Difference of urban development in China from the perspective of passenger transport around Spring Festival

Xu, Jing; Li, Aoyong; Liu, Dong; Liu, Yu; Du, Yunyan; Pei, Tao; Ma, Ting; Zhou, Chenghu

doi:10.1016/j.apgeog.2017.07.014

Cited by 102 publications

(61 citation statements)

References 32 publications

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“…Little has been done to explore such interplay patterns from the perspective of transient demographic changes and their linkage with the built environment using big data. Spring Festival, a special event in China each year, causes large-scale inter-urban human movement [10]. A large number of people return to their hometowns for a family reunion before the Spring Festival (a week-long holiday for Chinese New Year) and then go back to the cities where they live after the festival.…”

Section: Introductionmentioning

confidence: 99%

Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival

Huang

Liu

Zhao

et al. 2019

IJGI

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Public transport plays an important role in developing sustainable cities. A better understanding of how different public transit modes (bus, metro, and taxi) interact with each other will provide better sustainable strategies to transport and urban planners. However, most existing studies are either limited to small-scale surveys or focused on the identification of general interaction patterns during times of regular traffic. Transient demographic changes in a city (i.e., many people moving out and in) can lead to significant changes in such interaction patterns and provide a useful context for better investigating the changes in these patterns. Despite that, little has been done to explore how such interaction patterns change and how they are linked to the built environment from the perspective of transient demographic changes using urban big data. In this paper, the tap-in-tap-out smart card data of bus/metro and taxi GPS trajectory data before and after the Chinese Spring Festival in Shenzhen, China, are used to explore such interaction patterns. A time-series clustering method and an elasticity change index (ECI) are adopted to detect the changing transit mode patterns and the underlying dynamics. The findings indicate that the interactions between different transit modes vary over space and time and are competitive or complementary in different parts of the city. Both ordinary least-squares (OLS) and geographically weighted regression (GWR) models with built environment variables are used to reveal the impact of changes in different transit modes on ECIs and their linkage with the built environment. The results of this study will contribute to the planning and design of multi-modal transport services.

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Section: Introductionmentioning

confidence: 99%

Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival

Huang

Liu

Zhao

et al. 2019

IJGI

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“…Under the impetus of modern transportation and information technology, the barrier effect of geographical distance has gradually weakened, and a number of urban-related studies from the perspective of "flow" has been conducted [1][2][3]. Traffic flow is the link between cities.…”

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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“…In addition to movement, when all individuals have been georeferenced, their connections like mobile phone calls and social ties can be summed up to form spatially-embedded networks which measure spatial interactions from a new perspective [3]. Recently, most studies have constructed spatial-interaction networks by applying social-media check-in data [4,5], mobile-phone records [6], taxi-trajectory data [7][8][9], survey-based migration data [10][11][12], and internet data [13,14] to analyze regional structure.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Change Characteristics of Nodes’ Heterogeneity in the Directed and Weighted Spatial Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China

Yang

Liu

et al. 2019

Sustainability

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Spatial heterogeneity patterns in cities are an essential topic in geographic research and urban planning. This paper analyzes the spatial heterogeneity of places and reflects on the urban structure in cites based on spatial interaction networks. To begin with, we constructed 24 sequentially directed and weighted spatial interaction networks (DWNs) on the basis of points of interest (POIs) and taxi GPS data in Beijing. Then, we merged 24 sequential networks into four clusters: early morning, morning, afternoon, and evening. Next, we introduced the weighted D-core decomposition method in view of the complex network method and weighted distance in a geographic space in order to obtain the in-coreness/out-coreness of places. Finally, three indices (the entropy index, the node symmetry index, and the t-test) were used to measure the heterogeneity of places from both the strength dimension and the direction dimension. The results showed: (1) For the strength dimension, the spatiotemporal strength characteristics of the nodes in the DWN are uneven on weekdays or on the weekends, and the strength heterogeneity on weekdays is more obvious than on weekends; (2) for the direction dimension, out-flows and in-flows are different in the early morning and evening on weekends. In addition, the direction of the DWN is not obvious. The city networks present flat characteristics. This study used the weighted D-core method to identify the heterogeneity of nodes in the DWN, which has certain theoretical and practical value for the planning of urban and urban systems and the coordinated development of cities.

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Difference of urban development in China from the perspective of passenger transport around Spring Festival

Cited by 102 publications

References 32 publications

Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival

Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival

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

Spatiotemporal Change Characteristics of Nodes’ Heterogeneity in the Directed and Weighted Spatial Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China

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