Identifying Shrinking Cities with NPP-VIIRS Nightlight Data in China

Jiang, Zhuwu; Zhai, Weixin; Meng, Xiangfeng; Long, Ying

doi:10.1061/(asce)up.1943-5444.0000598

Cited by 49 publications

(23 citation statements)

References 40 publications

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“…However, since the Chinese economic reform, urban systems are complicated and consist of strongly interconnected parts, including human networks and their connections with buildings and the natural environment [60][61][62], which often spread across administrative boundaries. In addition, intensified by current rapid global urbanization, urban morphology and agglomeration of functional areas have undergone tremendous changes in the past few decades [63,64], and especially in China, this change is more obvious [65][66][67]. Therefore, there are inconsistencies between the administrative boundaries of traditional cities and the real central cities.…”

Section: City System In Chinamentioning

confidence: 99%

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Niu

Xia

Wang

et al. 2020

IJGI

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As the land use issue, caused by urban shrinkage in China, is becoming more and more prominent, research on urban shrinkage and expansion has become particularly challenging and urgent. Based on the points of interest (POI) data, this paper redefines the scope, quantity, and area of natural cities by using threshold methods, which accurately identify the shrinkage and expansion of cities in the Yellow River affected area using night light data in 2013 and 2018. The results show that: (1) there are 3130 natural cities (48,118.75 km2) in the Yellow River affected area, including 604 shrinking cities (8407.50 km2) and 2165 expanding cities (32,972.75 km2). (2) The spatial distributions of shrinking and expanding cities are quite different. The shrinking cities are mainly located in the upper Yellow River affected area, except for the administrative cities of Lanzhou and Yinchuan; the expanding cities are mainly distributed in the middle and lower Yellow River affected area, and the administrative cities of Lanzhou and Yinchuan. (3) Shrinking and expanding cities are typically smaller cities. The research results provide a quick data supported approach for regional urban planning and land use management, for when regional and central governments formulate the outlines of urban development monitoring and regional planning.

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Section: City System In Chinamentioning

confidence: 99%

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Niu

Xia

Wang

et al. 2020

IJGI

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“…Therefore, these two kinds of data are mainly applied in macroscopic large-scale studies such as those of urban agglomeration. As a result, the application of these two data in small-scale research of a single city is relatively inadequate [ 26 , 27 ]. In October 2018, Luojia-01, an experimental satellite launched by Wuhan University, began providing free NTL data with a spatial resolution of 130 m to researchers around the world.…”

Section: Introductionmentioning

confidence: 99%

Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development

Zhang

Yuan

Tan

et al. 2021

IJERPH

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As one of the most important methods for limiting urban sprawl, the accurate delineation of the urban–rural boundary not only promotes the intensive use of urban resources, but also helps to alleviate the urban issues caused by urban sprawl, realizing the intensive and healthy development of urban cities. Previous studies on delineating urban–rural boundaries were only based on the level of urban and rural development reflected by night-time light (NTL) data, ignoring the differences in the spatial development between urban and rural areas; so, the comprehensive consideration of NTL and point of interest (POI) data can help improve the accuracy of urban–rural boundary delineation. In this study, the NTL and POI data were fused using wavelet transform, and then the urban–rural boundary before and after data fusion was delineated by multiresolution segmentation. Finally, the delineation results were verified. The verification result shows that the accuracy of delineating the urban–rural boundary using only NTL data is 84.20%, and the Kappa value is 0.6549; the accuracy using the fusion of NTL and POI data on the basis of wavelet transform is 93.2%, and the Kappa value is 0.8132. Therefore, we concluded that the proposed method of using wavelet transform to fuse NTL and POI data considers the differences between urban and rural development, which significantly improves the accuracy of the delineation of urban–rural boundaries. Accurate delineation of urban–rural boundaries is helpful for optimizing internal spatial structure in both urban and rural areas, alleviating environmental problems resulting from urban development, assisting the formulation of development policies for urban and rural fringes, and promoting the intensive and healthy development of urban areas.

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“…In 36 European countries, 19.8% of cities greater than 5000 inhabitants underwent shrinkage between 1990 and 2010 [11]. Urban shrinkage is also becoming a challenge even in rapidly urbanising countries such as China [12,13] and India [14]. However, during the last three decades, it is the post-socialist Europe that has suffered the most from urban decline [15][16][17], both in terms of prevalence and severity of urban shrinkage, with Baltic states, Bulgaria and Romania reporting that more than 70% of their cities shrunk between 1990 and 2010 [11].…”

Section: Introductionmentioning

confidence: 99%

Patterns of Urban Shrinkage: A Systematic Analysis of Romanian Cities (1992–2020)

2021

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EU post-socialist countries are nowadays the epicenter of urban shrinkage, despite economic growth trajectories reported during the last decades. However, systematic assessments of urban shrinkage patterns for this part of the continent are surprisingly insufficiently addressed in the literature, and the relationship between urban demographic decline/growth and economic decline/growth is still to be understood. This paper first delivers a state-of-the-art of the peculiarities of urban shrinkage in East-Central EU countries. Secondly, it employs an analysis grid to assess severity, prevalence, persistence, speed and regional incidence of urban decline in Romania—one of the most affected post-socialist countries within the European Union. Thirdly, it explores the statistical association between urban shrinkage severity and economic growth, on one hand, and between urban shrinkage severity and municipality revenues, on the other. Results show that urban shrinkage is currently increasing in prevalence and severity among Romanian cities, thus continuing an alarming trend that started in 1990. Secondly, the results pinpoint a statistically significant association between demographic shrinkage, local economic output and municipalities’ own-source revenues. However, the size effects are rather weak, suggesting a more nuanced relationship between economic and demographic urban growth than that predicted by some theories of urban change.

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Identifying Shrinking Cities with NPP-VIIRS Nightlight Data in China

Cited by 49 publications

References 40 publications

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development

Patterns of Urban Shrinkage: A Systematic Analysis of Romanian Cities (1992–2020)

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