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

Li, Lin; Zhao, Kaixu; Wang, Xinyu; Zhao, Sidong; Liu, Xingguang; Li, Weiwei

doi:10.3390/land11030415

Cited by 37 publications

(14 citation statements)

References 147 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the GI and the CR values and the findings of available studies, this paper classifies spatial heterogeneity into three grades and concentration into six grades. According to the research protocols of scholars such as Zhao [100], Miyamoto [101], Bain [102], and Li [103] and the classification criteria of the United Nations Development Program, a classification criterion for spatial differentiation and concentration are proposed in this paper (Table 1). Created by the Boston Consulting Group in the 1970s, BCG is a method for analyzing and optimizing existing business portfolios that is mainly applied in the fields of business management and economics.…”

Section: Spatial Heterogeneity and Agglomeration Methods: CV Gi Cr Hhimentioning

confidence: 99%

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Zhang

Zhao

et al. 2022

Land

Self Cite

View full text Add to dashboard Cite

Making efforts to promote rationalized urban construction land change, distribution, allocation, and its performance is the core task of territory spatial planning and a complex issue that the government must face and solve. Based on the Boston Consulting Group matrix, a decoupling model, and a GIS tool, this paper constructs a new tool that integrates “dynamic analysis + performance evaluation + policy design” for urban construction land. We reached the following findings from an empirical study of Gansu, China: (1) Urban construction land shows diversified changes, where expansion is dominant and shrink cannot be ignored. (2) Most cities are in the non-ideal state of LH (Low-High) and LL (Low-Low), with a small number in the state of HH (High-High) and HL (High-Low). (3) Urban construction land change and population growth, economic development, and income increase are in a discordant relationship, mostly in strong negative decoupling and expansive negative decoupling. (4) The spatial heterogeneity of urban construction land change and its performance are at a high level, and they show a slow upward trend. Additionally, the cold and the hot spots show obvious spatial clustering characteristics, and the spatial pattern of different indexes is different to some extent. (5) It is suggested that in territory spatial planning Gansu should divide the space into four policy areas—incremental, inventory, a reduction development policy area, and a transformation leading policy area—to implement differentiated management policies and to form a new spatial governance system of “control by zoning and management by class.” The change of urban construction land, characterized by dynamics and complexity, is a direct mapping of the urban growth process. The new tools constructed in this paper will help to reveal the laws of urban development and to improve the accuracy of territory spatial planning in the new era. They are of great theoretical significance and practical value for promoting high-quality and sustainable urban development.

show abstract

Section: Spatial Heterogeneity and Agglomeration Methods: CV Gi Cr Hhimentioning

confidence: 99%

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Zhang

Zhao

et al. 2022

Land

Self Cite

View full text Add to dashboard Cite

show abstract

“…It should be pointed out that currently, scientific research uses different indexes of defining the concentration, including Gini [44,45], entropy [46], localization and Theil [47]. But empirical testing reveals that all concentration indexes have strong correlation [48].…”

Section: Dependent Variablementioning

confidence: 99%

“…The novelty of this study is the combination of the assessment of the significance of models with the control of spatial effects, which accumulate in connection with the "reduction" of the research level to local. To achieve this, firstly, we control the significance factor of the region (Model 4 and 5), and additionally evaluate the spatial autocorrelation of residues based on the global Moran index I [45,54].…”

Section: Models and Estimationmentioning

confidence: 99%

Econometric Modeling of Creative Industries Concentration Process in the Siberian and the Urals Single-Industry Towns

Antonova,

Pchelintsev

2023

Mathematics

View full text Add to dashboard Cite

Creative industry is considered the driver of modern urban development. It raises the new wave of issues of re-industrialization policy in single-industry towns. Nevertheless, the algorithms of current mathematical modeling in regional economies are not complex enough, leaving out spatial errors and variety in models used. We present eight steps of econometric analysis, considering local-level data. For the research, a balanced data panel was formed for 38 single-industry towns in Siberia and the Urals in Russia, for the period of 2013–2017. For mathematical modeling of the process of concentration of creative industries in single-industry towns, first, we present the specific indices of concentration, variety, and spatial disparities. Then, we test the full list of possible models for the variables. Then, we evaluate the regional offset. We describe the sufficient F-test, Hausman test and Breusch–Pagan Lagrange multiplier tests, choosing the most appropriate model. Finally, we evaluate the spatial autorepression of residuals. This algorithm allows us prove the data period and identify the tendency of spatial heterogeneity growth. We assume it to be the growing spillover effect in creative industries. At the same time, despite the positive trend of decreasing concentration of creative industries in single-industry towns, mono-industry continues to have a meaningful impact on their development, which forms the basis of path dependence. In this regard, the main actor of development in towns is city-forming enterprise, through the tools of corporate social responsibility. In view of the latter, it is proposed to develop tools for corporate creative responsibility in single-industry organizations within cities and regions. Finally, the general concern about the growth of spatial differentiation at the level of cities and regions is not yet significant.

show abstract

“…Of the economic factors, GDP represents the impact of the size of the economy [78]. Per-capita GDP represents the impact of the stage and quality of economic development [79,80] and fiscal self-sufficiency rate represents the government's ability to intervene in the economy [81,82]. In terms of natural factors, topography represents the impact of topographic complexity [83], average temperature represents the impact of climate change, especially the urban heat island effect [84], and ventilation coefficient represents the impact of regional wind environment and urban air quality [85,86].…”

Section: Machine Learning Regression (Mlr)mentioning

confidence: 99%

Evolution Model, Mechanism, and Performance of Urban Park Green Areas in the Grand Canal of China

Cai,

Zhao,

Huang

et al. 2023

Land

Self Cite

View full text Add to dashboard Cite

Urban park green areas are part of territorial space planning, shouldering the mission of providing residents with high-quality ecological products and public space. Using a combination of several measurement models such as the BCG (Boston Consulting Group) matrix, ESDA (Exploratory Spatial Data Analysis), MLR (Machine Learning Regression), GWR (Geographically Weighted Regression), and GeoDetector, this paper presents an empirical study on the changes in Urban Park Green Areas (UPGAs) in the Grand Canal of China. By quantitatively measuring the spatio–temporal evolution patterns of UPGAs, this study reveals the driving mechanisms behind them and proposes policy recommendations for planning and management based on performance evaluation. The spatio–temporal evolution of UPGAs and their performance in China’s Grand Canal are characterized by significant spatial heterogeneity and correlation, with diversified development patterns such as HH (High-scale–High-growth), HL (High-scale–Low-growth), LH (Low-scale–High-growth), and LL (Low-scale–Low-growth) emerging. The evolution performance is dominated by positive oversupply and positive equilibrium, where undersupply coexists with oversupply. Therefore, this paper recommends the implementation of a zoning strategy in the future spatial planning of ecological green areas, urban parks, and green infrastructure. It is also recommended to design differentiated construction strategies and management policies for each zoning area, while promoting inter-city mutual cooperation in the joint preparation and implementation of integrated symbiosis planning. Furthermore, the spatio–temporal evolution of the UPGAs in the Grand Canal of China is influenced by many factors with very complex dynamic mechanisms, and there are significant differences in the nature, intensity, spatial effects, and interaction effects between different factors. Therefore, in the future management of ecological green areas, urban parks, and green infrastructure, it is necessary to interconnect policies to enhance their synergies in population, aging, industry and economy, and ecological civilization to maximize the policy performance.

show abstract

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

Cited by 37 publications

References 147 publications

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Econometric Modeling of Creative Industries Concentration Process in the Siberian and the Urals Single-Industry Towns

Evolution Model, Mechanism, and Performance of Urban Park Green Areas in the Grand Canal of China

Contact Info

Product

Resources

About