Urban expansion in Shenzhen since 1970s: A retrospect of change from a village to a megacity from the space

Yu, Wenjuan; Zhang, Yujia; Zhou, Weiqi; Wang, Weimin; Tang, Rong

doi:10.1016/j.pce.2019.02.006

Cited by 40 publications

(17 citation statements)

References 44 publications

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“…With the development and promotion of national macro strategies such as 'One Belt and One Road' [64] and 'western development', the urban built-up area of Kunming has expanded significantly in recent years [65]. According to the statistical yearbook of Yunnan province in 2019, Kunming covers an area of 21 473 km 2 , with an urban built-up area of 437 km 2 . Although the main urban area of Wuhua District, Guandu District, Xishan District and Chenggong District is only 2542 km 2 , the urban built-up area reaches 434.4 km 2 , which is also the reason why the five main urban areas of Kunming City are selected as the study area (figure 1) [50,66].…”

Section: Study Areamentioning

confidence: 99%

“…In recent decades, with the rapid urbanization of China and the intensification of agricultural modernization in urban built-up areas, cities have undergone unlimited expansion and great changes have also occurred [ 1 , 2 ]. Urban built-up areas are the carrier of all urban activities, the main gathering areas of population and economic activities, and the prominent manifestation of urbanization [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

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Extraction of urban built-up area based on the fusion of night-time light data and point of interest data

Zhang

Yang

2021

R. Soc. open sci.

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The accurate extraction of urban built-up areas is an important prerequisite for urban planning and construction. As a kind of data that can represent urban spatial form, night-time light data has been widely used in the extraction of urban built-up areas. As one of the geographic open-source big data, point of interest (POI) data has a high spatial coupling with night-time light data, so researchers are beginning to explore the fusion of the two data in order to achieve more accurate extraction of urban built-up areas. However, the current research methods and theoretical applications of the fusion of POI data and night-time light data are still insufficient compared with the dramatically changing urban built-up areas, which needed to be further supplemented and deepened. This study proposes a new method to fuse POI data and night-time light data. The results before and after data fusion are compared, and the accuracy of urban built-up area extracted by different data and methods is analysed. The results show that the data fusion can avoid the shortage of single data and effectively improve the extraction accuracy of urban built-up areas, which is greatly helpful to supplement the study of data fusion in urban built-up areas, and also can provide decision-making guidance for urban planning and construction.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extraction of urban built-up area based on the fusion of night-time light data and point of interest data

Zhang

Yang

2021

R. Soc. open sci.

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“…In addition to economic development, Shenzhen has also put great effort into ecological conservation. For example, in 2005, Shenzhen created a "Basic Ecological Control Line" to restrict urban expansion, which has slowed down the loss of forests (Yu et al 2019). The highly hybridized social and natural features in this city make it an ideal place to quantify urban form.…”

Section: Study Area and Datamentioning

confidence: 99%

Integrating structure and function: mapping the hierarchical spatial heterogeneity of urban landscapes

et al. 2020

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Background Cities are social-ecological systems characterized by remarkably high spatial and temporal heterogeneity, which are closely related to myriad urban problems. However, the tools to map and quantify this heterogeneity are lacking. We here developed a new three-level classification scheme, by considering ecosystem types (level 1), urban function zones (level 2), and land cover elements (level 3), to map and quantify the hierarchical spatial heterogeneity of urban landscapes. Methods We applied the scheme using an object-based approach for classification using very high spatial resolution imagery and a vector layer of building location and characteristics. We used a top-down classification procedure by conducting the classification in the order of ecosystem types, function zones, and land cover elements. The classification of the lower level was based on the results of the higher level. We used an object-based methodology to carry out the three-level classification. Results We found that the urban ecosystem type accounted for 45.3% of the land within the Shenzhen city administrative boundary. Within the urban ecosystem type, residential and industrial zones were the main zones, accounting for 38.4% and 33.8%, respectively. Tree canopy was the dominant element in Shenzhen city, accounting for 55.6% over all ecosystem types, which includes agricultural and forest. However, in the urban ecosystem type, the proportion of tree canopy was only 22.6% because most trees were distributed in the forest ecosystem type. The proportion of trees was 23.2% in industrial zones, 2.2% higher than that in residential zones. That information “hidden” in the usual statistical summaries scaled to the entire administrative unit of Shenzhen has great potential for improving urban management. Conclusions This paper has taken the theoretical understanding of urban spatial heterogeneity and used it to generate a classification scheme that exploits remotely sensed imagery, infrastructural data available at a municipal level, and object-based spatial analysis. For effective planning and management, the hierarchical levels of landscape classification (level 1), the analysis of use and cover by urban zones (level 2), and the fundamental elements of land cover (level 3), each exposes different respects relevant to city plans and management.

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“…In recent decades, with the rapid urbanization of China and the intensification of agricultural modernization in urban built-up areas, cities have undergone unlimited expansion, and changes have also occurred [1], [2]. Urban built-up areas are the carrier of all urban activities, the main gathering areas for the population and economic activities, and a prominent manifestation of urbanization [3], [4].…”

Section: Introductionmentioning

confidence: 99%

The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China

2021

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With the urban built-up area becoming one of the most prominent forms of urban expansion, accurately extracting the urban built-up area is becoming more and more important to judge the urbanization process and evaluate the urban environment. Since it is difficult to significantly improve the accuracy of single satellite data in the extraction of urban built-up areas, this study proposes a method integrating POI (Point of Interest) data and Luojia-1A data to improve the extraction accuracy of urban built-up areas. In this study, integrated Density Graph, OSTU and geometric mean are used to extract urban built-up areas respectively. The highest precision of urban built-up areas extracted before data integration is 74.3% with the highest Kappa value of 0.54; while the highest precision of urban built-up area extracted after data integration is 91.4%, with the highest Kappa value of 0.91. Therefore, it can be concluded that compared with the existing widely used night-light-based methods, this method can integrate the advantages of POI data and Luojia-1A data, that is, it can not only solve the long-term oversaturation phenomenon of night light data, but also can extract urban built-up areas in a more refined way. The method used in this study, which integrates POI data and Luojia-1A data, can not only provide a new method for urban built-up area extraction, but also can play an active guiding role in urban planning and construction.

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Urban expansion in Shenzhen since 1970s: A retrospect of change from a village to a megacity from the space

Cited by 40 publications

References 44 publications

Extraction of urban built-up area based on the fusion of night-time light data and point of interest data

Extraction of urban built-up area based on the fusion of night-time light data and point of interest data

Integrating structure and function: mapping the hierarchical spatial heterogeneity of urban landscapes

The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China

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