Spatial Pattern Evolution and Influencing Factors of Tourism Flow in the Chengdu–Chongqing Economic Circle in China

Abstract

Based on Ctrip’s ‘tourism digital footprint’, the spatial pattern of tourism flows in the Chengdu–Chongqing Economic Circle from 2018 to 2021 is explored, social network analysis and spatial visualisation of tourism information data are conducted, and factors affecting the network structure of tourism flows are analysed using linear weighted regression methods. The results show that tourism flows in the Chengdu–Chongqing Economic Circle show a significant ‘dual core’ polarisation effect. At the end of 2019, as a turning point, the density value of the tourism flow network shows an irregular inverted ‘U’ distribution. Kuanzhai Alley, Hong Ya Dong and Chunxi Road have irreplaceable competitive advantages in the tourism flow network. The density of highways, the number of star-rated hotels and the regional GDP per capita are positively correlated with the effective size of the structural hole of the administrative unit. Finally, based on the research results, countermeasures are proposed to optimise the tourism development of the Chengdu–Chongqing Economic Circle.

A network science-based model for the evolution of environmental mass events

“…This paper proposes a network science-based evolutionary model of environmental group events, which aims to improve the performance of detecting group events in environmental group events by using the NodeAL group event detection method.The NodeAL method first uses the SimDecide algorithm to determine the best similarity calculation index of event nodes at different time periods, and then uses the MicroEvol algorithm Then, the MicroEvol algorithm is used to determine the matching relationship between evolutionary sequences and real event nodes during the evolution of environmental group events, so as to quantify the evolutionary fluctuations of event nodes and finally compare them with the group event detection threshold to determine the events. In order to verify the performance of the NodeAL method, we conducted experiments on the real environmental group event datasets VAST and PX and came to the following conclusions: (1) In the real environmental group event, the SimDecide algorithm can respond to the evolutionary fluctuation of nodes and give the best similarity calculation index under different time periods of different event nodes; (2) MicroEvol algorithm can quantify the evolutionary fluctuations of environmental group events and match the real event nodes according to the evolutionary sequence; (3) in the process of independent events and persistent events on environmental group events, the NodeAL method can show better sensitivity from the perspective of evolutionary fluctuations of event nodes and is more conducive to group event detection. Future research can be conducted to address two shortcomings:…”

“…Data show that environmental mass incidents have been increasing at an average annual rate of 29% since 1996, and at the same time have sparked widespread concern and continuous discussion in society. In analyzing the occurrence and evolution of environmental mass incidents, scholars have carried out research at different levels, of which the analysis of the evolution pattern of environmental mass incidents and the detection of environmental mass incidents are two important directions of relevant research at present [2][3]. The former lays the methodological foundation for the detection of environmental mass events, while the latter characterizes the evolutionary patterns of mass events by tracking and analyzing the fluctuating performance of environmental mass events at different stages.…”

“…To determine the best similarity weight array for environmental group events in different time periods. For this purpose, we proposed the SimDecide algorithm and further extended equation(2) on this basis to obtain the extended similarity formal…”

Research on the spatio-temporal evolution pattern of traffic dominance in Shaanxi Province

“…At the same time, with the rapid development of Geographic Information Systems (GIS) in recent years, researchers have new research methods and more intuitive ways of displaying the analysis of transportation advantage in different regions [13][14][15][16]. Overall, fully utilizing regional transportation advantages and avoiding transportation disadvantages can play an important role in the formulation of regional economic development strategies, industry selection, and spatial structure optimization [17][18][19] .…”

Spatial distribution pattern and driving mechanism of tourist attractions in Gansu Province based on POI data

“…Zhang Ai [18], Zhan Zirui [19] and Zhang Shengrui [20] selected national rural tourism key villages, Chinese national rural tourism towns and ethnic minority regions in northwest China to explore the spatial patterns and influencing factors of their rural tourism resources. Scholars such as Wang Yuewei [21], Chen Xuejun [22] and Zhao Junyuan [23] have studied the spatial pattern of tourism eco-efficiency in Inner Mongolia, the evolution of tourism flows in China's Chengdu-Chongqing Economic Circle and the spatial and temporal patterns and driving mechanisms of tourism eco-safety in the Yellow River Basin. For the study of the spatial pattern of tourism in Gansu Province, China.…”