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• W2583001811 abstract "Abstract Polycentricity belongs to the most versatile and fuzzy concepts in urban geography. It basically points to the existence of more than one center within a conurbation. Previous studies have mostly referred to the spatial distribution of employment density for (sub-) center identification. In contrast, our study draws on large area 3D building models derived from ubiquitous remote sensing data. We use stereoscopic Cartosat-1 digital surface models in combination with building footprints. These geoinformation reflect the spatial configuration of the built dimension and allow a physical approach to the concept of polycentricity. For (sub-) center identification we thoroughly analyze conceptually different kinds of threshold approaches (global, region-specific and distance-based) applied to concentrations of urban masses. After evaluating the advantages and disadvantages of the threshold approaches applied, we combine these methods to overcome their individual shortcomings. Last but not least, we establish a framework consisting of mapping techniques and site- and non-site specific statistics to evaluate polycentricity at fine-grained spatial intra-urban scale. In general we find that urban mass concentrations are a reasonable proxy for commonly used employment density data. We address the polycentricity issue across four German city regions—Frankfurt, Cologne, Stuttgart and Munich—and we find all of them to still be morphologically dominated by their core cities. Nevertheless, our analysis reveals striking differences of the urban spatial structure highlighting a rather monocentric pattern in the Munich region on the one hand, and a polycentric-dispersed distribution of urban mass concentrations in the Stuttgart region on the other hand." @default.
• W2583001811 created "2017-02-03" @default.
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• W2583001811 date "2017-07-01" @default.
• W2583001811 modified "2023-10-10" @default.
• W2583001811 title "Measuring morphological polycentricity - A comparative analysis of urban mass concentrations using remote sensing data" @default.
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• W2583001811 doi "https://doi.org/10.1016/j.compenvurbsys.2017.01.005" @default.
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