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, Jianhua; Yi, Disheng; Liu, Jingjing; Liu, Yusi; Zhang, Jing

doi:10.3390/su11226359

Cited by 4 publications

(4 citation statements)

References 32 publications

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“…Based on the above research, to investigate the interaction patterns between urban structure and traffic flow, we used the spatial hotspot analysis method [18] and the complex network [16] analysis method to explore spatio-temporal hotspots [19][20][21] and studied the urban traffic flow network from the perspective of network centrality [22]. We divide the study area into fine-grained grids [23] to avoid administrative districts to fragment geographic entities and propose a new trajectory direction description field model.…”

Section: Related Workmentioning

confidence: 99%

Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework

Zhang

Zheng

Chen

et al. 2021

IJGI

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The urban structure is the spatial reflection of various economic and cultural factors acting on the urban territory. Different from the physical structure, urban structure is closely related to the population mobility. Taxi trajectories are widely distributed, completely spontaneous, closely related to travel needs, and massive in data volume. Mining it not only can help us better understand the flow pattern of a city, but also provides a new perspective for interpreting the urban structure. On the basis of massive taxi trajectory data in Chengdu, we introduce a network science approach to analysis, propose a new framework for interaction analysis, and model the intrinsic connections within cities. The spatial grid of fine particles and the trajectory connections between them are used to resolve the urban structure. The results show that: (1) Based on 200,000 taxi trajectories, we constructed a spatial network of traffic flow using the interaction analysis framework and extracted the cold hot spots among them. (2) We divide the 400 traffic flow network nodes into 6 communities. Community 2 has high centrality and density, and belongs to the core built-up area of the city. (3) A traffic direction field is proposed to describe the direction of the traffic flow network, and the direction of traffic flow roughly presents an inflow from northeast to southwest and an outflow from southeast to northwest of the study area. The interaction analysis framework proposed in this study can be applied to other cities or other research areas (e.g., population migration), and it could extract the directional nature of the network as well as the hierarchical structure of the city.

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Section: Related Workmentioning

confidence: 99%

Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework

Zhang

Zheng

Chen

et al. 2021

IJGI

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“…Big data overcome the shortcomings of traditional data to some extent [23,24], reflecting more accurate population movement in real time. Some scholars have made use of big data of population mobility to carry out researches on the intercity connection and urban network [6,7,[25][26][27][28][29]. For example, the paper uses Baidu migration big data to study the characteristics of the rich club of the population mobility network during the Spring Festival travel rush in China [30] and the multisource migration big data of Baidu, Tencent, and Qihoo to study the travel characteristics of the population during the Spring Festival travel rush in China [25].…”

Section: Review Of the Related Literaturementioning

confidence: 99%

Club Characteristic and the Proximity Effect of Travel Links between Cities in China

Chen

Zhang

2020

Complexity

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With the increasingly close relations between cities in China, it is of great significance to explore the regular characteristics of the intercity connection. Through Tencent’s population migration heat and Baidu map big data, this paper analyzes the regular characteristics of the relations between complex cities based on such index as the rich node propensity index, preference level index, and relative heat index and also investigates the influence of geographical proximity factors on the external relations of different cities. The research has the following results. Firstly, the relations between cities have obvious club characteristics. The rich nodes tend to connect with the rich nodes, while the nonrich nodes tend to connect with the nonrich nodes. Secondly, the connection between cities has the effect of hierarchical proximity, and cities mainly establish spatial connections with cities of the same level and adjacent level. Thirdly, the relations between cities also have the effect of geographical proximity, and the degree of influence of geographical proximity in low-level cities is greater than that in high-level cities. Fourthly, the external connection mode of high-level cities is to establish close contact with high-level cities adjacent to the level, with strong attraction to low-level cities adjacent to the location at the same time. The low-level cities are closely related to the high-level cities adjacent to the location and other cities of geographical proximity or adjacent level. This study helps to further understand the complex characteristics and laws of intercity connections and urban networks.

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“…Previous studies showed that there is a considerable variation at different hours and different days regarding the number of taxi pick-ups and drop-offs [17,18]. Therefore, we selected, separately, two groups of OD flows that appeared on weekdays and weekends.…”

Section: Spatio-temporal Patterns Of Urban Travelling Hotspotsmentioning

confidence: 99%

“…Some of them explored the spatial distributions of travels for diverse social activities [15] and the geographical characteristics of urban travel demand [16]. Apart from that, Yang et al evaluated the diverse popularity of places in the urban area by human travelling [17,18]. Other research focused on the spatio-temporal characteristics of urban travelling hotspots [2,19,20].…”

Section: Introductionmentioning

confidence: 99%

Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing

Liu

Qin

et al. 2020

Sustainability

Self Cite

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Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.

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

Cited by 4 publications

References 32 publications

Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework

Urban Fine-Grained Spatial Structure Detection Based on a New Traffic Flow Interaction Analysis Framework

Club Characteristic and the Proximity Effect of Travel Links between Cities in China

Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing

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