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<article language="en">
	<journal>
		<journal_title>Natural Hazards and Earth System Science</journal_title>
		<journal_url>www.nat-hazards-earth-syst-sci.net</journal_url>
		<issn>1561-8633</issn>
		<eissn>1684-9981</eissn>
		<volume_number>9</volume_number>
		<issue_number>2</issue_number>
		<publication_year>2009</publication_year>
	</journal>
	<doi>10.5194/nhess-9-393-2009</doi>
	<article_url>http://www.nat-hazards-earth-syst-sci.net/9/393/2009/</article_url>
	<abstract_html>http://www.nat-hazards-earth-syst-sci.net/9/393/2009/nhess-9-393-2009.html</abstract_html>
	<fulltext_pdf>http://www.nat-hazards-earth-syst-sci.net/9/393/2009/nhess-9-393-2009.pdf</fulltext_pdf>
	<start_page>393</start_page>
	<end_page>403</end_page>
	<publication_date>2009-03-19</publication_date>
	<article_title content_type="html">Validation of a social vulnerability index in context to river-floods in Germany</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>A. Fekete</name>
			<email>fekete@ehs.unu.edu</email>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">United Nations University, Institute for Environment and Human Security (UNU-EHS), UN Campus, Hermann-Ehlers-Str. 10, 53113 Bonn, Germany</affiliation>
	</affiliations>
	<abstract content_type="html">Social vulnerability indices are a means for generating information about
people potentially affected by disasters that are e.g. triggered by
river-floods. The purpose behind such an index is in this study the
development and the validation of a social vulnerability map of population
characteristics towards river-floods covering all counties in Germany. This
map is based on a composite index of three main indicators for social
vulnerability in Germany – fragility, socio-economic conditions and region.
These indicators have been identified by a
factor analysis of selected demographic variables obtained from federal
statistical offices. Therefore, these indicators can be updated annually
based on a reliable data source. The vulnerability patterns detected by the
factor analysis are verified by using an independent second data set. The
interpretation of the second data set shows that vulnerability is revealed
by a real extreme flood event and demonstrates that the patterns of the
presumed vulnerability match the observations of a real event. It comprises
a survey of flood-affected households in three federal states. By using
logistic regression, it is demonstrated that the theoretically presumed
indications of vulnerability are correct and that the indicators are valid.
It is shown that indeed certain social groups like the elderly, the
financially weak or the urban residents are higher risk groups.</abstract>
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</article>

