when external players become involved with the system
and the response efforts. To create and maintain effective information flow, it is important not
only to improve ad hoc collaboration among actors from different jurisdiction and sectors, but
also to disseminate key information to irregular players (such as international relief organizations)
to identify best partners from the new system and thus establish an interactive tie between them
and the regular actors.
20
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Appendix 1: Network Map Key for Node Colors and Shapes (modified for UCINET analysis)
Organizational Source of Funding Organizational Jurisdiction
Source Color Jurisdiction Shape
Public Local Circle in Square
Private Provincial Box
Non-Profit National Circle
International Down Triangle
Appendix 2: Abbreviations for Organizations used in Network Analysis (both ORA &
UCINET Analysis)
Organization Name Acronym
Source of
Funding Jurisdiction
Abrizal Bakri Province ABP public provincial
ADF Health Survey Team ADFHT public international
ADF Engineer Reconnaissance Team ADFRT public international
Aceh-Nias Rehabilitation and Reconstruction
Board (BRR) AHBRR public national
Aceh Legislative Assembly ALA public provincial
ANTARA ANTARA public national
Office of Aceh Vice Governor (Muhammad
Nazar) AOVG public national
Aceh Provincial Government APG public provincial
Association of Aceh Islamic Boarding Schools ASAIBS nonprofit provincial
Association of Indonesian Tours and Travels ASITA nonprofit national
Andalas University AU public national
Australian Defense Force AusADF public international
Royal Australian Air Force AusAF public international
Australian Agency for International Development AusAID public international
Australian Defense Force AusDF public international
Australian Department of Foreign Affairs and
Trade, AusDFAT public international
Australian Embassy in Indonesia (Paul Robilliard) AusEMB public international
Australian Federal Police AusFP public international
Australian Military AusM public international
Australian, Office of Foreign Minister (Stephen
Smith) AusOFM public international
Australia, Office of Prime Minister (Kevin Rudd) AusOPM public international
Australian SAR Team AusSAR public international
AXIS Communications AXIS private national
23
Organization Name Acronym
Source of
Funding Jurisdiction
National Development Planning Board (Bappenas) Bappenas public national
National Search and Rescue Agency BASARNAS public national
Batavia Air BATAIR private national
Puncak Niaga Holdings Bhd BHD private international
Meteorology, Climatology and Geophysics
Agency BMKG public national
Blood Transfusion Unit (Bengkulu) BnUTDC nonprofit local
PT BTD (utility) BTD public local
Blood Transfusion Unit (Bukit Tinggi) BTUTDC nonprofit local
Embassy of Canada in Jakarta CANEMB public international
Canadian Ministry of Foreign Affairs CanMIFA public international
China Red Cross ChRC public international
Bank Bumiputra Commerce Islamic (CIMB) CIMB private national
Canada SAR CSAR public international
Command Post of the Coordination Unit for
Disaster Control CUDC public local
Church World Services CWS nonprofit international
European Commission EC public international
Emergency Capacity Building Consortium of
Indonesia ECB nonprofit national
European Commission for Humanitarian Aid and
Development ECHA public international
Office of the President of the European
Commission (Jose Barroso) ECOP public international
East Kutai Regent EKR public local
The Earth (Kusmayanto Kadiman) Observatory of
Singapore (Kerry Sieh) EOS public international
European Union SAR EUSAR public international
France's Civil Security Detachment FCSD public international
Tithe forum FOZ FOZ nonprofit national
France Rescue Team FSAR public international
Garuda Indonesia GARIndo public national
Government of Australia GoAUS public international
Government of China GoCHN public international
Government of Denmark GoDEN public international
Government of France GoFRA public international
Government of Germany GoGER public international
Government of Indonesia GoINDO public national
Government of Italy GoIta public international
24
Organization Name Acronym
Source of
Funding Jurisdiction
Government of Japan GoJAP public international
Government of Malaysia GoMAL public international
Government of Norway GoNOR public international
Government of Qatar GoQAT public international
Government of Russia GoRUS public international
Government of Saudi Arabia GoSAU public international
Government of Singapore GoSIN public international
Government of South Korea GoSK public international
Government of Switzerland GoSWI public international
Government of Taiwan GoTAI public international
Government of Thailand GoTHA public international
Government of the United Arab Emirates GoUAE public international
Government of the United Kingdom GoUK public international
Government of USA GoUSA public international
Germany SAR GSAR public national
Gama Study Center GSC private local
Hungarian Directorate General for Disaster
Handling HDG public international
HK Logistics HKL private international
Hope HOPE nonprofit international
Halim Perdanakusumah Air Force Airport HPAFPT public national
Cipto Mangunkusmo Hospital HpCM public national
Crown Medical Center Hospital, Malaysia HpCMC public national
M. Jamil Hospital HpMJ public provincial
M. Jamil Hospital(Victim Identification Center) HpMJVIC public national
Hungarian Search and Rescue HSAR public international
Ambacang Hotel HtlAMB private national
Hotel Baskoni Plaza HTLBP private national
Hang Tuah Hotel HTLTH private national
Indonesian Air Carriers Association IANACA nonprofit national
Imam Bonjol University IBUniv public national
International Red Cross IFRC nonprofit international
Bank International Indonesia, Padang Branch IIBANK private national
Indonesian Association of Congresses and
Conventions INCDCA nonprofit national
Indonesian Air Force IndoAF public national
Indosat IndoSAT public national
United National Insarag (Switzerland SAR) INSARAG public international
25
Organization Name Acronym
Source of
Funding Jurisdiction
Adityawarman Museum IoAM nonprofit national
Office of the Indonesian Ambassador to Malaysia
(Da'I Bactiar) IoAmb public national
National Disaster Mitigation Agency IoBNPB public national
Indonesian Cabinet IoCab public national
Office of the Coordinating Minister for Political,
Legal, and Security Affairs (Widodo AS) IoCMPLSA public national
Office of the Coordinating Minister for People's
Welfare (Aburizal Bakri) IoCMPW public national
European Commission for Humanitarian
Operations, Jakarta IoECHO public international
Indonesia Foreign Affairs Ministry IoFAM public national
Indonesian Fire Fighters IoFF public national
Indonesian Tithe Center IoITC nonprofit national
International Organization for Migration IOM nonprofit international
Indonesian Ministry of Communication and
Information IoMCI public national
Indonesia Ministry of Culture and Tourism IoMCT public national
Indonesian Ministry of Finance IoMF public national
Indonesian Ministry of Health IoMH public national
Indonesian Ministry of Home Affairs IoMHA public national
Indonesian Ministry of Health's Crisis Control
Center IoMHCCC public national
Ministry of Higher Education IoMHE public national
Indonesian Ministry of Health's Regional Crisis
Control Center IoMHRCCC public national
Indonesian Ministry of Industry IoMI public national
Indonesian Ministry of Law and Human Rights IoMLHR public national
Ministry of Manpower and transmigration IoMMT public national
Indonesian Ministry of Public Transportation IoMPT public national
Indonesian Ministry of Public Works IoMPW public national
Indonesian Ministry of Religious Affairs IoMRA public national
Indonesian Ministry of Research and Technology IoMRT public national
Indonesia Ministry of Trade IoMTD public national
Indonesian Navy IoN public national
National Archive and Library IoNAL public national
National Defense Forces IoNDF public national
National Mandate Party IoNMP public national
(Indonesian) National Police IoNP public national
26
Organization Name Acronym
Source of
Funding Jurisdiction
National Security Division IoNSD public national
Office of the Coordinating Minister for Economy
(Sri Mulyani Indrawati) IoOCME public national
Office of the Cabinet Secretary (Sudi Silalahi) IoOCS public national
Office of the First Lady (Ani Yudhoyono) IoOFL public national
Office of Military Chief (General Djoko Santosa) IoOMC public national
Office of the President of Indonesia (Susilo
Bambang Yudhoyono) IoOOP public national
Office of Police Chief (General Bambang
Hendarso Danuri) IoOPC public national
Office of the State Secretary (Hatta Rajasa) IoOSS public national
Office of Vice President of Indonesia (Jusuf Kalla) IoOVP public national
Office of Vice President (Boediono) IoOVP_B public national
Indonesian Red Cross IoRC public national
Indonesian SAR Team IoSAR public national
Indonesian Tourism Association Board IoTAB nonprofit national
International SAR team (Rescue Workers) ISAR public international
Islamic Studies Students ISS private international
Italian Red Cross ItaRC nonprofit international
Indonesian Tourism and Travel Fair ITTF public national
Jakarta Health Office JakHO public national
Jakarta Provincial Government JakPG public provincial
Jakarta Convention Center JCC public national
Japanese Foreign Affairs Ministry (Katsuya
Okada) JFAM public international
Jambi Governor JG public local
Japan International Cooperation Agency JICA public international
Blood Transfusion Unit (Jakarta) JkUTDC nonprofit local
Japanese Disaster Relief Team (Japan SAR Team) JSAR public international
Kerinci District Coordination Board for Disaster
Mitigation KDCBDM public district
Kerinci District Public Relations and Protocol KDPRP public district
Kepahiyan Geophysics Station KGS private provincial
Regional government of Klanten district, Central
Java province KlaRG public district
Kuala Lumpur International Airport KLIPT public national
Melaka Islamic University College (KUIM) KUIM public national
Malaysia's Air Asia MAA private international
Malaysian Armed Forces MalAF public international
27
Organization Name Acronym
Source of
Funding Jurisdiction
Malaysian doctors MalDOC public international
Malaysia Building Experts MalExp public international
Office of the Prime Minister of Malaysia (Najib
Razak) MalOPM public international
Maybank MBANK private international
Mercy Corps International MCI nonprofit international
Office of the Chief Minister, Melaka Province
(Mohd Ali Rustam) MelOCM public international
Melaka Provincial Government, Malaysia MelPG public international
Medical Emergency Rescue Committee MERC public national
Office of the Foreign Ministry, Malaysia MFM public international
Maninjau Hydro-electric power plant MHEPP private provincial
Mercy International MI nonprofit international
Mercy Malaysia MM nonprofit international
Media Prima Bhd MPB private international
Maninjau power plant MPP private provincial
Minangkabau Airport MPT public national
Malaysian Search and Rescue Team (Smart) MSAR public international
Muhammadiyah MUHAM nonprofit national
National Alms Collecting Agency NACA nonprofit national
Provinical Government of Nanggroe Aceh
Daraussalam NAD public provincial
National Coordinating Agency for Surveys and
Mapping (Bakosurtanal) NCASM public national
Norway's Ministry of Environment and
International Development (Erik Solheim) NMEID public international
NSTP-Media Prima Disaster Fund NSTP nonprofit national
The New Straits Times Press Berhad NSTPB private international
Nahdlatul Ulama NU nonprofit local
Padang's Culture and Touism Office (head:Edi
Hasmi) PaCTO public local
Padang Government PadGov public local
Office of Mayor, Padang (Fauzi Bahar) PadOOM public local
Office of the Deputy Mayor, Padang City (Marlis
Rahman) PadOPM public local
Padang Seaport PadSPRT public local
Palembang City Government PalCG public local
Rapid-UK PAPID-UK public international
Blood Transfusion Unit (Pekan Baru) PBUTDC nonprofit local
28
Organization Name Acronym
Source of
Funding Jurisdiction
PDAM Water Company PDAM public national
Blood Transfusion Unit (Padang) PdUTDC nonprofit local
Pertamina Fuels PERTA public national
Primary Health Care Team (International
Physicians) PHCT public international
Indonesian Hotels and Restaurants Association PHRI nonprofit national
Makassar Municipal Legislative Council of the
Prosperous Justice Party PKS nonprofit local
PKS Post Disaster Management Center PKS_PDMC nonprofit national
Blood Transfusion Unit (Palembang) PleUTDC nonprofit local
Pauh Limo power plant PLPP private provincial
Blood Transfusion Unit (Palarawan Riau) PlUTDC nonprofit local
Padangpanjang Meteorology, Climatology and
Geophysics Agency PPBMG public provincial
Office of Padang Pariman District Head (Muslim
Kasim) PPODH public local
PIP power plant PPP public national
Padang Pariaman Regional Government PPRG public district
Padangpariaman Public Works Department PPWD public local
Padang State University PSU public national
PT PLN Electricity Company PTPLN public national
Riau Governor RGP public local
Ria Public Works RPWD public local
Russian Ministry of Emergency Situations RusMES public international
Office of the President of Russia (Dmitry
Medvedev) RusOOP public international
Yayasan Salam Malaysia Salam nonprofit international
Saudi Arabia SAR SASAR public national
Sultan Azlan Shah Foundation SASF nonprofit international
Sulit Air Sepakat Organization SASO private national
Coordination Unit for Disaster Mitigation
(Satkorlak) of the Disaster Management Agency SATKOR public national
Saudi Arabian Embassy in Indonesia SauEmb public international
Scout Movement SCOUT nonprofit national
Simpang Empat power plant SEPP private provincial
Singkarak Hydro-electric power plant SHEPP private provincial
Soekarno Hatta International Airport SHPRT public national
Blood Transfusion Unit (Sijunjung) SjUTDC nonprofit local
South Korea SAR SKSAR public international
29
Organization Name Acronym
Source of
Funding Jurisdiction
Syiah Kuala Univeristy SKU public national
Blood Transfusion Unit (Solo) SlUTDC nonprofit local
Singapore Office of Prime Minister (Lee Hsien
Loogn) SOPM public international
Singapore Search and Rescue Team SSAR public international
South Sumatra Healthcare Team SSHT public provincial
South Sumatra SAR SSSAR public provincial
Save the Children (International) STC nonprofit international
Save the Children (Indonesia) STCIo nonprofit national
Water Association of Selangor, Kuala Lumpur and
Putrajaya (SWAn) SWAn private national
The Cultural Park TCP public national
PT Telkom Group Telkom public national
Tabung Gempa Nusantara (Malay Archipelago
Earthquake Fund) TGN nonprofit international
Indonesian Army (TNI) TNI public national
Turkey SAR TSAR public national
Turkey Humanitarian Aid TurHA public international
the Economic and Trade Office of the Taipei
Economic and Trade Office TwETO public international
Taiwan Redcross TwRC nonprofit international
UK Department for International Development UKDFID public international
UK Embassy in Jakarta UKEMB public international
UK Office of the Prime Minister (Gordon Brown) UKOPM public international
Office of UK Secretary of State (Douglass
Alexander) UKOSS public international
UK Red Cross UKRC nonprofit international
UK Search and Rescue Team UKSAR public international
United Nations UN public international
United Nations Disaster Assessment and
Coordination UNDAC public international
UNESCO UNESCO public international
United Nations Children's Fund UNICEF public international
United Nations Office for the Coordination of
Humanitarian Affairs UNOCHA public international
UN Office of the Secretary General (Ban Ki-
moon) UNOSG public international
United Nations Population Fund UNPF public international
United States Agency for International
Development USAID public international
30
Organization Name Acronym
Source of
Funding Jurisdiction
US Consulate General, Medan USCGM public international
US Disaster Assistance Response Team (DART) USDART public international
U.S. Defense Ministry USDOD public international
US State Department's Office of Foreign Disaster
Assistance USDOFA public international
US Emergency Hospital in Indonesia USEH public international
US Embassy in Jakarta (Osius) USEMB public international
United States Geological Survey USGS public international
Harvard University, Boston USHU nonprofit international
United States Military USM public international
Office of the President of the United States
(Barack Obama) USOOP public international
United States SAR USSAR public international
The University of North Sumatra USU public national
UTDD Riau (Blood Transfusion?) UTDD nonprofit local
World Bank WB nonprofit international
World Food Programme (WFP) WFP public international
World Health Organization (WHO) WHO public international
West Java Provincial Government WJPG public provincial
West Sumatra's local Meteorology, Climatology
and Geophysics WSBMKG public provincial
West Sumatra BPBD WSBPBD public provincial
West Sumutra Culture and Tourism Office WSCTO public provincial
Office of West Sumatra Deputy Governor (Marlis
Rahman) WSDG public provincial
Office of West Sumatra Governor (Gamawan Fauzi) WSOOG public provincial
Office of the Vice Governor of West Sumatra WSOVG public provincial
West Sumatra Province WSP public provincial
West Sumatra Provincial Government WSPG public provincial
West Sumatra Public Works Department WSPWD public provincial
West Sumatra Satkorlak WSSAT public provincial
West Sumatra's Tourism Service WSTS public provincial
West Sumatra Wira Brja Military Resort WSWBMR public national
World Vision (International) WV nonprofit international
World Vision (Indonesia) WVIo nonprofit national
XL (Telecommunications) XL private national
Zipur Public Works ZPWD public local
31
Appendix 3: Standard Network Analysis Report (ORA Analysis)
Network Level Measures
Input network(s) for all measures: Organization x Organization
Measure Value
Row count 286.000
Column count 286.000
Link count 284.000
Density 0.003
Isolate count 99.000
Component count 117.000
Reciprocity 0.022
Characteristic path length 3.598
Clustering coefficient 0.036
Network levels (diameter) 8.000
Network fragmentation 0.773
Krackhardt connectedness 0.227
Krackhardt efficiency 0.988
Krackhardt hierarchy 0.906
Krackhardt upperboundedness 0.537
Degree centralization 0.072
Betweenness centralization 0.026
Closeness centralization 0.002
Reciprocal? No (2% of the links are reciprocal)
32
Node Level Measures
Measure Min Max Avg Stddev
Total degree centrality 0.000 0.075 0.004 0.008
Total degree centrality [Unscaled] 0.000 43.000 2.126 4.358
In-degree centrality 0.000 0.080 0.004 0.009
In-degree centrality [Unscaled] 0.000 23.000 1.063 2.687
Out-degree centrality 0.000 0.070 0.004 0.008
Out-degree centrality [Unscaled] 0.000 20.000 1.063 2.339
Eigenvector centrality 0.000 1.000 0.186 0.333
Closeness centrality 0.003 0.004 0.003 0.000
Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000
Betweenness centrality 0.000 0.026 0.000 0.002
Betweenness centrality [Unscaled] 0.000 2098.222 32.937 192.378
Hub centrality 0.000 1.000 0.081 0.237
Authority centrality 0.000 1.000 0.103 0.281
Information centrality 0.000 0.020 0.003 0.005
Information centrality [Unscaled] 0.000 2.712 0.484 0.681
Clique membership count 0.000 15.000 0.280 1.208
Simmelian ties 0.000 0.000 0.000 0.000
Simmelian ties [Unscaled] 0.000 0.000 0.000 0.000
Clustering coefficient 0.000 1.000 0.036 0.119
33
Key Nodes
This chart shows the Organization that is repeatedly top-ranked in the measures listed below.
The value shown is the percentage of measures for which the Organization was ranked in the top
three.
Total degree centrality
The Total Degree Centrality of a node is the normalized sum of its row and column degrees.
Individuals or organizations who are "in the know" are those who are linked to many others and
so, by virtue of their position have access to the ideas, thoughts, beliefs of many others.
Individuals who are "in the know" are identified by degree centrality in the relevant social
network. Those who are ranked high on this metrics have more connections to others in the same
network. The scientific name of this measure is total degree centrality and it is calculated on the
agent by agent matrices.
34
Input network: Organization x Organization (size: 286, density: 0.00347205)
Rank Organization Value Unscaled Context*
1 IoOOP 0.075 43.000 20.653
2 GoINDO 0.047 27.000 12.597
3 WSOOG 0.035 20.000 9.072
4 IoOCS 0.030 17.000 7.561
5 IoOSS 0.030 17.000 7.561
6 IoRC 0.028 16.000 7.058
7 UN 0.028 16.000 7.058
8 IoMPT 0.026 15.000 6.554
9 PadOOM 0.025 14.000 6.051
10 IoMPW 0.025 14.000 6.051
* Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.004 Mean in random network: 0.003
Std.dev: 0.008 Std.dev in random network: 0.003
In-degree centrality
The In Degree Centrality of a node is its normalized in-degree. For any node, e.g. an individual
or a resource, the in-links are the connections that the node of interest receives from other nodes.
For example, imagine an agent by knowledge matrix then the number of in-links a piece of
knowledge has is the number of agents that are connected to. The scientific name of this measure
is in-degree and it is calculated on the agent by agent matrices.
Rank Organization Value Unscaled
1 IoOOP 0.080 23.000
2 GoINDO 0.077 22.000
3 IoOCS 0.056 16.000
4 IoRC 0.049 14.000
5 IoOSS 0.042 12.000
6 IoMPW 0.035 10.000
7 WSP 0.031 9.000
8 IoMI 0.031 9.000
9 IoMPT 0.028 8.000
10 IoBNPB 0.024 7.000
35
Out-degree centrality
For any node, e.g. an individual or a resource, the out-links are the connections that the node of
interest sends to other nodes. For example, imagine an agent by knowledge matrix then the
number of out-links an agent would have is the number of pieces of knowledge it is connected to.
The scientific name of this measure is out-degree and it is calculated on the agent by agent
matrices. Individuals or organizations who are high in most knowledge have more expertise or
are associated with more types of knowledge than are others. If no sub-network connecting
agents-to-knowledge exists, then this measure will not be calculated. The scientific name of this
measure is out degree centrality and it is calculated on agent by knowledge matrices. Individuals
or organizations who are high in "most resources" have more resources or are associated with
more types of resources than are others. If no sub-network connecting agents-to-resources exists,
then this measure will not be calculated. The scientific name of this measure is out degree
centrality and it is calculated on agent by resource matrices.
Input network(s): Organization x Organization
Rank Organization Value Unscaled
1 IoOOP 0.070 20.000
2 WSOOG 0.052 15.000
3 UN 0.049 14.000
4 PadOOM 0.042 12.000
5 IoOMC 0.031 9.000
6 AusOFM 0.031 9.000
7 IoOPC 0.028 8.000
8 GoAUS 0.024 7.000
9 IoCMPW 0.024 7.000
10 AusADF 0.024 7.000
Betweenness centrality
The Betweenness Centrality of node v in a network is defined as: across all node pairs that have
a shortest path containing v, the percentage that pass through v. Individuals or organizations that
are potentially influential are positioned to broker connections between groups and to bring to
bear the influence of one group on another or serve as a gatekeeper between groups. This agent
occurs on many of the shortest paths between other agents. The scientific name of this measure is
betweenness centrality and it is calculated on agent by agent matrices.
36
Input network: Organization x Organization (size: 286, density: 0.00347205)
Rank Organization Value Unscaled Context*
1 GoINDO 0.026 2098.222 0.086
2 IoOOP 0.026 2075.705 0.083
3 UN 0.011 861.667 -0.042
4 IoBNPB 0.009 696.778 -0.058
5 AusOFM 0.007 526.833 -0.076
6 WSPG 0.006 512.833 -0.077
7 IoCMPW 0.005 371.648 -0.092
8 IoOVP 0.003 222.667 -0.107
9 IoOCS 0.003 203.865 -0.109
10 UNOCHA 0.002 201.583 -0.109
* Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.000 Mean in random network: 0.016
Std.dev: 0.002 Std.dev in random network: 0.120
Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Rank Organization Value Unscaled
1 WSOOG 0.020 2.712
2 IoOOP 0.020 2.704
3 UN 0.019 2.669
4 PadOOM 0.019 2.589
5 IoOMC 0.017 2.420
6 AusOFM 0.017 2.408
7 IoOPC 0.017 2.344
8 GoAUS 0.016 2.252
9 IoMPT 0.016 2.248
10 AusADF 0.016 2.245
37
Clique membership count
The number of distinct cliques to which each node belongs. Individuals or organizations who are
high in number of cliques are those that belong to a large number of distinct cliques. A clique is
defined as a group of three or more actors that have many connections to each other and
relatively fewer connections to those in other groups. The scientific name of this measure is
clique count and it is calculated on the agent by agent matrices.
Rank Organization Value
1 IoOOP 15.000
2 GoINDO 9.000
3 IoBNPB 6.000
4 WSOOG 4.000
5 AusOFM 4.000
6 PadOOM 3.000
7 IoCMPW 2.000
8 IoOCME 2.000
9 IoOCS 2.000
10 IoOSS 2.000
Clustering coefficient
Measures the degree of clustering in a network by averaging the clustering coefficient of each
node, which is defined as the density of the node's ego network.
Rank Organization Value
1 MBANK 1.000
2 MM 0.500
3 IoMHA 0.479
4 IoOMC 0.479
5 IoOPC 0.479
6 IoMI 0.479
7 IoCMPLSA 0.472
8 IoMCI 0.472
9 IoMLHR 0.472
10 IoMPT 0.417
38
Key Nodes Table
This shows the top scoring nodes side-by-side for selected measures.
Rank Betweenness
centrality
Closeness
centrality
Eigenvector
centrality
In-degree
centrality
Out-degree
centrality
Total degree
centrality
1 GoINDO GoUSA IoOOP IoOOP IoOOP IoOOP
2 IoOOP UKEMB IoMHCCC GoINDO WSOOG GoINDO
3 UN AusAID JakHO IoOCS UN WSOOG
4 IoBNPB AusADF HpCM IoRC PadOOM IoOCS
5 AusOFM UKDFID AOVG IoOSS IoOMC IoOSS
6 WSPG PadGov SATKOR IoMPW AusOFM IoRC
7 IoCMPW USAID IoMCT WSP IoOPC UN
8 IoOVP GoUK Bappenas IoMI GoAUS IoMPT
9 IoOCS MCI IoMF IoMPT IoCMPW PadOOM
10 UNOCHA RusOOP STC IoBNPB AusADF IoMPW
Produced by ORA developed at CASOS - Carnegie Mellon University
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