47

Geographic Database on The Natural and Socioeconomic
Conditions of Reef Islands
Hiroto SHIMAZAKI,1 Hiroya YAMANO,1,2 Hiromune YOKOKI,3
Toru YAMAGUCHI,4 Masashi CHIKAMORI,5 Masayuki TAMURA,6
Hajime KAYANNE7
1

Social and Environmental Systems Division, National Institute for Environmental Studies,
16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan2
UR 128 CoRéUs, Institut de Recherche pour le Développement,
BP A5 98848 Nouméa Cedex, New Caledonia
3
Center for Water Environment Studies, Ibaraki University,
4-12-1 Nakanarusawa, Hitachi, Ibaraki 316-8511, Japan
4
Department of Archaeology, Keio University,
2-15-45 Mita, Minato, Tokyo 108-8345, Japan
5
Faculty of Informatics, Teikyo Heisei University,
2289-23 Uruido-Otani, Ichihara, Chiba 290-0171, Japan
6
Department of Urban and Environment Engineering, Kyoto University,
Yoshida-Honmachi, Sakyo, Kyoto 606-8501, Japan
7
Department of Earth and Planetary Science, The University of Tokyo,
3-7-1 Hongo, Bunkyo, Tokyo 113-0033, Japan

Abstract
Reef-island countries constitute the smallest and most isolated low-lying habitable land on Earth,thus they are vulnerable to the environmental consequences of climatic change. To effectively provide
measures for adapting to climate change situations, a better understanding of the diversity of the naturaland socioeconomic conditions of reef islands is needed. We have established a global geographic database that includes data on: (1) natural factors affecting the geological, physical or ecological processes
that control the formation and maintenance of reef islands; (2) factors characterizing the socioeconomic
state of reef-island countries; and (3) geographic features that can be referred to for location on a map.
The geographic database could serve as a basis for breaking the natural and socioeconomic conditions
down into several patterns, and help in identification of reef islands where similar adaptation measures
could be applicable. This report outlines the data we have collected and the processes we have employed to develop our geographic database.
Key words: atoll, geographic database, global, reef island

1. Introduction
Reef-island countries are distributed throughout
the tropical and sub-tropical waters of the world.
They constitute the smallest and most isolated
low-lying habitable land on Earth, thus they are vulnerable to the environmental consequences of climaticchange (e.g., a faster rise in sea level, more frequent
heat waves and droughts, and more extreme weather
events). Mitigation efforts based on reduction ofgreenhouse-gases emissions and on enhancement of
CO2 absorption can slow down the rate of climatechange but cannot prevent its occurrence (Ahmed etat., 1999). Without proper adaptive measures tocope with climate change situations, the sustainability
Global Environmental Research9(1)/2005: 47-55
printed in Japanof reef-island countries will be undermined further
and, in extreme cases, reef islands may become uninhabitable.
Reef islands are diverse in size, structure and components (Andréfouët et al., 2001a; Yamano et al., accepted), probably as a result of differences in factors
affecting the natural processes that control the formation and maintenance of reef islands. In addition, theresponse to climate change situations may vary depending on the socioeconomic conditions of reefisland countries (e.g., territorial and economic sizes,
population, industries, agricultural products, and natural resources). Hence a better understanding of thediversity of the natural and socioeconomic conditionsof reef islands will be needed to effectively provide©2005 AIRIES

48

H. SHIMAZAKI et al.

measures for adapting to climate change situations.
We established a geographic database that provides us with insights into the diversity of natural andsocioeconomic conditions of reef islands over several
spatial scales varying from global to regional. The
data stored in the geographic database were compiled
from many diverse sources and were integrated in a
spatial context. Although the data stored in the geographic database have not necessarily been standardized with respect to spatio-temporal resolution, they
can serve as a basis for breaking natural and socioeconomic conditions down into several patterns, and canhelp the identification of reef islands where similar
adaptation measures may be applicable. This reportoutlines the data we have collected and the processes
we have employed to develop the geographic database.

2. Overview of Geographic Database
A geographic database can be thought of as an integrated set of geographic data conveying the locations and attributes of real world phenomena. The
primary objective of our geographic database was toprovide synoptic views of the natural conditions of thewaters in which reef islands are located and also toprovide rapid access to information on the socioeconomic state of reef-island countries. The secondary
objective was to highlight the difference or similarityof the natural and socioeconomic conditions among
the reef islands of the world. The tertiary objective
was to examine relationships between the island structure and natural conditions in order to ascertain themost important factor(s) to island formation and maintenance. Thus, our geographic database needed toinclude geographic data on both natural and socioeconomic conditions over a wide spatial range to encompass the full extent of tropical and sub-tropical watersof the world, without losing too much spatial resolution to explain the variation of the conditions between
atolls or within an atoll.
Development of the geographic database involveda series of sequential steps. The workflow commenced with attribute selection, followed by the stepsof assembling data from various sources, extracting
the intended attributes from the source data, reformatting the attributes into a form suitable for use in a
database, and adding the reformatted data to the database. Attribute selection is the decision of what geographic phenomena should be included in the database.
This decision is especially important in the workflow
because it dominates the future options for analyses.
The rationale of attribute selection is described in thenext section, ‘Rationale of attribute selection.’ The
data processing methods included in the subsequent
steps, which can vary depending on the form of source
data, are explained in the section titled ‘Compilationof geographic data.’
The geographic data we collected can be catego-

rized into three broad groups: (1) factors affecting thenatural processes that control the formation and maintenance of reef islands; (2) factors characterizing thesocioeconomic state of reef-island countries; and (3)geographic features that can be referred to for location
on a map. Depending on the type of corresponding
geographic phenomena, the form of geographic data
could be either or both: raster representation or vector
representation. In a raster representation, the spaceis divided into an array of grid cells of a fixed size,
and attributes are assigned to the cells. The locationof a cell is implicitly determined by the ordering of
the cells. In a vector representation, attributes areassociated with geometric objects represented bypoints, lines (polylines), or areas (polygons). The
locations and shapes of the geometric objects are explicitly defined by coordinates. A summary of thegeographic data we collected is given in Table 1, andsome example maps of the data are shown in Fig. 1.

3. Rationale of Attribute Selection
3.1 Natural conditions
Reef islands are generally regarded as combined
products of geological, physical, and ecological processes (Gourlay, 1988). Historically, reef islands aresuggested to have been formed by a relative sea-level
fall in the late Holocene. McLean and Woodroffe(1994) suggested that the islands formed after the establishment of reef flats, and this was possibly due tothe relative sea-level fall in the late Holocene(Schofield, 1977; Scoffin, 1993; Yamano et al., 2001;
Woodroffe & Morrison, 2001). Hence attributeinformation on the Holocene relative sea-level history
was selected as a factor related to geological processes
that have triggered the formation of reef islands.
Once reef islands have formed, they are maintained by coherent accumulation of sand and gravelsize material derived from broken-down remains ofcoral, algae and other reef-building organisms produced in the adjacent reef environment. The presence and morphological characteristics of reef islands
may result from the balance among sediment supply,
sediment transport and available space for deposition.
Sediment materials are transported by physicalprocesses that operate on time scales varying from thedaily rhythm of the tides to the seasonal changes ofweather and oceanographic conditions (Woodroffe,
2002). The major factors affecting these physicalprocesses may include tides, surface winds, wind
waves and swells which produce currents or water
circulation (Andrews & Pickard, 1990), and tropicalstorms which transport the coral rubble which forms
reef islands (e.g., Maragos et al., 1973; Scoffin, 1993).
Hence information on attributes of these oceanographic and climatological conditions was selected asthey are factors affecting the physical processes thattransport sediment materials forming reef islands.
Coral reefs produce a depositional environment

Geographic Database on The Natural and Socioeconomic Conditions of Reef Islands

suitable for the maintenance of reef islands, by acting
as a natural breakwater that protects reef islands from
erosion and as a vital source of bioclastic materials forthe reef islands. Kleypas et al. (1999) suggested thatthe potential for coral reef development could be defined by surface solar irradiance, the water attenuation
coefficient of light, sea surface temperatures, nutrient
concentrations, carbonate ion concentrations andsalinity. Hence information on attributes of theseclimatological and oceanographic conditions was selected to take into account the potential for coral reefdevelopment.
Development of coral reefs may also be affectedby tides, wind waves, swells and tropical storms.
Tides limit the vertical development of reef flats bydefining the levels of Mean Low Water Neaps(MLWN) and Mean Low Water Springs (MLWS)(Kleypas & Hopley, 1992). Wind waves and swells
have also been suggested to control the susceptibilityof the reef framework to storm breakage (Yamano etal., 2003). Tropical storms are the most common
and possibly devastating disturbance of coral reef
communities (Treml et al., 1997). Hence information on attributes of these oceanographic and climatological conditions was selected as they are factors
having an indirect influence on the maintenance ofreef islands through the effect on ecological processesof coral reef development.
The distribution of reef-building organisms on thereef flat is important because it affects the supply ofmaterials forming reef islands. The major constituents of reef islands are benthic foraminifera, calcareous algae, hermatypic corals, and molluscs. Of these,
the benthic foraminifera are suggested to be the most49

important contributor to the sand mass of reef islands(Yamano et al., 2000). Hence information on attributes of the biogeography of benthic foraminifera wasselected as a factor explaining the potential for
reef-island maintenance.
The present-day ecological zonation of coral reefs
is mainly attributed to water exchange and circulationinduced by tides, surface winds, wind waves andswells (Geister, 1977; Done, 1983; Hubbard, 1988;
Grigg, 1998). Water exchange promotes the input ofadditional nutrients from ocean waters (Pugh &Rayner, 1981).
Water circulation promotes thecycling of nutrients within coral reef ecosystems anddetermines the dispersal of materials in coral reefs(Yamano et al., 1998). Hence information on theattributes of these climatological and oceanographic
conditions was considered important also as they arefactors affecting the ecological processes that control
the growth and zonation of reef-building organisms
inhabiting shallow reef flats.
Land plants (e.g., Pemphis acidula and Scavola
frutescens) contribute to preservation of reef islands
by stabilizing their constituent materials (Chikamori,2001). The factors affecting the growth of land
plants on reef islands may include surface solar irradiance, surface air temperature, and precipitation. Of
these factors, precipitation may be a key factor, because the distribution of flora on reef islands is suggested to have a close relationship to the amount ofprecipitation (Stoddart, 1992). In our geographic
database, information on the attributes of these climatological conditions was included to take into accountthe potential for natural land protection by land plants.

Table 1 List of source data we collected.
SpatialTemporal
Data name
Data provider
Covergae
Resolution
Covergae
Resolution
Oceanographic
Wind waves
36°N-36°S 1.5° GRID 1990/01/01-1999/12/31
6 hours
ERA-40
ECMWF
Primary swells
36°N-36°S 1.5° GRID 1990/01/01-1999/12/31
6 hours
ERA-40
ECMWF
Tidal range
Global
0.5° GRID----NAO.99b
Matsumoto et al. (2000)
Nutrients (Nitrate)
Global
1.0° GRID
1772-2001
Monthly
WOA01
NOAA NODC
Nutrients (Phosphate)
Global
1.0° GRID
1772-2001
Monthly
WOA01
NOAA NODC
Nutrients (Silicate)
Global
1.0° GRID
1772-2001
Monthly
WOA01
NOAA NODC
Salinity
Global
1.0° GRID
1772-2001
Monthly
WOA01
NOAA NODC
Sea surface temperatures
Global 9.28 km GRID
1985-1997
5 days mean
Path finder SST
NASA JPL
Climatological
Surface winds
Global2.5° GRID
1980-1995
Monthly climatology
GGUAS
NOAA NCDC
Surface air temperatures
Global2.5° GRID
1980-1995
Monthly climatology
GGUAS
NOAA NCDC
Surface solar irradiance
Global
1.0° GRID
1983/07-1991/06
Monthly
SeaWiFS Solar
NASA GISS
Precipitation
Global2.5° GRID
1979-2003
Monthly
GPCP-Ver.2
GPCP
Indo-Pacific
Points1945-2002
6 hours
Best Track
JTWC
Tropical storms
Atlantic
Points1851-2003
6 hours
HURDATNOAA NHC
Polygons----Dickinson (2003)
Geological Late Holocene sea-level change history Pacific
Human
Settlement period
Pacific
Polygons----Dickinson (2003)
Biogeographical Distribution of foraminifera
GlobalPoints----Langer & Hottinger (2001)
Socioeconomic
Population
Global--2004
Annual stat.
The World Factbook
CIA
GDP per capita
Global--2004
Annual stat.
The World Factbook
CIA
Land use
Global--2004
Annual stat.
The World Factbook
CIA
Natural resources
Global--2004
Annual stat.
The World Factbook
CIA
Industries
Global--2004
Annual stat.
The World Factbook
CIA
Agricultural products
Global--2004
Annual stat.
The World Factbook
CIA
Base map
Atoll locations
GlobalPoints----Reef locatiions
Reef Base
Shorelines
GlobalPolylines----GSHHS Ver. 1.2
Univ. of Hawaii
Administrative boundaries
GlobalPolygons----DCWESRI Inc.
Topograpy
Global
1/30° GRID----ETOPO2NOAA NGDC
Type

Geographic phenomena

50Fig. 1 Example maps of the data we collected.

H. SHIMAZAKI et al.

Geographic Database on The Natural and Socioeconomic Conditions of Reef Islands*The tropical storm frequency at each grid was determined from the number of hourly
storm positions falling within a 200 km radius around the grid center.

Fig. 1 (Continued)

3.2 Socioeconomic conditions
Even if the natural conditions affecting the formation and maintenance of reef islands are similar, reefislands under countries with different socioeconomic
conditions may have different responses toward theclimate change situations. Hence information on attributes that describe the socioeconomic conditions ofeach reef-island country should be included in ourgeographic database. The range of attributes should
cover information on territorial and economic sizes,
population, industries, agricultural products, and natural resources.
3.3 Base map features
In addition to geographic data on natural andsocioeconomic conditions, geographic data on features to be used for location references on maps arehelpful for visualization purposes. Such geographic
features include shorelines, administrative boundaries,and seafloor and land elevations. Hence these geographic features are stored in our geographic database.

4. Compilation of Geographic Data
Once the geographic phenomena to include in the
database were selected, we had to pay attention toquestions about how geographic phenomena should be
represented as geographic data, and how much detailshould be described. The method of representing
geographic phenomena could be either or both ofraster representation or vector representation. Thisdecision could be determined from the type of geographic phenomenon. Geographic phenomena concerning natural conditions which vary continuously
across space would be better off coded by raster representation. In contrast, a lot of the socioeconomic
conditions would be better off linked to the discretegeometric objects representing the locations or shapesof reef-island countries.
It is best to decide the level of detail of geographic
data after consideration of the spatial and temporalvariability of the geographic phenomenon. Somegeographic phenomena included in our database arenaturally dynamic and some are comparatively staticand slow to change. The processes that control theformation and maintenance of reef islands operate ontime scales ranging from the daily rhythm of the tides

51

to thousands of years of geological activities. Of thefactors affecting the controlling processes, the climatological and oceanographic factors exhibit relatively
high spatial and temporal variability and, therefore,
need to be coded as geographic data with high spatial
and temporal resolutions or with summary statistics
that appropriately represent their dynamic nature (Fig.
1).
The source data for our geographic database wereobtained predominately in digital form, but also inanalog form such as paper maps, which needed to bedigitized to encode the locations and attributes. The
source data digitized were converted into geographic
data in a form suitable for use in our geographic database while maintaining their original spatial and temporal resolutions. As needed, summary statistics
were derived from the source data and stored in ourgeographic database. Although the original values
could be redundant, the reformatted version of source
data was kept stored in the database to cope flexibly
with changes in future applications. The source dataand processing methods for each geographic
phenomenon are explained below.
4.1 Natural conditions
Wave energy flux: We obtained data on significant wave height H (m), mean wave period T (s) andmean wave direction D (degrees from north) for eachof the wind waves and swells from the ECMWF
Re-analysis (ERA-40) wave model product. The
data had a spatial coverage between 36°N and 36°S
and spatial resolution of a 1.5 by 1.5 degree grid.
The temporal coverage of the data was the period
from 1990 to 1999 and the temporal resolution was
6-hours.
From the data obtained, we derived mean energy
flux P (W/m) for each of the wind waves and swells
using equation (1).
ρg 2 2
P = EC g =
H T
(1)
32π
where E is energy density (J/m2); Cg is group velocity(m/s); ρ is the mass density of water (kg/m3); and g is
gravitational acceleration (m/s2). Then we summarized the energy flux of each of the wind waves andswells by computing mean and standard deviation for
the time frames of the entire period, year, and month.
We also summarized the mean wave direction of eachof the wind waves and the swells by calculating mean
and angular deviation (Zar, 1999) for the time framesof the entire period, year, and month.
Tropical storm: We obtained track data of tropical storms including typhoons, cyclones and hurricanes, from which we derived parameters on storm
intensity. The track data was taken from the official
archives provided by the NOAA/National Hurricane
Center (NHC) for Atlantic storms, and by the U.S.
Navy’s Joint Typhoon Warning Center (JTWC) for allother storms. The data consisted of 6-hourly center
locations and maximum sustained wind speeds for all

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H. SHIMAZAKI et al.

tropical storms over the past 50 years.
Prior to undertaking the processes of deriving
parameters on storm frequency and intensity, we refined the track data by removing inconsistently duplicated records. Then we estimated the hourly center
locations and maximum sustained wind speeds using
the method of spline interpolation. Finally we derived parameters on storm frequency and intensity at a
spatial resolution of a 0.5 by 0.5 degree grid. The
storm frequency in each grid was determined from thenumber of hourly storm positions falling within a
200 km radius of the grid center. The storm intensity
in each grid was defined by summarizing the Wind Intensity Index (WII) of the hourly storms within arange of 200 km from the grid center.
The WII was an index of storm intensity that was
preliminarily determined in this study, to take into
account the asymmetry of the wind field. In general,
the strongest winds in a storm are found on the right(left) side with respect to the storm’s motion in the
Northern Hemisphere (Southern Hemisphere), because the motion of the storm also contributes to its
swirling winds. The WII was calculated by equation(2).
WII = Vmax × (cos( α − β + ( π / 2 ) × δ ) + 2)(2)
where Vmax is maximum sustained wind speed, α is theazimuth from a grid point to the storm center, β is
direction of the storm’s motion, and δ is 1 for thestorms in the Northern Hemisphere and is −1 for thestorms in the Southern Hemisphere.
Tidal range: We calculated the range of astronomical tides at a spatial resolution of a 0.5 by 0.5
degree grid using data on harmonic constants of thefour major component tides (M2, S2, K1 & O1). The
harmonic constants used in the calculation wereobtained from the tidal prediction system ‘NAO.99b’(Matsumoto et al., 2000).
Surface solar irradiance: The original daily aswell as the monthly mean products on a 2.5 by 2.5
degree grid for the period from July 1983 to June
1991 was produced at the NASA/Goddard Institute forSpace Studies (GISS) using a fast atmospheric radiative transfer algorithm (Bishop & Rossow, 1991).
The data we obtained were a spatially interpolated
version of the original monthly mean product at a spatial resolution of from a 2.5 by 2.5 degree grid to a 1.0
by 1.0 degree grid. The interpolated data were distributed by the Distribute Active Archive Center(DAAC) at the NASA/Goddard Space Flight Center(GSFC). Using the monthly data for the period from
July 1983 to June 1991, we derived climatologies(mean and standard deviation) of the surface solar
irradiance by summarizing the data for the timeframes of the entire period, year, and month.
Surface air temperatures and winds: The data weobtained was monthly climatologies (mean and standard deviation) for the atmosphere represented on a
2.5 by 2.5 degree grid for the period from 1985 to
1995. The data was distributed by the NOAA/

National Climatic Data Center (NCDC) as a productnamed ‘The Global Gridded Upper Air Statistics(GGUAS) Version 1.1.’ The source of the GGUAS
dataset was semidiurnal analyses for the period from1985 to 1995, produced by the European Centre for
Medium-Range Weather Forecasts (ECMWF). Using monthly climatologies data for the period from1985 to 1995, we derived mean and standard deviation
values of the surface air temperature and surface windspeed for the entire period. We also summarized the
direction of surface winds by calculating mean andangular deviation (Zar, 1999) for the entire period.
Precipitation rate: The data we obtained were theproduct named ‘The Global Precipitation Climatology
Project Version 2 Combined Precipitation Data Set(GPCP Version 2 Data Set),’ which was developed andcomputed by the NASA/Goddard Space Flight
Center’s Laboratory for Atmospheres as a contribution
to the GEWEX Global Precipitation Climatology Project, and was distributed by the NOAA/National Climatic Data Center (NCDC). The dataset contains
monthly, 2.5 by 2.5 degree gridded fields of two products: the combined satellite-gauge precipitation estimate, and the combined satellite-gauge precipitation
error estimate. It covers the 21-year period fromJanuary 1979 to the delayed present. Using themonthly data for the period from January 1979 to
December 2003, we derived climatologies (mean andstandard deviation) of precipitation rate by summarizing the data for the time frames of the entire period,
year, and month.
Sea surface temperatures (SST): The data we obtained were the pentad (5-day) climatology on a
9.28 km grid for the period from 1985 to 1997. The
data were distributed by the Physical Oceanography
Distributed Active Archive Center (PO.DAAC) at theNASA/Jet Propulsion Laboratory (JPL) as a productnamed ‘AVHRR Pathfinder Global 9 km SST Climatology (JPL).’ The product was derived from thehighest quality SST data estimated from AVHRR
Pathfinder Oceans Pathfinder Data by a method of
spatial and temporal Gaussian interpolation (Casey &Cornillon, 1999). Using the pentad climatology data
for the period from 1985 to 1997, we derived meanand standard deviation values of SST for the timeframes of the entire period, year, and month.
Nutrient concentrations and salinity: Annual andmonthly (or seasonal) statistics (mean and standard
deviation) on nutrient elements and salinity for the
0-10 meters depth layer of the ocean represented on a
1.0 by 1.0 degree grid were obtained from the dataset
named ‘World Ocean Atlas 2001 (WOA01).’ The
WOA01 was prepared by the NOAA/National
Oceanographic Data Center (NODC) and its detailed
description has been published elsewhere (Conkrightet al., 2002). Although the WOA01 is based on datasubmitted to the NOAA/NODC up to August 2001
from 1772, the period of time with available data varies depending on the elements and the locations.

Geographic Database on The Natural and Socioeconomic Conditions of Reef Islands

Late Holocene sea-level history: Data on the spatial variation of the timing of the relative sea-level fallin the late Holocene were obtained from a paper map
prepared by Dickinson (2003). The hardcopy analog
map was first converted into a digital image by scanning. Then the scanned map was geocoded with reference to coastline data and converted into a form ofvector representation (polygons) by the heads-up
digitizing method, using ERDAS IMAGINE 8.7.
Attributes concerning the timing of the relative
sea-level fall in the late Holocene were assigned toeach polygon object.
Biogeography of foraminifera: Data on the biogeography of foraminifera were also obtained frompaper maps, which were prepared by Manger andHottinger (2000). In the same way as the map of thelate Holocene sea-level history, the maps of the biogeography of foraminifera were converted into vector
data (points) indicating locations where the following
foraminifera were observed: Marginora vertebralis,
Amphistegina spp., Calcarina spp., and Baculogypsina sphaerulata. These species are dominant on
shallow reef flats and could contribute to reef-island
formation (Yamano et al., 2000; Yamano, 2002).
4.2 Socioeconomic conditions
Socioeconomic state: Data on the socioeconomic
states of reef-island countries were obtained from theonline edition of The World Factbook 2004 (http://
www.cia.gov/cia/publications/factbook/). The online
data include comprehensive statistical information
about more than 260 countries and territories withfull-color maps and flags, and are maintained and published by the Central Intelligence Agency (CIA) of the
U.S.A. The original data coded as standard HTML
format were converted into a tabular format that could
be linked with geographic features representing atoll
locations (points) or administrative boundaries (polygons).
4.3 Base map features
Atoll locations: Data on atoll locations were extracted from the data named ‘Reef locations’ available
from ‘ReefBase (http://www.reefbase.org).’ ReefBase
is a global, online information system on coral reefs,
providing a wealth of relevant information to coral
reef researchers and coastal zone managers as well as
the general public. ReefBase was funded in its initial
year by a grant from the Swedish International Development Cooperation (SIDA) and is currently supported by the United Nations Foundation through a
project by WorldFish Center and the International
Coral Reef Action Network (ICRAN).
Shorelines: The shoreline data we obtained were
‘Global Self-consistent Hierarchical High-resolution
Shorelines (GSHHS) version 1.2.’ GSHHS is ahigh-resolution shoreline dataset amalgamated fromtwo databases in the public domain, and has been developed and maintained by Dr. Paul Wessel of the

53

School of Ocean and Earth Science and Technology(SOEST), University of Hawaii, and Dr. Walter H. F.
Smith of the NOAA/National Geophysical Data Center (NGDC). The dataset is available both from the
SOEST and NGDC; it comes with access software androutines to facilitate data decimation based on a standard line-reduction algorithm. The methods of processing and assembling GSHHS data were described by
Wessel and Smith (1996).
Administrative boundaries: Data on administrative boundaries were obtained from the dataset named‘Digital Chart of the World (DCW) 1993 version.’
The DCW is a comprehensive 1:1,000,000-scale vector basemap of the world. It consists of geographic,
attribute, and textual data that can be accessed, queried, displayed, and modified with ArcInfo software.
The dataset was originally developed by Environmental Systems Research Institute, Inc. (ESRI) in
Vector Product Format (VPF) for the U.S. Defense
Mapping Agency (DMA). The primary source forthe dataset is the DMA Operational Navigation Chart(ONC) series that are produced by Australia, Canada,
the United Kingdom, and the U.S.A. Here we shouldnote that the DCW dataset we used was developed in
1991/1992 and national boundaries reflect the politicalreality as of that time. Modern country names andboundaries have not been added and their omission
does not indicate support or non-support of any nation.
Topography: The topographical data we obtained
were ‘2-Minute Gridded Global Relief Data
(ETOPO2)’ distributed by the NOAA/National Geophysical Data Center (NGDC). ETOPO2 is a digital
elevation model that includes both land elevation andocean bathymetry for the entire world. It consists ofmore than 58 million elevation values that are regularly spaced at 2 minutes of latitude and longitude.
At the equator, the horizontal resolution of ETOPO2 isapproximately 3.7 km. Vertical positions are specified in meters, and horizontal positions, in geographic
coordinates. Source data, and thus data quality, varyfrom region to region.

5. Concluding Remarks
We have presented our effort to develop a global
geographic database of the factors that affect the formation and maintenance of reef islands, and that characterize the socioeconomic state of each reef-island
country. A change in any one factor can promotechanges in the geomorphological characteristics of areef island and have a potential to induce different
responses toward climate change situations. Gathering reef-island-related data into one information repository would facilitate a better understanding of thediversity of the natural and socioeconomic conditionsof reef islands, and this will be crucial to providing
reef islands with effective adaptation measures toward
climate change situations. Although the range of

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H. SHIMAZAKI et al.

geographic data included in our database is alreadyvast, further integration of additional data will beneeded. Possible data include: (1) regional scale dataon antecedent reef topography and present-day geomorphology of reef islands, and (2) global scale dataon tectonic events and eustatic variations, both ofwhich control the modern geomorphology of reef islands. Predictions of how reef islands will respondtoward climate change situations rely on a full understanding of how reef islands have been affected by thecurrent and historical environmental conditions.

Acknowledgements
This research was financially supported by Global
Environment Research Fund of the Ministry of theEnvironment, Japan (Project No. B15, Principal Investigator: HK).
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(Received 30 March 2005, Accepted 2 May 2005)