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GNS Science Consultancy Report 2009/321 
December 2009 
  
In Collaboration with 
 
 
 Pacific Islands Applied Geoscience Commission 
Joint Contribution Report 197 
Pacific Exposure Database 
Inception Report  
 
ADB TA 6496-REG: Regional Partnerships for  
Climate Change Adaptation and Disaster  
Preparedness  
 
SOPAC
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Asian Development Bank/World Bank joint initiative 
GNS Science 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Pacific Exposure Database − 
Inception Report 
 
ADB TA 6496-REG: Regional Partnerships for 
Climate Change Adaptation and Disaster 
Preparedness 
 
GNS Science Consultancy Report 2009/321 
December 2009 
 
In Collaboration with 
 
Pacific Islands Applied Geoscience Commission 
Joint Contribution Report 197 
 
 
in association with 
 
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Project Number: 440W1345 
 
 
CONFIDENTIAL 
 
This report has been prepared jointly by the Institute of Geological 
and Nuclear Sciences Limited (GNS Science) and the Pacific 
Islands Applied Geoscience Commission (SOPAC) in association 
with the Pacific Disaster Center (PDC) exclusively for and under 
contract to the Asian Development Bank (ADB). Unless otherwise 
agreed in writing, all liability of GNS Science, SOPAC or PDC to any 
other party other than ADB in respect of the report is expressly 
excluded. 
 
The data presented in this Report are 
available to GNS Science for other use from 
December 2009 
 
©Institute of Geological and Nuclear Sciences Limited 2009 
 
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ii 
 
EXECUTIVE SUMMARY 
The Asian Development Bank (ADB) are funding Technical Assistance, TA 6496-REG 
Regional Partnerships for Climate Change Adaptation and Disaster Preparedness to develop 
exposure databases - information on the built environment and its relationship to hazards - 
that support greater resilience to climate impacts and natural disasters through facilitating 
decision-making on hazard exposure and risk minimization. The databases will also support 
an assessment of the feasibility of a regional pooled catastrophe insurance scheme and its 
subsequent development, an interrelated World Bank project, which requires data form this 
project to carry out loss modelling. 
 
GNS Science International Ltd in association with the Pacific Disaster Center, and the Pacific 
Islands Applied Geoscience Commission (SOPAC) have been contracted to carry out the 
TA. Eight Pacific island countries: Cook Islands, Fiji, Papua New Guinea, Samoa, Solomon 
Islands, Tonga, Tuvalu and Vanuatu are to be involved in this initial project. Critical to the 
success of the project is the partnership with the SOPAC, who have an understanding of 
existing data, and have built relationships with these countries over many years. 
 
As part of the inception mission the following activities have taken place 
•  Preparation of a précis of the project concept  
•  Engagement with SOPAC in September 2009, where the composition of the project 
team and timetable were discussed, along with a review of SOPAC’s existing data 
holdings for the eight PICs; 
•  Presentation of the Project to the SOPAC Annual Session, Vanuatu; 
•  Attendance at the FEMM meeting, Cook Islands, October, 2009 and engagement with 
Cook Island government officials; 
•  Development of Project introductory letters to country counterpart agencies;  
•  A data capture trial in Nadi, and 
•  An inception meeting held at SOPAC 2-4 December 2009 to discuss the inception 
activities and finalise work programme, in conjunction with personnel from a related 
World Bank project. 
 
SOPAC and the countries involved have some infrastructure data. Five capital cities (Apia, 
Nuku’alofa, Honiara, Suva and Port Vila) have building data collected as part of the Pacific 
Cities project captured by SOPAC 1998 to 2001. Other infrastructure and physiographic data 
are held within the countries but appear to be inconsistent in coverage and often poorly 
attributed. This TA will collect existing infrastructure and physiographic data, and where data 
are lacking or absent, the location and attributes of buildings will be collected by field survey 
and extrapolation from census data. The field data collection will be reliant on SOPAC and 
in-country staff and will utilise integrated handheld data capture devices. Building foot prints 
and major, above-ground assets will be captured by digitizing from aerial or satellite imagery 
carried out by SOPAC and AIR Worldwide, prior to field data collection. 
 
All data collected will have a spatial reference but will be in a form that is GIS platform 
independent, i.e. it can be utilized in any GIS. This will ensure that the data have greater 
utility in risk modelling. Experience has shown that when a database is dependent on third-
party proprietary software it introduces additional cost in implementation and problems 
following new releases of the proprietary software. We envisage using an open source 
database and GIS management software that can be accessed by existing proprietary GIS 
software or open source GIS. 
 
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CONTENTS 
EXECUTIVE SUMMARY ......................................................................................................... II
 
1
 
INTRODUCTION .......................................................................................................... 1
 
2
 
INCEPTION ACTIVITIES ............................................................................................. 3
 
2.1
 
Initiation Report.................................................................................................3
 
2.2
 
Engagement with SOPAC/Fiji, September 2009 ..............................................3
 
2.3
 
Presentation of Project − SOPAC Annual Session, Vanuatu ...........................4
 
2.3.1
 
STAR Seminar ...........................................................................4
 
2.3.2
 
Special meeting .........................................................................4
 
2.4
 
FEMM meeting, Cook Islands, October 2009...................................................5
 
2.5
 
Project introductory letters to country counterpart agencies.............................6
 
2.6
 
Data capture trial, Nadi .....................................................................................6
 
2.7
 
Inception Meeting, 2 - 4 December 2009..........................................................6
 
3
 
INCEPTION FINDINGS................................................................................................ 7
 
3.1
 
Hazard data ......................................................................................................7
 
3.1.1
 
Tropical Cyclone ........................................................................7
 
3.1.2
 
Earthquake.................................................................................8
 
3.2
 
Infrastructure Data ..........................................................................................10
 
3.2.1
 
Buildings ..................................................................................10
 
3.2.2
 
Building Data Analysis – Pacific Cities, RiskScape .................12
 
3.2.3
 
Building data analysis - Nadi trial.............................................13
 
3.2.4
 
Transportation..........................................................................15
 
3.2.5
 
Underground Pipe Networks....................................................18
 
3.2.6
 
Power Distribution Networks....................................................19
 
3.3
 
Physiography ..................................................................................................20
 
3.3.1
 
Imagery ....................................................................................20
 
3.3.2
 
Topography..............................................................................20
 
3.3.3
 
Bathymetry...............................................................................21
 
3.3.4
 
Soils/Geology...........................................................................23
 
4
 
PROJECT IMPLEMENTATION PLAN ...................................................................... 25
 
4.1
 
Activity 1: Inception, engagement and liaison.................................................25
 
4.2
 
Activity 2: Database design ............................................................................25
 
4.3
 
Activity 3: Data Collection ...............................................................................26
 
4.3.1
 
Data collection Phase 1 ...........................................................26
 
4.3.2
 
Data collection Phase 2 ...........................................................26
 
4.3.3
 
Methodology for data capture in the field.................................26
 
4.3.4
 
Hazard data collection .............................................................28
 
4.3.5
 
Physiographic data ..................................................................28
 
4.4
 
Activity 4: National and Regional Systems .....................................................28
 
4.5
 
Activity 5: Training ..........................................................................................30
 
5
 
ACKNOWLEDGEMENTS .......................................................................................... 33
 
6
 
REFERENCES ........................................................................................................... 33
 
 
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Appendices 
Appendix 1:
 
Country Contacts – ADB Pacific Exposure Database Project.  TA- 6496-REG
 
Appendix 2:
 
Participants of the special session held at SOPAC Annual Session in Vanuatu, October 
2009
 
Appendix 3:
 
Example letters sent to country Government officials
 
Appendix 4:
 
Satellite imagery data and coverage maps held by SOPAC
 
Appendix 5:
 
Attribute forms and reference material for field trial − Nadi
 
Appendix 6:
 
Minutes of the Inception meeting and Attribute Table
 
Appendix 7:
 
Status of SOPAC Map servers for each country
 
Appendix 8:
 
Detailed project plan
 
Appendix 9:
 
Detailed data collection plan
 
 
 
Figures 
Figure 1: 
 
The eight Pacific Island Countries involved in the project are Cook Islands, Fiji, Papua New 
Guinea, Samoa, Solomon Islands, Tonga, Tuvalu and Vanuatu........................................................2
 
Figure 2:
 
The average number of tropical cyclones per year passing within 555 km (a circle of radius 
equal to 5° of latitude) of the main island groups of the Southwest Pacific over the full 
cyclone season (November through May) (source Glassey 
et al.
 2004). ...........................................8
 
Figure 3:
 
Epicentres of earthquakes of M≥5.5 from the Pacific Catastrophe Risk Financing Initiative 
simulated 10 000 year catalogue (source Pacific Catastrophe Risk Financing Initiative)...................9
 
Figure 4:
 
Field checking of building footprints captured from high resolution satellite imagery .......................14
 
Figure 5:
 
SW Pacific map showing bathymetry survey locations carried out under SOPAC/EU funding 
2003-2008........................................................................................................................................23
 
Figure 6:
 
GIS Options for the inventory database ...........................................................................................29
 
 
 
Tables 
Table 1:
 
Mean return period (in years) of tropical cyclones for the 8 PICs (source PCRFI 2008) ....................8
 
Table 2:
 
Mean return period (in years) of earthquake magnitudes occurring close (<200 km) to the 
capital cites of the eight selected countries (source Pacific Catastrophe Risk Financing 
Initiative). The distance is the site-to-rupture distance not site-to-epicentre.....................................10
 
Table 3:
 
RiskScape earthquake building classes parameters and possible values. ......................................12
 
Table 4:
 
PCRFI building classes parameters and possible values.................................................................12
 
Table 5:
 
PC database building class parameters and possible values. .........................................................13
 
Table 6:
 
Accuracy of building attributes data collected in the Nadi field trial..................................................15
 
Table 7:
 
Existing Topographic data................................................................................................................21
 
Table 8:
 
Existing Geology and Soils data ......................................................................................................24
 
Table 9:
 
Proposed workplan ..........................................................................................................................32
 
 
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1 INTRODUCTION 
South Pacific nations are exposed to a range of natural hazards such as earthquakes, floods, 
tsunami, volcanic eruption, cyclones and severe storms. There is a need to compare the risk 
posed by each hazard in a consistent way using potential impacts such as cost and 
casualties. Such a comparison of risk can then support decision-making by: 
•  Determining which hazard represents the greatest risk to various communities; 
•  Enabling mitigation investments to be prioritised (e.g. earthquake strengthening of 
buildings versus cyclone strengthening); 
•  Avoiding inappropriate land development through planning; and 
•  Contributing to effective emergency management plans. 
 
The Asian Development Bank (ADB) are funding Technical Assistance, TA 6496-REG 
Regional Partnerships for Climate Change Adaptation and Disaster Preparedness to develop 
exposure databases - information on the built environment and its exposure to hazards - that 
will support greater resilience to climate impacts and natural disasters. The TA will: 
•  Support the development of up to eight national infrastructure inventories for 
countries most exposed to the risk of earthquakes and cyclone; and 
•  Develop a consolidated, regional database encompassing hazard to create an 
exposure database and, if available, hazard-specific fragility functions. 
 
The data will enhance the preliminary assessment of the feasibility of a Pacific Catastrophe 
Risk Financing Initiative (PCRFI), which is being carried out as part of a related World Bank 
project (Phase 2). The information gathered will enhance the World Bank project in their 
development of country specific risk models. In addition it will assist informed decision-
making in Disaster Risk Reduction, minimizing the negative social and environmental 
impacts of catastrophic events. 
 
The exposure databases will build upon existing information. The key tasks for this TA are to: 
•  Assess the current in-country use of hazards and risk information; 
•  Collate and analyse existing hazard models and risk profiles;  
•  Collect national georeferenced building and infrastructure (inventory) data from the  
Pacific Islands Applied Geosciences Commission (SOPAC) and local government 
agencies to develop national and regional exposure databases; 
•  Incorporate historical natural disasters and climate change models into risk profiling 
and disaster exposure; 
•  Involve policy makers in stakeholder consultations, meetings, and relevant training to 
develop an awareness of the type of information available and its range of uses; 
•  Identify and implement an appropriate GIS platform for acquiring, managing, 
analysing, and displaying hazard and exposure data and risk information; and 
•  Provide in-country training, user manuals, and reports. 
 
Key to the success of the project is SOPAC who have an excellent understanding of 
available data as well as relationships with Pacific Island Countries (PICs) developed over 
many years. The eight Pacific Island Countries (PICs) that are involved in the project are 
Cook Islands, Fiji, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu and 
Vanuatu (Figure 1). These PICs have agreed with the ADB to support the initiative, 
predominantly via the ministries of finance and/or planning (Appendix 1). 
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Figure 1:  
The eight Pacific Island Countries involved in the project are Cook Islands, Fiji, Papua 
New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu and Vanuatu. 
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2 INCEPTION 
ACTIVITIES 
It was in initially planned to visit all eight Pacific Island Countries involved in the project to 
engage with government officials as part of this inception activity. However, on discussion 
with SOPAC, it became obvious that this approach would be unrealistic in achieving effective 
engagement in such a short timeframe, and very costly. It was decided to take advantage of 
a number of existing meetings, such as the SOPAC Annual Session in Vanuatu and the 
Forum Economic Ministers Meeting in the Cook Islands, to present the project concept to the 
countries. The project was also presented at the Pacific GIS and RS users Conference to be 
held in Suva from the 1-4 December 2009. Full engagement will be done prior to data 
collection in each country. Inception activities are described below. 
 
2.1 Initiation 
Report 
A précis (Glassey, 2009) of the project concept was prepared and supplied to ADB as 
requested. 
 
2.2 
Engagement with SOPAC/Fiji, September 2009 
The international project team engaged with SOPAC counterparts in Suva Fiji from the 28 
September until the 2 October 2009 and activities included: 
•  Discussing the project team composition (Michael Bonte-Grapetin, Litea Biukoto); 
•  Presenting the project concept to SOPAC staff and University of South Pacific (USP) 
Staff (Conway Pene and Eberhard Weber); 
•  Determining SOPAC data holdings for each country, (Joy Papao, Wolf Forstreuter – 
SOPAC); 
•  Engaging with the SOPAC Ocean and Islands team (Arthur Webb, Robert Smith and 
Herve Damlamian); 
•  Engaging with the SOPAC Economic Assessment Unit (Paula Holland) and Pacific 
Disaster Network coordinator (Jutta May); 
•  Discussing and planning the data capture process, including running a data capture 
trial in Nadi, based on Pacific Cities attributes and utilising 60 USP students to assist 
with data collection; and 
•  Revision of the Project Implementation Plan to consolidate the work plans and 
maximise synergies with the related World Bank-led Pacific Catastrophe Risk 
Financing Initiative (PCRFI) Phase 2. 
 
In addition we took the opportunity to meet with the following people based in Suva 
•  John Prasad, Acting Permanent Secretary, Finance, Fiji; 
•  Rasmih Rita (Fiji Land Information System), regarding the content and availability of 
data on Fiji, including census data; 
•  Terrence Erasito, Structural Engineer, Suva, Fiji to discuss building construction in 
the Pacific, and; 
•  Alain Goffeau of the ADB, South Pacific Regional Office, Suva. 
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2.3 
Presentation of Project − SOPAC Annual Session, Vanuatu 
2.3.1 STAR 
Seminar 
The opportunity was taken to present the project at the Science, Technology and Research 
(STAR) seminar of the SOPAC Annual Session in Vanuatu in October 2009. A presentation 
entitled “Hazard and Risk in the Pacific: Towards an understanding of exposure with 
RiskScape
1
 and the development of a Pacific exposure database” was given by David Heron 
of the Project Team, and outlined the previous work done by the World Bank and ADB, as 
well as the concept of this current project. The asset types (buildings and other structures, 
roads and other infrastructure) that would be captured under the ADB project were described 
together with the hazards (earthquake, tsunami, and cyclone), and RiskScape used to 
illustrate how these spatial asset data could be overlain on spatial hazard maps to determine 
the exposure (that is the number of buildings, kilometres or roads, etc. that are exposed to 
the hazards), vulnerability and loss. 
 
2.3.2 Special 
meeting 
During the STAR session in Vanuatu a side meeting was held and facilitated by David Heron 
and Michael Bonte-Grapetin (SOPAC) to further brief the country counterparts that attended 
from the Cook Islands, Papua New Guinea, Samoa, Tonga and Vanuatu on the project 
(Appendix 2). Fiji, Solomon Islands and Tuvalu were not represented at this meeting.  
 
The country delegates were briefed on the purpose of the project and potential applications 
of the national exposure databases. Details on the types of hazard and asset data to be 
collected were discussed and the organisations in each country most likely to hold relevant 
data were identified. Also discussed were the points of contact within each country and the 
best approach to take. It was clear that further multi-stakeholder discussions in-country will 
be needed to ascertain the best focal point to host and sustain the national exposure 
database. 
 
ADB has made initial contact with the officials in each country as per Appendix 1, but it was 
considered at this meeting, that a follow up letter with copies to Internal Affairs, the National 
Disaster Management Office and other departments and ministries to whom the project 
would be of interest and/or that could hold relevant data, would be appropriate. Further, it 
was suggested SOPAC should equally inform in writing the SOPAC National 
Representatives and copy these letters to the other relevant ministries. The following 
ministries were considered relevant in the named countries: 
 
Cook Islands 
ο  Ministry of Finance (initial contact) 
ο  Ministry of Foreign Affairs 
ο  Ministry of Infrastructure and Planning 
ο  Cook Islands Statistics Office 
ο  National Environment Services (NES) 
 
Kingdom of Tonga 
ο  Ministry of Finance (initial contact) 
                                                 
 
1
 RiskScape is a multi-hazard risk assessment system being developed in NZ 
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ο  Ministry of Lands, Survey, Natural Resources & Environment 
ο  Ministry of Works 
ο Department 
of 
Statistics 
ο Met 
Office 
 
Vanuatu 
ο  Office of the Prime Minister (initial contact) 
ο  Ministry of Finance 
ο  Ministry of Lands and Natural Resources 
ο  Department of Geology, Mines and Water Resources 
ο  Vanuatu Statistics Office 
 
Samoa 
ο  Ministry of Finance (initial contact) 
ο  Department of Foreign Affairs 
ο  Department of Lands, Survey & Environment 
ο  Public Works Department 
ο Department 
of 
Statistics 
ο  Electricity Power Corporation 
ο  Samoa Water Authority 
 
Papua New Guinea 
ο  Department of National Planning and Monitoring (initial contact) 
ο  Ministry for Finance and Planning 
ο  Ministry for Works and Supply 
ο  Department of Minerals Policy and Geohazards Management 
ο  National Statistics Office 
 
2.4 
FEMM meeting, Cook Islands, October 2009  
Phil Glassey, TA project team leader, along with Edy Brotowisoro of the ADB, and Olivier 
Mahul and Francis Ghesquiere of the World Bank, visited Rarotonga from the 27–30 October 
to attend the Forum Economic Ministers Meeting (FEMM) and engage with Cook Island 
government officials. The project was discussed with Dr Gerald Haberkom, Manager of the 
Statistics and Demography programme at South Pacific Commission (SPC), who indicated 
that the census data that they held will be available for the project. 
 
This team also met with Cook Island government officials from the Ministry of Infrastructure 
and Planning (MOIP), National Environment Service (NES), Ministry of Agriculture, 
Emergency Management Cook Islands (EMCI), the Prime Ministers Office, Central Policy 
and Planning and the Police. We visited Timote Tangiruaine, the MOIP GIS specialist, and 
determined that the Cook Islands hold much data that can be utilised for the project. The 
Secretary of the Ministry of Infrastructure and Planning expressed interest in supporting the 
project and along with the National Environment Service agreed to make staff available for 
the field survey in the Cook Islands planned for February 2010. 
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2.5 
Project introductory letters to country counterpart 
agencies 
Letters of introduction to the project will be sent by the International Team to each of the 
primary contacts in each country (Appendix 1) and copied to other government officials well 
in advance of data collection. An example of such a letter sent to the Cook Islands 
Government is attached as Appendix 3. These letters will be followed up with further 
correspondence to confirm logistics. 
 
SOPAC are also sending letters to the SOPAC National representatives for each country as 
per Appendix 3. Another request to the national representative will be made immediately 
prior to going on mission into the country, which is a SOPAC operating requirement. 
 
2.6 
Data capture trial, Nadi 
A trial in capturing building attributes was carried out in Nadi from 27−30 October to 
capitalize on the offer of 60 students from the University of the South Pacific and to test field 
methodologies and data templates. Building attributes, based on the Pacific Cities data, were 
collected utilizing different methods to determine the most efficient and reliable means of 
data collection. Two methods were trialled, a paper-based system and hand held devices. In 
both cases photographs were taken of the buildings and the image number recorded. 
Reference materials in the form of field guides and various maps were provided as part of 
the exercise. 
 
2.7 
Inception Meeting, 2 - 4 December 2009 
The draft inception report was discussed at the inception meeting held at SOPAC in Suva, 
Fiji, 2-4 December 2009. Representatives of the World Bank and AIR Worldwide, who will be 
carrying out the risk modelling as part of a related World Bank project, attended these 
meetings, and the attributes to be captured have been discussed and prioritised. AIR 
Worldwide will be capturing major assets utilising satellite imagery, and this imagery will be 
utilised to capture building footprints to which attributes will be assigned as part of this ADB 
project. The minutes of this meeting along with a table of data attributes to be captured are 
included as Appendix 6. At the same time the project was presented at the Pacific GIS /RS 
conference held at University of South Pacific (USP), Suva Fiji. 
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3 INCEPTION 
FINDINGS 
3.1 Hazard 
data 
Numerous online catalogues of hazard events are available for tropical cyclone, earthquake, 
tsunami and volcanic activity. The Pacific Catastrophe Risk Financing Initiative recognised 
that because of the short time over which historic records have been kept, these databases 
do not represent all possible future events. Consequently they created synthetic earthquake 
and cyclone catalogues representing 10 000 years of events that are statistically consistent 
with, but not identical to, the historical catalogue. These catalogues contain 150 000 cyclone 
and 2.2 million earthquake events and provide a better estimate the hazard. These data, or 
at least links to the data, should be included in the hazard database created by this project. 
 
Regional Natural Hazards Potential Maps of the Circum Pacific Region have been compiled 
by Johnson 
et al.
 (1995) at a scale of 1:10 million. These maps include earthquakes, 
cyclones, landslides, tsunami, floods, bushfires, volcanoes and droughts. Multi-hazard maps 
for major urban areas in Fiji (Suva, Nausori, Labasa, Nadi, and Ba) were reported by Blong 
(1994). PDC has developed and maintains an Asia Pacific Hazards and Vulnerabilities Atlas 
(
http://atlas.pdc.org
) which has now been extended to become the Global Hazards 
Information Network (GHIN). This resource, plus the Pacific Disaster Net hosted by SOPAC, 
contain information on hazards, historical damage from hazard events, as well as hazard 
models and risk surfaces (probability surfaces) for storms and earthquakes. 
 
3.1.1 Tropical 
Cyclone 
Figure 2 shows tropical cyclone occurrences in the South West Pacific for all years for the 
period 1970/71 to 2003/04, during the modern period of satellite records. Tropical cyclones 
develop in the South Pacific over the wet season, usually from November through April. Peak 
cyclone occurrence is usually during January, February and March. On average nine tropical 
cyclones occur during the November to April season, but this can range from as few as four 
in 1994/95 and 2003/04, to as many as 17 in 1997/98, during the strong El Niño episode 
(Glassey 
et al.
 2004). 
 
Tracks of tropical depressions (<34 knots wind speed), tropical cyclones (34–64 knots) and 
severe tropical cyclones (> 64 knots) are held in the US Navy Global Tropical/Extra-tropical 
Cyclone Climatic Atlas database (GTECCA)
2
 and the Joint Typhoon Warning Center’s 
(JTWC) Southern hemisphere Best Track data. These data include date, time, position, 
storm-stage and, where known, maximum wind speed, minimum central pressure and storm 
name. 
 
The Tropical Cyclone Hazard Model developed by AIR Worldwide as part of the World Bank, 
Pacific Catastrophe Risk Financing Initiative (PCRFI) project, uses this historic catalogue as 
well as other data from Bureau of Meteorology (BoM, Australia) and the Fiji Meteorology 
Service, to generate a synthetic catalogue of 10 000 events to calculate wind speeds and 
precipitation for a 2.5º latitude/longitude grid, and estimate storm surge. Return periods for 
various category storms for the eight different countries are report by the PCRFI as in Table 
1. 
                                                 
 
2
 http://navy.ncdc.noaa.gov/products/gtcca/gtccamain.html 
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Figure 2: 
The average number of tropical cyclones per year passing within 555 km (a circle of 
radius equal to 5° of latitude) of the main island groups of the Southwest Pacific over the full cyclone 
season (November through May) (source Glassey 
et al.
 2004). 
 
Table 1: 
Mean return period (in years) of tropical cyclones for the 8 PICs (source PCRFI 
2008) 
Saffir-
Simpson 
Category 
Cook Islands 
Fiji 
PNG Samoa Solomon 
Islands 
Tonga Tuvalu 
 Vanuatu 
≥1 5 
103 
20 4 
41 
≥2 8 
200 
13 
40 5 
83 
≥3 21 
12 
500 
35 
88 160 
182 
16 
≥4 294  169 
2500 
185 
270 
313 
769 
400 
5 10000 
10000 
3333 
3333 
3333 
10000 
10000 
10000 
Note:  Estimated mean return periods higher than 1000 years should be interpreted with caution as they are 
calculated from 10 or less simulated events out of 10000. 
 
A Tropical Cyclone hazard model, determining return periods for wind speeds using a 5 000 
event synthetic catalogue, has been developed for Port Vila, Vanuatu (Shorten 
et al
. 2003) 
and losses estimated. 
 
3.1.2 Earthquake 
The South Pacific is one of the most active seismic regions in the world (Figure 3). 
Earthquakes result in multiple threats to island nations. Ground shaking can be amplified by 
soft sediments, resulting in significant damage to buildings and infrastructure at some 
distance from the epicentre. Water-saturated sediments around wharves can settle, 
disrupting these facilities. Ground shaking can also trigger landslides in areas of unstable 
steep slopes. However it is tsunami that can be the most widespread and damaging 
consequence of an earthquake. It is intended that the hazard database include the 
epicentres of earthquakes that generated tsunami affecting the island nations together with 
any locations where tsunami impacts have been recorded 
 
 
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The SOPAC Pacific Cities Database includes an assessment of the ground conditions in the 
capitals of five of the eight island nations that are the focus of this project. Similar data will be 
captured for other heavily populated centres, where available, to assist in earthquake loss 
modelling. 
 
The Pacific Catastrophe Risk Financing Initiative showed a short return period for large 
earthquakes occurring within 50 km of the capital cities of the nations included in this project 
(Table 2). The data indicate that the Solomon Islands, Tonga and Vanuatu have a high 
earthquake hazard whereas Cook Islands and Tuvalu have a low earthquake hazard. 
 
A probabilistic earthquake hazard model was developed for Vanuatu by Suckale 
et al.
 (2005) 
and recently published (Suckale and Grünthal, 2009). The earthquake risk for Suva was 
reported by the South Pacific Disaster Reduction Programme (SPDRP 2002) and a 
probabilistic earthquake hazard assessment for Fiji was carried out by Jones (1998). 
Denham and Smith (1993) gave a review of earthquake risk in the Southwest Pacific region. 
Ripper and Letz (1993) determined return periods and probabilities of occurrence of 
earthquakes greater than M 7.0 normalized 10 000 km
2
 areas of Papua New Guinea. 
 
Figure 3
Epicentres of earthquakes of M≥5.5 from the Pacific Catastrophe Risk Financing 
Initiative simulated 10 000 year catalogue (source Pacific Catastrophe Risk Financing Initiative). 
 
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Table 2: 
Mean return period (in years) of earthquake magnitudes occurring close (<200 km) 
to the capital cites of the eight selected countries (source Pacific Catastrophe Risk Financing 
Initiative). The distance is the site-to-rupture distance not site-to-epicentre. 
 
Distance 
from 
Capital 
 
 
Earthquake 
magnitude 
 
Cook 
Islands 
 
Fiji 
 
Papua 
New 
Guinea 
 
Samoa 
 
Solomon 
Islands 
 
Tonga 
 
Tuvalu 
 
Vanuatu 
<50 km 
5
M<6 
- 23 182  270  2  3  - 
<100 km 
6
M<7 
- 40 124  135  3  3 2000  1 
<150 km 
7
M<8 
- 244 98  128 
8  12  - 
<200 km 
M
- - - 1667 94 106 -  833 
 
Tsunami hazard models have been attempted for a number of the nations under 
consideration. Modelling of the wave as it approaches the coast can be achieved using one 
of several different open-source software models available (e.g. ANUGA, MOST, COMCOT, 
etc.) and existing bathymetric data. Inundation beyond the coastline is more problematic 
because of the coarseness of the elevation data currently available. At best, elevation data 
are based on interpolated 2 m contours but good modelling requires elevations down to cm 
accuracy. Because of the uncertainty created by this lack of key data, no inundation 
modelling will be included in the hazard database. 
 
3.2 Infrastructure 
Data 
As part of the first project inception mission at SOPAC (28 Sep–2 Oct), the International 
Team was provided with digital GIS data by SOPAC analysts. These data were catalogued 
by PDC analysts and assessed against preliminary criteria. SOPAC has commissioned 
inventories of geospatial data related to tsunami inundation and risk modelling regionally and 
for some individual countries (SOPAC 2008, 2008a-d). They also collected building data for 5 
Pacific capital cities − Apia, Nuku’alofa, Suva, Port Vila and Honiara between 1998 and 2001 
(Biukoto 
et al.
 2001). In addition other data such as roads and land use for example were 
supplied by the governments or other sources. Analysis of the Pacific Cities database and its 
utility in loss estimation is discussed in Section 3.2.2 below. Other infrastructure data may be 
available in the countries, but is not held by SOPAC. 
 
3.2.1 Buildings 
A key focus of this activity is the development of a building database for each country and 
the region. At a minimum, buildings will be represented as point features. Building “footprint” 
datasets will be retained as polygons and converted to points for consistency. Attributes 
which characterize buildings such that they can be assigned a fragility class to enable loss 
computations will be collected. Replacement costs are required to compute direct 
replacement costs and hence financial loss to hazards. For the hazards under consideration 
(seismic and wind), fragility can be estimated using attributes including: use type, age, 
structural type, construction materials, roof configuration, number of stories, area and floor 
level. The attributes to be captured have been discussed and prioritised with AIR Worldwide 
who will be carrying out the loss modelling as part of the related World Bank project and 
included in Appendix 6. The following assessment of the building data, either held by 
SOPAC, or known to exist in each country, was based on these information requirements. 
 
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Cook Islands:  
None held by SOPAC. However, from the inception mission to the Cook Islands in October 
2009, it was discovered that the Cook Islands do have building information including location 
(point), use and type of structure for buildings constructed since 1996. These data are 
collected as part of the building permitting and control system. Hence some building data is 
available for Rarotonga. In addition building locations and use have been captured for 
Aitutaki, Atiu, Mauke, Mitiaro, and Mangaia as part of a survey conducted under an NZODA 
Outer Islands Water & Power Reticulation Feasibility Study (
http://www.maps.gov.ck
). 
 
Fiji Islands:
  
Suva only – Pacific Cities data.
 
 
Papua New Guinea:  
There are five separate building footprint datasets for different areas of the City of Port 
Moresby. However, the information (values) within the attribute tables is indiscernible. 
Bougainville, Chimbu, East New Britain, Enga, East Sepik, Madang, Manus, Milne Bay, 
Morobe, New Ireland, Oro, Port Moresby, Southern Highlands, Western, Western Highlands, 
West New Britain and West Sepik have health building layers that include the following 
attributes: Name, Alternate_Name, CU_Name, Census2000_Key, Feature, Province, District, 
LLG, Ward, RMU, Access and Health_Type. These areas also have a school buildings layers 
that includes the following attributes: Name, Alternate_Name, CU_Name, Literacy_Name, 
Village_Key, Province, District, LLG, LLG NSO Map, Access and School. 
 
Samoa:  
There is one dataset (AassetsGPS) of GPS collected buildings for the Apia area only 
captured as part of Pacific Cities. This dataset has very detailed attributes that include the 
type of structure (e.g., house, commercial, church, etc.), wall materials, windows, roof 
materials, roof shape, roof pitch, number of stories, maximum height, base floor area, and 
date of survey. 
 
Solomon Islands: 
Honiara Only – Pacific Cities data. 
 
Tonga: 
Building locations for the whole of Tonga were captured from satellite imagery as part of the 
CERM project 2004-2007. Large buildings (>1000 m
2
) were captured as polygons and 
smaller ones as points. There are few attributes assigned to these buildings. There is a 
points layer (Nasset) for Nuku’alofa captured as part of the Pacific Cities Project that includes 
attributes such as building use (e.g., house, church, commercial, etc.), base floor area, 
height above sea level, foundation type, wall materials, number of stories, roof material, roof 
shape, roof pitch, % of windows, and burglar bars. 
 
Tuvalu: 
The following islands have buildings footprints, but no attributes: Vaitupu, Nukulaelae, 
Nukufetau, Nui, Nuitao, Niulakita, Nanumea, and Nanumaga. The island of Funafuti has a 
detailed buildings polygon dataset (funafuti_Govbuild) with attributes including building type 
(household, government, etc.), water storage tank capacity, presence of gutters, gutter 
material, roof type (coded) and roof material. 
 
 
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Vanuatu: 
There is a points dataset (Vasset) for Port Vila, collected as part of the Pacific Cities project, 
with attributes that include building use (e.g., house, church, commercial, etc.), base floor 
area, height above sea level, foundation type, wall materials, number of stories, roof material, 
roof shape, roof pitch, % of windows, and burglar bars. Also for Port Vila is a point dataset 
(Vsites) with places such as hotels, public places, government buildings, etc. 
 
3.2.2 
Building Data Analysis – Pacific Cities, RiskScape 
Prior to undertaking the data capture trial in Nadi, analysis was undertaken on the Pacific 
Cities (PC) data to determine how readily they might be used for loss estimation. The data 
were compared to those used as input to RiskScape and the Pacific Catastrophe Risk 
Financing Initiative (PCRFI). The data were further analysed in an attempt to provide several 
general building categories to speed up field data capture. In general, the Pacific Cities (PC) 
data can be used for loss modelling but requires some assumptions to be made. For 
earthquake hazard, building data in RiskScape are classified into 72 construction classes 
using 5 parameters (Table 3). PCRFI lists 45 building construction classes of in Annex 4 
(Table 4) based on 4 parameters. 
 
The PC data contain a total of 20 781 points representing buildings in Apia, Honiara, Port 
Vila, Suva, and Nuku’alofa. Attributes are listed in Table 5. The database holds a 
classification of use, building name, minimum elevation of floor above ground, roof material, 
slope and shape, building size and the proportion of walls in windows plus the presence of 
concrete cantilever balconies. 
 
It was determined that with the use of a suitable lookup table the Pacific Cities data can be 
translated to fit within the proposed building classification and therefore can be used to form 
the basis of the building dataset for this project. 
Table 3: 
RiskScape earthquake building classes parameters and possible values. 
General Description 
Load System 
Height 
Age Band 
Quality 
Reinforced Concrete 
Shear wall 
Low (1-3 storeys) 
pre-1940 
Sound 
Steel Moment-resisting 
frame 
Medium 
(4-7 storeys) 
pre-1970 
Deficient 
Timber stud, steel stud 
Braced frame 
High (8+ stories) 
1960-1979 
 
Reinforced masonry 
Tilt-up panel 
 
post-1970 
 
Reinforced concrete 
Portal frame 
 
 
 
Light Industrial 
Energy absorption 
 
 
 
Advanced Design 
Unreinforced 
 
 
 
Brick Masonry 
 
 
 
 
 
Table 4: 
PCRFI building classes parameters and possible values. 
Building Type 
Construction Class 
Height 
Year of 
Construction 
Traditional 
Thatched / Palm leaf 
Low (1-3 storeys) 
pre-1990 
Shacks 
Other 
Medium (4-7 storeys) 
pre-1990-2005 
Detached 
Unreinforced masonry 
High (8+ stories) 
post 2005 
Detached / Flat 
Confined masonry walls 
 
 
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Flat / Commercial / Industrial 
Reinforced confined masonry walls 
 
 
Commercial / Industrial 
Reinforced concrete 
 
 
 Light 
metal 
 
 
 Other 
metal 
 
 
 
Table 5: 
PC database building class parameters and possible values. 
Building use 
UD material 
UD structure 
Wall material 
Storeys 
House Slab Soft 
Concrete 
Flats wooden 
poles 
Stiffened 
Timber 
Shed concrete 
columns 
 
Metal 3 
Commercial 
steel columns 
 
fibre-cement sheet 
public services 
load bearing walls 
 
Brick 
health services 
 
 
 
Accommodation  
 
 
etc 
Etc    
 
 
 
 
3.2.3 
Building data analysis - Nadi trial 
Building attributes were collected for over 1 200 buildings in both residential and commercial 
areas in Nadi. Some buildings were visited by two survey groups and so only 714 buildings 
were surveyed. Based on these trials a daily building attribute collection rate of between 50 
and 100 buildings (depending on building density and visibility) per survey group is estimated 
and can be used to evaluate time requirements for future survey work. 
 
About 100 (10%) of the buildings surveyed required the roof polygon captured from satellite 
imagery to be split (that is to say a single roof polygon represented 2 or more individual 
buildings) or were missing from the roof polygon dataset (Figure 4). 
 
A preliminary analysis of the data collected shows that 53% of the sampled buildings were 
residential, 31% commercial, 10% public or communal facilities, and the remainder were 
critical facilities, hazardous facilities, other facilities or unknown.  
 
For residential buildings most were either single or double storeys. The predominant roof 
pitch was low (56%), the predominant shape was gable (49%), and the predominant material 
was sheet metal (83%). The foundations were predominantly slab on ground (91%). Of those 
buildings with wood or concrete piles, wood poles or concrete column foundations very few 
had bracing. 83% had concrete walls - they were assumed to be plastered concrete block. 
For 38% of these, concrete columns were visible. It is assumed that where the building is 
above 1 storey those buildings without visible concrete columns had internal concrete 
columns and where it is 1 storey it is likely they consist of a mix of internal concrete columns 
and thin concrete columns (the width of a concrete block) between the wall blocks. 
 
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Figure 4: 
Field checking of building footprints captured from high resolution satellite imagery 
 
For commercial buildings most were double storeys (49%) with 13% being 3 storeys and 1% 
being 4 storeys. The predominant roof pitch was a flat (55%), and where the roof was not flat 
the predominant shape was gable (45%). The predominant material was sheet metal (50%) 
with concrete a close second (40%). The foundations were predominantly slab on ground 
(91%). Of those buildings with concrete column foundations most had some form of bracing. 
87% had concrete walls. There were assumed to be plastered concrete block. For 66% of 
these, concrete columns were visible. It is assumed that where the building is above 1 storey 
those buildings without visible concrete columns had internal concrete columns and where it 
is 1 storey it is likely they have internal concrete columns. 
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Only a cursory analysis of survey accuracy has been possible. Five buildings surveyed by a 
trainer were compared with the surveys of others who had undergone ½ day of training. In no 
instance was any building described completely consistently. An accuracy assessment of the 
survey is provided in Table 6. 
 
Table 6: 
Accuracy of building attributes data collected in the Nadi field trial 
Building Attribute 
Percent Correct 
Within 1 Class 
Note 
Main Use 
80 
 
 
Number of Storeys 
100 
 
 
Roof Pitch 
60 
40 
Roof Shape 
40 
 
Roof Material 
60 
 
Foundation 100 
 
Foundation Bracing 
 
 
 
Wall Material 
100 
 
 
Wall Structure 
80 
 
 
Windows 40 
 
Shutters 80 
 
 
Defects 60 
 
 
Parapets/Towers 100   4 
Concrete Cantilever 
80 
 
Plan Shape 
80 
 
 
Base Floor Area 
40 
20 
Minimum Floor Height 
20 
 
Notes: 
1: In most instances it is not possible to see the roof clearly, especially on commercial buildings due to observer’s position. 
Consequently the information in these fields is often inferred rather than observed. 
2: All foundations in the sample were slab. Analysis of random photos indicated some surveyors were confused whether to 
record concrete columns below the living levels as foundation or wall structure. 
3: The data capture form was ambiguous and as a result some surveyors understood ‘building with windows forming less than 
70% of the wall’ as ‘building with less than 70% wall ‘. 
4: None of these occurred in the sample. 
5: Concrete cantilevers are any unsupported concrete structure extending from a building wall a significant distance (>1 m). On 
commercial buildings concrete cantilevers are used to provide shelter to the foot path. In a number of instances the concrete 
had been covered with timber and so was difficult to recognise. 
6: Base Floor Area can be taken from the roof polygons but in the sample it was compared to the estimate made by the trainer.  
7: In the Nadi survey Minimum Floor Height was estimated as in m. Only in one instance was the sample estimate the same as 
the trainer. However future surveys will use classes and had they been used the accuracy would have been 60%.  
 
3.2.4 Transportation 
Transportation infrastructure, including roads, bridges, airports, and seaports, constitutes a 
significant category of assets exposed to hazards. As with building datasets, attributes that 
characterize the degree to which a hazard will damage or destroy the asset are required 
along with information that can be used to estimate replacement costs. 
 
Key attributes for roads include number of lanes, traffic flow (one or two way), road surface 
(sealed, unsealed, etc), and highway number/name. Additional attributes for bridges, often 
depicted as point features, include span (length), construction type and materials. 
 
Airports, either represented as lines or polygons, should include basic information on runway 
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length, width and height above sea level. Additional information on associated structures 
(e.g., terminals, hangers, communications equipment, etc.) would be required to fully 
estimate replacement costs. 
 
Cook Islands: 
There is a major roads dataset for Aitutaki Island, but it has no attributes. There is also a 
polygon layer for the airstrip data on Aitutaki. There are several roads datasets for Rarotonga 
with varying accuracy and completeness. The most detailed dataset (RaroRoads_WGS84) 
has attributes including name, type (minor or major) and surface type. However, not all 
features have completed attribute data. Roads have been captured for Aitutaki, Atiu, Mauke, 
Mitiaro, and Mangaia as part of a survey conducted under an NZODA Outer Islands Water & 
Power Reticulation Feasibility Study (
http://www.maps.gov.ck
). 
 
Fiji Islands: 
There are two national level roads datasets (Major roads and Roads). Major roads has a 
name attribute, however it is not complete. Neither dataset has any other attributes other 
than coded ones that appear to be symbology related. These codes relate to attributes such 
as pavement type or number of lanes. 
 
There are various roads datasets for very small areas of Fiji which appear to be based on 
cities or small level administrative units. Some examples are Suva (SUVroad), Nausori 
(nAUSORI) and Lau (LAUroad). These datasets do not have attributes. 
 
Papua New Guinea: 
There are national level transportation datasets for:  
•  The nation’s airstrips including attributes for airstrip name, longitude and latitude, 
category (e.g., national, provincial, private, etc.), capacity and owner. 
•  The nation’s roads including attributes for road name, surface type, condition and 
type (e.g., major, minor, etc.). 
 
There are also several other transportation datasets organized by province/region: 
• Bougainville: An airstrips layer with type, lanes and approximate span (m); apparent 
airport buildings data; a bridges layer with standard attributes
2
, and a roads layer with 
standard attributes
3
. The column for road names is incomplete. 
• Chimbu: An airstrips layer with standard attributes
1
 and a roads layer with standard 
attributes
3
•  East New Britain: An airstrips layer with airstrip name, longitude and latitude, 
elevation, category, owner and capacity; a bridges layer with standard attributes
2
; a 
roads layer with standard attributes
3
; plus a very incomplete road number column. 
• Enga: An airstrips layer with standard attributes
1
; a bridges layer with standard 
attributes
2
 and a roads layer with standard attributes
3
• East 
Sepik: An airstrips layer with standard attributes
1
; a bridges layer with standard 
attributes
2
; and a roads layer with standard attributes
3
.  The column for road names is 
incomplete. 
• Madang: An airstrips layer with airstrip name, longitude and latitude, elevation, 
category, owner and capacity; a roads layer with standard attributes
3
; plus a very 
incomplete road number column. 
• Manus: An airstrips layer with standard attributes
1
; a bridges layer with standard 
attributes
2
; and a roads layer with standard attributes
3
•  Milne Bay: A roads layer with standard attributes
3
.  The road name column is very 
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incomplete. 
• Morobe: An airstrips layer with standard attributes
1
; a bridges layer with standard 
attributes
2
; and a roads layer with standard attributes
3
.  The road name column is 
incomplete. 
•  New Ireland: An airstrips layer with standard attributes
1
 and a bridges layer with 
standard attributes
2
• Oro: An airstrips layer with standard attributes
1
; a bridges layer with standard 
attributes
2
; and a roads layer with standard
3
 but very incomplete attributes. 
• Port 
Moresby: A roads layer with no discernable attributes. 
• Southern 
Highlands: An airstrips layer with standard attributes
1
 and a roads layer with 
standard attributes
3
• Western: An airstrips layer with standard attributes
1
 and a roads layer with standard 
attributes
3
 plus a very incomplete road number column.   
•  Western Highlands: An airstrips layer with standard attributes
1
 and a roads layer with 
no discernable attributes.  
•  West New Britain: An airstrips layer with standard attributes
1
 and a roads layer with 
no attributes. 
• West Sepik: An airstrips layer with standard attributes
1
 and a roads layer with 
standard attributes
3
.  The road name column is very incomplete. 
 
1
Standard attributes for an airstrips layer are airstrip name, longitude and latitude, category (national, provincial, private, etc.), 
capacity and owner.   
2
 Standard attributes for a bridges layer are with type, lanes and approximate. span (m).   
3
 Standard attributes for a roads layer are road name, surface type, condition and type (major, minor, etc.). 
 
Samoa: 
There is a roads layer for each of the main islands - Savaii (S_ROAD) and Upolu (U_ROAD). 
However, this dataset has no attributes. There are three transportation layers for the town of 
Apia (i.e., bridge, roadsealed and roadunsealed). These datasets only have an elevation 
attribute. 
 
Solomon Islands: 
There is one roads dataset of Honiara (H_roads) but it has no attributes. Also, there is a 
polygon layer of the Honiara Airport (sb_airport) but it does not have attributes either. 
 
Tonga: 
There is a national level road dataset for Tonga collected as part of the CERM Project 
between 2005 and 2007 and held by Ministry of Lands, Survey, Natural Resources and 
Environment (MLSNRE). Attribute fields include Road-id, Name, Status, Surface, Width, 
Number_of_lanes, Description. There are also layers for Tracks (Track_id, Track_name, 
Status, Surface), Bridges (Bridge-id, Name, Status, Type, Construction, Surface, Use), 
Airport runway (Airstrip_id, Airstrip_name, Type, Surface, Status), large and small wharves 
(Wharf_id, Name, Use, Owner) and boatramps. However, it should be noted that many of the 
attribute fields have not been populated. The airport terminals are captured in the building 
database. 
 
Tuvalu: 
There are airport runway datasets for Nukufetau and Funafuti. There are also roads datasets 
for the following islands: Vaitupu, Nukulaelae, Nui, Niutao, Niulakita, Nanumea, and Funafuti. 
None of the roads datasets have any attributes. 
 
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Vanuatu: 
There is an airports point dataset (Airports) with attributes including name, type, runway 
length, surface type and direction. There are roads data for Malampa, Penama, Sanma, 
Shefa, Tafea and Torba with attributes including length, classification, number of lanes, 
surface type, and width. Some attribute data are not populated. There is also a national level 
wharfs dataset including attributes for name and island. 
 
3.2.5 
Underground Pipe Networks 
Utilities represent another significant category of infrastructure exposure to hazards. They 
have been broken into underground pipes (3.2.5) and power distribution (3.2.6) since they 
each have unique attributes and hazard exposure modes. 
 
Underground pipe networks include features such as pipes, reservoirs, valves and hydrants, 
some of which exist above ground, or partially above ground. Key attributes for risk 
assessment include physical properties like construction materials, age, quality and 
diameter. For this project, up to three separate networks are anticipated: 1) potable water 
reticulation, 2) sewage reticulation, and 3) storm-water reticulation. The data need to be 
attributed with replacement costs (e.g. $ replacement cost per meter of storm water pipe in a 
given area) and elevation, or level above the surrounding ground as well. 
 
The components of the networks will need to be modeled as lines, points or polygons. The 
linear components will comprise primarily the water pipelines (either principal or lateral), 
while the nodal components will include features associated with the movement of water 
(pumps, valves, hydrants, meters, etc.). Polygons will be used to represent water storage 
tanks and reservoirs. 
 
Cook Islands: 
No data is held by SOPAC. However as part of the survey conducted under an NZODA 
Outer Islands Water & Power Reticulation Feasibility Study (
http://www.maps.gov.ck
) a water 
reticulation network has been captured for Aitutaki, Atiu, Mauke, Mitiaro, and Mangaia. 
These data include attributes on water intakes and pipe diameter at least. Similar data are 
available for the water network on Rarotonga. 
 
There is no reticulated sewage in the Cook Islands. 
 
Fiji Islands: 
None held by SOPAC. However, there might be some data held by PWD Water and Sewage 
Dept. 
 
Papua New Guinea: 
Port Moresby has the following datasets: 
•  A reservoir dataset with name attribute; 
•  Water (waterPS) and sewage (sewagePS) datasets. What exact features they 
represent is unknown as attributes are coded; 
•  A water tank dataset with identification (TAG) and name attributes; 
•  A water trunk dataset with diameter and description attributes; 
•  A borehole dataset that includes well-code and altitude attributes. 
 
Port Moresby, Oro and Western PNG have drainage datasets.  However, it is not clear 
what their attributed values represent. 
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Samoa: 
None held by SOPAC. GIS data is held by the Samoa Water Authority 
 
Solomon Islands: 
The city of Honiara appears to have a water pipeline dataset (Hpipes) with detailed attributes 
including system network name, pipeline size, and date. Most of the attributes are coded 
and, therefore, need a data dictionary to fully interpret, while some are not complete. There is 
also a valves dataset (H_av) for Honiara with attributes of size, year installed and location. 
Most attribute values for most features are not present. There are three other datasets that 
appear to be related to the water network in Honiara (H_bf, H_fh and Hdrain). More 
information is needed from SOPAC about the exact nature of these datasets. 
  
Tonga: 
There are data layers for water towers (owner, construction, capacity (not complete) and 
comments), as well as Water tanks (Tank_id, Owner, Construction, Capacity), and Water 
bores (Wellbore_id, Name, Type, Depth, Owner, Pump_type, Motor, Volume, PH). Not all 
fields have been populated. 
 
Tuvalu: 
There is a water tanks dataset (funafeti_Tanks2_84) for Funafuti Island with attributes 
including construction material, year installed, condition, size and capacity. Also, well 
locations are available for Nukufetau, Nui and Niutao, but with no attributes. 
 
Vanuatu: 
Detailed water reticulation data are apparently held by a French company (UNELCO). 
 
3.2.6 Power 
Distribution Networks 
The Power Distribution Network is anticipated to include 1) information on distribution circuits 
operated either via underground cables or overhead lines, 2) substations, and 3) service 
area. Relevant attributes for the risk assessment include the size and the material of the 
cables and substations, the voltages, the year of installation, the relationship with the other 
components of the grid or the typology. Information on replacement costs is also required. 
 
Cook Islands:  
Power generation stations, sub-stations, pillar boxes and power poles have been collected 
for Aitutaki, Atiu, Mauke, Mitiaro, and Mangaia as part of a survey conducted under an 
NZODA Outer Islands Water & Power Reticulation Feasibility Study 
(
http://www.maps.gov.ck
), and are also available for Rarotonga. 
 
Solomon Islands: 
There is a dataset held by Solomon Islands Electricity Authority (SIEA) which contains power 
poles, power lines, transformers, meters, switches and street lights for Honiara. 
 
No data have been sighted for any other PIC’s although it exists at least for Tonga and parts 
of PNG. 
 
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3.3 Physiography 
3.3.1 Imagery 
All high and moderate resolution image data available at SOPAC are listed in the SOPAC 
Image Data Catalogue, including relevant meta-data, such as acquisition date, resolution, 
sensor, and image type. These data are partly available online at www.sopac.org/map or 
geonetwork.sopac.org. These data sets include: 
 
Cook Islands: 
There are high resolution satellite images available for the islands of Aitutaki, Atiu, Mauke, 
Mitiaro, Mangaia, Manihiki, Pukapuka and Rarotonga. The latest imagery for Rarotonga 
appears to be May 2009. 
 
Fiji Islands: 
There are various images of Viti Levu and Rotuma islands. Coverage of Rotuma is complete 
but scattered for Viti Levu as most imagery is focused on the towns of Suva and Nadi. 
 
Papua New Guinea: 
There is high resolution imagery for the towns of Lae, Manam, Port Moresby, Sissano 
lagoon, Vanimo and Wewak.  
 
Samoa: 
There are several high resolution images of Apia, one moderate resolution (3.5m) image of 
the island of Savaii and various images (at different resolutions) of Upolu. New imagery will 
most likely be available, taken shortly after the September 2009 tsunami. 
 
Solomon Islands: 
High resolution images of Gizo, Ranongga, Simbo, Vellalavella and a small part of 
Kolombangara. Moderate resolution images of Savo and Honiara. 
 
Tonga: 
There are multiple sets of high resolution imagery for Tongatapu as well as high resolution 
imagery for Niuas, and Vava’u. Quickbird imagery, 2003–2006 was purchased for the whole 
of the Kingdom of Tonga part of the World Bank CERM Project. 
 
Tuvalu: 
There are high resolution images of Nanumanga, Nanumea, Niulakita, Nui, Nuitao and 
Nukulaelae. There are also moderate resolution imagery of Nukufetau, Funafuti and Vaitupu. 
 
Vanuatu: There are high resolution images of Port Vila. There is also moderate resolution 
imagery of the entire island of Efate. 
 
Maps for each country showing imagery extent plus tables showing metadata etc are given in 
Appendix 4.  
 
3.3.2 Topography 
Table 7 shows available topographic data for each of the countries based on maps and 
information available at SOPAC and would need to be verified in-country. In general, there 
are National Topographic Map series available in all countries, with the exception of Tuvalu. 
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Contour intervals are rarely less than 20 m and the existing relief information available for the 
low-lying coastal areas is inadequate to allow meaningful inundation modelling of tsunami, 
storm-surge or river flooding hazard. 
 
Map scales are usually 1:50 000 or larger, with the exception of Papua New Guinea, where 
the national mapping scale is 1:100 000. In some places, (e.g. Honiara) larger scale maps 
are available for urban centres providing more topographic detail. For the five capital cities 
Apia, Honiara, Nuku’alofa, Suva and Vila large-scale digital terrain models (DTMs) with a 
resolution of 5 m are available as part of the Pacific Cities Project. The only DTMs currently 
available nationwide for all countries are either based on the topographic maps or SRTM 
data with ground resolutions of 70 m and less. Vanuatu, Solomon Islands and Papua New 
Guinea have recently been mapped using airborne radar, which might allow production of 
higher resolution DTMs. Some very high resolution (dm) data might be available for 
Fongafale, the main population area of Funafuti, Tuvalu (the data have not been reviewed by 
SOPAC) and a small area around Avatiu Harbour, Cook Islands, which had been used for 
storm-surge modelling (ADB 2005). Fiji has just commissioned a high-resolution aerial 
survey with the aim to produce large-scale topographic maps for selected coastal areas with 
1 m contours. 
 
Table 7: 
Existing Topographic data 
Topography Maps 
Country 
Scale Contour 
interval  
Year Scanned 
Raster 
Contour 
Layer 
Cook Islands 
1:25 000
10 -15 m 
1989 
 
2
 
Fiji 1:50 
000
3
 20 
m  1989 √ 
√ 
Papua New Guinea 
1:100 000
4
 20 
m  1965   
√ 
Samoa 1:50 
000
5
 20 
m  2000 √ 
√ 
Solomon Islands 
1:50 000
6
 
1:10 000
7
 
20 m 
1.5 m 
1976 
1971/6 
 
√ 
Tonga 1:25 
000
8
 1.5 
m  1975  
 
Tuvalu 1:10 
000 
n/a 
1971 
√ 
 
Vanuatu 1:50 
000
9
 
 
40 m 
 
1968  
 
1
Rarotonga Only 
2
Partly unclear reference and data source, interpolated(?) 2m contour lines for coastal areas and 10 –15 m contour  
3
For various areas around Fiji including Lautoka and Suva 
4
National topographic map series 
5
 Various maps of Upolu and Savaii 
6
Guadalcanal 
7
Honiara Town West/East 
8
Tongatapu group, otherwise 5 m contour interval 
9
Efate 
 
3.3.3 Bathymetry 
Traditionally, nautical charts have been the source of depth information, although relatively 
detailed in some areas, they are dedicated to safety in navigation and are therefore not fully 
suited as fundamental background information for multiple applications in science, 
engineering, geophysical and environmental studies, as well as coastal resource and 
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ecosystem management. In particular, the assessment of hazards due to storm tides or 
tsunamis relies on a high-resolution, precise, and seamless bathymetric terrain model that 
extends from the littoral zone (shallow water depths from about 10-20 m) to the shoreline and 
topography for at least a distance of 20 m run-up inland. 
 
While there is a growing list of near shore sites that have been surveyed by high-resolution 
multibeam bathymetry systems (see Figure 5 below for example), there is an almost 
complete lack of data for the shallow waters of the intertidal zone including reef flat and reef 
crest areas. These shallow water areas present the primary focus of hazard studies, but are 
the most poorly resolved and understood. The reef provides many ecosystem goods and 
services such as the production of sediments and providing a protective barrier during 
extreme storm wave conditions. Detailed shallow water bathymetry is also a vital component 
in modelling the contribution of wave-induced radiation stresses on lagoon circulation, 
sediment transport and water quality. It is therefore essential that the morphology and 
shallow water bathymetry of the reefs and atoll rim is accurately determined. The nature of 
shallow reef environments does not permit the use of traditional hydrographic survey 
methods, and an airborne LiDAR surveys present the best option to fill this data gap. 
 
Cook Islands:  
There is a bathymetry line dataset for Penrhyn Island.  However, there are no available 
attributes. Data is available for Aitutaki. 
 
Fiji Islands: 
Only a small area of the southern coast of Viti Levu. 
 
Papua New Guinea: 
There are bathymetry line datasets for the Bougainville, East New Britain, East Sepik, 
Madang, Manus, Milne Bay, Morobe, NIP, Oro, Western, West New Britain and West Sepik 
areas. 
 
Samoa: 
Upolo and Savaii. 
 
Solomon Islands: 
Some bathymetry around Honiara, Marovo and Gizo. 
 
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Figure 5: 
SW Pacific map showing bathymetry survey locations carried out under SOPAC/EU 
funding 2003-2008 
 
Tonga: 
Tongatapu. 
 
Tuvalu: 
Bathymetry exists for Funafuti, Nanumanga, Nanumea, Niulakita, Nui, Nuitao, Nukulaelae, 
Nukufetau, and Vaitupu. 
 
Vanuatu: 
Efate. 
 
3.3.4 Soils/Geology 
Physiographic data of the countries needs to be available including geology (to determine 
soil class for earthquake shaking, amplification and liquefaction hazard). Available geological 
soil data are summarised in Table 8. All larger, high-volcanic Pacific Island Countries have 
Regional Geological Map series and mapping programmes, which cover major parts of the 
country in scales of 1:50 000 to 1:250 000. Some of these data are available in vector format 
(PNG and Fiji) and include stratigraphic information and major fault lines. 
 
Digital soils data and borehole information suitable for assessing ground shaking 
amplification exists for the capital cities of Fiji, Samoa, Solomon Islands, Tonga and Port Vila 
through the SOPAC Pacific Cities database. Where possible, similar data will be created for 
the heavily populated areas not covered by the Pacific Cities Database in the Cook Islands 
(Avarua), Fiji (Nadi and Lautoka) and Papua New Guinea (Port Moresby, Mt Hagen and 
Lae), Tuvalu (Funafuti), and Vanuatu (Luganville) from the available geological maps. 
 
AIR Worldwide have indicated that in the absence of soils and geology data, topography can 
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be used to assign a soil class based on slope, as a surrogate. 
 
Cook Islands: 
Geology for Rarotonga based on geomorphic units (two units). Soils for Rarotonga mapped 
by Landcare Research Ltd 
 
Fiji Islands: 
Regional geological and soil maps (Twyford and Wright 1965), including soil texture and FAO 
classes for major islands, Viti Levu, Vanua Levu and Taveuni 
 
Papua New Guinea: 
Regional geological maps available in vector format. 
 
Samoa: 
There are soils and land use layers for the island of Savaii, however these have coded 
attributes which need interpretation by SOPAC. There is also a geological map at 1:100 000 
but it is not digital. 
 
Solomon Islands: 
Geological maps of some areas available at 1:100 000 and 1:50 000 and have been 
scanned. 
 
Tonga: 
There is a soils dataset with soil code attribute for approximately half of the islands 
 
Tuvalu: 
None 
 
Vanuatu: 
A regional geological map at 1:100 000 – not digital 
 
Table 8: 
Existing Geology and Soils data 
Geology Maps 
Soil Maps 
Country 
Scale  
Year 
Scanned 
Raster 
Vector 
Data 
Scale  
Year 
Scanned 
Raster 
Vector 
Data 
Cook Islands 
 
 
 
 
 
 
 
√ 
Fiji 1:50 
000
1
 1960/70s √ 
 
 
 
 
√ 
Papua New 
Guinea 
1:250 000
3
 1970s 
 
√ 
 
 
 
 
Samoa 1:100 
000
4
 
1958 
   
 
  
Solomon 
Islands 
1:100 000
5
 
1:50 000 
1970/80s 
1969 
√ 
 
 
 
 
√ 
 
Tonga 
       
 
  
Tuvalu 
       
 
  
Vanuatu 1:100 
000
6
 
 
 
 
 
 
 
√ 
1
Regional Geological Map series 1:50 000 for most parts of Viti Levu and Vanua Levu 
2
Generalised vector data (1:250 000) available as national data set, more detailed geology (1:50 000) vectorised, but individual 
sheets are not yet edited to form one comprehensive data set 
3
Regional Geological Map series 1:250 000, e.g. Tingey and Grainger (1976): Markham <Lae>, PNG Sheet SB 55/10 
4
Kear, D., Wood, B.L. (1959). The Geology and Hydrology of Western Samoa. New Zealand Geological Survey Bulletin, No.63. 
5
Regional Geological Map series 1:100 000, e.g. BGS (1986): Geology of the New Georgia Group, Solomon Islands, report 
MP/86/6, 7 sheets  
6
Regional geological map series 1:100 000, e.g. Ash et al. (1974): Geology of Efate and offshore islands 
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PROJECT IMPLEMENTATION PLAN 
 
4.1 
Activity 1: Inception, engagement and liaison 
In consultation with SOPAC, and taking into account existing work programme commitments, 
it was decided that engagement will be done prior to data collection in each country. There 
will be regular liaison with the ADB and the World Bank and its consultants during the 
project, and particularly at progress meetings. An inception report meeting was held in Suva, 
Fiji 2-4 December in Suva and included representatives from the World Bank and Air 
Worldwide. From this meeting we have determined the attributes needed for loss modelling, 
who will be responsible for collecting the information and have finalized a work plan 
summarized in Table 9, with a detailed project plan given as Appendix 8. In addition, during 
this time we presented the project to the Pacific GIS RS conference held at the University of 
South Pacific, liaised with representatives form countries, and staff from the South Pacific 
Commission. 
 
Output: From this activity include this inception report and finalised work programme (see 
Table 9 and Appendix 8 and 9) 
 
4.2 
Activity 2: Database design 
Based on the review of data held by SOPAC, and missions to Fiji and the Cook Islands, and 
discussions with World Bank project consultants, we have analysed the existing 
infrastructure data and determined the data gaps. There is some building data in each 
country and detailed data albeit dated for 5 major cities as part of the Pacific Cities database. 
There are generally country-wide coverage of roads but limited attributes. Utilities data 
appears to be limited but may well sit with the organisation responsible for the utilities in each 
country. In some cases the lack of the metadata to fully describe dataset means that 
interpretation of the features is required. We anticipate that many of these gaps will be filled 
when we data is collected in each country. 
 
As part of the inception meeting, the attributes required by AIR Worldwide were defined, and 
it was determined which project would collect the information. The attributes to be collected 
are given in table 1 of Appendix 6. Clearly there is a trade-off between the level of detail 
collected and the number of buildings that can be surveyed in a given time. Discussion on 
the level of detail required by AIR Worldwide has determined that some attributes have 
higher priority than others.  
 
A database structure will be designed prior to field data surveys, and the Nadi data trial 
menus (Appendix 5) will be modified to accommodate the essential attributes. The database 
design will be re-iterated during and after the data capture. It will be imperative to ensure that 
any new data can be easily incorporated into to existing in country databases. 
 
Output: From this activity a draft database design to store collected data, menus for data 
collection, will be developed and field data collection devices purchased and programmed. 
The database design will be refined following all field data collection. 
 
 
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4.3 
Activity 3: Data Collection 
Details of the data collection phase of the work are given in Appendix 9. Detailed building 
data will be captured by this project. Larger infrastructure data such as dams, airports and 
large industrial facilities will be captured by the World Bank project using high resolution 
imagery. Existing transport, network and physiographic data will be collected from the 
countries where it exists by this project. SOPAC will be the central repository for all data, but 
data for each country will be held by the nominated country host organisation.  
 
Output:
 Infrastructure data collected for each country. Regional database held by SOPAC 
 
4.3.1 
Data collection Phase 1 
Data collection will start in the Cook Islands in February 2010 and will be carried out by staff 
experienced in the use of handheld devices. The Cook Islands is a relatively small country 
with existing, mature GIS datasets. The Cook Islands data capture exercise will be reviewed 
and lessons learnt incorporated into training for the remainder of the team in Fiji in early 
March before continuing with Phase 1 data collection in the Solomon Islands and Vanuatu. 
Data will be collected by SOPAC staff in some parts of Fiji in this period. 
 
Papua New Guinea has potential exposure greater than six of the other countries combined, 
and during the inception meeting, it became apparent that Papua New Guinea data collection 
should be a priority. However as PNG is large and highly-rural, data collection should be 
concentrated where hazard was greatest (i.e. Lae, Madang, and Raubal). Data collection in 
PNG has been bought forward into Phase 1 which extends this data collection period. Given 
this, it is strongly recommended that in-country data capture involve additional SOPAC staff 
and International Team personnel that can be exchanged with the listed SOPAC personnel 
and consultants, due to the concentrated and prolonged Phase 1 field data capture period. 
 
4.3.2 
Data collection Phase 2 
Following a progress report, review of phase 1 data collection and a short break, Phase 2 of 
the data collection will start in Fiji in July, and simultaneously be carried out in Tuvalu. Once 
Fiji is complete data collection will move to Samoa and Tonga, finishing towards the end of 
September 2010. 
 
4.3.3 
Methodology for data capture in the field 
From the field trial it became apparent that the preferred method of data capture is portable 
handheld devices such as a small laptop or PDA. Preconfigured portable devices allow for 
maps, building ID’s, GPS, images and reference information to be stored and allow for the 
immediate upload of information into GIS files. Hence, field data will be captured using 
integrated handheld devices with pre-programmed attribute menus. The field survey 
equipment will be specified and purchased based on the requirements of the database 
design but devices like the Trimble Juno SB (
http://www.trimble.com/junosb.shtml
), 
SurveyLab IkeGPS (
http://www.ikegps.com/
) or TopCon GMS-2 
(
http://www.topconpositioning.com/products/mapping-and-gis/hand-held-devices/field-
controllers/gms-2.html
), will be used for the field data collection, although the latter two may 
be to expensive or not meet the field data capture requirements. 
 
The field data capture trial in Nadi proved useful to get an estimate of the time to collect 
building attribute data using handheld devices, the training effort required, and also exposed 
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other issues to be considered such as, the need for a structural engineer(s) to review 
building classifications, and be involved in the training, and that there is insufficient time to 
capture all asset data. From the data trial it is likely that more than ½ a day of training is 
required to get accurate building assessments from previously unskilled people. With 
additional training, an updated field guide, and the use of class values rather free entry fields, 
it is expected that a significantly higher level of accuracy could be achieved for individual 
buildings. 
 
Data will be collected in partnership between the International Team, SOPAC and country 
counterparts. Local counterparts will be integrated into field survey activities whenever 
possible to utilize local knowledge and act as liaison in communities when necessary or 
appropriate. The collection of infrastructure data will build capacity of local staff to capture 
their own data which can be extended to less populated areas as required. 
 
Laminated field guides with photos and descriptions of terms were beneficial for field workers 
to gain familiarity with the building attribute terms and concepts, and to reference when 
particularly challenging buildings were surveyed (Appendix 5). It is recommended that prior 
to field surveys, time is allowed to interview local architects and construction companies, to 
gain a better understanding of local building practices. Training from structural engineers and 
construction companies based in Fiji is planned for the project team along with training in the 
use of field equipment and has been incorporated into the workplan. 
 
Digitizing of building plans (rooftop areas) from satellite imagery before conducting a survey, 
as is currently being done by SOPAC as part of a World Bank project, allows for ground-
truthing during field data collection. Because of resolution, visibility and changes after 
acquisition imagery is obtained, there will be some errors and assumptions when performing 
initial rooftop digitizing. Reference maps of the areas surveyed were useful for navigation 
and to provide building identification numbers for survey forms. An index map of the entire 
area surveyed should be produced with appropriate labelling showing adjacent 
corresponding maps should be placed on the reference maps to assist in navigation during 
surveys. Hardcopy maps become less important when using portable devices with GPS and 
integrated digital maps or imagery. 
 
Access to buildings may be limited by gates, foliage, or other buildings. Such cases were few 
during the trial, but there were some buildings that posed particular challenges for recording 
attributes because of access or visibility.  
 
Field survey areas can vary greatly in climate, cultural and social settings, and physical 
terrain. Before a field campaign, field surveyors should consider appropriate precautions to 
protect themselves, equipment, and field documents against adverse weather and potential 
environmental hazards such as aggressive animals (dogs, insects, etc.), confrontational 
residents, or unsafe areas (construction sites, poisonous plants, unsafe neighbourhoods). 
 
Informational materials and/or press releases could be used to notify residents of the survey 
work and to minimize confusion about the purpose of surveys and potentially disarm any 
hostility from residents towards field workers. 
 
Where there are no field surveys, attributes will be inferred based on similar styles of 
buildings in areas where attributes are available. Census data from the countries or supplied 
by SPC, which contains information on dwellings and households, will be used to populate 
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the building database in rural areas. 
 
It is our understanding that digital Census data are available for most of the eight countries, 
held by SPC or the statistics departments of each country. Vanuatu and the Solomon Islands 
are to hold censes within the next six months or so. These data contain some household and 
dwelling information that can be used to extrapolate building attributes, particularly in rural 
areas, using basic assumptions about the dwelling types. 
 
4.3.4 
Hazard data collection 
The International Team will also determine which earthquake and cyclone models and 
climate change models are available and which ones are commonly used or most 
appropriate for risk assessment. The models will be catalogued in a data repository and 
reported. For this we will be reliant on the existing PDC Global Hazards Information Network 
(GHIN) and the Pacific Disaster Net hosted by SOPAC. These contain both information on 
hazard models and historical hazard event damage reports. 
 
Output: Hazard model data collected for region and countries held by SOPAC 
 
4.3.5 Physiographic 
data 
The International team will collect any imagery, topographic, bathymetric and soils/geology 
data as available when in country. 
 
Output: Catalogue of data stored at SOPAC 
 
4.4 
Activity 4: National and Regional Systems 
Once data are collected they will be analysed to produce recommended “Pacific” vulnerability 
classes based on construction type and materials, for example. Replacement costs will be 
reported for each country which is likely to vary from country to country depending on the 
availability of timber, sand for concrete, etc. 
 
National databases, developed to hold hazard models and asset data for each country, will 
be consolidated to form a regional database, most likely in the form of a Pacific “RiskScape” 
or some equivalent and held by SOPAC and updated by them and the countries. However, 
Risk Models are not likely to be included as this will be done by World Bank. 
 
Currently the 8 countries run predominantly MapInfo GIS software, although ESRI ArcGIS is 
also used. Some open source software is also used but it is not commonplace. All of the 
countries have MapServers which serve the data via the internet. However, the status of 
these servers varies (Appendix 7) – some have not been maintained, some have been 
disconnected due to the internet costs, and some have failed. The hardware requirements of 
each country to store and maintain there databases will be assessed during the data 
collection phase. 
 
The Project Team will design and deploy a GIS-based information system to store, manage 
and serve the regional and national databases that are developed under this project. The 
system’s proposed architecture is shown in Figure 6. It is anticipated that SOPAC will host 
the system. Some of the scenarios for such a system include: 
• 
Act as a central repository for the regional and national databases; 
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• 
Serve as a data store for running RiskScape models; 
• 
Publish a browser-based map viewer to view the GIS data online; 
•  Disseminate the GIS using various open standards for GIS data services (e.g. 
WMS, WFS, KML) − these data services can be consumed by desktop GIS 
clients or as general web services; 
• 
Support periodic data updates from national GIS departments; and  
• 
Support exporting GIS data for extraction to other systems.  
 
Concurrent with the data collection effort, the International Team will:  
• 
Develop a Concept of Operations (CONOPS) for the system. The CONOPS will 
formalize use cases, information flow, and high-level functional requirements; 
• 
Develop a system architecture and hardware specification; 
• 
Procure any necessary hardware and software; 
• 
Perform the system installation and confirmation at SOPAC; 
• 
Develop Standard Operating Procedures (SOPs) addressing data management, 
updates and dissemination as well as system maintenance; and  
•  Create user guides and training materials for system installation and 
maintenance.  
 
Some specific technologies are suggested in Figure 6, but the software components 
ultimately selected will balance SOPAC’s operating environment, the project budget, 
SOPAC’s existing infrastructure and skills, and the overall project requirements.  
 
 
 
Figure 6: 
GIS Options for the inventory database 
 
The architecture will focus on standards for GIS system interoperability, such as the OGC 
standards WMS, WFS, and KML (
http://www.opengeospatial.org
), regardless of the software 
chosen. Using these standards in place of proprietary data structures, communications, and 
application programming interfaces (APIs) will prevent a “vendor lock-in” situation, where a 
customer becomes so dependent on a given vendor’s products that it is too costly or difficult 
to switch to other solutions. These customers often find themselves buying additional 
software or extensions just because it interacts with the proprietary software they already 
own. It is anticipated, however, that some of the components, especially those used to edit 
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and update GIS database and metadata files, may be commercial-off-the-shelf (COTS) 
products, such as ArcGIS or MapInfo, which must be licensed from a vendor. These products 
may offer functionality that doesn’t exist elsewhere, or enable an enhanced workflow that 
saves time. Where possible, a free or low-cost alternative will be identified that can fulfil a 
given need. 
 
Each nation will have the option to create a similar GIS infrastructure to host their national 
database. The installation instructions and any source code generated for the SOPAC-
hosted solution will be made available to the nations. Each nation can then create a solution 
that is sized for their capacity and needs. At the most basic level, a GIS department could 
have shape files available on a walk-up computer for users to view/edit the data with free 
desktop GIS software. For nations with more resources and better Internet connectivity, they 
can replicate the online system delivered to SOPAC for their own nation. The nations may 
also have some existing infrastructure, into which they integrate the new data generated by 
this project. The budgets available to replicate these solutions at the national level could vary 
greatly from one nation to another, further underscoring the need for free alternatives to any 
COTS software chosen. 
 
Standard Operating Procedures (SOP) will be developed to facilitate two-way updating of the 
GIS data. The following considerations will be accounted for:  
• 
The updates may be generated by SOPAC or the individual nations;  
•  The national level GIS databases may have more detail than is needed at the 
regional level, or may include attributes that are not relevant to the regional risk 
assessments. Procedures will ensure that the attribute fields and values map 
between the originating source and the project database and vice versa; 
•  SOP’s will pay special attention to use file/data formats that are easily 
interoperable with various systems; 
•  The data must be re-projected into the coordinate system supported by the 
recipient; and  
•  The “database update” SOP must account for bulk updates and for situations 
where just a few edits are made. 
 
The RiskScape tool aids disaster management in that scenarios can be generated quickly. 
The hazard and exposure data collected as part of this TA would have this end-use in mind, 
and ultimately a Pacific Risk modelling tool could provide the vehicle for regional and country 
risk analyses. In this way SOPAC could interrogate any asset data and analyse impacts from 
hazard events. If suitable, such a system could be based in each country such that they can 
analyse their own data, run simplistic analyses, receive upgraded hazard and fragility 
functions as they are developed, and easily incorporate population casualties and indirect 
losses. 
 
Outputs: 
National and regional infrastructure databases, connected to existing GIS 
systems. Standard Operating Procedures (SOP) developed to facilitate 
updating of the GIS data. 
 
4.5 Activity 
5: 
Training 
We have met with the president of the Fiji Institute of Engineers (FIE). In conjunction with the 
Institute of Professional Engineers New Zealand Inc. (IPENZ), the FIE are launching a South 
Pacific Engineers Association (SPEA) in March 2010. We have tentatively agreed to 
contribute to this launch and will arrange seminars with Fiji structural engineers for the 
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TA/SOPAC team to gain a better understanding of Pacific building construction. This will 
precede our field surveys in seven of the 8 countries. Training of in-country staff in field data 
capture will occur at the beginning of the mission into each country and with ongoing 
supervision during data capture. This training has been incorporated into the revised work 
programme. 
 
The FIE have also indicated that they will provide structural engineering contacts for each of 
the countries. They will also provide replacement cost and construction cost, as well as 
building codes/guidelines for each country where available to the project and AIR Worldwide. 
 
Training in the use of the asset database and regional risk system will occur in March 2011. 
In SOPAC’s experience of providing capacity building to PICs is that it is more sustainable 
and efficient to target training in GIS applications and DRR at a national level, in terms of 
people trained/training costs and impact created. SOPAC and the TA team will undertake in–
country training once the databases have been completed. This would be subject to ADB 
approval to use the provisional funds allocated for training to cover travel costs of SOPAC 
and International Team trainers. Otherwise regional training will be held in Fiji 
 
It is proposed that a presentation of the project and its planned interventions will be given at 
next Pacific DRM Platform meeting in 2010. Further, project results and achievements will be 
disseminated at a side-meeting of the Pacific DRM Platform meeting in 2011 to senior 
government officials (CEOs for Finance/Planning and disaster management) and 
international Partners. 
 
Output: In-country staff trained in collection of field data and database system. 
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Table 9: 
Proposed workplan 
Activity 
Name 
Description 
Who 
Duration 
Output 
Project Start 21 Sep 09 
Initiation report for presentation at WB meeting Istanbul 
Inception Mission 
(28 Sep 09–24 Nov 09) 
Engage with SOPAC and plan country engagement. Introduce 
project at SOPAC Annual session and FEMM meeting in Cook 
Islands. From this the detailed project plan will be formulated. 
Nadi Data trial 
Int’l TA team 
SOPAC 
 
10 weeks 
Country government engagement 
 
Analyse of existing data. 
Inception report 24 Nov 09 
Inception report by 24 Nov 2009 
Inception Meeting Fiji, Dec 2009 
Inception report finalised and approved by ADB Dec 2009 
Database Design 
(26 Oct 09–28 Jan 10) 
 
Define level of infrastructure data that can be obtained and feasibly 
collected and finalise attributes. Develop database design and 
structure. Specify and purchase field survey equipment.  
Document and advise countries. Revised and finalised in Oct 10 
once data collection complete. 
Int’l TA team 
SOPAC 
Countries 
12 weeks 
Draft database design document 
 
Guidelines for capturing field data 
 
Specification, tender and purchase field equipment 
Collect Data Phase 1 –
see Appendix 8 
(11 Feb 10–21 May 10) 
 
TA team Training Fiji 
(4 Mar 10–8 Mar 10) 
Collect data in Cook Is, Vanuatu, Solomon Is and Papua New 
Guinea.  SOPAC and in-country staff to gather data using 
integrated PDAs etc. Utilize foot prints captured from 
aerial/satellite imagery. Involve policy makers in stakeholder 
consultations and meetings. 
 
Training in building construction and field capture devices for the TA 
team in Suva in association with launch of South Pacific 
Engineers Association. 
SOPAC 
Countries 
Int’l TA team 
Geoscientist 
 
Int’l TA team 
SOPAC 
Structural engineer 
subcontract 
13 weeks 
Data captured in 4 countries 
 
Revised database design 
 
Database hardware requirements determined 
 
All TA team trained in use of field equipment, and in building 
construction nomenclature. Reference material updated 
First progress report 31 May 10 
Progress report and meeting 
Collect data (Phase 2) 
See Appendix 8 
(12 Jul 10–24 Sep 10) 
Capture data in Tuvalu, Fiji, Tonga and Samoa 
SOPAC 
Countries 
Int’l TA team  
GIS Specialist 
11 weeks 
All field data collected 
 
Database hardware requirements determined 
Mid term report 29 Oct 10 
Mid-term report and meeting 
National and Regional 
database 
development 
(1 Nov 11–28 Feb 11) 
 
Specify in Country database hardware needs and purchase 
 
Develop national databases, populate and consolidate 
encompassing hazard and vulnerability data. Recommend 
classes. Report replacement costs. 
 
Develop consolidated database system and/or Pacific Risk Develop 
web-based system for loading national updates to the regional 
database  
 
Build database of existing hazard models 
Int’l TA team 
SOPAC 
16 weeks 
Database structure developed and documented for each 
country. 
 
Regional system developed and documented 
Regional System  and 
in-country training 
(1 Mar 11–13 May 11) 
 
Train Fiji and SOPAC in the use of the software design user 
interface 
 
Install Regional system  
 
Install country systems and train 
Int’l TA team 
SOPAC 
10 weeks 
Regional system installed in SOPAC and staff trained to use.  
Training modules and documentation for in-country training 
prepared 
 
Hardware and database installed in each country. Country 
counterparts trained in use and update of database. 
 
Training report. 
Final DRAFT report - 30 Jun 11 
Final Draft report due. Meeting 2 Aug 11 to discuss. 
Final report - 16 Aug 11 
 
Project Closed - 30 Sep 11 
 
 
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5 ACKNOWLEDGEMENTS 
This report has been prepared by Phil Glassey and Dave Heron (GNS Science), Chris 
Chiesa (PDC) and Steven Clegg, Joy Papao, Atu Kaloumaira of SOPAC. Michael Bonte-
Grapetin, Litea Biukoto and Susan Vocea (SOPAC) have also contributed significantly. The 
authors also acknowledge the input of Edy Brotowisoro (ADB), Olivier Mahul and Francis 
Ghesquerie and Stuart Gill (World Bank), SOPAC, Phil Bright (SPC), Paolo Bazzurro and 
Bishwa Pandey (AIR Worldwide), Pratarp Singh (president FIE), the representatives of the 
various government agencies that the team met with, and USP and its students. This report 
has been reviewed by Andrew King and Dr. Terry Webb of GNS Science. Delia Strong 
assisted with some of the diagrams. 
 
 
6 REFERENCES 
ADB, 2005: 
Climate Proofing – A risk based approach to adaptation
, Pacific Studies Series, 
Publication Stock No. 030905. 
 
Blong, R., 1994: Natural Perils and Integrated Hazard Assessment in Fiji; Final Report for 
Queensland Insurance (Fiji) Limited, National Insurance Company of New Zealand and Fiji 
Reinsurance cooperation. 
 
Biukoto, L., Swamy, M., Shorten, G. G., Schmall, S., and Teakle, G., 2001a: Pacific Cities 
CD, Nuku’alofa. GIS Hazards Dataset, Version 1.1. SOPAC Data Release Report, 3. 
 
Denham, David and Warwick Smith, 1993: 
Earthquake hazard assessment in the Australian 
and Southwest Pacific Region, 
Annali di Geofisica, 36, 3-4, 27-39. 
 
Glassey P.J., 2009: Pacific Exposure Database – Project Initiation report. ADB TA 6496-
REG: Regional Partnerships for Climate Change and Disaster Preparedness. GNS Science 
Client report CR 2009/256. 
 
Glassey, P., Heron, D., Ramsey, D., and Salinger, J., 2005: Identifying Natural Hazards and 
the Risks they pose to Tonga. Study Report 4: GeoSource Tonga. Cyclone Emergency 
Recovery and Management Project: Component B2: Land Hazards and Information 
Management. 
 
Johnson, W., Blong, R., and Ryan, C. (compilers), 1995: Natural Hazards Potential Map of 
the Circum Pacific Region (1995) 1:10 M. 
 
Jones T., 1998: 
Probabilistic earthquake hazard assessment for Fiji
. Australian Geological 
Survey Organisation AGSO Record 1997/46, 1998 
 
Ripper, I.D. and Letz, H., 1993
: Return periods and probabilities of occurrence of large 
earthquakes in Papua New Guinea
. Papua New Guinea Geological Survey Report 
93/1. 
 
Shorten, G., et al. (2003): Catastrophe Insurance Pilot Project, Port Vila, Vanuatu: SOPAC 
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GNS Science Consultancy Report 2009/321 
34 
 
Joint Contribution Report 147. 
 
Shorten G., Shapira A., Regnier M., Teakle G., Biukoto L., Swamy M., and Vuetibau L. 
(Compilers), 2001: Site-specific earthquake hazard determinations in capital cities in the 
South Pacific. Second Edition. 
SOPAC Technical Report
. 300. 
 
SOPAC, 2008: 
Inventory of Geospatial Data and Options for Tsunami Inundation & Risk 
Modelling – Pacific Island Country Summary
, SOPAC Miscellaneous Report 657. 
 
SOPAC, 2008a: 
Inventory of Geospatial Data and Options for Tsunami Inundation & Risk 
Modelling – Tonga
, SOPAC Miscellaneous Report 651. 
 
SOPAC, 2008b: 
Inventory of Geospatial Data and Options for Tsunami Inundation & Risk 
Modelling – Solomon Islands
, SOPAC Miscellaneous Report 654. 
 
SOPAC, 2008c: 
Inventory of Geospatial Data and Options for Tsunami Inundation & Risk 
Modelling – Fiji Islands
, SOPAC Miscellaneous Report 655. 
 
SOPAC, 2008d: 
Inventory of Geospatial Data and Options for Tsunami Inundation & Risk 
Modelling – Tuvalu,
 SOPAC Miscellaneous Report 656. 
 
South Pacific Disaster Reduction Programme (SPDRP), 2002: Suva Earthquake Risk 
Management Scenario Pilot Project (SERMP), Summary Report Part 1. 
SOPAC 
Joint Contribution 
139
 
Suckale, J. and Grünthal, G., 2009: A probabilistic seismic hazard model for Vanuatu, 
Bulletin of the Seismological Society of America, 99, 4, 2108-2126. 
 
Suckale, J., Grünthal, G., Regnier, M., and Bosse, C. (2005): Probabilistic Seismic Hazard 
Assessment for Vanuatu, Geo Forschungs Zentrum Potsdam, Scientific Technical report 
STR 05/16, ISSN 1610-0956. 
 
Twyford, I.T. and Wright, A.C.S., 1965: The Soil Resources of the Fiji islands, 2 Volumes, 
Government Printer, Suva, Fiji. 
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Appendix 1: Country Contacts – ADB Pacific Exposure Database Project.  TA- 6496-REG 
Country 
First 
Name 
Last 
Name 
Position Ministry 
Location  Address 
City 
Cook Islands  Garth  
Henderson  Aid Manager 
Ministry of Financial and 
Economic Development  
 
PO Box 120  
Avarua, 
Rarotonga  
Fiji John 
Prasad 
Acting Permanent 
Secretary for Finance 
Ministry of Finance 
Government Buildings 
PO Box 2212, 
Suva 
Papua New 
Guinea 
Joseph Lelang  The 
Secretary 
Department of National 
Planning and Monitoring 
Level 3, Vulupindi Haus 
PO Box 631 
Waigani, 
N.C.D. 
Samoa 
Hinauri 
Petana 
Chief Executive Officer 
Ministry of Finance 
  
PO Box 30017  Apia 
Solomon 
Islands 
Sadrach Fanega 
Permanent Secretary of 
Finance and Treasury 
Ministry of Finance 
  
PO Box 26 
Honiara 
Tonga Aisake 
Eke 
Secretary of Finance 
and Economic Planning 
Ministry of Finance and 
National Planning 
  
PO Box 87 
Nuku’alofa 
Tuvalu Temate 
Melitiana 
Assistant 
Secretary 
Ministry of Finance and 
Economic Planning 
Headquarter Division 
Vaiaku, 
Funafuti 
Vanuatu George 
Maniuri 
Director 
General 
Ministry of Finance and 
Economic Management 
  
Private Mail 
Bag 9058 
Port Vila 
 
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Appendix 2: Participants of the special session held at SOPAC Annual Session in Vanuatu, October 2009 
 
 
Name Position 
Ministry/Dept 
Address Location 
Country 
Mr Keu Mataroa 
Executive Officer 
Works 
PO Box 102 
Rarotonga 
Cook Islands 
Mr Kelepi Mafi 
Director of Geology 
Lands, Survey, Natural 
Resources & Environment 
PO Box 5 
Nuku’alofa 
Kingdom of Tonga 
Ms Esline Garaebiti 
Geohazards Manager 
Geology, Mines and Water 
Resources 
Private Bag 9001 
Port Vila 
Vanuatu 
Mr Michael Mangawai 
Director of Lands 
Lands and Natural 
Resources 
Private Mail Bag 9007 
Port Vila 
Vanuatu 
Mr Lameko Talia 
Principal Scientific 
Officer 
Natural Resources and 
Environment 
PO Box 3020 
Apia 
Samoa 
Mr Laurence Anton 
Senior Seismologist 
Mineral Policy & Geohazards 
Management 
Private Mail Bag 
Port Moresby, NCD 
Papua New 
Guinea 
 
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Appendix 3: Example letters sent to country Government officials 
10 November 2009 
 
 
 
 
 
 
 
Garth Henderson 
Aid Management Division 
Ministry of Financial and Economic Management 
PO Box 120 
Rarotonga 
Cook Islands 
 
 
 
 
Dear Sir 
 
ADB RETA 6496: REGIONAL PARTNERSHIP FOR CLIMATE CHANGE ADAPTATION AND 
DISASTER PREPAREDNESS – PACIFIC EXPOSURE DATABASE 
 
GNS Science International Ltd, a subsidiary of GNS Science, has been awarded the above Asian 
Development Bank (ADB) contract to develop a national and regional exposure databases for the 
Pacific. I understand from the ADB that you have been nominated as the focal point for the Cook 
Islands in relation to the project. 
 
Along with representatives of the ADB and World Bank, I visited Rarotonga from the 27–30 
October and discussed the project with Cook Island government officials including representatives 
from the Ministry of Infrastructure and Planning (MOIP), National Environment Service (NES), 
Ministry of Agriculture, Emergency Management Cook Islands (EMCI), the Prime Ministers Office, 
Central Policy and Planning and the Police. I noted that you were out of the country during our 
mission to Cook Islands. 
 
From this visit we determined that the Cook Islands already has significant amounts of relevant 
data and we want to extend the existing data for use in the project and for the benefit of the Cook 
Islands. The development of the database involves the collection of data on building construction 
and infrastructure in each of the countries. My team is planning to be in the Cook Islands for about 
two weeks in late February 2010 to undertake field surveys. To expedite the data collection we will 
require local assistance and have discussed this with the MOIP and Environment staff which you 
have made a commitment to providing. The Secretary of the Ministry of Infrastructure and Planning 
expressed interest in supporting the project and along with the National Environment Service 
agreed to make staff available for the field survey. 
 
We intend that our visit in February will firstly involve a briefing of all interested parties on the 
project. We will then train the Cook Island counterparts and undertake the field survey with them. 
The data collected will be left with the Cook Islands government and we recommend it be 
incorporated into the GIS held by MOIP. Follow up training is planned for later in the project 
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We will be in contact with you once our planning has advanced. Please advise if you are happy for 
us to liaise with MOIP regarding the logistics. If you require any further information regarding the 
project please contact me via e-mail at 
p.glassey@gns.cri.nz
, phone +64 3 4799684 or fax at +64 
3 4775232. 
 
 
 
Yours sincerely 
 
 
 
 
 
 
Phil Glassey 
Project Leader 
 
 
 
Cc 
 
Edy Brotoisworo, Senior Safeguards Specialist, Pacific Department, Asian Development Bank, 6 
ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines 
 
Michael Bonte-Grapetine, SOPAC Secretariat, Private Mail Bag, GPO, Suva, Fiji. 
 
Mike Mitchell  National Representative to SOPAC and Secretary, Ministry of Foreign Affairs & 
Immigration, PO Box 105, Rarotonga Cook Islands 
 
Charles Carson Emergency Management Cook Islands, Office of the Prime Minister Private Bag, 
Avarua, Rarotonga, Cook Islands 
 
Taukea Raui, Secretary, Ministry of Infrastructure & Planning PO Box 383, Te Atukura, Avarua, 
Rarotonga, Cook Islands. 
 
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TECHSEC: 
     16 
November 
2009 
 
 
National Representative to SOPAC <CK, FJ, PG, SB, TO, TV, WS> 
 
 
Dear  
 
WB/ADB/SOPAC Initiative on Risk Exposure Databases for Disaster Risk Reduction and 
Risk Financing  
 
The first phase of the World Bank (WB) Risk Financing Initiative for the Pacific developed preliminary risk profiles for 
8 Pacific Island countries. These profiles once fully developed and endorsed by the subject Pacific countries will help to 
inform catastrophe risk financing options for future consideration. Under Phase 2 the risk models will be refined and the 
initiative is being extended to all 14 Pacific ACP countries and Timor Leste.  
 
Further, the Asian Development Bank (ADB) is contributing to the collection of hazard, vulnerability and risk 
information to facilitate the development of risk databases in the initial 8 Pacific Island countries.  The information and 
data gathered will help to inform sound decision-making for disaster risk reduction and climate change adaptation 
initiatives.   
 
To facilitate the work under these initiatives a series of in-country missions are being planned with GNS Science and 
SOPAC for data collection and training.  In this connection a mission to <country> from to <dates> has been planned. 
During the mission we would like to meet with you and other relevant stakeholders to discuss how best to move 
forward.  
 
We also aim to collect data/information relevant to the development of the national risk database and humbly request 
the use of existing national datasets for these initiatives.  We are aware of the concerns around data ownership and 
would like to assure you that the World Bank and ADB have acknowledged that these would remain the property of all 
the countries concerned.  In turn, we have also had agreement that any data or information products from these 
initiatives will be released to countries.   
 
 
We look forward to your favourable consideration of these initiatives and your continued support. 
 
 
Yours sincerely, 
 
 
 
 
Cristelle Pratt 
Director  
 
CC  
ADB/WB focal point in-country 
NDMO 
Relevant Technical Agencies 
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Appendix 4: Satellite imagery data and coverage maps held by SOPAC 
 
Overview of available imagery, resolution, population covered and other available GIS data: P=Places 
(Towns, Villages, Settlements), R=Roads, C=Critical facilities, U=Utilities, A=Agriculture, B=Buildings 
Countries 
  
Islands/Areas 
Imagery available at 
SOPAC and resolution 
Percent of 
population  
Asset vector 
data available 
Cook Islands  
Rarotonga 
Aitutaki 
Atiu 
Mangaia 
Manihiki 
Mauke 
Mitiaro 
Pukapuka 
1m 
2.4m 
0.6m 
1m 
4m 
0.6m 
0.6m 
0.6m 
72%  
10% 
3% 
3% 
2% 
2% 
1% 
3% 
P, R, C, U 
Fiji 
Greater Suva 
Nadi  
Nausori 
Labasa  
Ba 
Lautoka 
Lami 
Sigatoka 
Coral Coast 
Rotuma 
0.6m 
0.6m 
0.6m 
0.6 
0.6m 
0.6m 
0.6m 
0.6m 
4m 
0.6m  
21%  
5% 
6% 
4% 
3% 
6% 
2.5% 
1% 
n/a  
0.2% 
P, R, C, U ,A, 
B(Suva) 
Papua New 
Guinea 
Port Moresby  
Lae 
Kavieng 
Vanimo 
Wewak 
Manam 
0.6m 
0.6m 
0.6m 
0.6m 
0.6m 
0.6m 
5% 
2% 
0.2% 
0.2% 
0.4% 
>0.1% 
P, R 
Samoa 
Apia 
Upolo 
0.6m 
1 m 
35% 
65% incl. Apia 
P, R, C, B 
Solomon 
Islands 
Honiara 
Gizo 
Rannonga 
Simbo 
Savo 
0.6m 
1m 
0.6m 
0.6m 
2.4m 
12%  
1% 
<1% 
<1% 
<1% 
P, R, B 
Tonga 
Tongatapu Group 
Vavau Group 
Ha’apai Group 
Nuia’s 
1m, 2.5m 
2.5m 
2.5m 
2.5m 
70% 
15% 
10% 
5% 
P, R, C, B, A  
Tuvalu  
Funafuti 
Vaitupu 
Nanumanga  
Nanumea  
Niulakita 
Niutao 
Nui  
Nukufetau 
Nukulaelae 
1m 
0.6m 
0.6m 
0.6m 
0.6m 
0.6m 
0.6m 
0.6m 
0.6m 
47% 
17% 
6% 
7% 
0.4% 
7% 
6% 
6% 
4% 
P, R, U 
Vanuatu 
Port Vila 
Luganville 
0.6m 
0.6m 
15% 
5% 
P, R, C, B (Vila) 
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NB: - Quickbird imagery at 2.5 m resolution (2003-2006) was purchased for the government of Kingdom of Tonga in 
2006 
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Appendix 5: Attribute forms and reference material for field trial − Nadi 
 
Name 
  
  
  
 
Date 
  
 
 
Camera 
  
  
  
 
Photo No 
  
 
 
Building ID 
  
  
  
 
Location 
  
 
 
GPS No 
  
  
  
 
GPS Waypoint 
  
 
 
 
 
 
 
 
 
 
 
 
Use Main 
use  
Subsidary use 
 
 
 
 residential 
 
 
 
 
 
 
 
house 
  
 
  
(single household in a single building)   
 
 
flats 
  
 
  
(multiple households in single 
building)  
 
 
 
fale 
  
 
  
(open house)) 
 
 
 
 
shed 
  
 
  
(building that is not used for sleeping)   
 
 commercial/industrial 
 
 
 
 
 
 
 
commercial 
  
 
  
(shops, offices, restuarant) 
 
 
 
industry 
  
 
  
(factory, workshop, warehouse) 
 
 
 
accommodation 
  
 
  
(hotel, motel, resort) 
 
 
 public/communal 
 
 
 
 
 
 
 
 
government 
  
 
  
(government department buildings) 
 
 
 
public services 
  
 
  
(bus station, boardcasting, libraries, 
etc)  
 
 
church, temple etc.    
 
  
 
 
 
 
 
community facilities    
 
  
(meeting halls) 
 
 
 
 
education 
  
 
  
(high school, university, kindergarten)   
 
 critical 
facilities 
 
 
 
 
 
 
 
 
police station 
  
 
  
 
 
 
 
 
fire station 
  
 
  
 
 
 
 
 
defence 
  
 
  
(army or navy building) 
 
 
 
health services 
  
 
  
(health clinics, hospital, chemist) 
 
 
 
communication 
  
 
  
(telephone exchange, cellphone) 
 
 
 
utility 
  
 
  
(electricity, water, gas) 
 
 
 hazardous 
facility 
 
 
 
 
 
 
 
 
petrol station 
  
 
  
 
 
 
 
 
chemical store 
  
 
  
 
 
 
 
 other 
  
 
  
(specify ……….……………………….…) 
 
 unknown 
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
Age 
  
 
 
 
 
 
 
 
 
 
 
 
 
No of storeys 
  
 
 
Roof pitch 
Flat  
 
 
 
   
 
 
 
Low (1-25) 
  
 
 
Roof shape 
hip  
 
 
 
Moderate (25-40) 
  
 
 
 
gable 
  
 
 
Steep (>40) 
  
 
 
 
arch 
  
 
 
 
    
other 
 
  
Roof material 
concrete  
 
 
 
 
 
 
 
 
  
  
sheet metal 
 
 
 
 
sheet 
  
or 
  
fibre-cement 
sheet 
 
 
 
 
  
  
metal tile 
 
 
 
 
tile 
  
or 
  
heavy tile 
 
 
 
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wooden shakes 
  
 
 
 
 
 
 
 
traditional material 
  
 
 
 
 
 
 
 
other 
  
(specify ………………………………………….…………..) 
 
unknown  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Foundation 
slab  
 
 
 
Bracing 
timber wall 
  
 
 
wooden/conc piles 
  
 
 
 
concrete wall 
  
 
 
wooden poles 
  
 
 
 
masonry wall 
  
 
 
concrete columns 
  
 
 
 
other (specify) 
  
 
 
steel columns 
  
 
 
 
 
 
 
 
load bearing wall 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Wall materials 
concrete  
 
 
 
Wall structure 
timber frame 
  
 
 
fibre-cement sheet 
  
 
 
 
wooden poles 
  
 
 
fibre-cement board 
  
 
 
 
concrete columns 
  
 
 
timber  
 
 
 
 
steel columns 
  
 
 
masonary  
 
 
 
 
load bearing wall 
  
 
 
metal  
 
 
 
 
unknown  
 
 
 
traditional material 
  
 
 
 
 
 
 
 
other 
  
(specify ……………………………………….) 
 
 
 
unknown  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Windows 
<75% wall 
  
 
 
Shutters 
present  
 
 
 
>75% wall 
  
 
 
 
brackets 
  
 
 
open space 
  
 
 
 
none 
  
 
 
none 
  
 
 
 
unknown 
  
 
 
 
 
 
 
 
 
 
 
Parapet/tower 
 
  
 
 
Defects 
none  
 
 
 
 
    
minor 
cracks 
 
  
Concrete cantilever 
  
 
 
 
major cracks 
  
 
 
 
    
minor 
rot 
 
  
Plan shape 
regular 
  
 
 
 
major rot 
  
 
 
irregular 
  
 
 
 
poor construction 
  
 
 
unknown  
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
< 50 sqm 
  
 
 
Min floor 
height (m) 
 
 
 
Base floor area 
(sqm) 
50-100 sqm 
  
 
 
 
 
 
 
 
100-200 sqm 
  
 
 
 
 
 
 
 
200-400 sqm 
  
 
 
 
 
 
 
 
> 400 sqm 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Name 
  
  
  
 
Date 
  
 
 
Camera 
  
  
  
 
Photo No 
  
 
 
Building ID 
  
  
  
 
Location 
  
 
 
GPS No 
  
  
  
 
GPS Waypoint 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
Foundation 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Foundation bracing 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Wall material 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Tick 1 only 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Roof material 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Roof shape 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Roof slope 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Tick 1 only 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Age 
  
 
 
 
No of storeys 
  
 
 
 
 
  
 
 
 
 
 
 
Windows 
<75% wall 
  
 
 
Shutters 
present  
 
 
 
>75% wall 
  
 
 
 
brackets 
  
 
 
open space 
  
 
 
 
none 
  
 
 
none 
  
 
 
 
unknown 
  
 
 
 
 
 
 
 
 
 
 
Parapet/tower 
 
  
 
 
Defects 
none  
 
 
 
  
 
 
 
minor 
cracks 
 
 
 
Concrete cantilever 
  
 
 
 
major cracks 
  
 
 
 
 
 
 
 
minor rot 
  
 
Plan shape 
regular 
  
 
 
 
major rot 
  
 
 
irregular 
  
 
 
 
other 
  
 
 
unknown  
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
Base floor area 
< 50 sqm 
  
 
 
Min floor 
height (m) 
 
 
 
wood/conc piles
slab
load 
bearing 
walls
 
timber
sheet 
metal
unbraced
timber
 
sheet  
metal
 
braced
 
timber
sheet 
metal
hip
l
 
m
  
gable
 
l
 
m
sheet metal
h
 
h
flat
concrete
tiles
 
hip
 
gable
m
  
h
 
m
h
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(sqm) 
50-100 sqm 
  
 
 
 
 
 
 
 
100-200 sqm 
  
 
 
 
 
 
 
 
200-400 sqm 
  
 
 
 
 
 
 
 
> 400 sqm 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Use Main 
use  
Subsidary use 
 
 
 
 residential 
 
 
 
 
 
 
 
house 
  
 
  
(single household in a single building)   
 
 
flats 
  
 
  
(multiple households in single 
building)  
 
 
 
fale 
  
 
  
(open house)) 
 
 
 
 
shed 
  
 
  
(building that is not used for sleeping) 
 
 
 commercial/industrial 
 
 
 
 
 
 
 
commercial 
  
 
  
(shops, offices, restuarant) 
 
 
 
industry 
  
 
  
(factory, workshop, warehouse) 
 
 
 
accommodation 
  
 
  
(hotel, motel, resort) 
 
 
 public/communal 
 
 
 
 
 
 
 
 
government 
  
 
  
(government department buildings) 
 
 
 
public services 
  
 
  
(bus station, boardcasting, libraries, 
etc) 
 
 
 
church, temple etc.    
 
  
 
 
 
 
 
community facilities    
 
  
(meeting halls) 
 
 
 
 
education 
  
 
  
(high school, university, kindergarten) 
 
 
 critical 
facilities 
 
 
 
 
 
 
 
 
police station 
  
 
  
 
 
 
 
 
fire station 
  
 
  
 
 
 
 
 
defence 
  
 
  
(army or navy building) 
 
 
 
health services 
  
 
  
(health clinics, hospital, chemist) 
 
 
 
communication 
  
 
  
(telephone exchange, cellphone) 
 
 
 
utility 
  
 
  
(electricity, water, gas) 
 
 
 hazardous 
facility 
 
 
 
 
 
 
 
 
petrol station 
  
 
  
 
 
 
 
 
chemical store 
  
 
  
 
 
 
 
 other 
  
 
  
(specify 
…………….……………………….…)  
 unknown 
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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BUILDING CLASSIFICATION  
 
 
 
 
 
 
 
 
FIELD GUIDE 
 
 
November 2009
background image
 
 
INTRODUCTION 
This document is to be used as a field guide for the classification of building and roof types for the 
ADB RETA 6496 Regional Partnerships for Climate Change Adaptation and Disaster Preparedness 
and the World Bank Pacific Catastrophe Risk Financing Initiative Phase 2 projects. This guide 
contains a glossary of the technical terms that will be used during the data collection phase of the 
projects. This guide is to be used in conjunction with the building data entry sheets and/or the pre-
programmed data dictionaries for mobile digital devices, such as PDAs. Also contained within this 
document is a summary of the overall observations made during the Nadi building survey which 
was carried out in October this year. 
 
MAPPING BUILDINGS WITH REMOTE SENSING 
High resolution satellite imagery is utilized to identify buildings and to create digitized polygons of 
estimated roof areas.  Georeferenced images are loaded into a GIS and buildings are identified 
individually by a GIS user.  Polygons representing building roof areas are constructed based on the 
best estimate taken from the imagery.  Satellite imagery may not reflect recent changes such as new 
construction or damages to existing buildings.  Therefore, digitized rooftop polygons are verified 
during field survey work and corrections are made accordingly. 
 
Buildings are assigned a unique identification number (Building ID#) which becomes the primary 
reference for cataloguing and for attaching attributes collected during building and roof 
classification surveys. 
 
GLOSSARY 
 
ROOF TYPE 
All existing roofs over occupied areas are described by the pitch, shape and material of the roof. In 
the case of more than one roof type is used to construct the roof all pitches, shapes and materials 
will be listed. This excludes different roof types for verandas or entries and/or tower like features as 
in the picture below of a flat roof, which will be listed as parapet/tower. 
 
Roof Pitch
 
The roof pitch refers to the angle of the roof.  
 
Term Description 
Example 
Flat 
A roof that is horizontal (0°) 
 
Low 
A roof that is nearly 
horizontal or at a low angle 
(1°- 25°) 
 
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Term Description 
Example 
Moderate  A roof with a moderate slope 
(25°- 45°) 
 
Steep 
A steep roof (>45°) 
 
 
Roof Shape 
This refers to the shape of the roof. 
 
Term Description 
Example 
Gable 
The building wall extends up 
to meet the roof forming a 
triangle. This includes roofs 
sloping only to one side and 
forming half a gable 
 
Hip 
Roof meets top of wall at 
same height on all sides 
 
Arch  
Roof forms an arch 
 
Other 
Any other roof shape not 
covered above 
 
 
Roof Material 
This refers to the material used to construct the roof. 
 
Term Description 
Example 
Concrete 
Usually only used for flat 
roofs 
 
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Term Description 
Example 
Sheet Metal 
Roof made from 
corrugated iron or metal 
sheets. It does not break 
but often rusts and bends. 
 
Fibre-cement 
Sheets 
Similar to sheet metal 
except that it does not rust 
or bend. Hard to 
differentiate from sheet 
metal from a distance. 
 
Metal Tiles 
Thin tiles, usually with 
stones glued on. Typically 
stones come off, may rust 
 
Heavy Tiles 
Tiles are made of concrete 
or clay and are thicker 
than metal tiles. Look at 
ridge line for concrete 
plaster 
 
Wooden 
Shakes 
Thin wood tiles, generally 
grey 
 
Traditional 
materials 
Roof material consists of 
thatching or other 
traditional materials 
 
Other 
Any other materials. 
Please specify 
 
 
 
FOUNDATION 
The foundation is a part of a building, usually below the ground, that transfers and distributes the 
weight of the building onto the ground. 
 
Term Description 
Example 
Concrete Slab  
Concrete slab sits on 
ground and building 
walls sit on slab 
 
Wooden/Concrete 
Pile 
 
Timber or concrete post 
less than 1m tall that 
supports the floor 
 
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Term Description 
Example 
Wooden Pole 
Timber pole greater than 
1m tall that support the 
floor (typically timber). 
Normally poles are less 
than 3m tall. 
 
Reinforced 
Concrete 
Columns 
Concrete column more 
than 1m tall supports the 
floor, typically concrete. 
Normally columns are 
3m tall.  
 
Steel Column 
Steel column supports 
the floor, typical in 
multi-storey commercial 
buildings. 
 
 
Foundation Bracing 
This refers to material that is used to brace (or support) the building foundation. 
 
Term Description 
Example 
Timber 
Wall 
Timber wall built between 
timber piles or poles or 
concrete columns 
 
Concrete 
Wall 
Concrete wall built between 
concrete columns 
 
Masonry 
Brick or block wall built 
between concrete columns 
 
Other 
Other bracing material (e.g., 
steel sheet or timber struts), 
please specify 
 
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BUILDING TYPE 
More than one wall material and construction type can be present in one building and will be noted. 
In the case this can be used to distinguish between entire buildings, these will be assessed separately 
and more than one form will be filled.  
 
Wall Material 
This refers to the material used to construct the walls of the building’s occupied levels.  
 
Term Description 
Example 
Concrete 
Concrete used to 
construct walls. The 
concrete  may or may not 
be reinforced 
 
Fibre Cement 
sheets 
Sheets of fibre-cement 
used in walls, normally 
has thin timber over 
vertical joints 
 
Fibre Cement 
Board 
Looks like timber but 
often has strong visible 
grain pattern and joiners 
at end of boards 
 
Masonry 
Brick or block used in 
walls.  
 
Timber 
Timber planks or sheet 
used for walls 
 
Metal Sheet 
Sheet metal used for walls
 
Traditional 
Material 
Wall constructed from 
thatching or other 
traditional materials 
 
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Term Description 
Example 
Other 
Any other material not 
mentioned above 
 
 
Building Structure 
This refers to the building structure above the foundation. 
 
Term Description 
Example 
Timber 
Frame 
This is the assumed structure 
for wall materials comprising 
of metal, timber or fibre-
cement 
 
Wooden 
Pole 
Wooden poles hold up the 
roof 
 
Concrete 
Column 
Building has concrete 
columns to support the roof 
 
Steel 
Column 
Usually used for factories 
and warehouses without 
internal walls 
 
Load 
Bearing 
Wall 
Concrete walls bear the 
weight of the roof without 
concrete columns 
 
Unknown - 
 
 
Windows 
This section looks at the percentage of wall space with windows along a wall with the most 
windows. 
 
Term Description 
Example 
<75% 
wall 
Less than 75% of the wall 
with the most windows is 
covered in windows 
 
>75% 
wall 
More than 75% of the wall 
with the most windows is 
covered in windows 
 
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Open 
Space 
The majority of the building 
is open 
 
None 
The walls do not have 
windows 
 
 
 
OTHER BUILDING CHARACTERISTICS 
 
Plan Form 
The plan form refers to the way the building roof appears when viewed from above. 
 
Term Description 
Example 
Regular 
Building is square or 
rectangular in plan form 
(when viewed from above) 
 
Irregular 
Building is not square or 
rectangular in plan form 
(when viewed from above) 
 
 
Defects 
This section covers defects or faults in building structures. 
 
Term Description 
Example 
Minor 
Cracks 
Concrete wall  has small 
cracks  
 
Major 
Cracks 
A lot of cracking visible in 
the concrete wall 
 
background image
 
 
Term Description 
Example 
Minor Rot 
Small amount of rotting 
visible in timber structure 
 
Major Rot 
Large amount of rot 
observed in timber structure 
 
Poor 
Construction 
Building has been 
constructed haphazardly 
 
None 
No visible defects 
 
 
Miscellaneous  
Below are other factors that need to be taken into consideration. 
 
Term Description 
Example 
Parapet/Tower  When a building has a 
projection above the roof 
line.  
 
Cantilever 
When a building has an 
unsupported balcony 
 
Shutters/ 
Brackets 
Windows have shutters or 
brackets/hinges for shutters 
on them for protection 
against wind damage 
 
 
 
 
Base Floor 
Area 
Estimated area of building 
footprint  
Minimum 
Floor Height 
Floor height of occupied 
level above ground level. 
 
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OBSERVATIONS FROM NADI SURVEY 
The following observations were made during the building survey in Nadi: 
•  Flat roofs are most likely to be made of concrete 
•  If a building has wooden piles or posts then the wall materials and structure will also be 
timber 
•  All of the buildings surveyed during the Nadi survey had either concrete or sheet metal roofs 
•  It is not possible to distinguish between masonry and concrete walls once the walls have 
been plastered 
•  Quite a number of buildings had roof shapes that could not be classified under the 3 classes 
provided in this field guide 
•  Roof tanks and signs mounted on rooftops are not to be considered as parapets 
•  There was some confusion over what a cantilever was 
•  Quite often you cannot see foundation bracing from the outside 
•  Some individuals had trouble estimating minimum floor areas 
•  Determination of window coverage percentage was challenging for some individuals to 
visualize 
•  Some residents  uneasy with strangers surveying their property 
•  Access to buildings may be limited by gates, foliage, or other buildings. 
•  Buildings inaccessible due to fences or unable to access routes 
•  It was found that there had been quite a few changes in building composition since the aerial 
photo of the survey area was taken 
•  People collecting data need to be aware that it is ok to tick “other” or “unknown”. During 
the Nadi survey it was observed that some groups felt compelled to categorise buildings into 
one of the labelled/identified categories 
•  Using only paper sheets for data collection requires significant post-processing time for data 
entry and integrating into a GIS, adding to the total time needed to build datasets.  
Additionally, using paper sheets provides challenges in carrying large numbers of paper 
sheets in the field which can be heavy to carry and potentially be damaged or lost by adverse 
weather such as rain or wind.  
•  During field data collection reference materials in the form of field guides and various maps 
were used to assist in the exercises.  Laminated field guides with photos and descriptions of 
terms were beneficial for field workers to gain familiarity with the building types 
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Appendix 6: Minutes of the Inception meeting and Attribute Table 
Inception Meeting of Joint World Bank - Asian Development Bank - SOPAC Initiative on Risk 
Exposure Databases for Disaster Risk Reduction and Risk Financing in the Pacific Region 
 
2 - 4 December 2009 
at SOPAC EU Conference Room, Suva, Fiji 
 
The Inception Meeting of the Joint Initiatives Risk Exposure Databases for Disaster Risk 
Reduction and Risk Financing in the Pacific Region
 of the World Bank, Asian Development 
Bank and SOPAC was held from 02 – 05 December 09 at the SOPAC Secretariat in Suva, Fiji 
Islands.   
 
The meeting was attended by:  
ADB  
Edy Brotoisworo,  
AIR  
Paolo Bazzurro, Bishwa Pandey,  
GNS 
Phil Glassey,  
PDC 
Todd Bosse,  
WB  
Stuart Gill,  
SPC  
Phil Bright and  
SOPAC  Mosese Sikivou, Michael Bonte-Grapentin, Steven Clegg, Susan Vocea, Joy Papao and 
Litea Biukoto. 
 
Agenda 
 
1) Welcome
 – Mosese Sikivou 
2)  Introduction of the Inception meeting - 
Edy Brotoisworo 
3)  Presentation of Inception Report ADB RETA  6496 -
 Phil Glassey 
4)  Implementation of PCRFI - Phase 2 -
 Paolo Bazzurro 
5) 
<OPTIONAL> 
Data capture and data sharing tools
 – Stuart Gill  
6)  Update on SOPAC Project Activities - 
Michael Bonte-Grapentin 
7) Discussion
 
a.  Comments on the Draft Inception Report 
b. 
Implementation plans (i.e. how we will collect the data and on what timeline)
 
i. 
Methodology, e.g. building/infrastructure parameters
 
ii. 
Focal areas for field data capture/ground-truthing 
 
iii. 
Timelines
 
c. 
Information exchange and sharing (data formats and metadata standards)
 
d. 
Other skills/personnel that may be needed
 
8)  Any other business
 
9) Next meeting
 
10) Close
 
 
 
Presentation of Inception Report ADB RETA  6496 - Phil Glassey, GNS 
 
•   ADB RETA 6496 will develop a Regional Exposure Database consisting of asset 
information and hazard models available in the region. Phil stressed that the RETA would 
only look at an inventory of hazard models available on the public domain such as 
earthquake and tropical cyclone models  
 
•  Agreed that a complete historical events database though important was beyond the scope 
of the two initiatives but recognised that some Pacific countries like Vanuatu had developed 
similar databases.  The WB Catastrophe Risk Initiative collected information relating to 
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disaster losses   
 
•  AIR to provide an inventory of models plus metadata documented in their report to the 
World Bank for Phase 1 of PCRFI.  The countries in Phase 1 included CK, FJ, PG, WS, SB, 
TO, TV, VU. An inventory of the models documented by AIR in their report to the World 
Bank be circulated including metadata 
 
•  GNS and SOPAC carried out a field trial in Nadi with University of the South Pacific 
students to test both the data collection template and methodology.  Data collected via 
onscreen digitising would need to be verified using field data.   
 
•  Attributes collected under the ADB project were detailed and would require training as well 
as guidance from an engineer familiar with the construction methods in the area.  Tools 
used for the data collection included a data collection template uploaded on PDAs or GPS 
plus a camera.  The pictures taken could be used to verify the data collected. 
 
•  Engineers would be contracted to assist with field data verification and training of data 
collectors. A 2-3 day training is planned around 4 March. 
  
•  Due to the different timelines, the data collected through the ADB project prior to the WB 
deadline would be made available for input into the PCRFI models 
 
•  The template of the features collected and attributes required was being refined to ensure 
that only the data needed would be collected.  The field data collection in Nadi, inputs from 
previous studies and discussions from the current meeting would help finalise this. 
 
•  The meeting recognised that infrastructure data was often hard to come by and not readily 
shared by the utility companies. To help facilitate requests made for data, the meeting 
agreed to a list of features and associated attributes (Table 1).   
 
•  The PCRFI required full coverage of a country to develop a realistic risk profile.  A sample 
methodology to cover areas where field collection is not carried out needs to be developed.  
This is particularly important for large areas as well as rural areas.  In the instance of Papua 
New Guinea, which is the largest land area and has the highest population ergo risk 
exposure, it drives the risk models for the region.   
 
•  Data sharing agreements needed to be established with countries to use data.  
Reciprocally, the new data produced would also be shared with the countries. 
 
•  High resolution satellite imagery and field data would be used to collect asset data.  Mid 
resolution imagery covering the entire country would be used for first an unsupervised 
classification followed by a supervised classification using field visits and high resolution 
imagery. 
 
•  Census information for countries where the data exists would be extracted from the 
PopulationGIS packages developed by the Secretariat of the Pacific Community.   
 
•  GNS, PDC and SOPAC will carry out field data capture under the ADB project in two 
phases: Phase 1: CK, VU, SB from FEB-MAY 2010 and Phase 2: TO, WS, FJ, TV, PG from 
JUN-SEP 2010. 
 
•  Joint missions by ADB and WB project staff for ground truthing and field data capture are 
planned for SB and VU and it was strongly suggested that a joint mission to Papua New 
Guinea should be included into the first phase of data capture under the ADB project.  Field 
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data capture in PG would be a challenge in terms of size and cost of travel within the 
country but would provide significant impact on quality of the exposure model by AIR. 
 
•  Tentatively a joint field mission to PG could be accommodated by GNS/SOPAC beginning 
of May, but would require approval by a) ADB to postpone the submission of the progress 
report to 1 June 2010 and b) by WB for extending the deadline for exposure field data input 
from SOPAC by 2 weeks. 
 
•  AIR signalled it could provide additional resources for in-country support and SOPAC would 
be able to provide additional staff resources to fast-track data capture under the ADB 
project so that it can be utilised in time for the WB/AIR model. 
 
Implementation of PCRFI - Phase 2 -
 Paolo Bazzurro AIR Worldwide 
 
 
Phase 1 
Phase 2 
Exposure residential, 
commercial, 
industrial, public assets 
residential, commercial, 
industrial, public asset, main 
infrastructure, major crops 
and population 
Perils 
EQ – shaking and local 
(regional) tsunamis, TC – 
wind, surge and rain 
EQ (ground shaking and 
tsunami), TC (wind, storm 
surge, rain) Rainfall using 
characteristics of developing 
storm - central P, radius, max 
wind speed.  Calibrated using 
observed rainfall.  EXCL 
FLOODING!!  Note TC – with 
defined central P or lower but 
ex Excl tropical storms, 
depressions.   
teletsunamis (events in 
Pacific basin) 
 
•  Modelling includes direct losses to exposure and impact on population.  Indirect losses are 
not included. 
 
•  Emergency losses to be sustained by Government in the aftermath of large events is not 
modelled directly 
 
•  Results presented by perils and in combination plus by country or aggregation 
 
•  Phase 2 exposure classification would be done by satellite imagery and field visits. Also 
ground truthing using publically available high resolution imagery such as on Google Earth.  
Use of low and medium resolution imagery for automatic unsupervised classification and 
then high resolution imagery for supervised classification.  Manual digitising 
 
•  Opportunities with other projects being implemented e.g. Tonga on biomass project WB 
funded, NZ Kiwi Image for NZ and protectorates though not for commercial use. 
 
•  Base imagery and derived products 
 
•  Modelling incorporates historical events of TC Category 3-5 from 1945 – 2007 and EQ 
noting that some countries are at risk to both perils 
 
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•  The components of the Catastrophe Risk Model include hazard, Exposure - replacement 
cost of assets, Vulnerability, Losses, Mitigation.   
 
•  The exposure model shows replacement cost of assets in PG USD 18,168m and FJ USD 
6,155m for existing building stock 
 
•  Vulnerability of assets – type, construction class and height-storeys, location, age (pre 
1990, 1991-2005, post 2005).  Useful to calculate replacement costs.  Though the 
information may not be readily available incountry the Rawlinsons Construction Handbook 
could provide a guide to average costs in the Pacific 
 
•  Developed a catalogue of potential future EQ and TC.  10,000 realisations of the next year 
activity generated based on historical activity and data.  Validated using observed 1min 
sustained wind speed.  Force proportional to (wind speed)^1/2.  Slight differences in wind 
speed affect force 
 
•  Modelled storm surge using tracks. Compared with ACE, Munich RE and CSIRO storm 
track. 
 
•  Tsunamigenic EQ >8.5 are modelled noting that tsunamis generated in the region within 
the last 20 years have generally been generated by smaller shallow earthquakes. 
 
•  Model Inundation and losses 
 
Data capture and data sharing tools
 – Stuart Gill  
 
•  Need to communicate and collaborate on the two initiatives.  Documents to be shared 
amongst the partners 
 
•  The data catalogue included in the ADB Inception report needs to be checked and updated 
 
•  Features mapped and attributes collected to be agreed to and finalised.  The field data 
questionnaire was reviewed and prioritised (Table 1).   
 
Update on SOPAC Project Activities - 
Michael Bonte-Grapentin 
 
•  As part of the WB project SOPAC will digitise manually buildings for agreed target areas in 
14 PICs. The digitising has started and will be completed by 15 March 2010. For all areas, 
where ground-truthing or field data capture is planned under the ADB and WB projects 
building footprints will be available prior to field data collection. 
 
•  Ground-truthing for WB project is planned jointly for SB (March), VU (April) and tentatively 
PG (May). Additionally, areas in FM and PW (February) and Fiji (March-April) will be 
covered. 
 
•  First trial of the building data collection carried out in Nadi using pre-defined 
building/infrastructure parameters and unique IDs. 
 
•  Formulae to reduce the digitised roof area to calculate floor area thus discounting the 
overhang needed.  This will be looked at during field verification exercises. 
 
•  SOPAC/GNS will circulate project implementation schedule to all partners 
 
 
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Other matters 
 
•  Information exchange and sharing (data formats and metadata standards) 
 
•  SOPAC to be the data clearinghouse and facilitate information exchange 
 
•  Shapefiles with native file formats kept as backup 
 
•  Imagery – native formats 
 
•  A video conference is proposed for Friday 5 March 2009 at 9am and the next project 
meeting is tentatively scheduled for 1
st
 week of June, venue to be confirmed. WB 
suggested, since this would coincide with the global Understanding Risk conference to 
have the meeting in Washington DC. 
 
•  MDA agreed verbally for imagery purchased under the joint SOPAC/PDC/SPC license 
agreement to be used by external consultants (e.g. AIR) for this initiative. WB to formally 
request in writing, PDC volunteered to draft agreement. 
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Action Items 
by Stuart Gill WB 
 
WB - Assess if we can send GNS AIR reports under an NDA (in particular exposure report)  
AIR / GNS - Develop taxonomy of typical buildings for each country for each major construction type  
AIR - Share hazard literature research and data with GNS [GNS Build upon this for their project]  
WB - Send WB metadata standards to GNS / AIR  
ALL (10-Dec) - Review WB Metadata standards and send minimum requirements and comments to PDC  
PDC (16-Dec) - Collate comments and determine metadata requirements  
PDC (30-Dec) - Determine from the following sources SOPAC / PDC / SPC the usage licenses of imagery 
and extracted features. In particular explore the possibility to temporarily (under agreement) move the data 
to AIR India to complete the feature extraction process. Have this confirmed in writing.  
WB - Google data download for PNG: determine the license for derived products  
GNS/ADB/SOPAC – determine if it is feasible to move PNG forward in the data collection schedule (to the 
end of May)  
WB – to determine if we can extend AIR contract by 3-4 weeks to accommodate a combined data collection 
mission to PNG  
GNS – Collate Friday afternoon’s discussion regarding the definition of building parameters. Create the 
definitive list and circulate for final approval. 
ALL – Review GNS’s collated list of building parameters.  
AIR – Create data needs matrix and circulate it for comments  
SOPAC – Share updated country survey timeline.  
AIR / SOPAC / WB – agree on final two countries for field surveying  
WB – to determine feasibility that the next face-to-face meeting be the second week of June after the 
Understanding Risk conference  
AIR / SOPAC – Bi-weekly teleconference for coordination and to assess progress  
SOPAC – to circulate the minutes from the first meeting to ALL for comments and ALL to add their notes 
from subsequent days  
 
Data to collect  
A comprehensive list of data requirements was discussed during the meeting and agreed upon. GNS is 
currently consolidating that list and will be circulating it for approval.  
 
Country timeline (SOPAC to provide detailed Gant chart)  
Note: this is the timeline for field surveys. The digitizing of the high-res imagery is a parallel process that will 
provide the base layer for the field surveys to amend. Note: The initial numbers refer to calendar weeks  
 
Countries within WB timeframe  
8/9, Cook Islands, ADB  
12/14, Vanuatu, Joint mission, (begun digitizing)  
15-17, Solomon Islands, Joint mission, (begun digitizing)  
19-21, Papua New Guinea, Joint mission  
12-13 Fiji, WB (begun digitizing)  
 
Additional WB field visits to be confirmed  
(Feb) Federated States of Micronesia (YAP, Chuuk)  
(Feb) Palau (begun digitizing)  
 
ABD surveys that are outside WB timeframe  
??, Fiji  
??, Tonga  
??, Samoa  
??, Tuvalu (begun digitizing)  
 
Countries just covered by manual digitizing and automated processes  
Niue, Nauru (begun digitizing), Marshall Islands, Kiribati and Timor Leste 
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Table 1: 
Asset features and attribute data, and other data to be collected under each 
respective project initiative. 
NB: all are of primary importance unless otherwise denoted 
with (ii) secondary importance and (iii) tertiary importance 
 
CATASTROPHE RISK FINANCING 
INITIATIVE (WB/AIR) 
RISK EXPOSURE DATABASE (ADB/GNS/SOPAC) 
BUILDINGS 
POLYGON 
 
ο Use 
 
  residential (including squatter) 
 Commercial 
 Industrial 
 
chemical plants, power plant, 
manufacturing, mining, 
 public 
assets 
churches, hospitals, schools, police 
stations, fire stations, and 
government buildings.  
ο  Area (calculated from footprint) 
POINT 
 
ο ID 
ο Use 
Group 
 residential 
 commercial/industrial 
 public/communal 
 critical 
facilities 
 hazardous 
facility 
 other 
 unknown 
ο Main 
Use 
 house 
 flats 
ο  Living Units - An estimate of the 
number of flats in apartment block 
 fale 
 shed 
 commercial 
 industrial/manufacturing 
 accommodation 
ο  Living Units - An estimate of the 
number of flats in apartment block 
 government 
 public 
services 
  church, temple etc. 
 community 
facilities 
 education 
 police 
station 
 fire 
station 
 defence 
 health 
services 
 communication 
 utility 
 petrol 
station 
 chemical 
store 
 chemical 
plant 
 power 
plant 
 mining 
  food and drug facility 
 other 
 unknown 
ο  Age (ii) only if easily captured 
ο Roof 
Pitch 
 Flat 
(true/false) 
 Low 
(true/false) 
  Moderate Roof Pitch (true/false) 
 Steep 
(true/false) 
 Unknown 
(true/false) 
ο Roof 
Shape 
 Hip 
(true/false) 
 Gable 
(true/false) 
 Monopitch 
(true/false) 
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CATASTROPHE RISK FINANCING 
INITIATIVE (WB/AIR) 
RISK EXPOSURE DATABASE (ADB/GNS/SOPAC) 
 Arch 
(true/false) 
 Other 
(true/false) 
 Unknown 
(true/fals 
ο Roof 
Material 
 Concrete 
(true/false) 
  Undifferentiated Sheet (true/false) 
  Sheet Metal (true/false) 
  Sheet Fibre-Cement (true/false) 
  Undifferentiated Tile (true/false) 
  Metal Tile (true/false) 
  Heavy Tile (true/false) 
  Wooden Shakes (true/false) 
 Traditional 
(true/false) 
 Other 
(true/false) 
 Unknown 
(true/false) 
ο Foundation 
 Slab 
(true/false) 
  Wood/Concrete Pile (true/false) 
  Wood Pole (true/false) 
  Concrete Columns (true/false) 
  Steel Columns (true/false) 
  Load Bearing Wall (true/false) 
 Unknown 
(true/false) 
ο  Foundation Bracing  
  Timber Wall (true/false) 
  Concrete Wall (true/false) 
  Masonry Wall (true/false) 
 Other 
(true/false) 
 Unknown 
(true/false) 
ο Wall 
Material 
 Concrete 
(true/false) 
  Fibre-Cement Sheet (true/false) 
  Fibre-Cement Board (true/false) 
 Timber 
(true/false) 
 Masonry 
(true/false) 
 Metal 
(true/false) 
  Traditional Material (true/false) 
 Other 
(true/false) 
 Unknown 
(true/false) 
ο Building 
Frame 
  Timber Frame (true/false) 
  Wooden Poles (true/false) 
  Concrete Columns (true/false) 
  Steel Columns (true/false) 
  Load Bearing Wall (true/false) 
 Tilt-up 
wall 
 Unknown 
(true/false) 
ο Window 
Size 
  <75% of Wall 
  >75% of Wall 
 Open 
Space 
 None 
ο Shutters 
 Present 
 Brackets 
 None 
 Unknown 
ο Plan 
Shape 
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CATASTROPHE RISK FINANCING 
INITIATIVE (WB/AIR) 
RISK EXPOSURE DATABASE (ADB/GNS/SOPAC) 
 Regular 
 L-shape 
 C-shape 
 Irregular 
 Unknown 
ο  Base Floor Area - Only for added buildings 
  < 50 m
2
 
 50-100 
m
2
 
 100-200 
m
2
  
 200-400 
m
2
 
  > 400 m
2
 
ο Parapet/Tower 
(true/false) 
ο  Concrete Cantilever (true/false) 
ο Defects 
 None 
(true/false) 
 Minor 
Cracks 
(true/false) 
 Major 
Cracks 
(true/false) 
 Minor 
Rot 
(true/false) 
 Major 
Rot 
(true/false) 
  Under construction (true/false) 
  Poor construction (true false) 
 Uninhabitable 
ο  Minimum Floor Height Above Ground (m) 
  0.0 - 0.1 
  0.2 - 0.3 
 0.4- 
1.0 
 1.1 
-3.0 
 >3.0 
ο  Maximum Floor Height Above Ground (m) 
  0.0 - 0.1 
  0.2 - 0.3 
  0.4 - 1.0 
 1.1 
-3.0 
 >3.0 
Site slope 
 Flat 
 Moderate 
 Steep 
ο Photo 
Number 
ο Source 
ο Reliability 
ο Follow 
up 
 
INDUSTRIAL FACILITIES 
POINT 
 
ο Chemical 
plants 
ο Mining 
ο Manufacture 
 Usage 
  High, Low level 
ο  Food and drug facility 
 
POINT 
 
Collected as for BUILDING USE 
ROADS 
LINE 
 
ο  Major roads  
LINE 
 
ο Road-ID 
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CATASTROPHE RISK FINANCING 
INITIATIVE (WB/AIR) 
RISK EXPOSURE DATABASE (ADB/GNS/SOPAC) 
ο Minor 
roads 
ο Class 
 Local 
 
 Collector 
 
 Arterial 
 
ο Surface 
 Sealed 
 Unsealed 
ο Width 
(m) 
ο  Number of lanes 
ο Source 
ο Reliability 
BRIDGES 
POINT 
 
ο Major 
bridges 
  Single span - Concrete 
  Single span - Steel 
  Multi span - Concrete 
  Multi span - Steel 
 Other 
 
POINT 
 
ο ID 
ο Length 
(m) 
ο Width 
(m) 
ο  Abutment Type (ii) 
 Concrete 
 Timber 
 Earth 
ο  Pier Type (ii) 
 Concrete 
 Steel 
 Timber 
ο  Span Type (ii) 
 Supported 
 Continuous 
 Cantilever 
ο Span 
material 
 Concrete 
 Steel 
 Timber 
ο Span 
Length 
(m) 
ο  Number of Spans 
ο  Year of Construction 
ο Source 
ο Reliability 
 
OTHER TRANSPORTATION  
 
POINT 
 
ο Ports 
 Type 
 
ο Commercial 
ο non 
commercial 
 
POINT 
 
Buildings such as Bus Stations and airport terminals 
to be included in the USE category of Buildings 
LINE 
 
ο  Airport  runway 
 Length 
 Surface 
type 
  No of Runways 
ο Railways 
(iii) 
 
 
PIPELINES 
LINE LINE 
 
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CATASTROPHE RISK FINANCING 
INITIATIVE (WB/AIR) 
RISK EXPOSURE DATABASE (ADB/GNS/SOPAC) 
 
ο  Potable water systems 
 Pipes 
(iii) 
ο Waste 
water 
systems 
 Pipes 
(iii) 
ο  Oil and Natural Gas systems  
 Pipelines 
(ii) 
 
Won’t be captured by field survey. Collect data as 
available  
 
ο ID 
ο Importance 
 System 
 
  Network Primary  
  Network Distribution Primary  
  Distribution Primary  
  Rural Distribution Primary  
 Secondary 
 
 Rural 
Secondary 
ο Flexibility 
 Brittle 
 Ductile 
ο Service 
 Waste 
Water 
 
  Drinking Water  
  Storm Water  
 Oil 
 
ο Support 
 Underground 
 
 On 
Ground 
 Suspended 
 Overhead 
ο Support 
Type 
 Wood 
Pole 
 Concrete 
Pole 
 Concrete 
Structure 
 Steel 
Tower 
 Bridge 
 Other 
ο Diameter 
ο Depth 
ο  Year of Construction 
ο Source 
ο Reliability 
 
NETWORK JUNCTION 
POINT 
 
ο  Potable water systems 
 Treatment 
plant 
 Storage 
tanks 
 Pumping 
stations 
(iii) 
ο Waste 
water 
systems 
 Treatment 
plant 
ο  Oil and Natural Gas systems 
 Refineries 
  Pumping plants (ii) 
 Tank 
farms 
  Gas compression stations (ii) 
  Fuel depots – storage 
POINT  
 
Will only be captured by field survey where obvious. 
Collect data from utilities where available 
 
ο ID 
ο Junction 
Type 
 Valve 
 
 Pump 
 
 Transformer 
 
  Switch/Distribution Box  
 Tower 
 Other 
ο Restraint 
 Unrestrained 
 Restrained 
ο  Year of Construction 
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CATASTROPHE RISK FINANCING 
INITIATIVE (WB/AIR) 
RISK EXPOSURE DATABASE (ADB/GNS/SOPAC) 
ο Source 
ο Reliability 
 
 
POWER 
POINT 
 
ο  Electric Power Generation  
 Power 
plant 
ο Hydro 
ο  
Diesel 
ο  
wind 
  Substations - Low, medium, and 
high voltage 
  Poles high voltage (ii) 
 
POINT 
 
Won’t be captured by field survey. Collect data from 
country/utility as available 
 
LINE 
 
ο  Electric Power Generation  
 Powerlines 
(iii) 
LINE High voltage only  
 
Won’t be captured by field survey. Collect from 
Utilities as available 
 
ο ID 
ο Importance 
 System 
 
  Network Primary  
  Network Distribution Primary  
  Distribution Primary  
  Rural Distribution Primary  
 Secondary 
 
 Rural 
Secondary 
ο Support 
 Underground 
 
 On 
Ground 
  
Suspended 
 Overhead 
ο Support 
Type 
 Wood 
Pole 
 Concrete 
Pole 
 Concrete 
Structure 
 Steel 
Tower 
 Bridge 
 Other 
ο Voltage 
ο  Year of Construction 
 
TELECOMMUNICATION 
POINT 
 
ο Communication 
system 
 Telephone 
central 
offices 
  Stations or transmitters 
 Sat 
dishes 
 Towers 
(iii) 
 Exchanges 
(iii) 
LINE 
 
Won’t be captured by field survey. Collect from 
Utilities where available 
 
ο ID 
ο Importance 
 System 
 
  Network Primary  
  Network Distribution Primary  
  Distribution Primary  
  Rural Distribution Primary  
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CATASTROPHE RISK FINANCING 
INITIATIVE (WB/AIR) 
RISK EXPOSURE DATABASE (ADB/GNS/SOPAC) 
 Secondary 
 
 Rural 
Secondary 
ο Support 
 Underground 
 
 On 
Ground 
  
Suspended 
 Overhead 
ο Support 
Type 
 Wood 
Pole 
 Concrete 
Pole 
 Concrete 
Structure 
 Steel 
Tower 
 Bridge 
 Other 
ο  Number of lines 
ο  Year of Construction 
ο Source 
ο Reliability 
Indicator 
 
DAM 
POINT 
 
ο Construction 
 Earth 
 Rock 
fill 
 Concrete 
ο  Storage volume (ii) 
 
 
 
 
Other data 
CATASTROPHE RISK FINANCING 
INITIATIVE (WB/AIR) 
RISK EXPOSURE DATABASE (ADB/GNS/SOPAC) 
ADMINISTRATION 
POLYGON 
 
ο  Country shape (political boundaries) 
ο  1st and 2nd level administration 
ο Census 
ο Land 
Usage 
 
 
GEOLOGY/SOILS 
 
POLYGON 
ADB Team to collect as available in country 
 
LANDUSE 
POLYGON 
 
ο Crops 
(cash) 
 
 Plantation 
ο Vegetation 
ο Human 
usage 
ADB Team to collect as available in country 
TOPOGRAPHY/ BATHYMETRY 
RASTER/VECTOR 
ADB Team to collect as available in country 
 
IMAGERY 
Low to medium resolution for areas not covered  SOPAC to order and supply 
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CATASTROPHE RISK FINANCING 
INITIATIVE (WB/AIR) 
RISK EXPOSURE DATABASE (ADB/GNS/SOPAC) 
 
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Appendix 7: Status of SOPAC Map servers for each country* 
Country 
Name 
On-Line 
Status 
Name Agency 
Contact 
Information 
Comments 
Cook 
Islands 
YES Mr.Timoti 
Tangiruaine, 
Urban/Rural Planner 
Ministry of Infrastructure and 
Planning 
P.O Box 102, Rarotonga,Cook Islands, 
Tel: (682) 20034  
Fax: (682) 21134 
E-mail: 
timoti@moip.gov.ck
 
Regular maintenance, data updates. Actively 
used. 
Go 
to www.maps.gov.ck
 
Fiji 
NO 
Mr. Ian Fong, 
Acting Director, DMR 
and National SOPAC, 
Fiji 
Mineral Resources 
Department 
 
Offline since 2008. 
PNG YES 
Mr.Arnold 
Lakamanga, 
Manager GIS Mineral 
Information 
Mineral Resources Authority 
P O Box 5, Harbour City Konedobu, NCD, 
Papua New Guinea 
Tel: (675) 321 3511, (675) 6863503 
E-mail: alakamanga@mra.gov.pg 
No active maintenance for approx. 1 yr. 
Samoa 
NO 
Ms. Siosinamele Lui, 
Science Officer 
Meteorology Division, 
Ministry of Natural 
Resources, Environment, 
and Meteorology 
P.O. Box 3020 Apia Samoa 
Tel: (685) 20855 / 20856 / 21521  
Fax: (685) 20857 
E-mail: 
Siosina.Lui@mnre.gov.ws
 
 
Offline since 2006. Previously actively 
maintained. Internet costs too high. 
Solomon 
YES 
Mr.Carlos Tatapu,  
IT Officer 
Ministry of Mines & Energy 
P O Box G37. Honiara, Solomon Islands 
Tel: (677)28609 / 21521  
Fax: (677) 25811 
E-mail: 
c_tatapu@mines.gov.sb
 
 
No regular maintenance. No data updates 
known. 
Tonga YES 
Mr. 
Taniela 
Kula, 
Government Geologist 
Ministry of Lands, Survey 
and Natural Resource 
PO Box 5, Nukualofa, Tonga 
Tel: (676) 25508  
Fax: (676) 23216 
E-mail: taniela@lands.gov.to 
Regular maintenance, data updates.  
Actively used. 
Tuvalu 
NO 
Mrs. Loia Tausi,  
Lands Officer 
Lands and Survey 
Departments, Ministry of 
Natural Resources & Lands 
Funafuti, Tuvalu  
Tel: (688) 20170  
Fax: (688) 20167 
E-mail: 
loia_tausi@yahoo.com
 
 
System inoperable after recent s/w update. 
Data reportedly lost. Previously had been 
actively maintained. 
Vanuatu 
NO 
Mr. Matt Temar,  
IT Specialist 
Lands Department 
PMB 9090, Port Vila, Vanuatu 
Tel: (678) 22892  
Fax: (678) 27708 
E-mail: 
mtemar@vanuatu.gov.vu
 
Offline since early 2009. No assigned staff to 
maintain.  Previously updated. For some time 
period only accessible in-country. 
*September 2009, Interview with Ms. Vilisi Tokalauvere SOPAC by Chris Chiesa, PDC 
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APPENDIX 8: Detailed project plan
Prepare Prelim Report/ppt ADB/WB
Prelim report and ppt due
Prelim Inception
PC Nadi Trial Data attributes
Vanuatu Inception
PC Nadi Trial
Cook Is. Inception
Draft Inception Report
Inception Report Due
Inception Meeting
Finalise Inception report
Hazard Model Database Design
Infrastructure Database Design
Spec Equipment
Purchase equipment
Set up Equipment
Draft Database design report
Programme Handhelds
Revise database design
Finalise database design report
Data Collection Cook Is
Training - Fiji
Data Collection Solomon Is
Data Collection Vanuatu
Data Collection PNG
Draft Progress report
First Progress report Due
First Progress meeting
Data Collection Fiji
Data collection Tuvalu
Data Collection Samoa
Data Collection Tonga
Draft Mid term report
Mid Term report Due
Mid Term meeting
Consolidate data
Develop Regional system
Test System
Install In SOPAC
Training SOPAC
Data Installation and Training In country
Draft Final report
Draft Final report Due
Final Draft Meeting
Finalise report
Final Report Due
Project Closed
2009
2010
2011
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
2. Database design
1.  Inception
Activity
Task Description
4. National and     
regional systems
5. training
6. Final reporting
3. Data Collection
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background image
 
 
 
Appendix 9: Detailed data collection plan 
 
TA Team personnel - Days 
Date 
Country / Area (Task) 
GNS  
 
SOPAC
1
 
  
  
  
PG  DH TB
∑ JP SC AK
SV MK LB MB ∑ 
PHASE 1 
Feb 11 - Feb 25 
Cook Islands 
14 
14 
  
28 
  
14 
  
  
  
14 
  
28 
Feb 11 - Feb 25 
Rarotonga (Training, Data Capture & Debrief) 
11 
14 
  
25 
  
14 
  
  
  
14 
  
28 
Feb 16 - Feb 19 
Aitutaki 
  
  
  
  
  
  
  
  
  
  
Mar 9 - Mar 30 
Solomons 
21 
  
21  42 
21 
  
  
21 
  
  
21 
63 
Mar 12 - Mar 30 
Honiara & N-Guadacanal (Consultation, Training, Data Capture & 
Debrief) 
14 
  
21  35 
16 
  
  
21 
  
  
13 
50 
Mar 10 - Mar 18 
Gizo/Noro & Western Province 
  
  
  
  
  
  
  
  
Mar 22 - Mar 27 
Auki, N Malaita 
  
  
  
  
  
  
  
  
  
  
Mar 8 - Apr 16 
Fiji - Phase 1 
  
  
  
  
  
19 
14 
19 
  
56 
Mar 8 - Mar 13 
Lami 
  
  
  
  
  
  
  
14 
Apr 6 - Apr 13 
Labasa 
  
  
  
  
  
  
  
  
21 
Apr 13 - Apr 20 
Navua – Pacific Harbour 
  
  
  
  
  
  
  
  
21 
Mar 28 - Apr 22 
Vanuatu 
21 
  
21  42 
21 
23 
  
  
  
13 
18 
75 
Mar 28 - Apr 22 
Port Vila & Efate (Consultation, Training, Data Capture & Debrief) 
17 
  
13  30 
16 
  
  
  
11 
39 
Mar 28 - Apr 7 
Luganville & E-Santo 
  
  
  
  
  
  
10 
  
18 
Apr 7 - Apr 14 
SW-Pentecost & Ambrym 
  
  
  
  
  
  
  
  
  
14 
Apr 14 - Apr 18 
Tanna (optional) 
  
  
  
  
  
  
  
  
Apr 23 - May 21 
PNG 
28 
28 
  
56 
25 
25 
  
18 
  
  
18 
86 
Apr 23 - Apr 26 
Port Moresby (Consultation, Training & survey of industrial sites?) 
  
  
  
  
  
  
  
  
  
Apr 26 - May 10 
Lae 
14 
14 
  
28 
14 
14 
  
  
  
42 
May 10 - May 19 
Lae - Madang, Madang & surrounds, Ramu Suga, Yonki? 
  
  
  
  
  
  
  
  
May 10 - May 19 
Rabaul/Kokopo 
  
  
  
  
  
  
23 
May 17 - May 20 
Kimbe (optional) 
  
  
  
  
  
  
  
  
  
May 19 - May 20 
Port Moresby (Consultation & Debrief) 
  
  
  
  
PHASE 2 
Jun 30 - Aug 2 
Fiji - Phase 2 
33 
  
26  59 
33 
26 
33 
33 
  
14 
14  153 
Jun 30 - Jul 19 
Lami-Suva-Nausori 
19 
  
12  31 
19 
12 
19 
19 
  
83 
Jul 19 - Aug 2 
Nadi-Lautoka-Ba 
14 
  
14  28 
14 
14 
14 
14 
  
70 
Jul 12 - Jul 19 
Tuvalu 
  
  
  
  
  
  
  
  
Jul 12 -  Jul 19 
Funafuti/Fongafale (Consultation, Training, Data Capture & Debrief) 
  
  
  
  
  
  
  
  
Aug 18 - Sep 3 
Samoa 
16 
16 
  
32 
16 
  
  
  
  
16 
  
32 
Aug 18 - Sep 3 
Apia & Upolu (Consultation, Training, Data Capture & Debrief) 
11 
16 
  
27 
16 
  
  
  
  
16 
  
32 
Aug 27 - Sep 1 
Savaii 
  
  
  
  
  
  
  
  
  
  
Sep 4 - Sep 20 
Tonga 
16 
16 
  
32 
  
  
  
16 
  
  
32 
Sep 4 - Sep 20 
Tongatapu  (Consultation, Training, Data Capture & Debrief) 
11 
16 
  
27 
  
16 
  
16 
  
  
  
32 
Sep 14 - Sep 19 
Vavau 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
GRAND TOTAL 
  
149
74  75 
  
116 105
52  102
19 
59 
71 
  
 
NB: 
1
Includes both World Bank checking and ADB project 
 
PG = Phil Glassey, DH = Dave Heron, TB = Todd Bosse 
JP = Joy Papao, SC = Steven Clegg, AK = Atu Kaloumaira, SV = Susan Vocea, MK = Miriam Kong, LB = Litea Biukoto, MB = Michael Bonte 
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1 Fairway Drive
Avalon
PO Box 30368
Lower Hutt
New Zealand
T +64-4-570 1444
F +64-4-570 4600
Dunedin Research Centre
764 Cumberland Street
Private Bag 1930
Dunedin
New Zealand
T +64-3-477 4050
F +64-3-477 5232
Wairakei Research Centre
114 Karetoto Road
Wairakei
Private Bag 2000, Taupo
New Zealand
T +64-7-374 8211
F +64-7-374 8199
National Isotope Centre
30 Gracefield Road
PO Box 31312
Lower Hutt
New Zealand
T +64-4-570 1444
F +64-4-570 4657
Principal Location
www.gns.cri.nz
Other Locations

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