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Delineation of Central Business Districts in mega city regions using remotely sensed data

Taubenböck, Hannes and Klotz, Martin and Wurm, Michael and Schmieder, J and Wagner, B and Wooster, M and Esch, Thomas and Dech, Stefan (2013) Delineation of Central Business Districts in mega city regions using remotely sensed data. Remote Sensing of Environment, 136, pp. 386-401. Elsevier. ISSN 0034-4257

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Abstract

Central Business Districts (CBDs) are an important urban structural type (UST), and an apparent structural feature of many large cities. CBD locations play a decisive role in the spatial arrangement of functions and exposures within cities. However, while past research underscores the importance of their spatial detection, delineation and cartographic representation, the definitions used aremostly functional and qualitative. Objective pre-defined methods/thresholds for the semi-automated spatial classification of CBDs, based on a quantitative approach, do not yet exist. This paper presents a conceptual framework to define the CBD using physical and morphological parameters, and tests the approach using 3-D city models of three European test sites (Canary Wharf in London, La Defense in Paris, and Levent in Istanbul). From these case studies, we develop a transferable method to detect and delineate CBDs over larger areas from a combination of Cartosat-1 digital surface models and multispectral Landsat ETM+ imagery. Applying this wide-area method to the entire extents of the three European megacities of London, Paris and Istanbul, we detect CBDs with a user accuracy of 75.7% and spatially delineate them with overall accuracies of 82.9%. Finally, we apply spatial metrics to analyze and compare the location and distribution of CBDs across the three mega cities, finding many similarities between London and Paris, but showing that Istanbul features a more complex urban footprint, and a different spatial CBD pattern.

Item URL in elib:https://elib.dlr.de/83030/
Document Type:Article
Title:Delineation of Central Business Districts in mega city regions using remotely sensed data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Taubenböck, Hanneshannes.taubenboeck (at) dlr.deUNSPECIFIED
Klotz, MartinUNSPECIFIEDUNSPECIFIED
Wurm, Michaelmichael.wurm (at) dlr.deUNSPECIFIED
Schmieder, J Munich ReUNSPECIFIED
Wagner, BUNSPECIFIEDUNSPECIFIED
Wooster, MDept. of Geography, King's College LondonUNSPECIFIED
Esch, ThomasThomas.Esch (at) dlr.deUNSPECIFIED
Dech, Stefanstefan.dech (at) dlr.deUNSPECIFIED
Date:September 2013
Journal or Publication Title:Remote Sensing of Environment
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:136
Page Range:pp. 386-401
Publisher:Elsevier
ISSN:0034-4257
Status:Published
Keywords:Urban remote sensing, Central Business District, Digital surface model, Cartosat-1, Landsat, 3-D city model, Megacity, Urban structure type, Spatial pattern, Spatial metrics, classification
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 - Vorhaben Zivile Kriseninformation und Georisiken (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Taubenböck, Dr. Hannes
Deposited On:24 Jun 2013 13:33
Last Modified:21 Nov 2013 13:45

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