The Identification and Dynamics of Urban Shadow Areas from the Perspective of People Flows—A Case Study of Nanjing
Autor(en): |
Weiting Xiong
Junyan Yang |
---|---|
Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Buildings, 22 November 2023, n. 12, v. 13 |
Seite(n): | 2934 |
DOI: | 10.3390/buildings13122934 |
Abstrakt: |
Urban shadow areas, formed by long-term unbalanced and inadequate development during the rapid process of urbanization, are of great significance to a city’s overall development. However, relatively little attention has been paid to identifying and characterizing urban shadow areas. Drawing upon a dataset on urban morphology and cellular signaling, and taking Nanjing as a case study, this paper proposes a method to identify urban shadow areas from the perspective of people flows. The empirical results show that there are 19 urban shadow areas within the downtown areas of Nanjing, 11 of which are distributed in the old downtown areas and the rest are relatively scattered in the periphery. As for morphological characteristics, these urban shadow areas differ from each other in terms of indicators such as building density and development intensity. Moreover, the empirical results show that these urban shadow areas are not isolated but closely connected with other parts of Nanjing. Based on the different spatio-temporal distribution patterns of their connections, the 19 urban shadow areas are divided into four types, and the characteristics of each type have been investigated by analyzing a representative shadow area. It is suggested that policies aiming to eliminate the negative effects of urban shadow areas should consider heterogeneity in their spatial distributions within a city, the temporal distribution of their external connections, and their dominant functions. |
Copyright: | © 2023 by the authors; licensee MDPI, Basel, Switzerland. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
5.15 MB
- Über diese
Datenseite - Reference-ID
10753802 - Veröffentlicht am:
14.01.2024 - Geändert am:
07.02.2024