Do Urban Agglomeration Effects and Household Amenities have a Skill Bias?*

The article provides an analysis of the agglomeration processes within a large territorial socio- economic system of Eastern Siberia - the Irkutsk urban agglomeration (IGA). Agglomeration processes are considered to be prerequisites of agglomeration effects, which are usually divided into two types: localisation and urbanisation effects. The evaluation of agglomeration effects is based on a regression analysis, where the effect is interpreted as a dependent variable, while agglomeration processes are regarded as the parameters that affect the intensity of agglomeration. They can be measured using a set of indices. The parameters of localisation have been estimated using the Moran index (territorial clusters have been identified), and the parameters of urbanisation effects - using the Shannon index.An assessment of the diversity of economic activity was carried out both for the territorial units composing the IGA, and for its separate sectors of the economy. Since different territorial units have a different composition of industries, the assessment of industry diversity was carried out using the normalised analogue of the Shannon index - the Pielu index. Based on the calculations, it can be concluded that the strongest agglomeration associated with localisation effects will be achieved in the urbanised IGA zone. However, the peripheral zone is to participate in the inter-district exchange of labour and other economic resources. Thus, agglomerationeffectsbecomethenecessaryprerequisitesforthefurtherintensificationofeconomic ties as a result of an increase in the number of new jobs and the diversification of the sectoral structure.

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Urban Agglomeration and CEO Pay

SSRN Electronic Journal ◽

10.2139/ssrn.1108757 ◽

2008 ◽

Cited By ~ 2

Author(s):

Bill B. Francis ◽

Kose John ◽

Iftekhar Hasan ◽

Maya Waisman

Keyword(s):

Urban Agglomeration ◽

Ceo Pay

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Research on Logistics Integration of Yangtze River Delta Urban Agglomeration

2020 International Conference on Urban Engineering and Management Science (ICUEMS) ◽

10.1109/icuems50872.2020.00012 ◽

2020 ◽

Author(s):

DaChuan Ding

Keyword(s):

Yangtze River ◽

Yangtze River Delta ◽

River Delta ◽

Urban Agglomeration

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Analysis on the Spatial Structure of Tourism Economy of Chengdu Chongqing Urban Agglomeration

2020 The 4th International Conference on E-commerce, E-Business and E-Government ◽

10.1145/3409929.3416791 ◽

2020 ◽

Author(s):

FENG XIAOBING

Keyword(s):

Spatial Structure ◽

Urban Agglomeration ◽

Tourism Economy

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The Spatio-Temporal Analysis of Urban-Rural Coordinated Development and Its Driving Forces in Yangtze River Delta

Land ◽

10.3390/land10050495 ◽

2021 ◽

Vol 10 (5) ◽

pp. 495

Author(s):

Daizhong Tang ◽

Mengyuan Mao ◽

Jiangang Shi ◽

Wenwen Hua

Keyword(s):

Influencing Factors ◽

Yangtze River ◽

Driving Forces ◽

Yangtze River Delta ◽

River Delta ◽

Urban Agglomeration ◽

System Level ◽

Actual State ◽

Coordinated Development ◽

Urban Rural

This paper conducts an analytical study on the urban-rural coordinated development (URCD) in the Yangtze River Delta urban agglomeration (YRDUA), and uses data from 2000–2015 of 27 central cities to study the spatial and temporal evolution patterns of URCD and to discover the influencing factors and driving forces behind it through PCA, ESDA and spatial regression models. It reveals that URCD of the YRDUA shows an obvious club convergence phenomenon during the research duration. The regions with high-level URCD gather mainly in the central part of the urban agglomeration, while the remaining regions mostly have low-level URCD, reflecting the regional aggregation phenomenon of spatial divergence. At the same time, we split URCD into efficiency and equity: urban-rural efficient development (URED) also exhibits similar spatiotemporal evolution patterns, but the patterns of urban-rural balanced development (URBD) show some variability. Finally, by analyzing the driving forces in major years during 2000–2015, it can be concluded that: (i) In recent years, influencing factors such as government financial input and consumption no longer play the main driving role. (ii) Influencing factors such as industrialization degree, fixed asset investment and foreign investment even limit URCD in some years. The above results also show that the government should redesign at the system level to give full play to the contributing factors depending on the actual state of development in different regions and promote the coordinated development of urban and rural areas. The results of this study show that the idea of measuring URCD from two dimensions of efficiency and equity is practical and feasible, and the spatial econometric model can reveal the spatial distribution heterogeneity and time evolution characteristics of regional development, which can provide useful insights for urban-rural integration development of other countries and regions.

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The Gradient Effect on the Relationship between the Underlying Factor and Land Surface Temperature in Large Urbanized Region

Land ◽

10.3390/land10010020 ◽

2020 ◽

Vol 10 (1) ◽

pp. 20

Author(s):

Yixu Wang ◽

Mingxue Xu ◽

Jun Li ◽

Nan Jiang ◽

Dongchuan Wang ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Landscape Planning ◽

Potential Gradient ◽

Surface Characteristics ◽

Ordinary Least Squares ◽

Urban Agglomeration ◽

Spatial Gradient ◽

Gradient Effect

Although research relating to the urban heat island (UHI) phenomenon has been significantly increasing in recent years, there is still a lack of a continuous and clear recognition of the potential gradient effect on the UHI—landscape relationship within large urbanized regions. In this study, we chose the Beijing-Tianjin-Hebei (BTH) region, which is a large scaled urban agglomeration in China, as the case study area. We examined the causal relationship between the LST variation and underlying surface characteristics using multi-temporal land cover and summer average land surface temperature (LST) data as the analyzed variables. This study then further discussed the modeling performance when quantifying their relationship from a spatial gradient perspective (the grid size ranged from 6 to 24 km), by comparing the ordinary least squares (OLS) and geographically weighted regression (GWR) methods. The results indicate that: (1) both the OLS and GWR analysis confirmed that the composition of built-up land contributes as an essential factor that is responsible for the UHI phenomenon in a large urban agglomeration region; (2) for the OLS, the modeled relationship between the LST and its drive factor showed a significant spatial gradient effect, changing with different spatial analysis grids; and, (3) in contrast, using the GWR model revealed a considerably robust and better performance for accommodating the spatial non-stationarity with a lower scale dependence than that of the OLS model. This study highlights the significant spatial heterogeneity that is related to the UHI effect in large-extent urban agglomeration areas, and it suggests that the potential gradient effect and uncertainty induced by different spatial scale and methodology usage should be considered when modeling the UHI effect with urbanization. This would supplement current UHI study and be beneficial for deepening the cognition and enlightenment of landscape planning for UHI regulation.

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