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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 and Lakes, Tobia and Zhu, Xiao Xiang and 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, pp. 1-15. Elsevier. DOI: 10.1016/j.compenvurbsys.2019.101372 ISSN 0198-9715

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

Abstract

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.

Item URL in elib:https://elib.dlr.de/129172/
Document Type:Article
Title:Has Dongying developed to a ghost city? - Evidence from multi-temporal population estimation based on VHR remote sensing and census counts
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Leichtle, Tobiastobias.leichtle (at) dlr.deUNSPECIFIED
Lakes, Tobiatobia.lakes (at) geo.hu-berlin.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Taubenböck, HannesHannes.Taubenboeck (at) dlr.deUNSPECIFIED
Date:November 2019
Journal or Publication Title:Computers, Environment and Urban Systems
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:78
DOI :10.1016/j.compenvurbsys.2019.101372
Page Range:pp. 1-15
Publisher:Elsevier
ISSN:0198-9715
Status:Published
Keywords:Urbanization VHR remote sensing 4d functional city model Population estimation Ghost city China
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Leichtle, Tobias
Deposited On:18 Sep 2019 09:46
Last Modified:18 Sep 2019 09:46

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