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  5. Size distributions of slums across the globe using different data and classification methods
 
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2019
Zweitveröffentlichung
Artikel
Verlagsversion

Size distributions of slums across the globe using different data and classification methods

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Hauptpublikation
Friesen.pdf
CC BY-NC 4.0 International
Format: Adobe PDF
Size: 3.17 MB
TUDa URI
tuda/4488
URN
urn:nbn:de:tuda-tuprints-86395
Autor:innen
Friesen, John
Taubenböck, Hannes
Wurm, Michael
Pelz, Peter F.
Kurzbeschreibung (Abstract)

More than 900 million people worldwide live in slums. These slums mainly can be found in cities of the global south and are characterized by poor living conditions and usually insufficient access to basic infrastructure such as water or energy. In order to improve the living conditions of slum inhabitants, information about the number, location and size of the slums is required to plan supply infrastructure. We therefore identify morphological slums in eight different cities in Africa, South America and Asia, using remote sensing data and analyse their size distributions. We show that 84.6% of all observed morphological slums have a size between 0.001 and 0.1 km2. These results rely on a consistent approach using a clear ontology and conceptual frame for classification. However, classification methods for these underserved areas differ. We show slum classifications based on different methods reveal a strong dependency between the particular method and the resulting size distribution. The study shows the relevance of remote sensing for the investigation of slums and the results can be used for infrastructure planning, as infrastructure improvement projects are often limited to the large known slums. Whereas, the large number of small slums distributed across the city is often neglected.

Sprache
Englisch
Fachbereich/-gebiet
16 Fachbereich Maschinenbau > Institut für Fluidsystemtechnik (FST)
DDC
600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
Institution
Universitäts- und Landesbibliothek Darmstadt
Ort
Darmstadt
Titel der Zeitschrift / Schriftenreihe
European Journal of Remote Sensing
Startseite
1
Endseite
13
ISSN
2279-7254
Verlag
Taylor & Francis
Publikationsjahr der Erstveröffentlichung
2019
Verlags-DOI
10.1080/22797254.2019.1579617
PPN
447800531

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