Assessment of Urban Spatial Integration Using Human Settlement Environmental Geographic Dataset: A Case Study in the Guangzhou–Foshan Metropolitan Area

Urbanization is an important process in China’s urban development, significantly contributing to resource allocation and the cooperative development of neighboring cities. In recent years, remote-sensing technology has emerged as a powerful tool in urbanization research. However, the disparity in development between urban and rural areas poses challenges in evaluating the degree of urbanization within a region. This paper addresses this issue by using LCZ (Local Climate Zone) data to provide a unified framework for analyzing a human settlement environmental geographic dataset. This study focuses on the spatial development and transformation of the Guangzhou–Foshan urbanization from 2000 to 2020. The LCZ data offer a suitable framework for examining urban–rural gradients, facilitating the analysis of spatial characteristics under varying development conditions. This unified framework enables a comprehensive analysis of the spatiotemporal characteristics of urban spatial integration. The results show that the analysis of the Guangzhou–Foshan metropolitan area reveals that the region has maintained a “core–edge” spatial structure over the past 20 years. The development rate has decelerated following policy changes in 2010, with the adjacent area experiencing significantly slower development compared to the overall study area. LCZ data are effective for comparative analysis of internal spatial development within urban areas, offering a novel approach to studying spatial integration amid urban development.

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Publication Details

Journal
Land
Published
2024-08-11
DOI
https://doi.org/10.3390/land13081262
Citations
3
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
1.13
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article

Assessment of Urban Spatial Integration Using Human Settlement Environmental Geographic Dataset: A Case Study in the Guangzhou–Foshan Metropolitan Area

Jing Xie, Rui Chen, Zifeng Li, Siyu Zhou et al.
3 citations
Land
Remote Sensing and Land Use
1.13
article

Assessment of Urban Spatial Integration Using Human Settlement Environmental Geographic Dataset: A Case Study in the Guangzhou–Foshan Metropolitan Area

Jing Xie, Rui Chen, Zifeng Li, Siyu Zhou, Shuyuan Liu
article en
3 citations

Abstract

Urbanization is an important process in China’s urban development, significantly contributing to resource allocation and the cooperative development of neighboring cities. In recent years, remote-sensing technology has emerged as a powerful tool in urbanization research. However, the disparity in development between urban and rural areas poses challenges in evaluating the degree of urbanization within a region. This paper addresses this issue by using LCZ (Local Climate Zone) data to provide a unified framework for analyzing a human settlement environmental geographic dataset. This study focuses on the spatial development and transformation of the Guangzhou–Foshan urbanization from 2000 to 2020. The LCZ data offer a suitable framework for examining urban–rural gradients, facilitating the analysis of spatial characteristics under varying development conditions. This unified framework enables a comprehensive analysis of the spatiotemporal characteristics of urban spatial integration. The results show that the analysis of the Guangzhou–Foshan metropolitan area reveals that the region has maintained a “core–edge” spatial structure over the past 20 years. The development rate has decelerated following policy changes in 2010, with the adjacent area experiencing significantly slower development compared to the overall study area. LCZ data are effective for comparative analysis of internal spatial development within urban areas, offering a novel approach to studying spatial integration amid urban development.

LandVol. 13(8)
Sun Yat-sen University (CN), Chinese Academy of Sciences (CN), Institute of Geographic Sciences and Natural Resources Research (CN), University of Chinese Academy of Sciences (CN)
Sustainable cities and communities
Openalex Percentile: Top 25%
Remote Sensing and Land Use
1.13
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