Evolution characteristics of TSI
Temporal evolution of TSI
In order to analyze the regional variations in the impact of the HSR on TSI and better display the obtained results, seven cities from each region are selected as the main representatives. The average TSI values from the various cities located in seven major regions to the seven representative cities are calculated over the past 13 years, illustrated in Fig. 1.
Trends in average TSI between the representative cities and seven regions from 2007 to 2019.
From Fig. 1, it can be observed that the intensity of TSI is impacted by the distance, where the strength of TSI between regions that are farther apart tends to weaken. When Beijing is chosen as the destination, the average TSI between North China and Beijing is comparatively stronger compared to the other six regions. Conversely, the average TSI values corresponding to Beijing are few when the regions are the southwest and northwest areas far away from Beijing. The southwest and northwest regions correspond to low average TSI values due to the long distance from them to Shanghai. These findings provide evidence of the distance decay effect in the TSI model. Therefore, distance decay effects confirm transportation costs as a key factor for the development of a city’s tourism.
Additionally, the persistent TSI intensity differentials reveal structural imbalances in China’s tourism. Specifically, the regions of North China, East China, and Central China consistently correspond to higher TSI values than other regions, which shows a close tourism connection between these regions and the representative cities. In particular, the TSI between the eastern region and Shanghai is the strongest, with a TSI value reaching approximately 250. In contrast, the northwest and northeast regions display the weakest TSI corresponding to Nanning, with the highest value being less than 10.
The high-value “Eastern Triangle” reflects agglomeration benefits from developed transport networks and complementary tourism resources.
In addition to the distance factor affecting the TSI intensity, some specific events in the city at different times also have an impact on the TSI value. In Fig. 1, there is an obvious decreasing trend during many periods. For example, the decline in Shanghai’s tourism attractiveness to East China in 2011 is mainly because the Shanghai World Expo in 2010 attracts a large number of tourists, leading to a natural drop in the number of tourists in 2011. In addition, the rapid development of the tourism industry in other cities in East China, such as Hangzhou, Nanjing, and Suzhou, also has a diversion effect on Shanghai. For example, in 2011, Suzhou successfully established itself as a national demonstration city for tourism standardization, ranked first in the tourist satisfaction survey among 50 large and medium-sized tourist cities in China.
In 2015, the tourism industry in Shenyang faces a series of negative events. The results of the special rectification campaign on the tourism market order released by the National Tourism Administration show that some scenic spots in Shenyang, such as the Shenyang Botanical Garden, are seriously warned for failing to meet the relevant standards.
Similarly, in 2016, the tourism attractiveness of Chongqing also declined. This is also mainly because of a series of negative events. For example, the Dragon Gorge scenic area is stripped of its 5A-level scenic area title due to serious tourism safety hazards, poor environmental sanitation, and chaotic tourism order. This negative news directly affects its tourism attractiveness. Generally, these cases illustrate that special events and negative incidents can significantly impact a city’s tourism attractiveness.
In addition, Fig. 1 shows that although the opening times of the HSR in different cities are different, there is an overall upward trend in the TSI values. This can be attributed to the increase in population, per capita GDP, and the popularity of tourist attractions in these cities.
However, the most critical driver of this trend is the reduction in intercity travel time, a finding supported by our empirical analysis. Specifically, the fixed-effects model in the Appendix 2 confirms that travel time between cities has the most significant impact on TSI, as it directly mediates the flow of tourists and resources, as shown in Tables A1–A7.
As illustrated in Figs. 2 and 3, the development and optimization of HSR have effectively shortened intercity travel time. From Fig. 2, it can be seen that the number of cities connected by HSR increases from 37 in 2007 to 226 in 2019, demonstrates China’s successful infrastructure investment. In Fig. 3, the travel time from each region to the representative cities shows a downward trend overall. The decrease in travel time facilitates an increase in people’s willingness to travel and strengthens the level of TSI between cities.
Number of cities with HSR from 2007 to 2019.
Variations of average travel time from each region to the representative cities by HSR from 2007 to 2019.
Lastly, Fig. 1 shows that during the study period from 2007 to 2019, the growth of average TSI can be roughly divided into two stages. The first stage (from 2007 to 2014) is characterized by a gentle increase of the TSI, where the values from the southwest, northwest, and northeast regions to the representative cities are relatively low. The second stage (from 2014 to 2019) represents a phase of dramatic growth, where the value corresponding to the southwest region experiences more significant growth compared to the northwest region. The reason for the above difference in the two stages is mainly due to the different changes in average travel time from each region to the seven typical cities during the period. From Fig. 3, a noticeable decrease in the travel time can be found during the second stage. Especially during the period from 2014 to 2015, the decrease in travel time is most significant due to the notable increase in the number of cities connected by HSR.
In addition, the value corresponding to the southwest region is more significant than the northwest region. Because the development of HSR in the southwest region is more advanced than that in the northwest region. For example, it is evident that after 2009, the southwest region consistently shows a higher frequency of HSR services compared to the northwest, illustrated in Fig. 4. It reveals the differences in the average number of HSR services between the southwest and northwest regions.
Variation of HSR services between the northwest and southwest regions.
Spatial differences in TSI
To indeed reveal the spatial differences, the change value and rate of the TSI between the seven representative cities and the cities located in different regions are calculated over the past 13 years as shown in Fig. 5.
Spatial change in the TSI to each representative city.
From Fig. 5, it can be seen that the change value of the TSI is weak when Nanning and Shenyang are selected as the representative cities, its maximum value only reached about 290. The rate of change is also weak when considering Beijing as the representative city, with its maximum value only reaching 1740. When Chongqing is selected as the representative city, the change value is most significant, and the maximum change value can reach 2023.59. Nevertheless, most of the large changes correspond to the adjacent areas of the representative cities. For instance, the TSI between Beijing and Tianjin is higher than that between Beijing and other cities. Additionally, Fig. 5 shows that the northern, central, and eastern regions consistently exhibit deeper colors, which means that their value of TSI changes greatly owing to the HSR’s impact.
Furthermore, Fig. 6 illustrates the sum of the change values and rates of the TSI corresponding to the seven representative cities over the past 13 years. The areas with great change value are predominantly concentrated in the eastern region and the Beijing-Tianjin-Hebei region in the northern region, as well as the southeastern coastal region, as shown in the left part of Fig. 6. For example, Beijing, Tianjin, and Shanghai all have a relatively great change value of the TSI, corresponding to 4394.98, 2813.61, and 2560.84, respectively. This reveals that the HSR can exert a significant impact on the TSI change in these regions. Differently, the areas experiencing high growth rates are primarily concentrated in Southwest, South, and Central China. Specifically, cities like Kunming, Nanning, and Shenzhen all have significantly changed the rate of TSI, corresponding to 233.03, 254.52, and 298.1, respectively. In fact, the pronounced rate of the TSI change corresponding to these areas aligns closely with the Chengdu-Mianyang-Leshan HSR line and the Liupanshui-Zhanyi HSR line. This reveals that the HSR can cause a large change in these cities and regions along HSR lines.
Totally spatial distribution of the TSI change value and rate.
Evolution characteristics of TSS
For investigating the TSS, the degree centrality and betweenness centrality of the seven representative cities within the tourism network structure during the study period are calculated, as shown in Fig. 7. From Fig. 7, it can be found that the centrality of the representative cities in the study period increases as a whole, except for the betweenness centrality of Beijing and Shanghai. This indicates that the tourism network structure tends to be stable and no longer depends on a single city. Initially, Beijing and Shanghai played hub roles in the tourism network. However, their roles have changed with the expansion and development of the HSRN. In other words, new hub cities are emerging in the tourism network structure, such as Wuhan and Chongqing. For example, the degree centrality of Chongqing and the betweenness centrality of Wuhan are larger than that of Beijing or Shanghai after 2017.
Evolution of the TSS corresponding to the representative cities in degree centrality and betweenness centrality.
In addition, from Fig. 7, we can catch a glimpse of the development status of tourism in China. Specifically, the tourism center of China’s tourism development is no longer concentrated in North and East China. New tourism centers have successively emerged in Central, Southwest, and Northwest China, indicating that China’s tourism development is in a state of all-around prosperity.
Furthermore, the overall growth rates of the centrality of different cities within the tourism network structure are different during the period from 2007 to 2019, as presented in Table 2.
As shown in Table 2, the degree centrality of Chongqing displays the highest growth rate, followed by Xi’an, while that of Beijing has the lowest growth rate. Because Beijing, the capital of China, has a high priority to develop HSR and has also opened and operated HSR early, the impact of the HSR on the TSS of Beijing is limited, compared to other cities. However, Chongqing is late in developing HSR and does not open and operate HSR until 2015. Despite the relatively late development in HSR, the continuous improvement of the HSRN has significantly increased the connectivity between Chongqing and other regions. This leads to Chongqing becoming an important hub in the tourism structure. Differently, the betweenness centrality of Beijing and Shanghai shows negative growth while that of Shenyang has the biggest growth. This confirms that with the opening of HSR stations in other cities, the importance of Beijing and Shanghai has decreased and has even been overtaken by other cities. Moreover, these findings reveal significant variations in the impact of HSR on the TSS across different regions.
The overall evolution characteristics of the TSS may be attributed to the changing of accessibility between cities within the tourism network. Specifically, the average number of reachable cities increased from 6.11 in 2007 to 57.48 in 2019, and the maximum number of reachable cities has also shown an upward trend, rising from 21 in 2007 to 149 in 2019, as shown in Table 3. Therefore, the TSS gradually presents a multi-core development pattern. Interestingly, the minimum number of reachable cities consistently remains at 1 from 2007 to 2009. This phenomenon may be attributed to geographical constraints and the regional HSR construction plan. As a result, certain cities, like Haikou and Sanya, have only a single HSR line passing through them, and consequently, they have only one directly reachable city.
Tourism role identification of different cities
Commonly, the role of a city can be divided into tourism destination function, tourism source function, and tourism transit function from the tourism activity perspective. Given the characteristics of the constructed indicators, they are only suitable for identifying and analyzing tourism destinations and transit points. Therefore, this study focuses on these two tourism-related functions and does not involve the identification of tourism source areas. Combined with the above result analysis of TSS, this study identifies the tourism role of different cities by using the SOM algorithm with degree centrality and betweenness centrality. The degree centrality of cities can be used to identify their role importance in the tourism destination function, while their betweenness centrality can be used to identify their role importance in the tourism transit function. It is noted that the paper classifies these cities into five levels. In this study, we refer to the number of classification levels from other research by combining with the economic classification results of Yicai to ensure that our classification results are scientifically grounded and can reflect the actual conditions of cities’ tourism development (Zhang et al., 2019; Liu et al., 2015). Additionally, 2007, 2011, 2015, and 2019 are selected as the time period of the clustering for clear visualization and interpretation, as shown in Fig. 8.
The distinct colors stand for different cluster categories, and the size of the bubbles in the chart corresponds to the tier of cities, with larger bubbles indicating a higher tier of cities.
In Fig. 8, the cities can be clustered into five tiers based on their importance of role of tourism destination function. From Fig. 8, it can be seen that the number of cities in all five tiers exhibits an upward trend. The number of medium-tier cities increases from 9 in 2007 to 33 in 2019, and the number of high-tier cities grows from 1 in 2007 to 11 in 2019. Specifically, the number of medium-high tier cities is relatively small, while the number of low and lower tier cities far exceeds that of other tier cities. For instance, in the year 2015, there are a total of 14 cities classified as higher and high tier, while the number of low and lower tier cities amounted to 121.
Although the number of medium-high tier cities is relatively small, these cities have gradually strengthened their roles as tourism destinations with the development of HSRN. For example, the tourism destination capacity of cities such as Hangzhou and Zhengzhou became high-tier in 2011. Additionally, the tourism destination function of these cities undergoes continuous evolution over time. Specifically, Wuhan and Chongqing, initially positioned as medium-low tier cities, have gradually transitioned into higher tier cities and play significant roles in the tourism destination landscape. Interestingly, certain cities such as Beijing and Shanghai have consistently maintained their status as prominent tourist destinations. This can be attributed to the fact that these two cities have convenient transportation conditions and are also modern urban centers, offering tourists a diverse range of travel experiences.
Furthermore, it can be found that the presence of the HSR facilitates the development of the destination cities in China. In Table 4, the majority of core destination cities are located in North China, Central China, East China, and Southwest China.
This distribution pattern is closely related to the method of selecting core destination cities, which we will elaborate on below. It is noted how to select the core destination cities. At first, we select cities from the top two levels of the SOM clustering results that have high degree centrality. Then, we further identify these selected cities according to provincial-level or sub-provincial cities.
The significant change in the spatial distribution of core destination cities is notable, which has expanded from solely encompassing North China to now including five distinct regions. In addition, the substantial impact of HSR is observed in East China, Southwest China, and Central China. Specifically, the number of core destination cities increases from 0 in 2007 to 5 in 2019 in East China, and from 0 in 2007 to 4 in 2019 in Southwest China.
In Fig. 9, the cities can also be classified into five tiers based on their importance of role of tourism transit function, in which the higher-tier cities are identified as the core transit cities due to their superior capacity in facilitating such transit compared to other tiers. The evolution of tourism transit cities can be observed in two distinct stages, shown in Fig. 9. In the initial stage, there is a noticeable increase in the number of cities across all tiers, from 2007 to 2015. In the subsequent stage, after 2015, a visible downward trend is observed, specifically among the medium-high tier cities. In other words, with the continuous development of the HSRN, a gap emerges between individual cities and other cities, resulting in the concentration of tourism transit capacity in specific cities. For example, Guangzhou, Changsha, and Zhengzhou are classified as core cities of tourism transit in 2019.
The distinct colors stand for different cluster categories, and the size of the bubbles in the chart corresponds to the tier of cities, with larger bubbles indicating a higher tier of cities.
Similar to the evolution of tourism destinations, there are also only a few medium-high tier in tourism transit cities, whereas the low and lower tier cities make up a larger proportion of the total number of cities. For instance, in 2007, there are a mere 11 medium-high tier cities, while the number of low and lower tier cities reaches 26. Likewise, the number of medium-high tier cities is 23 in 2019, whereas an overwhelming 194 low and lower tier cities are identified. In addition, cities like Zhengzhou and Shijiazhuang have ascended to the first and second tiers from 2015 to 2019, indicating a consistent strengthening of their transit functions. This indicates that the tourism transit function of cities, such as Jinan and Nanjing, changes over time. Furthermore, some cities, such as Guangzhou, Changsha, Nanjing, and Jinan, are identified as consistently occupying the core positions among tourism transit cities.
Finally, it can also be observed that there are regional differences in the impact of the HSR on cities’ tourism function. In Table 5, the core and sub-core transit cities are mostly located in North China during the early period. This distribution pattern is closely related to the method of selecting hub cities. Therefore, it’s important to note how to identify hub cities. Initially, we choose from high-betweenness centrality cities in clustering results. Subsequently, we further select these chosen cities according to provincial-level and sub-provincial cities and the list of the hub cities released by the National Development and Reform Commission in China. For example, in 2007, 3 of the core and sub-core component cities are located in North China. However, there has been a shift in the distribution of these transit cities toward East and Central China over time. In 2011, 5 of the core and sub-core component cities are located in East China, while 6 and 4 of the core and sub-core component cities are located in Central China, in 2015 and 2019, respectively. This suggests that the impact of HSR development is significant in Central China and East China.
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