Measuring urban spatial interaction in Wuhan Urban Agglomeration, Central China: A spatially explicit approach

He, Jifeng; Chun, Li; Yu, Yan; Liu, Yanfang; Huang, Junlong

doi:10.1016/j.scs.2017.04.014

Cited by 87 publications

(40 citation statements)

References 36 publications

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“…However, traditional impact factor research is mostly based on correlation analysis, principal component analysis and regression analysis. Among these analyses, correlation analysis mostly explores the influence of single influence factors on the dependent variables in a time series [24], and the mechanism behind the spatial distribution characteristics is less studied; principal component analysis and regression analysis can comprehensively judge the influence of factors on the dependent variables, but they ignore the relationship between the impact factor and the spatial distribution of the dependent variable, cannot determine the influence of the interaction between the factors on the spatial differentiation characteristics of the dependent variable [25], and lack quantitative exploration. As a new method based on geospatial space to detect spatial differentiation and to reveal the driving force behind spatial differentiation, geographical detectors [26] can detect not only the intensity of interpretation of a certain influence factor on the spatial differentiation of the dependent variable but also the intensity of interpretation of the independent variables after the interaction of the factors on the spatial differentiation of the dependent variable.…”

Section: Introductionmentioning

confidence: 99%

Exploring Spatial and Temporal Connection Patterns among the Districts in Chongqing Based on Highway Passenger Flow

et al. 2019

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Investigating regional connections and their influencing factors from the perspective of "flow" space is one of the foundations of promoting regional development. In this article, the data we used includes actual highway passenger flow data, National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light remote sensing data, and socioeconomic data. We analyzed the spatial distribution pattern, connection intensity and spatial autocorrelation of highway passenger flow in Chongqing during the working day, weekend and May Day and revealed the influencing factors by means of a geographic detector. Three key conclusions resulted from this research. First, highway passenger flow in Chongqing districts exhibits spatial agglomeration that is clearly higher in western Chongqing than in eastern Chongqing and forms an obviously dual-core "star" structure, with the main urban area and Wanzhou serving as the core. Second, a factor detector notes that the nighttime light area index (0.9251, 0.9512, 0.9541) has the strongest explanatory power for the spatial differentiation of passenger traffic in Chongqing districts, which is the key factor. Third, interaction detection shows that the interaction between the two factors displays an enhancement effect at different times. The nighttime light area index shows the strongest explanatory power under the synergy of tourist attractions, which are 0.9850, 0.9903 and 0.9908. But the per capita GDP and highway mileage have the most obvious enhancement effect after interaction (0.9544, 0.9661, 0.9652). Therefore, in future planning and development, we should pay attention to cooperation and exchanges between districts and use the nighttime light area index as an important reference factor to provide a scientific basis for the construction of public transport and economic construction in Chongqing.

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Section: Introductionmentioning

confidence: 99%

Exploring Spatial and Temporal Connection Patterns among the Districts in Chongqing Based on Highway Passenger Flow

et al. 2019

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“…The spatial integration is a spatial and functional interaction process within and between cities of urban agglomeration and reflects the level of cooperation between cities [25]. Recently, remarkable achievements of interaction features have been made by methods in advancing the analysis of regional interaction [26,27]. But most of these methods simplify cities into points, thus ignoring the spatio-temporal changes of land use and landscape in the process of urban integration.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Spatial Integration Patterns of Urban Agglomerations along an Inter-City Gradient

Wan

Deng

et al. 2019

Sustainability

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Understanding the integration process of urban agglomeration is essential for sustainable regional development and urban planning. However, few studies have analyzed the spatial integration patterns of metropolitan regions according to the impacts of landscape ecology along rail transit corridors. This study performed a comprehensive inter-city gradient analysis using landscape metrics and radar charts in order to determine the integration characteristics of an urban agglomeration. Specifically, we analyzed the evolution of spatial heterogeneity and functional landscapes along gradient transects in the Changsha–Zhuzhou–Xiangtan (CZT) metropolitan region during the period of 1995–2015. Four landscape functional zones (urban center, urban area, urban–rural fringe, and green core) were identified based on a cluster analysis of landscape composition, connectivity, and fragmentation. The landscape metric NP/LPI (number of patches/largest patch index) was proposed to identify the urban–rural fringe, which revealed that the CZT region exhibited a more aggregated form, characterized by a single-core, continuous development, and the compression of green space. The integration of cities has resulted in continued compression and fragmentation of ecological space. Therefore, strategies for controlling urban expansion should be adopted for sustainable urban development. The proposed method can be used to quantify the integration characteristics of urban agglomerations, providing scientific support for urban landscape planning.

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“…In the process of China's urbanization, urban agglomerations have replaced single cities and became the most dynamic areas in Chinese economic development [4]. As the most complex urban spatial pattern, urban agglomeration is the agglomeration of urban economies in terms of spatial organization and structure.…”

Section: Introductionmentioning

confidence: 99%

“…The spatial interaction of CO 2 emissions between two urban areas may be measured by the point-line interaction structure; however, it is difficult to show the spatial interactions of the whole area. Demonstrating spatial interactions in a network-based way is even more challenging [4]. Networks can identify the mechanisms of the spatial interactions of CO 2 emissions and give insight into the changes in spatial interactions.…”

Section: Introductionmentioning

confidence: 99%

Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis

et al. 2018

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Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO 2 emission distribution while boosting regional economic growth. Exploring this spatial association and dependence is conducive to the implementation of effective and coordinated policies for regional level CO 2 reduction. This study calculated CO 2 emissions from 2005-2016 in the Chengdu-Chongqing urban agglomeration with the IPAT model, and empirically explored the spatial structure pattern and association effect of CO 2 across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO 2 emission in the area is a complex network pattern, and in the sample period, the CO 2 emission association relations increased steadily and the network stabilization remains strengthened; (2) the centrality of the cities in this area can be categorized into three classes: Chengdu and Chongqing are defined as the first class, the second class covers Deyang, Mianyang, Yibin, and Nanchong, and the third class includes Zigong, Suining, Meishan, and Guangan-the number of cities in this class is on the rise; (3) the network is divided into four subgroups: the area around Chengdu, south Sichuan, northeast Sichuan, and west Chongqing where the spillover effect of CO 2 is greatest; and (4) the higher density of the global network of CO 2 emission considerably reduces regional emission intensity and narrows the differences among regions. Individual networks with higher centrality are also found to have lower emission intensity.

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Measuring urban spatial interaction in Wuhan Urban Agglomeration, Central China: A spatially explicit approach

Cited by 87 publications

References 36 publications

Exploring Spatial and Temporal Connection Patterns among the Districts in Chongqing Based on Highway Passenger Flow

Exploring Spatial and Temporal Connection Patterns among the Districts in Chongqing Based on Highway Passenger Flow

Quantifying the Spatial Integration Patterns of Urban Agglomerations along an Inter-City Gradient

Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis

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