Spatial and Temporal Pattern of Urban Smart Development in China and Its Driving Mechanism

Chen, Xiaohong; Wei, Luyao; Zhang, Haifeng

doi:10.1007/s11769-018-0976-0

Cited by 17 publications

(15 citation statements)

References 42 publications

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“…e spatial expansion of the industrial land is essentially a process of urban restructuring. A broad array of studies have been carried out in recent years, mainly focusing on the spatiotemporal changes [34][35][36]. Moreover, studies on driving forces have gradually increased attention.…”

Section: Analytical Framework For the Influencing Factors Of Urban Industrial Land Expansionmentioning

confidence: 99%

Urban Industrial Land Expansion and Its Influencing Factors in Shunde: 1995–2017

Xiong

Niu

2020

Complexity

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The change in the industrial land is of great significance to the sustainable development of cities. However, scholars have done relatively little research on this subject, especially on the urban industrial land expansion process and its influencing factors. This article selects Shunde, a typical Chinese industrial city of the Guangdong province, using remote sensing interpretation to analyze the spatiotemporal evolution of industrial land expansion from 1995 to 2017 and applies the multiple regression model to analyze the influencing factors. The main conclusions are as follows: the industrial land in Shunde has experienced the development trend of “slow expansion-rapid expansion-slow expansion,” and the “fragmentation” of the industrial land space is still prominent. Decentralization, marketization, capital, and labor force have passed the significance test of the model, which are important factors influencing the expansion of the industrial land in Shunde. Among them, decentralization is the primary factor, while marketization has the greatest impact on industrial land expansion in Shunde. The influence of globalization and technical progress is not significant.

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Section: Analytical Framework For the Influencing Factors Of Urban Industrial Land Expansionmentioning

confidence: 99%

Urban Industrial Land Expansion and Its Influencing Factors in Shunde: 1995–2017

Xiong

Niu

2020

Complexity

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“…Sufficient food supply is the foundation of human survival, social stability and economic development (Zheng et al, 2016). Meantime, rice is an important source of agricultural carbon emissions, which releases carbon in the form of greenhouse gases such as CO 2 , CH 4 , and N 2 O during its growth (Chen and Pan, 2022;Lin et al, 2022). The development of low-carbon agriculture is an important measure to achieve the goal of comprehensive energy conservation and emission reduction (Lin et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar

Wang²,

Sun³

et al. 2023

Front. Ecol. Evol.

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Accurate mapping of rice-growing areas is essential to ascertain the spatial distribution of rice fields, and ensure food security. It is a challenging task to timely and accurate identify rice under the complex terrain due to its diversified land cover, small- or middle-sized rice fields with fragmented distribution. In this paper, the time series VV and VH backscatter coefficient datasets were first constructed based on 411 sentinel-1 synthetic aperture radar (SAR) images in Chongqing city with complex terrain. Then, the rice multi-characteristic parameters, including SAR backscatter features, composite features, rice phenological parameters, texture features and topographic features, were generated. On this basis, the homogeneous image objects were produced. Furthermore, a rice identification algorithm combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization SAR (MPHO-DPSAR) was established. The research demonstrated that the MPHO-DPSAR algorithm can achieve accurate mapping of small and medium-sized and fragmented rice fields in regions under complex terrain according to the accuracy evaluation at three levels and the comparison with other three classical rice identification methods. The suitability and limitations of proposed MPHO-DPSAR algorithm were also discussed from the aspects of SAR data temporal and spatial resolution, rice phenology, and surface landscape complexity.

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“…This plan was concerned with upgrading the industrial structure, transforming the economic system, resolving the issue of employment growth, promoting the development of regional economic integration [38], enabling sustainable development of rural agriculture in the three northeastern provinces [39], and accelerating urbanization [40]. In addition, as a result of the national revitalization strategy and other policies, more and more scholars were able to shift their focus on the highquality development of Northeast China [41][42][43][44][45]. In response to the national high-quality development strategy, Northeast China is facing huge challenges and opportunities, thus contributing to the study of spatial quality in Northeast China.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Characteristics and Influencing Factors of Urban Spatial Quality in Northeast China Based on DMSP-OLS and NPP-VIIRS Nighttime Light Data

et al. 2022

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The quality of urban spaces is a pivotal part of high-quality spatial development. It is directly connected to the comprehensive, coordinated and sustainable development of a region. In recent years, Northeast China has characterized urban space contraction and development. To study the quality of urban space in Northeast China, this paper fitted the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data with 11 indicators related to high-quality urban development for the period 1992–2018. The feasibility of nighttime light data reflecting urban spatial quality was verified by a linear equation, and the temporal characteristics of urban spatial quality in Northeast China were obtained. The Exploratory Spatial Data Analysis Geographically and Temporally Weighted Regression (ESDA-GTWR) explores the spatial relevance and possible influencing factors of this kind of development. The results suggest that the overall trend of spatial quality in the three northeastern provinces is “initial slow growth and significantly weakened after”. The fast developing cities include Panjin, Liaoyang, Shenyang, and Dalian in the Liaoning Province. On the other hand, cities such as Heihe and Yichun in the Heilongjiang Province have relatively slow development speeds. Furthermore, the spatial quality development in the three northeastern provinces exhibits a trend of continuous concentration. The cities with high spatial qualities are concentrated near the Liaoning Province, with low spatial qualities in the north and high spatial qualities in the southern parts of the three provinces. As there is a notable gap between the northern and the southern regions, the central region represents an area in partial transition. The spatial quality of each city in the three northeastern provinces is the result of a number of intertwined factors, with significant differences in the degree of their influence. The significant degree of influence factors on spatial quality from higher to lower is urbanization, quality of life, rural revitalization, government promotion, and infrastructure.

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Spatial and Temporal Pattern of Urban Smart Development in China and Its Driving Mechanism

Cited by 17 publications

References 42 publications

Urban Industrial Land Expansion and Its Influencing Factors in Shunde: 1995–2017

Urban Industrial Land Expansion and Its Influencing Factors in Shunde: 1995–2017

A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar

Spatio-Temporal Characteristics and Influencing Factors of Urban Spatial Quality in Northeast China Based on DMSP-OLS and NPP-VIIRS Nighttime Light Data

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