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Citation: Xue, C.-H.; Bai, Y.-P.
Spatiotemporal Characteristics and
Factors Influencing Urban Tourism
Market Network in Western China:
Taking Chengdu as an Example.
Sustainability 2023,15, 8135.
https://doi.org/10.3390/
su15108135
Academic Editors: Wadim
Strielkowski, Bruno Barbosa Sousa
and Vasco Ribeiro Santos
Received: 30 March 2023
Revised: 14 May 2023
Accepted: 16 May 2023
Published: 17 May 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Spatiotemporal Characteristics and Factors Influencing Urban
Tourism Market Network in Western China: Taking Chengdu as
an Example
Chen-Hao Xue 1,2 and Yong-Ping Bai 1,*
1College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China;
xuechenhaovip@126.com
2College of management, Northwest Minzu University, Lanzhou 730030, China
*Correspondence: baiyp@nwnu.edu.cn
Abstract:
Urban tourism network attention is important for measuring the competitiveness of the
urban tourism industry, tourism attraction, and cultural soft power. In this study, we explored the
spatiotemporal patterns and factors influencing network attention in the tourist source market and
discussed how tourism cities can increase network attention, thus improving the competitiveness of
urban cyberspace and developing soft power. Taking Chengdu as a research case, we obtained data
on its tourism network attention from 31 provinces (autonomous regions and municipalities) between
2011 and 2021. We measured the spatiotemporal characteristics of network attention using the
inter-annual change index, seasonal concentration index, potential tourists’ concentration coefficient,
and ESDA model and analyzed the factors affecting spatiotemporal changes in network attention
using the geographic weighted regression (GWR) model. The results revealed that from 2011 to
2021, the network attention of Chengdu tourism showed an overall “M”-type fluctuation trend, with
significant seasonal differences and disequilibrium and significant differences in space, signifying an
overall “
∩
”-shaped distribution trend. This suggested a weak negative spatial correlation. Further,
the number of mobile Internet users, people in higher education per 100,000 people, per capita gross
domestic product, urbanization rate, and passenger throughput are important factors that affect the
network attention of Chengdu tourism. Thus, these results can be used by cities in western China to
optimize the network attention rating system of urban tourism, strengthen the promotion of urban
image, build a sustainable city, and transform network traffic into effective economic growth.
Keywords:
network attention; Chengdu; tourism; spatiotemporal pattern; factors influencing urban
tourism market network
1. Introduction
The tourist source market is the key factor that determines the survival and sustain-
able development of tourism destinations and tourism development competition among
tourism destinations. Research on tourist destination source markets is quite prevalent
in the fields of tourism geography, tourism management, and marketing [
1
,
2
]; tourism
destination branding based on tourists’ perceived image and potential tourist attention
have been widely proved to be effective as a common choice of marketing strategy in many
places [
3
–
8
]. This research focused on the spatial structure and evolution characteristics
of the tourist source market. Analyzing the spatial characteristics, seasonal changes, and
long-term trends of potential tourists and exploring the important factors that affect the
potential concerns of the tourist source market are significant for enhancing the network
attention of the source market, formulating tourist cities’ tourism development policies, en-
hancing destination attractiveness, and promoting the sustainable development of tourism
industries [
9
–
12
]. Scholars have examined the spatial characteristics of tourist cities source
Sustainability 2023,15, 8135. https://doi.org/10.3390/su15108135 https://www.mdpi.com/journal/sustainability
Sustainability 2023,15, 8135 2 of 21
market from three perspectives: the spatial distribution of tourist cities sources, the spa-
tiotemporal evolution of tourist source markets, and mathematical models [
13
–
15
]. The
corresponding research methods mainly include the tourist source radius, seasonal index,
geographical concentration index, reliability function, time series model, spatial use curve,
network analysis, and tourism background trend line model [16–21].
In the era of the Internet, changes in the spatial structure of the tourist source market
have become an important issue for scholars. Öörni studied the impact of Internet tourism
and the tourism market on the way tourists search before they consume products; the
research hypotheses were constructed by discussing the amount of search and consumer
objectives related to direct sales channels. He observed that the impact of the Internet-
based leisure tourism market on pre-order consumer searching was smaller than it was
expected to be, and most consumers extend their searches to electronic markets to improve
the search process, not necessarily the quality of their purchase decision [
22
]. Rodríguez
developed and implemented a hierarchical clustering method using smartphone geographic
location data to segment the tourism market and verified it in the Netherlands [
23
]. Xu
analyzed the discrepancy between actual and expected flows in scenic spots in an urban
destination at different distance ranges based on individual travel tips data extracted
from online sources [
24
]. Tourism information searching has become more convenient
and flexible owing to economic, scientific, and technological developments, such as the
popularity of smartphones; this has not only altered people’s lifestyles and concepts, but
also, channels of information dissemination. Before traveling, tourists usually searched
on the Internet to obtain traffic, weather, accommodation, scenic spot tickets, and other
information. These search processes are retained by the Internet in the form of search
traces called “network attention” or a “search index” [
25
]. As a statistical measure of urban
information dissemination and the recognition index of netizens, the network attention of
tourism cities has important practical implications for promoting the virtual IP creation of
city business cards, the value-added network of data information, and the promotion of
city characteristics.
Network attention indicators, such as Google Trends and the Baidu Index, can reflect
Internet users’ behavior preferences and public opinion recognition based on big data
from online searches. They can also reflect Internet users’ attitudes, feelings, and judgments
about real events and can comprehensively measure the attention and spatial differences
and content preferences of Internet users across the country in special urban areas [
26
–
28
].
There is also a positive correlation between city or scenic spot passenger flows and network
attention [
29
–
31
]. With the help of network attention data, the distribution of tourist
destinations can be clarified, the spatial pattern of tourism flow can be clarified, innovative
marketing methods for scenic spots can be innovated, and precise marketing promotion
can be implemented [
32
–
36
]. Chinese scholars use the Baidu index and other network
search data to predict the tourist flow of scenic spots, research tourism security network
concerns, the passenger flow using different client data, and the spatial pattern of scenic
spots’ concerns [
37
–
44
]. In addition to the Baidu Index, the network attention data of
different network platforms and social media data, such as Sina Weibo, user-generated
content, social network members, and users’ short video data have also been used to
examine tourist source markets [45–51].
At present, research on the spatial structure and dynamic change in network attention
in the tourist source market is limited to the dynamic change in the spatial structure
of the tourist source market in Nanjing and the characteristics and factors influencing
the change in the tourist source market structure in Zhangjiajie over recent years [
52
,
53
].
There are a few relevant studies on its medium- and long-term inter-annual evolution and
corresponding influencing factors. Additionally, only a few studies have examined the
network attention of the tourist source market in western cities in China, such as Chengdu.
As an important indicator of the precursor effect of actual passenger flow, assessing the
case of Chengdu can reflect the travel tendency of potential tourists and provide new ideas
for studying its tourist source market’s space–time characteristics.
Sustainability 2023,15, 8135 3 of 21
In the era of mass tourism and the information technology revolution, the Internet
has become the primary source of obtaining information regarding a tourist’s destination.
Tourism cities and scenic spots also use big data to analyze changes in tourists’ attention in
the source market to improve their marketing effectiveness. Therefore, based on time series
change, spatial structure change, and time section change in the Baidu Index’s network
attention of Chengdu’s tourist market, this study reveals the spatiotemporal characteristics
and changes within the network attention of the tourist market and analyzes the factors
influencing change and provides a reference for the marketing and cultural soft power
construction of Chengdu’s customer source market. This study enriches the research on
the urban tourism source market and network attention and provides a useful reference
for western cities in China to formulate tourism sustainable development policies. In
the second part of this article, an overview of the research area, research methods, and
research data sources are introduced. The third and fourth parts analyze the spatiotemporal
characteristics and influencing factors of Chengdu’s tourism network attention. Finally, the
influencing mechanism is explored in depth, and suggestions for urban tourism marketing
strategies are proposed.
2. Materials and Methods
2.1. Study Area
Chengdu (102
◦
54
0
–104
◦
53
0
E longitude; 30
◦
05
0
–31
◦
26
0
N latitude), located in the central
part of Sichuan Province, is the capital of Sichuan Province and one of the first famous
national historical and cultural cities in China (Figure 1). It was estimated that, in 2022,
the city’s gross domestic product (GDP) would be CNY 20818 trillion, an increase of 2.80%
over that of the previous year. By the end of 2022, Chengdu had 92 national A-level
scenic spots, including 2 5A scenic spots and 50 4A scenic spots. (A grade A scenic spot
is the quality level of China’s tourist attraction, which is divided into five levels. Among
them, 5A is the highest level of China’s tourist attraction, representing China’s world-class
tourist attractions.) From 2016 to 2020, Chengdu’s Globalization and World Cities Research
Network’s world city ranking jumped from 188 to 59, and it won the titles of “World’s
Best Tourism Destination”, “China’s Tourism and Leisure Demonstration City”, and so on.
In 2019 (before the outbreak of COVID-19), Chengdu’s total tourism revenue was CNY
466.4 billion, including CNY 455.11 billion of domestic tourism revenue and 276.42 million
domestic tourists.
Sustainability 2023, 15, x FOR PEER REVIEW 4 of 21
Figure 1. Location of Chengdu, Sichuan Province, China.
2.2. Methods
2.2.1. Inter-Annual Variation Index and Seasonal Concentration Index
The inter-annual variation index is used to reflect the relative amount of inter-annual
variation in tourism network aention; its calculation formula is as follows:
1
1
i
n
i
i
N
Y
N
n=
= (1)
where Y is the inter-annual change index, and Ni is the concern index of the i th year.
Generally, the inter-annual change in the tourist source network of a tourist destination
shows a slowly growing trend. The more the value of Y deviates from 100%, the greater
the inter-annual change in the tourist source network of Chengdu is, and the more unsta-
ble the degree of concern is.
The seasonal concentration index reflects the time concentration of tourists in
Chengdu; its calculation formula is as follows:
()
12 2
18.33 12
i
i
Rx
=
=−÷
(2)
where xi is the proportion of network aention in each month of Chengdu throughout the
year; R is the seasonal concentration index of tourists in Chengdu. R is close to 0, which
means that the more uniform the distribution in each month of the next year is, the greater
R is. This implies that the distribution of tourist’s network aention to Chengdu is large
and seasonal.
2.2.2. Concentration Coefficient of Potential Tourists
Referring to Zhu’s improvement of the geographical concentration index [54], the
concentration coefficient of potential tourists’ aention was adopted to measure the
Figure 1. Location of Chengdu, Sichuan Province, China.
Sustainability 2023,15, 8135 4 of 21
2.2. Methods
2.2.1. Inter-Annual Variation Index and Seasonal Concentration Index
The inter-annual variation index is used to reflect the relative amount of inter-annual
variation in tourism network attention; its calculation formula is as follows:
Y=Ni
1
n∑n
i=1Ni
(1)
where Yis the inter-annual change index, and N
i
is the concern index of the ith year.
Generally, the inter-annual change in the tourist source network of a tourist destination
shows a slowly growing trend. The more the value of Ydeviates from 100%, the greater the
inter-annual change in the tourist source network of Chengdu is, and the more unstable
the degree of concern is.
The seasonal concentration index reflects the time concentration of tourists in Chengdu;
its calculation formula is as follows:
R=q∑12
i=1(xi−8.33)2÷12 (2)
where x
i
is the proportion of network attention in each month of Chengdu throughout the
year; Ris the seasonal concentration index of tourists in Chengdu. Ris close to 0, which
means that the more uniform the distribution in each month of the next year is, the greater
Ris. This implies that the distribution of tourist’s network attention to Chengdu is large
and seasonal.
2.2.2. Concentration Coefficient of Potential Tourists
Referring to Zhu’s improvement of the geographical concentration index [
54
], the
concentration coefficient of potential tourists’ attention was adopted to measure the concen-
tration of potential tourists in the tourist source market in Chengdu. This measure is more
reasonable and scientific than the geographical concentration index (G) is, which reflects
deviation from the complete average distribution. The formula is as follows:
G0=∆G/G×100 (3)
where
G0
refers to the concentration coefficient of potential tourists’ attention in Chengdu’s
tourist source market. The larger the value is, the more concentrated the tourists’ attention
is, and vice versa. The general geographical concentration index used is as follows:
G=100 ×sn
∑
i=1
(Xi/T)2(4)
where nis the total number of tourist sources, X
i
is the network focus index of the ith
tourist source, and Tis the total focus of the destination. This value is affected by both the
uniform distribution of tourist sources and the number of tourist sources. Therefore, before
the influence of nis excluded, Gis the only variable reflecting the concentration of tourists,
which requires comparison.
G
It is the concentration index that calculates the complete
average situation of nregions based on the assumption that potential tourists are evenly
distributed among ndestinations or come from nsource markets:
G=100 ×q∑n
i=1(1/n)2(5)
Then, we introduce ∆Gto eliminate the impact of n:
∆G=100q∑n
i=1(Xi/T)2−q∑n
i=11/n2(6)
Sustainability 2023,15, 8135 5 of 21
∆G
is the difference between Gand
G
, and the ratio of this difference with
G
reflects
the concentration of potential tourists.
2.2.3. ESDA Model
ESDA (Exploratory Spatial Data Analysis) is a method used to explore the spatial
distribution pattern and spatial interaction mechanism of regional things or phenomena [
55
].
The common methods include global autocorrelation and local autocorrelation. See the
literature [56] for a specific formulaic expression.
2.2.4. GWR Model
The GWR model embeds the spatial location of research data into the regression
parameters and uses the locally weighted least squares method to estimate point-by-point
parameters. The weight is the distance function between the geographical/spatial location
of the study area unit and the geographical/spatial location of other units. This model
can better reveal the spatial correlation between research variables [
57
]. The calculation
formula is as follows:
yi=ai0(ui,vi)+∑p
i=1aik (ui,vi)xik +εi(7)
where
(ui,vi)
is the spatial location of the ith study area,
xik
is the independent variable
of the ith study area,
ai0(ui,vi)
and
∑p
i=1aik (ui,vi)xik
are the estimated values of constant
terms and parameters of the ith study area, respectively, and Pis the number of independent
variables of the ith sample point; εiis the error correction term.
2.3. Data Source
When the urban tourist source market is studied, a lack of comprehensive survey
data on provincial tourist source statistics can be replaced with network focus data [
58
].
By the end of 2021, Baidu accounted for 85.48% of the domestic search engine market in
China, making it the largest Chinese search engine in the world (data are from the Stat-
counter: https://gs.statcounter.com/search-engine-market-share/all/china/#monthly-
202101-202112, accessed on 27 February 2023). The Baidu Index is a data-sharing platform
embodying behavior data from a large number of Internet users. We used certain keywords
to find changes in network attention that occurred over the past week, month, year, or
even longer. The following keywords were used to collect the network attention data of
31 provinces (municipality and autonomous regions) in Chengdu from 1 January 2011 to
31 December 2021: “Chengdu Tourism”, “Chengdu Scenic Spot”, and “Chengdu Strategy”.
Since network attention data from tourist source markets in Chengdu are sparse, such as
Hong Kong, Macao, and Taiwan, these were not included in the study. Further, data from
the China Statistical Yearbook, the Chengdu Statistical Yearbook, the provincial statisti-
cal yearbook and statistical bulletin, and the civil aviation airport production statistical
bulletin from 2011 to 2021 were used to determine the factors influencing urban tourism
market networks.
2.4. Selection of Indexes
Network attention reflects tourists’ demand for destination information. Consequently,
the main factors influencing network attention are the indicators that affect tourists’ demand
and information acquisition. This paper analyzes the factors that affect the space–time
difference in Chengdu’s tourism network attention using the following: (1) population
size—the larger the population size of the tourist source area is, the more potential tourists
there are who are willing to travel to Chengdu, and the more common the generated
online information search behavior is, which is represented by the population at the end
of the year; (2) economic development level—the better the economic development level
of the potential tourist location is, the higher the per capita GDP is, and the stronger the
willingness to travel is, which is expressed by the per capita GDP indicator; (3) the level
of network development, which directly affects the degree of online attention of tourists,
Sustainability 2023,15, 8135 6 of 21
wherein an increasing amount of potential tourists conduct online searches via the mobile
internet, and this is expressed by the number of mobile internet users; (4) the education
level of potential tourists is an important factor that affects their online search, which
is expressed by the number of people in higher education per 100,000 people; (5) the
urbanization development level, wherein urbanization has an important impact on the
number of people who desire to travel in the city, which is expressed by the urbanization
rate indicator; (6) travel population—the travel population of the tourist source area reflects
the number of people traveling in the area, which is represented, to a certain extent, by the
passenger throughput index of each province (Table 1).
Table 1. The serial number and name of each factor.
Number X1 X2 X3 X4 X5 X6
Name Population at the
end of the year GDP per capita
Number of
mobile internet
users
Number of higher
education per
100,000 people
Urbanization rate Passenger
throughput
connotation Population size
Economic
development
level
Network
development
level
Education Urbanization
development level
Traveling
population
3. Results
3.1. Time Characteristics of Chengdu Tourism’s Network Attention
3.1.1. Inter-Annual Characteristics
According to the calculation of the inter-annual change index, the attention network
of Chengdu’s tourism shows an overall “M”-type fluctuation in terms of its development
trend (Figure 2). Here, an upward trend was observed in the periods of 2011–2014 and
2017–2019, whereas 2015–2016 and 2019–2020 showed a declining trend; 2020–2021 was
the recovery period. The inter-annual change index increased from 0.66 in 2011 to 1.28 in
2014, decreased to 0.99 in 2016, and was followed by an increase to 1.25 in 2019. Owing to
the impact of the COVID-19 pandemic, in 2020, the inter-annual change index of network
attention decreased to 0.53 and recovered to 0.60 in 2021. From 2015 to 2016, although the
numbers of domestic tourists remained high at 189 million and 200 million, the growth rates
fell to 2.59% and 4.71%, respectively. From 2017 to 2019, the number of domestic tourists
who arrived in Chengdu increased from 211 million to 276 million, and the annual growth
rate increased to 4.88%, 15.81%, and 15.20% per year, respectively. Due to COVID-19, it is
estimated that the annual average numbers of domestic tourists in Chengdu during 2020
and 2021 would have been 204 million and 205 million, respectively.
Sustainability 2023, 15, x FOR PEER REVIEW 7 of 21
3.1.1. Inter-Annual Characteristics
According to the calculation of the inter-annual change index, the aention network
of Chengdu’s tourism shows an overall “M”-type fluctuation in terms of its development
trend (Figure 2). Here, an upward trend was observed in the periods of 2011–2014 and
2017–2019, whereas 2015–2016 and 2019–2020 showed a declining trend; 2020–2021 was
the recovery period. The inter-annual change index increased from 0.66 in 2011 to 1.28 in
2014, decreased to 0.99 in 2016, and was followed by an increase to 1.25 in 2019. Owing to
the impact of the COVID-19 pandemic, in 2020, the inter-annual change index of network
aention decreased to 0.53 and recovered to 0.60 in 2021. From 2015 to 2016, although the
numbers of domestic tourists remained high at 189 million and 200 million, the growth
rates fell to 2.59% and 4.71%, respectively. From 2017 to 2019, the number of domestic
tourists who arrived in Chengdu increased from 211 million to 276 million, and the annual
growth rate increased to 4.88%, 15.81%, and 15.20% per year, respectively. Due to COVID-
19, it is estimated that the annual average numbers of domestic tourists in Chengdu dur-
ing 2020 and 2021 would have been 204 million and 205 million, respectively.
Figure 2. The annual change index of Chengdu tourism network aention in 2011–2021.
3.1.2. Seasonal Characteristics
The seasonal concentration index used to measure Chengdu’s tourism network at-
tention index during 2011–2021 was 8.38, 6.80, 8.15, 6.55, 5.25, 3.46, 4.18, 4.55, 12.73, 14.25,
and 11.21 per year, respectively, which fluctuated from high (2011–2015) to low (2016–
2018) and back to high (2019–2021), indicating that the seasonal difference in Chengdu’s
tourism network aention was significant. (A greater value implies that the distribution
of tourist’s network aention in Chengdu is large and seasonal (Figure 3).) The largest
seasonal difference was observed in 2020. The highest value of the seasonal concentration
index in most years from 2011 to 2021 was distributed from August to October. From 2011
to 2015, the high value of the seasonal concentration index was concentrated around the
New Year’s Day, National Day, and summer vacation, which corresponded with the tra-
ditional tourist peak season. From 2016 to 2018, the seasonal concentration index was
evenly distributed, with a maximum value of 8.64 in June 2018. From 2019 to 2021, the
seasonal difference significantly increased. Further, in 2019, the period from April to June
constituted the peak of the entire year; in 2020, the period from February to March 2020
constituted the first peak of the entire year, and in September, it aained the highest level
of 59.79 in a decade.
Figure 2. The annual change index of Chengdu tourism network attention in 2011–2021.
Sustainability 2023,15, 8135 7 of 21
3.1.2. Seasonal Characteristics
The seasonal concentration index used to measure Chengdu’s tourism network at-
tention index during 2011–2021 was 8.38, 6.80, 8.15, 6.55, 5.25, 3.46, 4.18, 4.55, 12.73, 14.25,
and 11.21 per year, respectively, which fluctuated from high (2011–2015) to low (2016–2018)
and back to high (2019–2021), indicating that the seasonal difference in Chengdu’s tourism
network attention was significant. (A greater value implies that the distribution of tourist’s
network attention in Chengdu is large and seasonal (Figure 3).) The largest seasonal differ-
ence was observed in 2020. The highest value of the seasonal concentration index in most
years from 2011 to 2021 was distributed from August to October. From 2011 to 2015, the
high value of the seasonal concentration index was concentrated around the New Year’s
Day, National Day, and summer vacation, which corresponded with the traditional tourist
peak season. From 2016 to 2018, the seasonal concentration index was evenly distributed,
with a maximum value of 8.64 in June 2018. From 2019 to 2021, the seasonal difference
significantly increased. Further, in 2019, the period from April to June constituted the peak
of the entire year; in 2020, the period from February to March 2020 constituted the first
peak of the entire year, and in September, it attained the highest level of 59.79 in a decade.
Sustainability 2023, 15, x FOR PEER REVIEW 8 of 21
Figure 3. Seasonal concentration index of Chengdu tourism network aention from 2011 to 2021.
3.1.3. Characteristics of Holidays
Chengdu tourism’s network aention data were selected from 1 January 2021 to 31
December 2021 (Figure 4). In 2021, the data revealed three peaks around “legal holidays”
and summer vacation. Specifically, since 12 February 2021 (Spring Festival), Chengdu
tourism’s network aention level gradually increased, and until 18 April, it showed a fluc-
tuation and rising trend of a “high intra-week index and low weekend index.” After a
small peak from 29 March to 2 April, there was a trough from 3 to 5 April (Qingming
holiday, which is the Pure Brightness Festival). This, along with the results of previous
studies, demonstrates that the search behavior of potential tourists in the source market
before they travelled led to an increase in network aention before the festival [48]. Similar
laws also appeared before and after the “May Day” holiday (International Labor Day).
From 19 April to 5 May, network aention peaked during the “May Day” holiday, with
the highest value appearing on 2 May. From 28 June to 30 July, Chengdu tourism’s net-
work aention remained high, which was consistent with the summer passenger flow
peak. Influenced by the resurgence of the local COVID-19 pandemic in Chengdu toward
the end of July 2021, network aention decreased significantly in August. Since 5 Novem-
ber, Chengdu tourism’s network aention has remained low.
Figure 4. Chengdu tourism network aention index in 2021.
3.2. Spatial Characteristics of Chengdu Tourism Network Aention
3.2.1. Spatial Concentration Trend
According to the calculation of the geographical concentration index (G) of Chengdu
tourism’s network aention and the potential tourist concentration coefficient (
'
G) (Table
2), we observed a disequilibrium in space. Further, the potential tourist concentration co-
efficient can reflect the deviation degree of the network aention concentration beer than
the geographical concentration index can. According to the geographical concentration
index, the G value of Chengdu tourism’s network aention fluctuated between 20.12 and
26.63 from 2011 to 2021, and the maximum fluctuation range was 1.93 between 2011 and
Figure 3. Seasonal concentration index of Chengdu tourism network attention from 2011 to 2021.
3.1.3. Characteristics of Holidays
Chengdu tourism’s network attention data were selected from 1 January 2021 to 31
December 2021 (Figure 4). In 2021, the data revealed three peaks around “legal holidays”
and summer vacation. Specifically, since 12 February 2021 (Spring Festival), Chengdu
tourism’s network attention level gradually increased, and until 18 April, it showed a
fluctuation and rising trend of a “high intra-week index and low weekend index”. After
a small peak from 29 March to 2 April, there was a trough from 3 to 5 April (Qingming
holiday, which is the Pure Brightness Festival). This, along with the results of previous
studies, demonstrates that the search behavior of potential tourists in the source market
before they travelled led to an increase in network attention before the festival [
48
]. Similar
laws also appeared before and after the “May Day” holiday (International Labor Day).
From 19 April to 5 May, network attention peaked during the “May Day” holiday, with the
highest value appearing on 2 May. From 28 June to 30 July, Chengdu tourism’s network
attention remained high, which was consistent with the summer passenger flow peak.
Influenced by the resurgence of the local COVID-19 pandemic in Chengdu toward the
end of July 2021, network attention decreased significantly in August. Since 5 November,
Chengdu tourism’s network attention has remained low.
Sustainability 2023,15, 8135 8 of 21
Sustainability 2023, 15, x FOR PEER REVIEW 8 of 21
Figure 3. Seasonal concentration index of Chengdu tourism network aention from 2011 to 2021.
3.1.3. Characteristics of Holidays
Chengdu tourism’s network aention data were selected from 1 January 2021 to 31
December 2021 (Figure 4). In 2021, the data revealed three peaks around “legal holidays”
and summer vacation. Specifically, since 12 February 2021 (Spring Festival), Chengdu
tourism’s network aention level gradually increased, and until 18 April, it showed a fluc-
tuation and rising trend of a “high intra-week index and low weekend index.” After a
small peak from 29 March to 2 April, there was a trough from 3 to 5 April (Qingming
holiday, which is the Pure Brightness Festival). This, along with the results of previous
studies, demonstrates that the search behavior of potential tourists in the source market
before they travelled led to an increase in network aention before the festival [48]. Similar
laws also appeared before and after the “May Day” holiday (International Labor Day).
From 19 April to 5 May, network aention peaked during the “May Day” holiday, with
the highest value appearing on 2 May. From 28 June to 30 July, Chengdu tourism’s net-
work aention remained high, which was consistent with the summer passenger flow
peak. Influenced by the resurgence of the local COVID-19 pandemic in Chengdu toward
the end of July 2021, network aention decreased significantly in August. Since 5 Novem-
ber, Chengdu tourism’s network aention has remained low.
Figure 4. Chengdu tourism network aention index in 2021.
3.2. Spatial Characteristics of Chengdu Tourism Network Aention
3.2.1. Spatial Concentration Trend
According to the calculation of the geographical concentration index (G) of Chengdu
tourism’s network aention and the potential tourist concentration coefficient (
'
G) (Table
2), we observed a disequilibrium in space. Further, the potential tourist concentration co-
efficient can reflect the deviation degree of the network aention concentration beer than
the geographical concentration index can. According to the geographical concentration
index, the G value of Chengdu tourism’s network aention fluctuated between 20.12 and
26.63 from 2011 to 2021, and the maximum fluctuation range was 1.93 between 2011 and
Figure 4. Chengdu tourism network attention index in 2021.
3.2. Spatial Characteristics of Chengdu Tourism Network Attention
3.2.1. Spatial Concentration Trend
According to the calculation of the geographical concentration index (G) of Chengdu
tourism’s network attention and the potential tourist concentration coefficient (
G0
) (Table 2),
we observed a disequilibrium in space. Further, the potential tourist concentration coeffi-
cient can reflect the deviation degree of the network attention concentration better than
the geographical concentration index can. According to the geographical concentration
index, the Gvalue of Chengdu tourism’s network attention fluctuated between 20.12 and
26.63 from 2011 to 2021, and the maximum fluctuation range was 1.93 between 2011 and
2019. However, Gvalue in 2020 was 6.51 times higher than the minimum value was (2019),
indicating that in 2020, the geographical concentration of Chengdu tourism’s network
attention level was higher than it had been in the previous years. However, according to
the concentration coefficient of potential tourists’ attention, the fluctuation range of
G0
value from 2011 to 2021 was 12.01 to 48.27, and the maximum fluctuation range between
2011 and 2019 was 10.79. In 2020, the
G0
value was 36.26 higher than the minimum value
was (2019). As indicated by similar calculation results in the relevant literature [
48
], the
potential tourist concentration coefficient,
G0
, can better explain the concentration degree
of tourists and more sensitively reflect the degree of deviation than the Gvalue can. The
concentration coefficient of potential tourists’ attention in each year demonstrates that
the actual distribution of network attention deviates more from the average geographical
concentration index. This indicates that the spatial distribution of network attention is
relatively concentrated.
Table 2. Tourist concentration coefficient of Chengdu tourism network attention.
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
G21.18 20.85 21.32 22.05 21.15 20.67 20.59 20.18 20.12 26.63 26.12
G17.96
∆G3.22 2.88 3.36 4.09 3.19 2.71 2.63 2.22 2.16 8.67 8.16
G017.95 16.06 18.68 22.80 17.77 15.09 14.62 12.35 12.01 48.27 45.43
3.2.2. Differences in Provincial Distribution
From 2011 to 2021, the inter-provincial differences in Chengdu tourism’s network
attention were significant, showing a rough “
∩
”-shaped distribution trend. The network
attention level in most regions peaked in 2014, 2018, and 2019, and fell from 2020 to 2021
(Figure 5). At the national scale, the network attention level of the eastern, western, and
central regions showed a decreasing trend. Network users from the eastern region, Beijing,
Jiangsu, and Guangdong paid the most attention to Chengdu tourism, whereas the amount
of network attention given to Hainan Province was the lowest in the eastern region, which
was equivalent to the values in Qinghai and Ningxia. The network attention level for
Henan and Hubei in the central region is significantly higher than those of other regions,
while the western region has an increased average value due to the high degree of attention
paid to Sichuan Province. Chongqing and Shaanxi are also at the forefront in the western
Sustainability 2023,15, 8135 9 of 21
region in terms of network attention. However, the average value of the western region
was still lower than that of the central region, excluding data for Sichuan Province.
Sustainability 2023, 15, x FOR PEER REVIEW 9 of 21
2019. However, G value in 2020 was 6.51 times higher than the minimum value was (2019),
indicating that in 2020, the geographical concentration of Chengdu tourism’s network at-
tention level was higher than it had been in the previous years. However, according to the
concentration coefficient of potential tourists’ aention, the fluctuation range of
'
G value
from 2011 to 2021 was 12.01 to 48.27, and the maximum fluctuation range between 2011
and 2019 was 10.79. In 2020, the
'
G value was 36.26 higher than the minimum value was
(2019). As indicated by similar calculation results in the relevant literature [48], the poten-
tial tourist concentration coefficient,
'
G, can beer explain the concentration degree of
tourists and more sensitively reflect the degree of deviation than the G value can. The
concentration coefficient of potential tourists’ aention in each year demonstrates that the
actual distribution of network aention deviates more from the average geographical con-
centration index. This indicates that the spatial distribution of network aention is rela-
tively concentrated.
Table 2. Tourist concentration coefficient of Chengdu tourism network aention.
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
G 21.18 20.85 21.32 22.05 21.15 20.67 20.59 20.18 20.12 26.63 26.12
𝐺 17.96
ΔG 3.22 2.88 3.36 4.09 3.19 2.71 2.63 2.22 2.16 8.67 8.16
'
G 17.95 16.06 18.68 22.80 17.77 15.09 14.62 12.35 12.01 48.27 45.43
3.2.2. Differences in Provincial Distribution
From 2011 to 2021, the inter-provincial differences in Chengdu tourism’s network
aention were significant, showing a rough “∩”-shaped distribution trend. The network
aention level in most regions peaked in 2014, 2018, and 2019, and fell from 2020 to 2021
(Figure 5). At the national scale, the network aention level of the eastern, western, and
central regions showed a decreasing trend. Network users from the eastern region, Bei-
jing, Jiangsu, and Guangdong paid the most aention to Chengdu tourism, whereas the
amount of network aention given to Hainan Province was the lowest in the eastern re-
gion, which was equivalent to the values in Qinghai and Ningxia. The network aention
level for Henan and Hubei in the central region is significantly higher than those of other
regions, while the western region has an increased average value due to the high degree
of aention paid to Sichuan Province. Chongqing and Shaanxi are also at the forefront in
the western region in terms of network aention. However, the average value of the west-
ern region was still lower than that of the central region, excluding data for Sichuan Prov-
ince.
Figure 5. Network aention of Chengdu tourism in 31 provinces (autonomous regions and cities)
in China from 2011 to 2021. Regional division: the eastern region includes 11 provinces
Figure 5.
Network attention of Chengdu tourism in 31 provinces (autonomous regions and cities)
in China from 2011 to 2021. Regional division: the eastern region includes 11 provinces (municipal-
ities), including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong,
Guangdong and Hainan; the central region includes Heilongjiang, Jilin, Shanxi, Anhui, Jiangxi,
Henan, Hubei and Hunan; the western region includes Inner Mongolia, Guangxi, Chongqing,
Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia Xinjiang and has 12 provinces
(autonomous regions and municipalities).
3.2.3. Spatial Correlation
We used the global Moran’s I index to examine the spatial autocorrelation of Chengdu
tourism’s network attention from 31 provinces (autonomous regions and municipalities)
(Figure 6). We found that this value between 2011 to 2020 was negative, with the highest
value of 0.08 appearing in 2011, and the lowest value of 0.14 appearing in 2019. This indi-
cates that Chengdu tourism’s network attention from 31 provinces (autonomous regions
and municipalities) presents a weak spatial negative correlation; that is, the higher the
dispersion of the spatial distribution position (distance) is, the greater the spatial difference
in network attention is. Specifically, provinces (autonomous regions and municipalities)
that pay lots of attention to Chengdu tourism tend to be scattered in space (Sichuan,
Beijing, Guangdong, and Jiangsu). In other words, network attention is not highly concen-
trated. From the perspective of time evolution, Moran’s I index demonstrated a fluctuating
downward trend, with values of 0.10 in 2016 and 0.14 in 2021, with a gradual increase in
spatial dispersion.
Sustainability 2023,15, 8135 10 of 21
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(municipalities), including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian,
Shandong, Guangdong and Hainan; the central region includes Heilongjiang, Jilin, Shanxi, Anhui,
Jiangxi, Henan, Hubei and Hunan; the western region includes Inner Mongolia, Guangxi, Chong-
qing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia Xinjiang and has 12 prov-
inces (autonomous regions and municipalities).
3.2.3. Spatial Correlation
We used the global Moran’s I index to examine the spatial autocorrelation of
Chengdu tourism’s network aention from 31 provinces (autonomous regions and mu-
nicipalities) (Figure 6). We found that this value between 2011 to 2020 was negative, with
the highest value of 0.08 appearing in 2011, and the lowest value of 0.14 appearing in 2019.
This indicates that Chengdu tourism’s network aention from 31 provinces (autonomous
regions and municipalities) presents a weak spatial negative correlation; that is, the higher
the dispersion of the spatial distribution position (distance) is, the greater the spatial dif-
ference in network aention is. Specifically, provinces (autonomous regions and munici-
palities) that pay lots of aention to Chengdu tourism tend to be scaered in space (Si-
chuan, Beijing, Guangdong, and Jiangsu). In other words, network aention is not highly
concentrated. From the perspective of time evolution, Moran’s I index demonstrated a
fluctuating downward trend, with values of 0.10 in 2016 and 0.14 in 2021, with a gradual
increase in spatial dispersion.
Figure 6. Moran’s I index of Chengdu tourism network aention from 2011 to 2021.
The local Moran’s I indexes of Chengdu tourism’s network aention for 2011, 2014,
2018, and 2021 indicate that the number of provinces (autonomous regions and munici-
palities) located in the first quadrant (HH quadrant) is gradually increasing (Figure 7).
This indicates that the number of provinces paying lots of aention to Chengdu tourism
is increasing, and these provinces are gradually forming clusters in space. In 2021, the
provinces formed a local aggregation state in the eastern and central regions, and the local
Moran’s I index is in the third quadrant (LL quadrant). Most of them are in north-east
China, Inner Mongolia, and Xinjiang. These concentrated provinces account for the lowest
amount of aention being paid toward Chengdu tourism. The fourth quadrant (HL quad-
rant) belongs to the region itself, which pays a lot of aention to Chengdu tourism, but
the surrounding provinces account for a low level of aention. The spatial difference be-
tween them is large from Beijing, Liaoning, Shandong, Sichuan, Jiangsu, and Guangdong
in 2011 to Liaoning, Sichuan, and Guangdong in 2021. The second quadrant (LH quad-
rant) demonstrates that the region itself pays less aention to Chengdu tourism, but the
surrounding areas pay a lot of aention to it. The spatial difference between them is large
and widely distributed in the north-west and south-west regions. The local Moran’s I in-
dex reveals that the network aention level of 31 provinces (autonomous regions and mu-
nicipalities) toward Chengdu tourism has gradually increased, but the network aention
Figure 6. Moran’s I index of Chengdu tourism network attention from 2011 to 2021.
The local Moran’s I indexes of Chengdu tourism’s network attention for 2011, 2014,
2018, and 2021 indicate that the number of provinces (autonomous regions and munic-
ipalities) located in the first quadrant (HH quadrant) is gradually increasing (Figure 7).
This indicates that the number of provinces paying lots of attention to Chengdu tourism
is increasing, and these provinces are gradually forming clusters in space. In 2021, the
provinces formed a local aggregation state in the eastern and central regions, and the local
Moran’s I index is in the third quadrant (LL quadrant). Most of them are in north-east
China, Inner Mongolia, and Xinjiang. These concentrated provinces account for the low-
est amount of attention being paid toward Chengdu tourism. The fourth quadrant (HL
quadrant) belongs to the region itself, which pays a lot of attention to Chengdu tourism,
but the surrounding provinces account for a low level of attention. The spatial difference
between them is large from Beijing, Liaoning, Shandong, Sichuan, Jiangsu, and Guang-
dong in 2011 to Liaoning, Sichuan, and Guangdong in 2021. The second quadrant (LH
quadrant) demonstrates that the region itself pays less attention to Chengdu tourism, but
the surrounding areas pay a lot of attention to it. The spatial difference between them is
large and widely distributed in the north-west and south-west regions. The local Moran’s I
index reveals that the network attention level of 31 provinces (autonomous regions and
municipalities) toward Chengdu tourism has gradually increased, but the network atten-
tion levels of each province (autonomous regions and municipalities) differ significantly
and have various characteristics.
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levels of each province (autonomous regions and municipalities) differ significantly and
have various characteristics.
Figure 7. (a–d) Local Moran’s index of Chengdu tourism network aention in 31 provinces (auton-
omous regions and municipalities) in 2011, 2014, 2018, and 2021.
Figure 7.
(
a
–
d
) Local Moran’s index of Chengdu tourism network attention in 31 provinces (au-
tonomous regions and municipalities) in 2011, 2014, 2018, and 2021.
3.3. Impact Characteristics of Geographically Weighted Regression (GWR) Variables on
Network Attention
3.3.1. Model Construction and Inspection
Multiple linear regression and GWR were used to determine the factors influencing
spatiotemporal differentiation. The network attention of the source market is linked to
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multiple factors. The multiple linear regression model is a classic model for analyzing
the factors that influence dependent variables. These are estimated using an optimal
combination of multiple independent variables. The analysis of spatial characteristics
demonstrated that Chengdu tourism’s network attention has a certain spatial correlation
and regional differences. Among the factors influencing the network attention of the
tourist source market, the mutual independence between regions required by the linear
regression model is no longer present [
55
–
57
]. Considering that the estimation results of the
multivariate linear regression model may not be accurate, the GWR model was introduced
to analyze the impact indicators of Chengdu tourism’s network attention from 31 provinces
(autonomous regions and municipalities) between 2011 and 2021. This method facilitated
the exploration of spatial variation characteristics and the laws of impact factors in different
geographical locations. Due to space limitations, in this study, we only compared the cross-
sectional data of 2018 (before the pandemic) with those of 2021 (after the normalization of
pandemic prevention and control) and used the GWR model to present the analysis.
We obtained information on various variables that affected the evolution of spatial
and temporal patterns of Chengdu tourism’s network attention in 2018 and 2021 using
the GWR tool in ArcGIS10.2 and AICc information criterion method (Table 3). It can be
seen that the adjusted goodness-of-fit (R2Adjusted) of the model is above 79%, except for
the population size index. Therefore, excluding the population index at the end of the
year, the GWR estimation model can better simulate the impact of other variables on the
development of Chengdu tourism’s network attention from 31 provinces (autonomous
regions and municipalities).
Table 3. GWR model results in 2018 and 2021.
Index X1 X2 X3 X4 X5 X6
2018
AICc 420.45 −23.47 −158.73 −77.26 36.66 59.79
R2 0.50 0.87 0.90 0.82 0.80 0.80
R2Adjusted 0.49 0.85 0.89 0.80 0.80 0.80
2021
AICc 513.82 12.79 −84.89 −47.65 −15.98 45.84
R2 0.51 0.82 0.88 0.86 0.81 0.82
R2Adjusted 0.50 0.81 0.86 0.84 0.81 0.82
3.3.2. Impact of Various Variables on Network Attention
The regression results of the GWR model (Figure 8) show that the regression coeffi-
cients of five explanatory variables are different in each province (autonomous regions and
municipalities). This indicates spatial differences in the impact of each explanatory variable
on Chengdu tourism’s network attention. The influence of five explanatory variables on
Chengdu tourism’s network attention can be explained as follows: the number of mobile
Internet users > the number of people in higher education per 100,000 people > GDP per
capita > the urbanization rate > passenger throughput.
Sustainability 2023, 15, x FOR PEER REVIEW 13 of 21
decreased. From the perspective of time, the regression coefficient of the number of
mobile Internet users also increased, and the impact intensity gradually increased.
(2) The number of people in higher education per 100,000 people also has a significant
impact on Chengdu tourism’s network aention in all the provinces (autonomous
regions and municipalities). In 2018, high values were observed in Beijing, Shandong,
Jiangsu, Shanghai, Zhejiang, Guangdong, and Fujian, whereas low values were ob-
served in north-west China, Tibet, inner Mongolia, and Heilongjiang. In 2021, the
coefficients for Sichuan, Chongqing, Shaanxi, and Hubei increased. Provinces (au-
tonomous regions and municipalities) with high regression coefficients comprised
abundant higher education resources and a large number of college students. From
the perspective of time, the extreme value of the regression coefficient expanded by
2.30 times over the three years, indicating that the spatial difference in the impact of
education level in various provinces (autonomous regions and municipalities) has
widened further.
(3) Per capita GDP has a significant and positive impact on the aention given to
Chengdu tourism’s network aention in all provinces (autonomous regions and mu-
nicipalities). In 2018, high values were observed in Beijing, Jiangsu, Shanghai,
Zhejiang, and Guangdong, whereas low values were observed in the north-west re-
gion, Tibet, inner Mongolia, and Heilongjiang. The coefficient of the “Hu Line” in the
south-east area is significantly higher than that in the north-west region. From the
perspective of time, the impact intensity of GDP per capita gradually increased. In
2021, the regression coefficient of Chongqing was higher than it was in 2018, while
that in other regions remained unchanged.
(4) The urbanization rate also has a significant impact on Chengdu tourism’s network
aention in each province (autonomous regions and municipalities). However, its
regression coefficient is smaller than those of GDP per capita, the number of mobile
Internet users, and the number of people in higher education per 100,000 people. In
2018, high values were observed in Beijing, Shandong, Jiangsu, Shanghai, Zhejiang,
Guangdong, and Tianjin, while low values were observed in the north-west region,
Tibet, and Heilongjiang. In 2021, the value increased in Liaoning, Hubei, and Fujian.
(5) Passenger throughput had the lowest impact on Chengdu tourism’s network aen-
tion in all provinces (autonomous regions and municipalities). In 2018, high values
were observed in Beijing, Jiangsu, Shanghai, Zhejiang, and Guangdong, whereas low
values were observed in the north-west, south-west, north-east, and central regions.
In 2021, the regression coefficients for Tianjin, Chongqing, Yunnan, and Hainan de-
creased, while that of Shandong increased. From the perspective of time, the extreme
value of the regression coefficient of passenger throughput reduced, and the impact
intensity gradually weakened. A high regression coefficient value appeared in eco-
nomically developed regions and the main tourist source markets in Chengdu.
Figure 8. Cont.
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Figure 8. Cont.
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Figure 8. Cont.
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Figure 8. Spatial distribution of regression coefficients of GWR model to Chengdu tourism network
aention in 31 provinces (autonomous regions and municipalities) in 2018 and 2021.
4. Discussion
4.1. Analysis of Driving Mechanism
To create opportunities for China’s digital technology development and encourage
the construction of a new double-cycle development paern, exploring the allocation ef-
fect of public Internet aention and its role in stimulating consumption and other eco-
nomic performance is crucial for promoting the high-quality transformation and sustain-
able development of China’s urban economy [59,60]. The new means of achieving Internet
transmission have become essential for tourist cities and scenic spots to aract network
aention. Aracting network aention has a positive effect on improving a city’s popu-
larity and promoting offline traffic agglomeration. Relevant literature shows that cities
with high degrees of network aention are often accompanied by significant tourism pop-
ularity and traffic agglomeration [61].
The Internet aracts urban tourist traffic because some exclusive information and
characteristic resources of the city are highly consistent with the aention and interests of
tourists. Potential tourists from source cities are usually aracted to food, scenic spots,
climate, and other factors that are indigenous to the destination city [62,63]. This araction
drives potential tourists to retrieve information from the Internet. This search usually uses
the combination of “city + tourism/strategy/food/scenic spots/weather” as keywords. The
search behavior of a large number of potential tourists in each source city constitutes the
Baidu index of the source city to the tourist destination.
According to the above analysis, network aention in Chengdu tourism is signifi-
cantly affected by the level of Internet development, higher education, economic develop-
ment, urbanization development, and the travel population size. The research conclusions
are similar to previous research results [29,31,53]. The level of Internet development has
the most significant impact on the network aention given to Chengdu in all provinces
(autonomous regions and municipalities). Regions with high levels of Internet users have
a high corresponding search index and high level of network popularity. As they were
affected by COVID-19, an increasing number of potential tourists searched for strategies
to avail Chengdu tourism and visit related scenic spots. The use of the mobile Internet to
experience VR tourism has ensured a continued increase in network popularity. The num-
ber of people in higher education also had a significant impact on the network aention
given to Chengdu tourism in each province (autonomous regions and municipalities).
Relevant research demonstrates that education level is the main demographic feature that
affects tourists’ perception and decision making [64]. Regions with rich higher education
resources and a large number of college students had a high degree of network aention
given to Chengdu tourism. College students groups are using tourist sources more; they
use the Internet to search for tourism destinations, which is one of the reasons for the
increase in network aention.
Figure 8.
Spatial distribution of regression coefficients of GWR model to Chengdu tourism network
attention in 31 provinces (autonomous regions and municipalities) in 2018 and 2021.
(1)
The number of mobile Internet users has the most significant and positive impact on
Chengdu tourism’s network attention in all the provinces (autonomous regions and
municipalities). In 2018, high values were observed in Shandong, Jiangsu, Shanghai,
Zhejiang, Guangdong, Fujian, and Sichuan, whereas low values were observed in the
north-west region, Tibet, inner Mongolia, and Heilongjiang. In 2021, the coefficients
of Sichuan, Shaanxi, Hebei, and Henan increased, while those of Fujian and Yunnan
decreased. From the perspective of time, the regression coefficient of the number of
mobile Internet users also increased, and the impact intensity gradually increased.
(2)
The number of people in higher education per 100,000 people also has a significant
impact on Chengdu tourism’s network attention in all the provinces (autonomous
regions and municipalities). In 2018, high values were observed in Beijing, Shan-
dong, Jiangsu, Shanghai, Zhejiang, Guangdong, and Fujian, whereas low values were
observed in north-west China, Tibet, inner Mongolia, and Heilongjiang. In 2021,
the coefficients for Sichuan, Chongqing, Shaanxi, and Hubei increased. Provinces
(autonomous regions and municipalities) with high regression coefficients comprised
abundant higher education resources and a large number of college students. From
the perspective of time, the extreme value of the regression coefficient expanded by
2.30 times over the three years, indicating that the spatial difference in the impact of
education level in various provinces (autonomous regions and municipalities) has
widened further.
(3) Per capita GDP has a significant and positive impact on the attention given to Chengdu
tourism’s network attention in all provinces (autonomous regions and municipalities).
In 2018, high values were observed in Beijing, Jiangsu, Shanghai, Zhejiang, and
Guangdong, whereas low values were observed in the north-west region, Tibet, inner
Mongolia, and Heilongjiang. The coefficient of the “Hu Line” in the south-east area is
significantly higher than that in the north-west region. From the perspective of time,
the impact intensity of GDP per capita gradually increased. In 2021, the regression
coefficient of Chongqing was higher than it was in 2018, while that in other regions
remained unchanged.
(4)
The urbanization rate also has a significant impact on Chengdu tourism’s network
attention in each province (autonomous regions and municipalities). However, its
regression coefficient is smaller than those of GDP per capita, the number of mobile
Internet users, and the number of people in higher education per 100,000 people. In
2018, high values were observed in Beijing, Shandong, Jiangsu, Shanghai, Zhejiang,
Guangdong, and Tianjin, while low values were observed in the north-west region,
Tibet, and Heilongjiang. In 2021, the value increased in Liaoning, Hubei, and Fujian.
(5) Passenger throughput had the lowest impact on Chengdu tourism’s network attention
in all provinces (autonomous regions and municipalities). In 2018, high values were
observed in Beijing, Jiangsu, Shanghai, Zhejiang, and Guangdong, whereas low values
were observed in the north-west, south-west, north-east, and central regions. In 2021,
Sustainability 2023,15, 8135 16 of 21
the regression coefficients for Tianjin, Chongqing, Yunnan, and Hainan decreased,
while that of Shandong increased. From the perspective of time, the extreme value of
the regression coefficient of passenger throughput reduced, and the impact intensity
gradually weakened. A high regression coefficient value appeared in economically
developed regions and the main tourist source markets in Chengdu.
4. Discussion
4.1. Analysis of Driving Mechanism
To create opportunities for China’s digital technology development and encourage the
construction of a new double-cycle development pattern, exploring the allocation effect
of public Internet attention and its role in stimulating consumption and other economic
performance is crucial for promoting the high-quality transformation and sustainable
development of China’s urban economy [
59
,
60
]. The new means of achieving Internet
transmission have become essential for tourist cities and scenic spots to attract network
attention. Attracting network attention has a positive effect on improving a city’s popularity
and promoting offline traffic agglomeration. Relevant literature shows that cities with high
degrees of network attention are often accompanied by significant tourism popularity and
traffic agglomeration [61].
The Internet attracts urban tourist traffic because some exclusive information and
characteristic resources of the city are highly consistent with the attention and interests
of tourists. Potential tourists from source cities are usually attracted to food, scenic spots,
climate, and other factors that are indigenous to the destination city [
62
,
63
]. This attraction
drives potential tourists to retrieve information from the Internet. This search usually uses
the combination of “city + tourism/strategy/food/scenic spots/weather” as keywords.
The search behavior of a large number of potential tourists in each source city constitutes
the Baidu index of the source city to the tourist destination.
According to the above analysis, network attention in Chengdu tourism is significantly
affected by the level of Internet development, higher education, economic development,
urbanization development, and the travel population size. The research conclusions are
similar to previous research results [
29
,
31
,
53
]. The level of Internet development has
the most significant impact on the network attention given to Chengdu in all provinces
(autonomous regions and municipalities). Regions with high levels of Internet users have
a high corresponding search index and high level of network popularity. As they were
affected by COVID-19, an increasing number of potential tourists searched for strategies
to avail Chengdu tourism and visit related scenic spots. The use of the mobile Internet to
experience VR tourism has ensured a continued increase in network popularity. The number
of people in higher education also had a significant impact on the network attention given
to Chengdu tourism in each province (autonomous regions and municipalities). Relevant
research demonstrates that education level is the main demographic feature that affects
tourists’ perception and decision making [
64
]. Regions with rich higher education resources
and a large number of college students had a high degree of network attention given to
Chengdu tourism. College students groups are using tourist sources more; they use the
Internet to search for tourism destinations, which is one of the reasons for the increase in
network attention.
The level of economic development also has a significant impact on network attention
given to Chengdu tourism for all provinces (autonomous regions and municipalities).
Simultaneously, the level of economic development is also an important factor affect-
ing the travel potential of tourist sources [
65
]. The more potential tourists there are in
economically developed regions, the more attention is paid to Chengdu tourism. As a
world-renowned tourism destination, Chengdu attracts the attention of potential tourists
in the country, especially, in economically developed regions, which are the main tourist
sources in Chengdu. There are many potential tourists and high-level network attention
regions. Further, the level of urbanization also has a significant impact on the network
attention given to Chengdu tourism for all provinces (autonomous regions and municipali-
Sustainability 2023,15, 8135 17 of 21
ties). The urbanization rate has a linear trend with the domestic travel rate and per capita
tourism expenditure of residents [
66
]. Urbanization is the developmental trend of modern
civilization, and the generation and development of tourism cannot be separated from cities.
With the improvement of urbanization, the disposable income of residents will increase, the
domestic travel rate of residents will increase significantly, and the corresponding network
attention given to tourism destinations will also increase. The scale of the travel population
had a relatively low impact on network attention given to Chengdu tourism for each
province (autonomous regions and municipalities). Passenger throughput is an important
indicator of the degree of regional economic development and the number of outbound
tourists. Since the outbreak of the COVID-19 pandemic, both the tourism industry and
the air transport industry have been affected. The number of tourists traveling via air has
declined year-on-year. In addition, the Chengdu–Chongqing Passenger Dedicated Line
and the Chengdu–Guiyang–Kunming High-speed Railway were successively opened. As a
result, the regression coefficient of passenger throughput in some regions decreased, and
the impact intensity of indicators gradually decreased.
To summarize the findings presented in this work, tourist cities should transform
network traffic into effective economic growth. Tourism effectively promotes economic
growth in economically underdeveloped areas, which is in line with the goal to “reduce
inequality within and among countries“ as part of the Sustainable Development Goals
(SDGs). The development of Internet technology and the popularization of new social
platforms have accelerated the transmission of the concept of urban tourism marketing
and fueled the comprehensive development of cities. Western cities can spread urban
tourism marketing to second- and third-tier cities in China, and also, they have a certain
reference significance according to other cities in the world. City managers should solve
the key problem of the transformation from “network attention dividend” to “development
power”, pay attention to the dynamic development of cities for a long time, avoid short-
sightedness and policies that seek to achieve quick success and instant benefits, and
reduce the unsustainable risks of tourism development, constantly improve the city’s
competitiveness and the supply capacity of tourism public services, transform the focus of
urban tourism network into the growth of the tourism population, and drive the growth of
per capita tourism consumption with the flow effect.
4.2. Limitations
As we were limited to focusing on the source of network attention, this study only
discussed Chengdu tourism network attention data based on the Baidu index from 2011
to 2021, and data from short video platforms and social networks were not included.
There was also no statistical analysis of the spatiotemporal characteristics of more detailed
administrative units (prefecture level) on Chengdu tourism network. In addition, this
paper used “Chengdu Tourism”, “Chengdu Scenic Spot”, and “Chengdu Strategy” as
keywords for the online search. Although it has some representativeness, it cannot fully
cover potential tourists’ attention given to Chengdu’s tourism network. Along with the
retrieved data, the dynamic change in the proportion of data from PC and mobile networks
and the characteristics of potential tourists behind them need to be further described
in detail.
5. Conclusions
Based on network attention data and related analysis methods, this study verifies
the spatial and temporal patterns of network attention from 31 provinces (autonomous
regions and municipalities) in China given to a single tourism city and examines the
factors that affect network attention. (1) From 2011 to 2021, Chengdu tourism’s network
from 31 provinces (autonomous regions and municipalities) in China showed an overall
“M”-type fluctuation trend, with significant seasonal differences. (2) Chengdu tourism’s
network attention from 31 provinces (autonomous regions and municipalities) was in a
state of disequilibrium and significantly differed in terms of space, with the overall trend of
Sustainability 2023,15, 8135 18 of 21
an “
∩
”-shaped distribution. The trend decreased from eastern to western to central regions.
The network attention value of users in the eastern region given to Chengdu tourism was
the highest, and those of the 31 provinces (autonomous regions and municipalities) showed
a weak spatial negative correlation. (3) The number of mobile Internet users, the number
of people in higher education per 100,000 people, GDP per capita, the urbanization rate,
and passenger throughput are important factors that affect the network attention given to
Chengdu tourism. The regression coefficient of each index is generally low in the west and
high in the east, and the coefficient of the south-east region of the “Hu Line” is significantly
higher than that of the north-west region.
Urban tourism network attention is an important scale for measuring the competitive-
ness of the urban tourism industry, tourism attraction, and cultural soft power, and it is
an important dimension of urban brand building and cyberspace governance. Improving
network attention, and thus, improving the competitiveness of urban cyberspace and devel-
oping soft power is an aspect that decision makers in many cities, especially tourism cities,
have been considering [
25
,
67
–
70
]. Therefore, cities in western China should strengthen
the following two aspects in the process of enhancing tourism attraction, soft power of
cyberspace and urban brand communication.
First, decision makers in cities should optimize the network attention rating system for
urban tourism. As they are influenced by factors such as the economic development level,
network communication level, education level, urbanization rate, and tourist population,
the Yangtze River Delta, Beijing–Tianjin–Hebei, and Guangdong–Hong Kong–Macao Bay
Area have formed growth poles of urban tourism network attention in the western region.
However, the results indicate that in addition to the relatively high level of network
attention from economically developed regions, the urban tourism network attention level
in the central and western regions was low and did not form an agglomeration effect.
Second, they should strengthen publicity and promote the city’s image. Tourism is
the main driving force for building a city’s image, cultural IP, and brand. A city’s image
is more vibrant and sustainable than economic industries are. From the perspective of
city image publicity, developed cities, characteristic cities, cultural cities, or tourist cities
can ensure the rapid dissemination of the city’s image over a certain period or through
a certain event in the Internet era. For example, during the Spring Festival and National
Day in Beijing, “Tavern” in Chengdu, “8D Chongqing”, and “Dingzhen’s hometown”
(Litang County, Ganzi Tibetan Autonomous Prefecture, etc.), the city’s network attention
and tourism activities are rapidly combined. In fact, the city’s business development,
urban renewal, cultural and tourism integration, and other in-depth integrations create
opportunities for the Internet economy and tourism economy. Western cities should seize
the geometric diffusion effect of the Internet, strengthen the publicity and promotion of
urban characteristics, build up the city’s brand, encourage tourism industry development,
and determine a new advantage for the development of western cities in the Internet era.
Author Contributions:
C.-H.X. was responsible for data collection and writing: Y.-P.B. proposed the
research ideas and methods of the manuscript; C.-H.X. and Y.-P.B. were responsible for creating the
figures and forms. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Project of the National Natural Science Foundation of
China (40771054), Foundation of Gansu Philosophy and Social Science Planning Project (2021YB025).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: Thanks to the hard-working editors and valuable comments from reviewers.
Conflicts of Interest: The authors declare no conflict of interest.
Sustainability 2023,15, 8135 19 of 21
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