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Has Dongying developed to a ghost city? - Evidence from multi-temporal population estimation based on VHR remote sensing and census counts

Leichtle, Tobias und Lakes, Tobia und Zhu, Xiao Xiang und Taubenböck, Hannes (2019) Has Dongying developed to a ghost city? - Evidence from multi-temporal population estimation based on VHR remote sensing and census counts. Computers, Environment and Urban Systems, 78, Seiten 1-15. Elsevier. doi: 10.1016/j.compenvurbsys.2019.101372. ISSN 0198-9715.

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Offizielle URL: https://www.sciencedirect.com/science/article/pii/S0198971519300833

Kurzfassung

With ongoing growth and continuous development of cities, the world is turning into an urban society. In this context, urbanization, population growth and migration towards urban areas are global trends. These processes are highly dynamic especially in China, with highest rates of urbanization worldwide. In contrast to these well-known trends, a recently emerging and rarely studied side effect is “ghost cities”, which became a notable phenomenon in China recently. A ghost city is commonly defined as a new urban development that is running at severe undercapacity with respect to population and businesses and where the availability of housing and public infrastructure significantly exceeds the practical demand. Against this background, this study presents a framework based on remote sensing for the assessment of the presence or absence of the ghost city phenomenon in a typical highly dynamic Chinese city. For this purpose, remote sensing data with very-high resolution (VHR) are employed for establishment of a 4d functional city model. Subsequently population capacity estimates are based on a statistical approach. The components of the functional 4d city model, i.e. the multi-temporal building model and the classification of building types associated with residential and non-residential function returned very high accuracies with κ of 0.73 and 0.89, respectively. The number of floors was estimated with coefficient of determination of 0.91. Compared to the numbers of official census counts, the multi-temporal population capacity estimation revealed a considerable mismatch of available living space based on VHR remote sensing data and actual population counts. According to the conceptual framework of this study, this disagreement indicates a high likelihood and significant evidence for the emergence and presence of the ghost city phenomenon for the city of Dongying. In addition, a detailed spatial assessment was conducted in terms of an index comparing the dynamics of residential developments and population numbers to provide an impression of specific regions of the urban area which are most likely to suffer from the ghost city phenomenon.

elib-URL des Eintrags:https://elib.dlr.de/129172/
Dokumentart:Zeitschriftenbeitrag
Titel:Has Dongying developed to a ghost city? - Evidence from multi-temporal population estimation based on VHR remote sensing and census counts
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Leichtle, Tobiastobias.leichtle (at) dlr.dehttps://orcid.org/0000-0002-0852-4437NICHT SPEZIFIZIERT
Lakes, Tobiatobia.lakes (at) geo.hu-berlin.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Zhu, Xiao Xiangxiao.zhu (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Taubenböck, HannesHannes.Taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126NICHT SPEZIFIZIERT
Datum:November 2019
Erschienen in:Computers, Environment and Urban Systems
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:78
DOI:10.1016/j.compenvurbsys.2019.101372
Seitenbereich:Seiten 1-15
Verlag:Elsevier
ISSN:0198-9715
Status:veröffentlicht
Stichwörter:Urbanization VHR remote sensing 4d functional city model Population estimation Ghost city China
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Fernerkundung u. Geoforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Institut für Methodik der Fernerkundung > EO Data Science
Hinterlegt von: Leichtle, Tobias
Hinterlegt am:18 Sep 2019 09:46
Letzte Änderung:21 Nov 2023 07:06

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