Abstract
With the rapid development of transportation and modern communication technology, “tourism flow” plays an important role in shaping tourism’s spatial structure. In order to explore the impact of an urban tourism flow network on tourism’s spatial structure, this study summarizes the structural characteristics of the tourism flow networks of 43 scenic spots in Nanjing from three aspects—tourism flow network connection, node centrality, and communities—using cellular signaling data and the social network analysis method. A comparative analysis revealed the tourism flow network structures of residents and non-local tourists. Our findings indicated four points. Firstly, the overall network connectivity was relatively good. Core city nodes displayed high spatial concentration and connection strength. However, suburban nodes delivered poor performance. Secondly, popular nodes were intimately connected, although there were no “bridging” nodes. Lesser-known nodes were marginalized, resulting in severe node polarization. Thirdly, regarding the network community structure, the spatial boundary between communities was relatively clear; the communities within the core city were more closely connected, with some parts encompassing suburban nodes. Most suburban communities were attached to the communities in the core area, with individual nodes existing independently. Fourthly, the primary difference in the tourism flow network structures between residents and non-local tourists was that the nodes for residents manifested a more balanced connection strength and node centrality. Core communities encompassed more nodes with more extensive coverage. Conversely, the nodes for non-local tourists showed wide discrepancies in connection strength and node centrality. Furthermore, core communities were small in scale with clear boundaries.
Highlights
Since the 1960s, due to the continued developments in modern science and technology, global networking has become a significant development tendency
This study carried out data processing on a variety of filter condition experiments, and the processing results, multiple times, meaning the relatively accurate basic data could be used to analyze the spatial structure of tourism flow within the city
This study performed two statistical analyses on different temporal and spatial scales, study performed statistical on different temporal and spatial usingThis cellular signaling data,two to examine theanalyses phenomenon of tourism flow resulting from scales, using cellular signaling data, to examine the phenomenon of tourism flow resulting the spatial displacement of tourist crowds
Summary
Since the 1960s, due to the continued developments in modern science and technology (including computer and network information technology, advanced transportation, modern communications, globalization, and informatization), global networking has become a significant development tendency. With the rapid development of information technology, the largescale statistics and a vast amount of data regarding tourists’ spatiotemporal behavior are readily available This data enables the study of tourism flow network structures, based on the social network analysis method, to move further toward refinement and quantification. This study carried out data processing on a variety of filter condition experiments, and the processing results, multiple times, meaning the relatively accurate basic data could be used to analyze the spatial structure of tourism flow within the city It highlights the combination of the social network analysis method and traditional space theory. Traditional space theory emphasizes static material space expression; this study used the social network analysis method to connect human activities with spatial structures, which can reflect the functional connection in urban space more accurately It analyses the differences between local residents and tourists in a tourism network structure. In addition to addressing the deficiency in the existing research, regarding the application of big data and the absence of research scale, it provides a scientific basis for the differentiated organization of tourism space and tourism routes, urban infrastructure, transportation planning, and tourism social management
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