Spatiotemporal evolution and driving mechanism of regional shrinkage at the county scale: The three provinces in northeastern China

Yu, Shangkun; Wang, Chengxin; Jin, Zhenxing; Zhang, Shuai; Miao, Yi

doi:10.1371/journal.pone.0271909

Cited by 5 publications

(5 citation statements)

References 31 publications

(28 reference statements)

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“…The method of calculating populationrelated shrinkage applies to both economy-related shrinkage and space-related shrinkage. In accordance with the grading of the shrinkage index in existing research [44,45] and the actual situation of the study area, the urban shrinkage in the three provinces of Northeast China was classified as severe shrinkage (S < −0.2), moderate shrinkage (−0.2 ≤ S < −0.1), mild shrinkage (−0.1 ≤ S < −0.05), and slight shrinkage (−0.05 ≤ S < 0).…”

Section: Shrinkage Modelmentioning

confidence: 63%

Spatiotemporal Evolution of Urban Shrinkage and Its Impact on Urban Resilience in Three Provinces of Northeast China

Wang

Zhang

et al. 2023

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Currently, Chinese cities are experiencing both overall growth and localized shrinkage. Therefore, it becomes crucial to quantify urban shrinkage and explore the transformation and sustainable development of shrinking cities from the perspective of urban resilience. This study focuses on the three provinces of Northeast China, which are representative areas of urban shrinkage, as its research subjects. Employing the analytic hierarchy process, a comprehensive evaluation system for urban shrinkage is constructed based on three dimensions: population, economy, and space. Furthermore, urban resilience is scientifically measured from four aspects: economy, society, ecology, and infrastructure. The study analyzes the spatiotemporal evolution characteristics of urban shrinkage and urban resilience in the three northeastern provinces from 2012 to 2018. It also examines the impact of urban shrinkage on urban resilience through regression analysis and mediation models. The results indicate the following: (1) Half of the cities in the three northeastern provinces experienced shrinkage, although the extent of shrinkage decreased with the implementation of the Northeast China revitalization strategy. Population-related shrinkage was the most extensive and continued to expand, while economy-related shrinkage was the most severe, and space-related shrinkage was the least severe. (2) The resilience of shrinking cities was lower than the average level. Population-shrinking cities and economy-shrinking cities exhibited low levels of economic resilience, and the gap between them continued to widen. Space-shrinking cities generally had low infrastructure resilience. (3) The urban shrinkage index had a significant positive impact on the urban resilience index, mediated through intermediary variables, such as innovation capability and cultural development. Notably, both the direct and indirect effects of innovation capability were the greatest. Population-related shrinkage had the largest impact on urban resilience, while more intermediary variables of economy-related shrinkage passed the significance test.

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Section: Shrinkage Modelmentioning

confidence: 63%

Spatiotemporal Evolution of Urban Shrinkage and Its Impact on Urban Resilience in Three Provinces of Northeast China

Wang

Zhang

et al. 2023

Land

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“…Shanghai in 2020 has a high level of urban intensification and a high economic output of urban land because the dense population leads to these phenomena. Heilongjiang (12), Jilin (18), and Liaoning (19) in the TNPs were always in a low-intensive urban state, which may be the reason for the insufficient utilization of urban space due to population loss in these areas [52,53].…”

Section: Urbanization In Chinese Provincesmentioning

confidence: 99%

A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data

et al. 2023

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Physical urban boundaries (PUBs) are basic geographic information data for defining the spatial extent of urban landscapes with non-agricultural land and non-agricultural economic activities. Accurately mapping PUBs provides a spatiotemporal database for urban dynamic monitoring, territorial spatial planning, and ecological environment protection. However, traditional extraction methods often have problems, such as subjective parameter settings and inconsistent cartographic scales, making it difficult to identify PUBs objectively and accurately. To address these problems, we proposed a self-supervised learning approach for PUB extraction. First, we used nighttime light and OpenStreetMap road data to map the initial urban boundary for data preparation. Then, we designed a pretext task of self-supervised learning based on an unsupervised mutation detection algorithm to automatically mine supervised information in unlabeled data, which can avoid subjective human interference. Finally, a downstream task was designed as a supervised learning task in Google Earth Engine to classify urban and non-urban areas using impervious surface density and nighttime light data, which can solve the scale inconsistency problem. Based on the proposed method, we produced a 30 m resolution China PUB dataset containing six years (i.e., 1995, 2000, 2005, 2010, 2015, and 2020). Our PUBs show good agreement with existing products and accurately describe the spatial extent of urban areas, effectively distinguishing urban and non-urban areas. Moreover, we found that the gap between the national per capita GDP and the urban per capita GDP is gradually decreasing, but regional coordinated development and intensive development still need to be strengthened.

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“…The constraints on the development of small towns are intricate due to the increasing level of interference as the scale of settlement decreases. Factors such as natural resource endowment, population density, market accessibility, location conditions, and political and economic structures can significantly impact the development of small towns [ 32 , 33 ]. However, the driving mechanisms of STS in China and the West differ due to the varying stages of economic development.…”

Section: Literature Reviewmentioning

confidence: 99%

Why small towns are shrinking: The spatial heterogeneity of small towns shrinkage and the impact of it from the perspective of rural-urban interaction in China

Han,

Deng,

2023

PLoS ONE

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Small towns play a crucial role in bridging urban and rural territory systems. While numerous studies have identified the characteristics and causes of small town shrinkage (STS), there remains an unexplored perspective on the reasons for their shrinkage from the perspective of the rural-urban relationship. To address this research gap, we investigated the relationship between STS and rural-urban interaction (RUI) in China. We hypothesized that a negative relationship existed between the degree of STS and the intensity of RUI. Using geo-statistical methods, such as the multi-scale geographical weighted regression (MGWR) model, the hypothesis was tested using Henan Province in China as a case study. The results indicated that the phenomenon of STS was observed extensively across the study region, with a 59% geographical overlap between the high-value area of STS and the low-value area of urban-rural interaction. Three distinct sub-types of STS regions were identified: shrinking regions along geographical borders, shrinking regions adjacent to metropolitan areas, and shrinking regions in ecologically fragile areas. The factors influencing STS demonstrated spatial heterogeneity and multi-scale characteristics. The findings will improve our understanding of urban shrinkage from a multi-level perspective and offer policy makers guidance for the sustainable development of small towns based on local conditions.

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Spatiotemporal evolution and driving mechanism of regional shrinkage at the county scale: The three provinces in northeastern China

Cited by 5 publications

References 31 publications

Spatiotemporal Evolution of Urban Shrinkage and Its Impact on Urban Resilience in Three Provinces of Northeast China

Spatiotemporal Evolution of Urban Shrinkage and Its Impact on Urban Resilience in Three Provinces of Northeast China

A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data

Why small towns are shrinking: The spatial heterogeneity of small towns shrinkage and the impact of it from the perspective of rural-urban interaction in China

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