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| Titel |
Applicability of Remotely Derived Global Population Datasets in Earthquake Risk Assessment of Istanbul, Turkey |
| VerfasserIn |
Rashmin Gunasekera, Helene Galy, Keiko Saito |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250038191
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| Zusammenfassung |
| There has been a considerable increase in the use of remotely-derived population data in
studies of public policy, disaster reduction and in commercial applications. To assess its
applicability (in terms of suitability and sensitivity) in natural hazard risk assessment, we
have analysed two globally consistent population datasets that incorporate Remote Sensing as
a means of estimating population count: LandScan Global Population Database
(LandScan, Dobson et al., 2000), and Global Rural Urban Mapping Project (GRUMP;
Balk et al., 2005). Both datasets provide population counts of resolution of up to1
km2.
LandScan data, intended to estimate ambient population at risk from natural hazards and
relies on a methodology based on using the second order administrative population data from
census and ancillary data sources. In contrast, the methodology of GRUMP aims to delineate
urban from rural areas using census and remotely sensed night time lights. The impact of
ancillary data used in these global population datasets plays a pivotal role in its applicability
to natural hazard risk assessment.
We analysed the two databases to evaluate the impact on residential population in
Istanbul, Turkey, for a potential earthquake scenario. The modelling results using both
LandScan and GRUMP data show divergence of results at districts levels where the
spatial areas of the district are large and population concentrations are low. The
total number of buildings damage was calculated to be 1,924,636 and 1,742,604
from LandScan and GRUMP datasets respectively. Provision of this sensitivity
analysis information from these complementary methods would help strengthen the
disaster risk reduction options and improve sustainable land use practices through
enhanced public participation in the decision making and governance processes. In
relation to further development of this technology, although panchromatic low-light
imaging data would be useful, multispectral low-light imaging data would also
provide valuable information on the type or character of lighting; potentially stronger
predictors of variables, such as ambient population density and economic activity. |
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