Spatial-Temporal Evolutionary Characteristics and Factors of

Network Attention to Rural Tourism: A Study Based on Big Data

Yuting Li

and Xuefeng Wang

School of Economics and Management, Beijing Jiaotong University, Beijing, China

Keywords: Rural Tourism, Network Attention, Big Data, Baidu Index.

Abstract: The development of rural tourism is conducive to promoting the implementation of rural revitalization strat-

egy. Visitors frequently search for information about rural tourism on online platforms, generating network

attention. Based on Baidu index, using web data mining and mathematical statistics, this paper analyze the

spatial-temporal evolutionary characteristics and factors of network attention to rural tourism. The findings

are as follows: (1) from 2011 to 2020, network attention to rural tourism has the characteristics of first rising

and then falling, and is the highest in spring and autumn, and the lowest in winter. (2) network attention to

rural tourism has no obvious agglomeration characteristics among the 31 provinces in China, but the whole

shows the trend of decreasing from the east to the middle to the west. (3) network attention to rural tourism

is related to climate comfort degree, economic development level, network development, socio-demography

characteristic, hospitality service ability, tourism resources endowment and accessibility. The study aims to

provide a reference basis for the planning and development of rural tourism destinations.

1 INTRODUCTION

As a typical information-intensive industry, tourism

cannot be separated from the development of the In-

ternet, and search engines have become an important

tool for the majority of tourists to obtain information.

Tourists usually use the Internet to search for infor-

mation about tourist destinations before their trips to

assist in decision making, and the records and traces

left on the websites reflect the degree of their atten-

tion to tourist destinations, i.e., the network attention.

Based on the search volume of a large number of In-

ternet users, the web focus scientifically analyzes and

calculates the search browsing of Internet users in

search engines for a certain keyword, which not only

reflects the travel preferences of consumers, but also

provides a portrait of target people for each tourism

destination. It has been found that network attention

has a precursor effect (Li et al., 2008). Web big data

may help predict visitor traffic and ultimately visitor

spending, complementing traditional data (Artola,

2015). Other scholars have studied the spatial and

temporal evolution characteristics and influencing

https://orcid.org/0000-0001-8610-8503

https://orcid.org/0000-0002-2836-6958

factors of tourism safety and tourism public opinion

network attention, which provide references for tour-

ism safety management and tourism destination pub-

lic opinion monitoring and governance (Liu et al.,

2019; Zou et al., 2015). Tourism destinations can

forecast and manage traffic based on the temporal dis-

tribution of network attention, and implement precise

marketing based on the source of potential visitors for

planning and management.

In recent years, the Chinese government has vig-

orously implemented the rural vitalization strategy.

As an important means to achieve rural revitalization,

rural tourism is conducive to increasing farmers' in-

come, improving the economic structure of rural ar-

eas, and achieving green and sustainable development

in rural areas (Zhao, 2007). To some degree, network

attention to rural tourism reflects the development

scale and trend of rural tourism, which can provide

some realistic reference for the construction of tour-

ism destinations. How to promote the development of

rural tourism has become the focus of attention at pre-

sent. Most studies have focused on the development

mode (Zhang et al, 2012; Ma et al, 2007), develop-

ment and operation of rural tourism and its impact

Li, Y. and Wang, X.

Spatial-Temporal Evolutionary Characteristics and Factors of Network Attention to Rural Tourism: A Study Based on Big Data.

DOI: 10.5220/0012072500003624

In Proceedings of the 2nd International Conference on Public Management and Big Data Analysis (PMBDA 2022), pages 225-232

ISBN: 978-989-758-658-3

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

225

(Wang, 2009; Du & Su, 2011; Yang, 2017). Network

attention directly affects the development of rural

tourism, and the current research has yet to analyse

the characteristics of the spatial and temporal evolu-

tion of rural tourism network attention and the factors

influencing it at a national level. How to promote ru-

ral tourism through increasing network attention at a

macro level is crucial to the implementation of the ru-

ral revitalization strategy. Based on the massive and

immediate nature of big data, this paper takes the net-

work attention of rural tourism in 31 provinces and

cities as the research object, explore how rural tour-

ism network attention changes in time and space, and

what factors influence the spatial and temporal distri-

bution of rural tourism network attention. Based on

Baidu index, this paper uses research methods such as

big data mining, index analysis and correlation anal-

ysis. The paper conducts research from the perspec-

tive of public attention, with a view to providing a

reference basis for the development and marketing of

rural tourism products and services.

Firstly, this paper examines the temporal evolu-

tionary characteristics of network attention to rural

tourism and factors; secondly, examines the spatial

evolutionary characteristics of network attention to

rural tourism and factors; and finally, there are con-

clusions and discussion.

2 METHODS AND MATERIALS

2.1 Data Collection

Baidu Index is a data sharing platform launched by

Baidu, based on Baidu's massive amount of internet

users' behavioural data, and is also one of the statisti-

cal analysis platforms with the highest data usage rate

and the largest number of people using it. Based on

the search volume of Internet users in Baidu, Baidu

index uses keywords as the statistical object to scien-

tifically analyse and calculate the weighted sum of the

search frequency of each keyword in Baidu web

searches.

Network attention to rural tourism is based on the

Baidu index, which is the frequency weighted sum of

users' searches for rural tourism related keywords on

the Baidu search engine. Essentially, it is the use of

big data to obtain information on user behaviour,

which can provide a comprehensive and accurate por-

trayal of the demand characteristics of potential tour-

ists. This paper takes the network attention of rural

tourism in 31 provinces, municipalities and autono-

mous regions in China as the research object. Using

the Baidu index comparison function, the three high-

est search indices were selected, namely rural tour-

ism, agritainment and picking. The Baidu index

search platform was used to obtain the web attention

data of 31 provincial-level administrative regions in

China from January 1, 2011 to December 31, 2020 for

each of the three keywords, and the sum of the daily

average network attention of the three keywords was

used to represent the daily average network attention

to rural tourism, and the network attention to rural

tourism mentioned subsequently were the sum of the

three.

2.2 Methodology

This paper uses the seasonal concentration index,

Herfindahl-Hirschman index, Gini coefficient and ge-

ographical concentration index to analyse the spatial

and temporal evolution characteristics of network at-

tention, and uses SPSS to conduct Person correlation

analysis to explore its influencing factors.

(1) Seasonal concentration index

𝑆=



(𝑥



−8.33)







/12

(1)

Where 𝑥



denotes the proportion of network at-

tention to rural tourism in month 𝑖 to network atten-

tion to rural tourism for the year, 𝑖 =1, 2, … ,12. S is

the seasonal concentration index. The larger the S-

value, the more concentrated the temporal distribu-

tion of rural tourism web attention and the more pro-

nounced the off-peak season, and vice versa, the more

dispersed the distribution.

(2) Herfindahl-Hirschman index

𝐻=𝑥











(2)

Where 𝐻 is the Herfindahl-Hirschman index,

which fluctuates between 1/12 and 1. The closer 𝐻

is to 1, the greater the temporal variation in network

attention and the higher the concentration, and vice

versa.

(3) Geographical concentration index

𝐺=100×



(𝑝



/𝑝)







(3)

Where 𝑝



is the annual rural tourism network at-

tention for 𝑖. 𝐺 is the geographical concentration in-

dex. The closer it is to 100, the more concentrated the

spatial distribution of rural tourism network attention

is, and vice versa, the more dispersed the spatial dis-

tribution is.

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(4) Gini coefficient

Gini=1+1/n

2/n

p(p

+2p

+3p

+…+np

)

(4)

Where 𝑝̅ is the average network attention of ru-

ral tourism. p

,…,p

is the ranking of the net-

work attention of rural tourism in 31 provinces and

cities in descending order. Gini is the Gini coeffi-

cient, which fluctuates between 0 and 1. The closer

Gini is to 1, the more disparate the spatial distribu-

tion of rural tourism is, and vice versa, the more even

it is.

3 RESULTS

3.1 Temporal Evolutionary

Characteristics and Factors

3.1.1 Temporal Evolutionary Characteristics

From the network attention of rural tourism in each

year from 2011 to 2020 (Figure 1), the network atten-

tion tends to rise and then fall, with overall rising.

Among them, agritainment rose and then fell, with

the overall trend declining; rural tourism rose and

then fell, with the overall trend rising; picking showed

an M, with the overall trend up. It can be seen that

during the decade, the popularity of Agritainment is

decreasing, but still at a high level overall, while the

popularity of rural tourism and picking is increasing,

with rural tourism increasing the most, which is con-

sistent with the stage of development of rural tourism

in China. As the primary form of rural tourism devel-

opment since the 1980s, China's agritainment has be-

come more mature and cannot meet the deeper needs

of the majority of tourists. Tourists tend to prefer

sightseeing and experiential products such as picking.

In addition, as the three issues of agriculture, the

countryside and farmers become more prominent in

the new era, China is paying more and more attention

to the development of the countryside, increasing the

development of rural tourism, and rural tourism has

become a hot topic.

In terms of months, the highest value of network

attention to rural tourism over the decade occurred in

August 2015 (Figure 2), probably due to the State

Council's issuance of Several Opinions on Promoting

Tourism Reform and Development in 2014, which

clearly proposed vigorous development of rural tour-

ism, reflecting the guiding role of policy on tourism

development. The lowest network attention occurred

in January 2020, probably due to two reasons: the

cold climate and the outbreak of the new crown epi-

demic. Throughout the year, the annual network at-

tention curve to rural tourism is relatively flat, indi-

cating that its network attention is spread out over

time, with no obvious off-peak season. The reason for

this may be that most visitors choose local and neigh-

bouring rural destinations and spend less time there,

so rural tourism does not have strong seasonal char-

acteristics. In order to observe the month-to-month

trend of network attention to rural tourism more intu-

itively, the proportion of network attention in each

month from 2011 to 2020 was calculated (Figure 3).

During the ten-year period, the network attention to

rural tourism showed a gentle bimodal pattern, with

attention mainly concentrated in April-June and Sep-

tember-October, i.e., the spring and autumn seasons,

with the highest peaks occurring in May and October,

and people having more leisure time during May Day

and November, which should be related to the holiday

system. The December-February period saw a low

level of interest in, probably due to low temperatures.

Figure 1: Network attention to rural tourism by year.

Spatial-Temporal Evolutionary Characteristics and Factors of Network Attention to Rural Tourism: A Study Based on Big Data

227

Figure 2: Network attention by month.

Figure 3: Share of network attention by month.

The seasonal concentration index and Herfindahl-

Hirschman index were calculated separately for each

year of rural tourism web attention (Table 1). As can

be seen, the seasonal concentration index fluctuates

between 1.034 and 1.751 with a small fluctuation, in-

dicating that the temporal variation in rural tourism

web attention is moderate for each month of the year,

while maintaining similar seasonal differences from

year to year. The largest seasonal concentration index

is in 2020, probably due to the outbreak of the New

Crown epidemic in the first half of 2020, when tour-

ism was almost at a standstill, and the gradual recov-

ery of tourism in the second half of the post-epidemic

period. Similarly, the Herfindahl-Hirschman index

fluctuates between 0.085 and 0.087, suggesting that

rural tourism network attention does not vary signifi-

cantly between decades and is more evenly distrib-

uted.

Table 1: Seasonal characteristics of Network attention to rural tourism.

Year 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

S 1.140 1.662 1.448 1.504 1.561 1.034 1.228 1.198 1.296 1.751

H 0.085 0.087 0.086 0.086 0.086 0.085 0.085 0.085 0.085 0.087

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3.1.2 Temporal Evolutionary Factors

There are seasonal differences in rural tourism net-

work attention, with a higher concentration of atten-

tion in April-June and September-October, with the

highest peaks in May and October, and a preliminary

judgement that the differences may be related to the

holiday system and climate comfort.

Leisure time affects the decision making of tour-

ists' travel. In order to scientifically and accurately an-

alyse the influence of the holiday system on network

attention, this paper refers to the research method of

setting up a virtual index by Ma et al (2011), consid-

ering the precursor effect of tourism network attention

and the length of each holiday (Li et al., 2008). The

virtual index is set to 0.75, 1 and 0.75 for the months

of March, April and May which are affected by the

Qingming Festival, Labour Day and Dragon Boat

Festival, 0.75 for the month of October where the Na-

tional Day is located, and 1 for the month of Septem-

ber which is affected by the National Day and Mid-

Autumn Festival. The virtual index for July and Au-

gust is set to 0.5 and 0.25 respectively, as the summer

holidays are not as widespread as the national holi-

days, so the virtual index for June is set to 0.75. The

collected data were collated and tallied (Table 2). The

results show that the holiday system did not pass the

significance test, indicating that the highest peaks in

May and October and the lowest peaks in December

and January may be due to other factors. For example,

although there are more holidays in February, the

countryside is less attractive and people are less will-

ing to travel as it is not the ripening season for fruits

and vegetables, and the temperature is lower.

Table 2: Impact of the holiday system.

Factors Person relevance Significance

Holiday system 0.275 0.386

Climate is often closely related to tourism activi-

ties and has a direct impact on the perceived experi-

ence of tourists. Climate comfort is an indicator that

evaluates human comfort in different climatic condi-

tions from a meteorological point of view. In this pa-

per, the temperature and humidity index is used as its

factor. Reference was made to the method of setting

up a virtual index by Ma (2011), assigning values to

each level of the temperature and humidity index.

This paper takes three representative provinces and

** indicates significant correlation at the 0.01 level (two-

sided); * indicates significant correlation at the 0.05 level

(two-sided).

cities in the north, middle and south of Beijing,

Jiangsu and Guangdong as examples, and selects the

cumulative monthly average temperature and

monthly average relative humidity data of each place,

calculates the temperature and humidity index, virtual

index and network attention, and uses SPSS software

to conduct correlation analysis on the virtual index

and network attention of each place (Table 3), the re-

sults show that climate comfort and rural tourism net-

work attention are very significantly The correlation

is that a comfortable climate is an important guarantee

for carrying out tourism activities, stimulating the de-

mand of tourists and increasing the popularity of the

destination.

Table 3: Impact of climate comfort

Region Person relevance Significance

Beijing 0.714

0.009

Jiangsu 0.672

0.017

Guangdong 0.606

0.037

3.2 Spatial Evolutionary

Characteristics and Factors

3.2.1 Spatial Evolutionary Characteristics

In terms of the difference between provinces, prov-

ince with the highest level of attention is Guangdong

(Figure 4). According to data released by the Baidu

Institute of Statistics and Traffic, among the prov-

inces, Guangdong has the largest number of Internet

users. Therefore, it may be related to the degree of In-

ternet development. Provinces ranked 2-3 were Bei-

jing and Zhejiang, all in the more economically de-

veloped eastern region, indicating that the market de-

mand for rural tourism in the eastern region is more

mature. All the three provinces with the least attention

are located in the western region of China, indicating

that the rural tourism market demand in the western

region needs to be cultivated. Considering the eco-

nomic differences between the eastern and western re-

gions, it is speculated that the network attention to ru-

ral tourism may be related to the level of economic

development.

The geographical concentration index and Gini

coefficient were calculated separately for each year

from 2011 to 2020 (Table 4). The geographical con-

centration index of rural tourism network attention for

each province fluctuated between 19.31 and 19.80

Spatial-Temporal Evolutionary Characteristics and Factors of Network Attention to Rural Tourism: A Study Based on Big Data

229

from 2011 to 2020, indicating that the inter-provincial

concentration of rural tourism network attention is

small and distributed. The Gini coefficient fluctuates

between 0.22-0.27 over the decade, with 0.2-0.29 in-

dicating a low index rating according to the UNDP

banding. It means that inter-provincial network atten-

tion is more evenly spread with insignificant differ-

ences. This means that public demand for rural tour-

ism, as represented by the degree of internet attention,

is dispersed, and that there is more market space for

rural tourism and high potential for development.

Figure 4: Total network attention by provincial districts.

Table 4: Inter-provincial differences in network attention to rural tourism.

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

G 19.67 19.66 19.80 19.86 19.61 19.49 19.43 19.35 19.45 19.31

Gini

0.25 0.25 0.26 0.27 0.25 0.24 0.23 0.23 0.24 0.22

3.2.2 Spatial Evolutionary Factors

According to the results of the above study, there are

certain inter-provincial differences and inter-regional

differences in rural tourism network attention. There

are two main sources of factors on the spatial evolu-

tion selected for this paper. Firstly, the above study

shows that there are regional differences in rural tour-

ism network attention, presumably related to the level

of regional economic development and the degree of

network development. Secondly, it has been found

that rural tourism network attention is related to tour-

ism resource endowment, hospitality service capacity,

and transportation convenience (Rong & Tao, 2020).

Another study chose the socio-demographic charac-

teristics factor to study its relevance (Zou et al., 2015).

Considering the above factors and the characteristics

of this study, five factors are analysed: level of eco-

nomic development, network development, socio-de-

mographic characteristics, hospitality service capac-

ity, tourism resource endowmen

and accessibility.

The data collected were collated for statistical pur-

poses (Table 5).

Table 5: Spatial evolutionary factors.

Factors Variable indicators Person relevance Significance

Level of economic

development

Regional GDP .867

.000

Disposable income per inhabitant .518

.003

Network development

Size of Internet users .729

.000

Internet

enetration rate .429

.016

Socio-demographic

characteristics

Age

0-14 years -.362

.045

15-64 years .006 .973

65+ years .589

.000

Education

level

Junior High School -.243 .188

High School .424

.017

Tertiary and above .098 .599

Hospitality

service capacity

Number of star-rated restaurants .736

.000

Number of homestays .754

.000

Tourism resource endowment National Rural Tourism Key Village .431

.015

Accessibility Road miles .394

.028

The level of regional economic development af-

fects people's willingness to travel. GDP and per cap-

ita disposable income of residents of each province in

2019 were selected as the indicators. It can be seen

that the two show a very significant correlation with

network attention, with disposable income per resi-

dent being the basis for people's consumption and a

prerequisite for undertaking rural tourism activities.

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Regional GDP has the strongest correlation of all in-

dicators, at 0.867, indicating that it has the greatest

influence on people's rural tourism decisions.

Internet development directly affects network at-

tention. Using the scale of Internet users and Internet

penetration rate in different regions as indicators, the

results show that they are significantly correlated with

network attention. At the same time, the more devel-

oped the regional network is, the faster and more ex-

tensive the information dissemination is, thus pro-

moting further increase in network attention.

Users with different attributes have different pref-

erences for tourism. For example, older people prefer

recreation tourism and younger parents prefer parent-

child tourism, and socio-demographic characteristics

may affect network attention to rural tourism. Using

age and education level as indicators, results show

that 0-14 years old age and network attention showed

a significant negative correlation. 0-14 years old pop-

ulation does not yet have economic ability and has

limited use of the Internet, so the more the proportion,

the lower the network attention. High school educa-

tion showed a significant correlation and age 65+

showed a highly significant correlation, suggesting

that age and literacy base also influence the magni-

tude of regional network attention to some extent.

The capacity of hospitality services is a guarantee

for the development of tourism destinations. The

number of star-rated restaurants and the number of

homestays are chosen as indicators, as people mostly

choose to travel freely, in addition, one of the main

features of rural tourism is to experience local food

and folklore. The results show that the two are very

significantly correlated with network attention, indi-

cating the importance of hospitality service capacity

in the development of rural tourism. As a representa-

tive of the quality of tourism facilities and service lev-

els, star-rated restaurants are important contact points

for tourists travelling and determine their experience.

At the same time, with the rapid development of the

experience economy and the upgrading of consumer

demand, especially in rural tourism, more and more

tourists are choosing local homestay.

Tourism resources are the core element of rural

tourism development, and are the premise and foun-

dation of rural tourism. The number of national rural

tourism key villages in each region is chosen as the

indicator, and the results show that it is significantly

correlated with network attention. The tourism re-

sources of a region will first radiate the surrounding

areas, forming a regional agglomeration effect, and

the number of tourism resources directly affects the

formation of rural tourism hotspots, thus affecting the

regional network attention.

Transportation directly affects the accessibility of

tourist destinations. Considering that tourists in rural

tourism mostly choose to drive themselves, road

miles was chosen as the indictor. The results show

that the road miles is significantly correlated with net-

work attention. To get rich, first build roads, roads

make tourist destinations more closely connected to

their sources, and are essential for the development of

rural tourism.

4 DISCUSSION

Based on the Baidu index, this paper analyses the spa-

tial and temporal evolutionary characteristics of net-

work attention to rural tourism and its factors. The

study combines rural tourism with online big data, en-

riching the research related to rural tourism. There are

also practical implications. The spatial-temporal evo-

lution of network attention shows that rural tourism

currently suffers from declining fervour, imperfect in-

frastructure construction and marketing and promo-

tion tools that need to be strengthened. Rural tourism

destinations should enrich rural tourism product sys-

tems, improve rural tourism infrastructure, develop

differentiated marketing strategies and strengthen

public relations management.

In addition, there are certain limitations. Firstly,

the measurement of network attention to rural tourism

is relatively single, considering only the Baidu index

and lacking comprehensive consideration of other

platforms. Secondly, the index system of factors

needs to be improved, for example, due to the lack of

data, it is difficult to quantify the government policy

orientation and other indicators. Rural tourism has

now entered a new period of development, and there

is still much room for research in the future.

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