Spatiotemporal Evolution of Multiscale Urbanization Level in the Beijing-Tianjin-Hebei Region Using the Integration of DMSP/OLS and NPP/VIIRS Night Light Datasets

Wang, Jingtao; Liu, Haibin; Liu, Hao; Huang, Hui

doi:10.3390/su13042000

Cited by 12 publications

(13 citation statements)

References 43 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research methods adopted in existing literature mainly include regression models, exploratory spatial analysis, system dynamics, and cellular automata, primarily used to analyze the current urbanization characteristics, trends, influence factors, countermeasures, and proposals [88][89][90]. Rabehi [91] predicted the level of urbanization of the Bay of Algiers, Algeria, by 2030 using a meta-automata model based on luminescence data.…”

Section: Methodsmentioning

confidence: 99%

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

Zhao

Wang

et al. 2022

Land

View full text Add to dashboard Cite

Urbanization has an abundant connotation in dimensions such as population, economy, land, and society and is an important sign to measure regional economic development and social progress. The use of Night Light Data from remote sensing satellites as a proxy variable can significantly improve the accuracy and comprehensiveness of the measurement of urbanization development dynamics. Based on the Night Light Data and statistical data from 2015 to 2019, this paper quantitatively analyzes the spatio-temporal evolution pattern of urbanization in Guangxi and its driving mechanism using exploratory time-space data analysis, GeoDetector and Matrix: Boston Consulting Group, providing an important basis for sustainable urban development planning and scientific decision-making by the government. The findings show that (1) there is a high level of spatial heterogeneity and spatial autocorrelation of urbanization in Guangxi, with the Gini index of urban night light index and urban night light expansion vitality index always greater than 0.5, the global Moran’s I greater than 0.17, the spatial differentiation converging but the spatial correlation increasing. (2) The spatial pattern of urbanization in Guangxi has long been solidified, but there is a differentiation in urban development trend, with the coexistence of urban expansion and shrinkage, requiring differentiated policy design for urban governance. (3) The development and evolution of urbanization in Guangxi present a complex intertwined dynamic mechanism of action, with interaction effects of bifactor enhancement and non-linear enhancement among factors. It should be noted that the influence of factors varies greatly, with the added value of the tertiary industry, gross domestic product, total retail sales of social consumer goods having the strongest direct effect on the urban night light index, while the added value of secondary industry, per capita GDP, gross domestic product having the strongest direct effect on the urban night light expansion vitality index. All of them are key factors, followed by some significant influence factors such as government revenue, population urbanization rate, per government revenue, population urbanization rate, per capita disposable income of urban and rural residents that should not be ignored, and the rest that play indirect roles mainly by interaction.

show abstract

Section: Methodsmentioning

confidence: 99%

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

Zhao

Wang

et al. 2022

Land

View full text Add to dashboard Cite

show abstract

“…The panel regression between the composite index and the average nighttime light after fusion showed a strong correlation. The accuracy test shows that the estimated value of the fusion average light calculated by the urbanization level estimation model fully represents the urbanization level (Wang et al, 2021). Jeff (2014) improved SOM algorithm and applied it to pattern classification of regional economic assessment.…”

Section: Related Workmentioning

confidence: 99%

Modeling and analysis of factors affecting the integration of Beijing Tianjin Hebei low carbon economy based on SDM model

Zhu

Wang

2023

Front. Energy Res.

View full text Add to dashboard Cite

Background: Under the guidance of carbon peak and carbon neutral targets, China’s economic development is facing unprecedented challenges. It is of great strategic significance and value to study the integrated development of regional low-carbon economy. The harmonious development of Beijing-Tianjin-Hebei (Beijing-Tianjin-Hebei) has become a national strategy, and its importance is prominent.Objective: To promote the regional economic integration of Beijing-Tianjin-Hebei is the top priority of promoting the development of Beijing-Tianjin-Hebei integration at present, and is the key to achieving the coordinated development and mutual benefit of Beijing-Tianjin-Hebei.Methods: In this context, this paper studies the fuzzy control evaluation algorithm of regional economy based on SDM model. Realized the prediction of the expansion of the low-carbon economy in Beijing-Tianjin-Hebei, and put forward the development strategy of the economic integration of Beijing-Tianjin-Hebei. The ranking of regional competitiveness of fuzzy control evaluation method is in line with the reality of today’s economic expansion. The application of fuzzy rules improves the speed, accuracy and objectivity of evaluation.Results: The final experiment shows that the accuracy of the algorithm is very high, reaching 95.14%. In addition, the algorithm has higher efficiency and better performance. The fuzzy control evaluation algorithm of regional economy can better predict the economic growth of Beijing, Tianjin and Hebei.Conclusion: Propose the development strategy of Beijing-Tianjin-Hebei low-carbon economic integration to further promote the development of Beijing-Tianjin-Hebei integration and promote the adjustment and optimization of China’s economic structure.

show abstract

“…In the existing NTL studies, most of them chose either the DMSP-OLS or NPP/VIIRS data [16,17] for research, or made a comparative study of the two [17]. However, there were few studies on the fusion of the two NTL data [19].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Global remote-sensing image data, especially DMSP-OLS (Defense Meteorological Satellite Program's Operational Linescan System) [16] and NPP/VIIRS (National Polar-orbiting Partnership and Visible Infrared Imaging Radiometer Suite) [17], have the advantages of objectivity, good economy, strong timeliness, large continuous time span, and wide space coverage. It is widely used in the field of spatial estimation of social and economic [18] indicators, urbanization process research [19] and other fields. The surface light intensity information recorded by the NTL data more directly reflects the difference in human activities [20].…”

Section: Introductionmentioning

confidence: 99%

The County-Scale Economic Spatial Pattern and Influencing Factors of Seven Urban Agglomerations in the Yellow River Basin—A Study Based on the Integrated Nighttime Light Data

et al. 2021

Self Cite

View full text Add to dashboard Cite

The integrated night light (NTL) datasets were used to represent the economic development level, and visual analysis was carried out on the evolution characteristics of the economic spatial pattern of various urban agglomerations in the Yellow River Basin (YRB), at a county-scale, in 1992, 2005, and 2018. The Global Moran’s I and the local Getis-Ord G methods were used to explore the overall spatial correlation and local cold–hot spot of economic development levels, respectively. The spatial heterogeneity of the influence of relevant factors on the economic development level at the municipal scale was analyzed by using the multi-scale geographically weighted regression (MGWR) model. The results show that the county-level economic spatial pattern of urban agglomeration in the YRB has an obvious “pyramid” characteristic. The hot spots are concentrated in the hinterland of the Guanzhong Plain, the Central Plains, and the Shandong Peninsula urban agglomeration. The cold spots are concentrated in the junction of urban agglomerations, and the characteristics of “cold in the west and hot in the east” are obvious. Labor input and import and exporthave a positive impact on the economic development level for each urban agglomeration, government force has a negative impact, and education shows both positive and negative polarization on economic development.

show abstract

Spatiotemporal Evolution of Multiscale Urbanization Level in the Beijing-Tianjin-Hebei Region Using the Integration of DMSP/OLS and NPP/VIIRS Night Light Datasets

Cited by 12 publications

References 43 publications

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

Modeling and analysis of factors affecting the integration of Beijing Tianjin Hebei low carbon economy based on SDM model

The County-Scale Economic Spatial Pattern and Influencing Factors of Seven Urban Agglomerations in the Yellow River Basin—A Study Based on the Integrated Nighttime Light Data

Contact Info

Product

Resources

About