Volume 28, Issue 7 pp. 2346-2356
RESEARCH ARTICLE

Evaluating Urban Land Resource Carrying Capacity With Geographically Weighted Principal Component Analysis: A Case Study in Wuhan, China

Binbin Lu

Binbin Lu

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China

Net Zero and Resilient Farming, Rothamsted Research, North Wyke, UK

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Yilin Shi

Yilin Shi

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

School of Computer Science, Wuhan University, Wuhan, China

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Sixian Qin

Sixian Qin

Wuhan Geomatics Institute, Wuhan, China

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Peng Yue

Peng Yue

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

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Jianghua Zheng

Corresponding Author

Jianghua Zheng

College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China

Correspondence:

Jianghua Zheng ([email protected])

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Paul Harris

Paul Harris

Net Zero and Resilient Farming, Rothamsted Research, North Wyke, UK

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First published: 03 September 2024

Funding: This work was supported by National Natural Science Foundation of China (Grant 42071368) and the Fundamental Research Funds for the Central Universities, China (Grants 2042022dx0001 and 2042024kf0005).

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.