Inferring floor area ratio thresholds for the delineation of city centers based on cognitive perception

Wurm, Michael; Goebel, Jan; Wagner, Gert G.; Weigand, Matthias; Dech, Stefan; Taubenböck, Hannes

doi:10.1177/2399808319869341

Cited by 14 publications

(14 citation statements)

References 36 publications

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“…The number of floors per building is an important factor of the urban spatial structure, and serves as an input variable for the population estimation procedure within this study (see Section 4.4). To derive the number of floors per building, we conducted a regression analysis that estimates the floors in dependence on the building height (e.g., Wurm et al, 2019a). Therefore, a sample was created containing 100 buildings from the original building footprint dataset.…”

Section: Building Floors From Google Street View Datamentioning

confidence: 99%

Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Stiller

Wurm

Stark

et al. 2021

JTLU

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A significant increase in global urban population affects the efficiency of urban transportation systems. Remarkable urban growth rates are observed in developing or newly industrialized countries where researchers, planners, and authorities face scarcity of relevant official data or geo-data. In this study, we explore remote sensing and open geo-data as alternative sources to generate missing data for transportation models in urban planning and research. We propose a multi-modal approach capable of assessing three essential parameters of the urban spatial structure: buildings, land use, and intra-urban population distribution. Therefore, we first create a very high-resolution (VHR) 3D city model for estimating the building floors. Second, we add detailed land-use information retrieved from OpenStreetMap (OSM). Third, we test and evaluate five experiments to estimate population at a single building level. In our experimental set-up for the mega-city of Santiago de Chile, we find that the multi-modal approach allows generating missing data for transportation independently from official data for any area across the globe. Beyond that, we find the high-level 3D city model is the most accurate for determining population on small scales, and thus evaluate that the integration of land use is an inevitable step to obtain fine-scale intra-urban population distribution.

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Section: Building Floors From Google Street View Datamentioning

confidence: 99%

Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Stiller

Wurm

Stark

et al. 2021

JTLU

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“…Although there is potential for further development of this method, it provides an opportunity to reveal comprehensive insights into development paths and effects of planning processes in cities and urban regions around the globe. Moreover, it can contribute to extent current research on the detection of city centers [16].…”

Section: Resultsmentioning

confidence: 97%

Deriving Urban Mass Concentrations Using TanDEM-X and Sentinel-2 Data for the Assessment of Morphological Polycentricity

Standfuß

Geiß

Kühnl

et al. 2020

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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Polycentricity refers to urban regions with more than one center. These additional (sub-) centers, e.g. spatial concentrations of jobs, are characteristic for the transformation of monocentric towards polycentric urban patterns. Frequently assessed with socioeconomic data, the phenomenon is also reflected in the built morphology of urban landscapes. Only recently, a methodology for largescale morphological characterization of built-up structures in urban areas relying on TanDEM-X and Sentinel-2 data has been introduced. Thus, a new way to investigate morphologic polycentricity in and among cities is provided. Relying on this approach, we derive the distribution of urban mass concentrations in four city regions. We identify high urban mass concentrations-proxies for (sub-) centersusing a threshold approach. A comparison between the studied regions reveals that only one city tends to have a polycentric urban structure. Our study highlights a new and promising possibility to study the urban morphologic development at global scales.

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“…Allen proposed that spatial resilience is based on the asymmetry, connectivity, and information exchange of complex systems and the ability of the system's spatial properties to generate resilient feedback when faced with external shocks [5]. Regarding research content, urban spatial resilience attempts to construct an indicator system with specific entry points, including urban road traffic [6], urban local space [7], multiscale urban resilience construction [8][9][10][11], etc. Evaluation indicators can be categorized into four types: urban social, economic, environmental, and institutional resilience.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Urban Spatial Resilience and Its Influencing Factors: Case Study of the Harbin–Changchun Urban Agglomeration in China

Chen

et al. 2022

Sustainability

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This study constructs a framework for evaluating urban spatial resilience based on five dimensions: scale, intensity, morphology, function, and benefit. Likewise, it empirically analyzes the spatial differences and influencing factors of urban spatial resilience in the Harbin–Changchun urban agglomeration from 2000 to 2020. Overall, the spatial resilience of the Harbin–Changchun urban agglomeration declined from 2000 to 2019. In addition, its ability to resist external disturbances weakened. The five dimensions of spatial resilience declined. However, urban spatial morphological resilience slightly increased. The spatial diversity of the Harbin–Changchun urban agglomeration is obvious, implying that the spatial resilience of cities in the central region, mainly in Suihua and Songyuan, is higher than in peripheral areas of the urban agglomeration, mostly in the Yanbian Korean Autonomous Prefecture, Siping, and Qiqihar. The period between 2000 and 2019 was dominated by cities with fluctuating spatial resilience. Furthermore, urban spatial resilience is influenced by a combination of factors, with economic support being the primary one. The selection of the urban spatial resilience research index system in this study is more spatially oriented and more accurately reflects the urban spatial resilience situation, which, in turn, provides a new planning perspective for urban planning in China.

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Inferring floor area ratio thresholds for the delineation of city centers based on cognitive perception

Abstract: Inferring floor area ratio thresholds for the delineation of city centers based on cognitive perception. Environment and Planning B: Urban Analytics and City Science.

Cited by 14 publications

References 36 publications

Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Deriving Urban Mass Concentrations Using TanDEM-X and Sentinel-2 Data for the Assessment of Morphological Polycentricity

Evaluation of Urban Spatial Resilience and Its Influencing Factors: Case Study of the Harbin–Changchun Urban Agglomeration in China

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