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Measuring polycentricity: a whole graph embedding perspective


Fu, Cheng; Nanni, Mirco; Yeghikyan, Gevorg; Weibel, Robert (2021). Measuring polycentricity: a whole graph embedding perspective. Santa Barbara: UC Santa Barbara: Center for Spatial Studies.

Abstract

Polycentricity is a critical characteristic of the spatial organization of cities. Many indices have been proposed to measure the degree of morphological polycentricity or functional polycentricity. However, selecting a proper set of polycentricity indices for cities in a particular region or country still needs prior expert knowledge. This study demonstrates that whole graph embedding, as a novel and efficient computational tool, can model the city polycentricity in an integrated manner without much prior knowledge. The new method can further support visual analytics and classification very well.

Abstract

Polycentricity is a critical characteristic of the spatial organization of cities. Many indices have been proposed to measure the degree of morphological polycentricity or functional polycentricity. However, selecting a proper set of polycentricity indices for cities in a particular region or country still needs prior expert knowledge. This study demonstrates that whole graph embedding, as a novel and efficient computational tool, can model the city polycentricity in an integrated manner without much prior knowledge. The new method can further support visual analytics and classification very well.

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Additional indexing

Item Type:Published Research Report
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2021
Deposited On:30 Sep 2021 14:12
Last Modified:14 Oct 2021 11:47
Publisher:UC Santa Barbara: Center for Spatial Studies
Series Name:GIScience 2021 Short Paper Proceedings
OA Status:Green
Publisher DOI:https://doi.org/10.25436/E2TG6C
Official URL:https://escholarship.org/uc/item/8t51k45t

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