Evaluating Urban Land Resource Carrying Capacity With Geographically Weighted Principal Component Analysis: A Case Study in Wuhan, China.
Published In: Transactions in GIS, 2024, v. 28, n. 7. P. 2346 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lu, Binbin; Shi, Yilin; Qin, Sixian; Yue, Peng; Zheng, Jianghua; Harris, Paul 3 of 3
Abstract
With the rapid urbanization in China, urban land resources gradually become the core of urban development. This study spatially evaluated the urban land resource carrying capacity (LRCC) with a case study of the built‐up area in Wuhan from 2015 to 2020. Following an evaluation index system, five critical LRCC indicators, including population density, GDP per land area, plot ratio, building density, and road network density, were selected by an analytical hierarchical process. The synthesis of indicators, however, is usually challengeable due to homogeneous assumptions of traditional techniques. In this study, we adopted a local technique, geographically weighted principal component analysis, to calculate a comprehensive carrying pressure (CCP) concerning spatially varying contributions of each indicator on their synthesis across different geographic locations. On mapping these spatial outputs of the built‐up area in Wuhan, the highest CCP was found in the central areas, where population size tends to be influential and the dominant variable in 62.69% of subdistricts. Furthermore, increased construction over the 5 years has led to an increased CCP in some of the peripheries of the built‐up area, and 55.22% of subdistricts show rising changes. With the GWPCA technique, this framework works well in evaluating and analyzing urban LRCC from a new local perspective. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Transactions in GIS. 2024/11, Vol. 28, Issue 7, p2346
- Document Type:Article
- Subject Area:Science
- Publication Date:2024
- ISSN:1361-1682
- DOI:10.1111/tgis.13241
- Accession Number:180851254
- Copyright Statement:Copyright of Transactions in GIS is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.