Understanding the Diversity of Urban–Rural Fringe Development in a Fast Urbanizing Region of China

Li, Guoyu; Cao, Yu; He, Zhichao; Ju, He; Wang, Jiayi; Fang, Xiaoqian

doi:10.3390/rs13122373

Cited by 28 publications

(18 citation statements)

References 49 publications

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“…Household registers and land management are the basis of the urban-rural dual system in China. Under the premise of maintaining the stability of the system, the rural-urban fringe played an important role in promoting the development of the urban economy [22]. Specifically, the rural collective land in the rural-urban fringe became the main source of urban construction at a low transaction price, and the floating population living there provided abundant labor resources for the city [2].…”

Section: Literature Review 21 Land Use In the Rural-urban Fringementioning

confidence: 99%

“…In addition to the traditional PLES, some land types have multiple Production-Living-Ecological Functions (PLEF), introducing difficulties into the research around PLES [43]. As a result, the concept of "compound functional space" was introduced, and space types, such as "production-ecological space, semi-production space, and weak ecological space" were derived, forming a variety of classification standards for PLES [22]. There are two main methods to identify and divide PLES, namely, the land use type combination and index system method.…”

Section: Ples and Land Usementioning

confidence: 99%

“…In this paper, the land use type combination was chosen to depict the PLES. First, combined with the characteristics of both agricultural and non-agricultural production in the rural-urban fringe, the traditional PLEF was divided into four functions: "Living, ecological, agricultural production, and non-agricultural production" [22]. Second, one land type usually had a variety of PLEFs, especially in China's rural-urban fringe, where land use had a more evident mixed feature in the PLEF.…”

Section: Ples Classificationmentioning

confidence: 99%

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Land Use Change and Its Driving Factors in the Rural–Urban Fringe of Beijing: A Production–Living–Ecological Perspective

Chen

Zhang

Liang

et al. 2022

Land

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Production–Living–Ecological Space (PLES) is a useful tool to identify land use status patterns and optimize land resource allocation. In this study, the spatial econometric model was chosen to analyze the driving factors of land use change in Chaoyang District, part of the rural–urban fringe in Beijing, from the perspective of PLES evolution, from 2005 to 2020. The results showed the following: (1) Production Space (PS) to Living-Non-Farm Production Space (LNPS) has been the most significant conversion process of PLES since 2005, making LNPS the PLES type with the highest proportion in the study area. (2) With the spatial order from near-to-far from the city center, the scale of PS was reduced and concentrated, Ecological Space (ES) was formed in a green belt at the periphery of Beijing, Eco-Agricultural Production Space (EAPS) and Living-Agricultural Production Space were rapidly reduced, and LNPS was rapidly expanded in the point-line-plane order. (3) The PS to LNPS conversion was mainly driven by economic development and industrial structure upgrades, while the PS to ES conversion was mainly due to the distribution of population density and also industrial structures. The conversion of EAPS to LNPS was driven by the increase of the urbanization rate and economic growth. This study confirmed the policy-driven effect of the conversion from PS to ES. Due to the “Concentric Circle” spatial structure of Beijing, the conversion of PLES is generally related to the distance from the city center.

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Section: Literature Review 21 Land Use In the Rural-urban Fringementioning

confidence: 99%

Section: Ples and Land Usementioning

confidence: 99%

Section: Ples Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Land Use Change and Its Driving Factors in the Rural–Urban Fringe of Beijing: A Production–Living–Ecological Perspective

Chen

Zhang

Liang

et al. 2022

Land

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“…The leading landscape patterns in the Nanjing urban center area also include landscape pattern indexes for cultivated lands. In this subregion, cultivated lands are mainly distributed along the edges, with small areas and scattered overall distribution (Li et al, 2021). The flood vulnerability of the cultivated land patches in this region is mostly caused by the disorderly expansion of urban built-up areas and the encroachment of built-up areas on cultivated lands and the surrounding green spaces.…”

Section: Leading Landscape Patterns In the Urban Center Areamentioning

confidence: 99%

Combining catastrophe technique and regression analysis to deduce leading landscape patterns for regional flood vulnerability: A case study of Nanjing, China

2022

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Urbanization leads to changes in land use, and the expansion of impervious surfaces leads to an increase in flood vulnerability. Predicting and analyzing these landscape pattern changes are important in the early stages of urban planning. In practice, the threshold for obtaining comprehensive and detailed hydrological and meteorological data is high, which makes it difficult for landscape and urban planners to quickly evaluate urban floods. To compensate for these trends, we took Nanjing, China, as the study site and discussed the leading flood vulnerability landscape patterns based on quantitative assessments. We introduced catastrophe theory to integrate three indicators and seven subfactors for flood vulnerability assessment: exposure, including precipitation; sensitivity, including elevation, slope, soil and drainage density; and adaptability, including land use and forest coverage. Then, we calculated the landscape pattern metrics (shape index, fractal dimension index, related circumscribing circle, contiguity index and landscape division index) at the class level. Finally, we divided the city into four subregions, established regression models for the subregions and the whole city, and deduced the leading flood vulnerability landscape patterns in each region and the whole city. We found that the leading landscape patterns varied among different regions. According to the research results, the landscape pattern indexes identified in this paper can be interpreted intuitively, which can provide a reference for modifying the planning layout of regional green infrastructure, optimizing the vulnerability of urban floods, and providing a basis for further improving Nanjing urban planning and alleviating the urban flood vulnerability. The methods proposed herein also will benefit land use and green infrastructure management in other regions lacking meteorological and hydrological data.

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“…With the proposal and intensively promotion of strategy for rural revitalization and strategy for urban finer governance, the importance of rural area is becoming more and more obvious. While the accurate identification of urban-rural area is the chief precise to achieve the finer governance of urban area [10,11].…”

Section: Introductionmentioning

confidence: 99%

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Chen

Deng

2022

IEEE Access

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The spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light (NTL) data to identify urban and rural areas, which is likely to have an impact on the identification results due to the large brightness difference of lights. Therefore, based on NTL data and combine with data level fusion algorithm, this study separately fuses point of interest (POI) data that representing the quantity distribution of urban infrastructure and Baidu migration big (BM)data that representing the change relationship of regional population mobility to identify urban and rural areas by using deep learning method. The results show that the highest accuracy of urban-rural spatial identification with single NTL data is 84.32% and kappa is 0.6952, while the highest accuracy identified by data fusion is 95.02% and kappa is 0.8259. It can be seen that the differences caused by light brightness are effectively corrected after data fusion, which greatly improves the accuracy of urban and rural spatial identification. By comparing the results of NTL data modified by different big data, this study analyzes and identifies the accuracy of urban and rural area by using deep learning method, which not only enriches the study of data fusion in urban area, but also provides a basis for analyzing regional urban-rural relations and urban-rural development. Therefore, this study is believed to have important practical value for the coordinated development of urban and rural areas.

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Understanding the Diversity of Urban–Rural Fringe Development in a Fast Urbanizing Region of China

Cited by 28 publications

References 49 publications

Land Use Change and Its Driving Factors in the Rural–Urban Fringe of Beijing: A Production–Living–Ecological Perspective

Land Use Change and Its Driving Factors in the Rural–Urban Fringe of Beijing: A Production–Living–Ecological Perspective

Combining catastrophe technique and regression analysis to deduce leading landscape patterns for regional flood vulnerability: A case study of Nanjing, China

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

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