Public Health Research on Night Tourism of Tourist Destinations

Based on Big Data: Takes the Ancient City of Lijiang as an Example

Wenjin Chen and Qianwen Kan

Zhejiang Gongshang University, Hangzhou, Zhejiang, China

Keywords: Public Health, Big Data, Night Travel, Lijiang Ancient City.

Abstract: With the continuous development of China's tourism industry, the forms of tourism are also gradually

diversified, among them, night tourism has become a widely popular way for tourists to travel. Compared

with daytime travel, night travel has a more sense of atmosphere, but due to the dim light at night, it is easy

to cause problems such as walking on air and stampede due to dense crowds. At the same time, due to the

epidemic, night travel also increases the difficulty of managing tourists. Therefore, the study of night

tourism helps to understand the trend of tourists' night tourism, and then maintain the public health of

destinations and tourists. This article in the ancient city of Lijiang as the research object, through data crawl

software to collect a certain time tourists comments on ctrip comments, and through the emotion analysis

and content analysis method to tourists in the ancient city of Lijiang at night travel location preference is

analyzed, and by strengthening the management of these sites to the maintenance of the ancient city of

Lijiang public health. The study found that: first, through high-frequency word analysis, experience

projects, ancient buildings and beautiful scenery can better attract tourists; second, through the emotional

analysis of tourists, a strong sense of atmosphere can more resonate with tourists, thus causing tourists to

gather.

1 INTRODUCTION

As a part of tourism, night tourism fills the time

vacancy of carrying out tourism activities, and also

promotes the rapid development of night economy,

which is an important form to realize the

development of China's tourism in China. With the

active support of the government, night tourism has

become a new way of tourism. In the study of night

travel, influenced by the regional, economic and

cultural factors, domestic and foreign for night

tourism research differences, night travel has not

been great attention by foreign scholars, related

research is less, Bromley scholars first found in

199424 hours city has become the focus of many

leisure and housing development (Bromley, 1994).

The Lemelin's field surveys of insect tourist

destinations have found that firefly travel is very

popular overnight in Mexico (Lemelin, 2021). The

above two scholars all found that night tourism can

effectively drive the development of the local night

economy, and showed a positive attitude towards

promoting night tourism. At the beginning of this

century, some scholars in China carried out relevant

research on night tourism, but they only stayed at

the theoretical level. After night tourism was

constantly praised by tourists, more scholars carried

out various research and analysis on night tourism.

Dai Bin believes that the development of night

tourism can give new forms of tourism and bring

new junction points for the integration of culture and

tourism (Dai, 2019). From the perspective of the

integration of cultural and tourism, Liu Yinjiang

studies how the night tourism can drive the

development of the cultural and tourism industry

and then promote the development of the economy

(Liu, 2019).

While night travel is increasingly popular with

tourists, it also has some problems endangering

public health. Due to the depth of the night, night

tourism will bring a sense of mystery to the tourist

destination. When tourists visit tourist destination at

night, they will get a completely different viewing

experience from during the day, but the night will

also bring restrictions to the vision of tourists at

night, making it prone to events harmful to their

own health during the tour. At the same time,

760

Chen, W. and Kan, Q.

Public Health Research on Night Tourism of Tourist Destinations Based on Big Data: Takes the Ancient City of Lijiang as an Example.

DOI: 10.5220/0012043700003620

In Proceedings of the 4th International Conference on Economic Management and Model Engineering (ICEMME 2022), pages 760-765

ISBN: 978-989-758-636-1

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

because the epidemic control requires scanning the

code and registration of tourists, it is easy for

tourists to directly enter the sightseeing places while

the managers do not pay attention. The large number

of tourists is not conducive to epidemic prevention

and control, and is prone to stampede, which

endangers public health. We can also start from the

emotional preferences of tourists to analyze the

unpleasant things encountered by tourists during

sightseeing, so as to prevent and control the places

prone to accidents. Many scholars have analyzed the

tourist destination from the perspective of tourists'

emotions. Taking Hong Kong Ocean Park as an

example, Huang Xiaoting studied the process of

tourism emotional experience quantitatively, and put

forward the concept of "tourism emotional path"

(Huang, 2015). Liu Yi and others analyzed the

emotional differences between Chinese tourists and

international tourists based on the evaluation data

released on three domestic tourism websites (Liu,

2017).

This paper takes Ctrip as the sample data

acquisition object, and takes the ancient city of

Lijiang as the case place, and analyzes preference

for sightseeing of the tourists participating in the

ancient city of Lijiang through data mining and data

analysis, so as to provide guidance for maintaining

the public health and safety of night tourism. First,

the octopus crawler software was used to climb the

comment data published on Ctrip, and then the data

text was analyzed through ROST CM6.0. Finally,

and preference for sightseeing of night tourists were

summarized, so as to enrich the research scope of

night tourism and promote the public health and

safety of tourist destinations.

2 STUDY DESIGN

2.1 Case Sites and Their Data Sources

The old city of Lijiang has achieved good results in

the development of night economy, so this paper

selects the old city of Lijiang as a case site. In 2021,

the list of the First Batch of National Night Culture

and Tourism Consumption Cluster Areas published

by the Ministry of Culture and Tourism plans to

focus on the development of night economy in 120

projects, and the ancient city of Lijiang is among

them. In the ancient city of Lijiang, the night tour is

not a new concept. Night tour products such as

bonfire jumping, river lights praying and a bar street

have long existed for many years. In recent years,

Lijiang ancient Town scenic spot and from the

supply side and the demand side of the two ends to

work together, innovate night tour, night

entertainment, night market, night shopping and

other diversified business forms, the 31 entrances

and exits of the ancient city scenic area are open 24

hours a year round, on the basis of the night scenery

of the traditional heritage buildings, Add the Mufu

Museum, Yanhe Tea and horse theme park, Sakura

Square and other regional night view lighting belt,

attract the "eyes" of tourists with distinctive scenes;

at the same time, encourage the catering culture and

entertainment operators to delay the operation,

create the theme landscape night scene boutique

catering, script kills immersive experience home

stay, Such as the Lijiang Ancient Town Folk Music

Festival, "Love in Lijiang Chinese Tanabata Love

Poetry Festival", street art performance, reading

club, music salon and other night cultural activities,

catch the hearts of tourists with an aftertaste

experience, let the ancient city of Lijiang night tour

products supply more diversified, The types of

products are even richer.

Ctrip travel can obtain rich data, so this paper

selects Ctrip as the source of data mining. Ctrip is a

platform that provides accommodation,

transportation and other services, with hundreds of

millions of users, which means that it is rich in big

data information. As a leading comprehensive travel

service company in China, Ctrip has successfully

integrated the high-tech industry with the traditional

tourism industry, providing a full range of travel

services to more than 141 million members.

Considering the richness and openness of the

platform data, this study selected Ctrip as the data

acquisition platform.

2.2 Research Technique

In the analysis of tourists' preference for visiting

places, the emotion analysis method and the content

analysis method are mainly used. First of all, the

selected case in this paper is the ancient city of

Lijiang, data source is ctrip, ctrip tourists tour

Lijiang ancient city of comments as a data sample,

and octopus crawler software data crawl, in order to

get the most accurate information, the data

collection time for January 2020 to January 2022,

after screening the invalid comments and irrelevant

comments, finally extract 325 valid comments,

collected the total number of about 26000 words,

and save it as txt documents for the next step.

Secondly, with the help of ROST CM6.0 software,

the document data previously saved in txt format is

frequently analyzed, and the analysis results are

Public Health Research on Night Tourism of Tourist Destinations Based on Big Data: Takes the Ancient City of Lijiang as an Example

761

mainly presented in the form of word cloud map and

semantic network graph. Finally, the ROST CM6.0

software was used to analyze the network text data

for emotions, and the emotions were divided into

three emotion types: positive, neutral, and negative

emotions. Therefore, according to the mined data

text, this paper conducts Word Cloud word and

cloud map analysis, semantic network analysis and

emotion analysis, and summarizes these analysis

results, and then puts forward relevant suggestions,

so as to analyze the influence of tourists' tour

preferences, and provide effective guidance for the

development of night tourism and the maintenance

of public health in the ancient city of Lijiang.

3 INTERPRETATION RESULT

The mined text data is analyzed by high-frequency

word and cloud map analysis, semantic network

analysis and emotion analysis, so as to seek the tour

preferences of tourists during the night tour.

3.1 Word Cloud Analysis

Make full use of Word Cloud word cloud map

analysis to extract text keywords from 325 data

collected. First of all, the octopus extraction about

tourists in Lijiang night tour 325 valid comments

with ROST CM6.0 word processing and word

frequency analysis, in order to make the results more

meaningful, and the "night", "Lijiang" and other

high frequency word, at the same time, the other

unrelated cities such as "Shanghai", "Beijing",

"Guangzhou" to all, after the above operation, meet

the requirements of the top 30 high frequency word

extracted, and form Word Cloud word cloud graph,

extraction results as shown in Figure 1.

High-frequency words can directly reflect the

network image of the ancient city of Lijiang and the

main location of tourists' night tour. According to

the results of Figure 1, the "one bar street" appears

the most frequently, reflecting that the bar is the

project that tourists want to experience most when

coming to the night tour. The ancient city of Lijiang

should strengthen the management on this street,

and take timely measures such as diversion and flow

restriction when the crowd is dense. At the same

time, "square street", "wood", "wood", "the" five

phoenix building "," snow mountain academy ","

antique "these building frequency is second only to"

bar street ", this shows that the ancient buildings is

tourists often go to sightseeing, buildings with local

characteristics can attract tourists to experience and

stop, the ancient city of Lijiang can be in these

places to strengthen management. Also can be seen

from the figure "alley", "wooden" house ","

five-arched "," shop "," wall " attractions such as

distinctive frequency is higher, combined with the

previous frequency appears higher vocabulary can

be seen, can bring visitors experience or bring

visitors more novel location can attract tourists, and

some performing arts products received less

attention from tourists. In addition, we can get from

the "wonderful", "beautiful", "gorgeous", "enjoy"

describe the emotional words seen in the form of

night tour of Lijiang, make the tourist body and

mind by great pleasure, tourists' mood is mainly

positive mood, it shows that tourists travel at night

basic not meet affect the mood of events, the ancient

city of Lijiang public health management in place.

To sum up, it can be seen from the

high-frequency vocabulary that the ancient city of

Lijiang attracts tourists more about experience

projects and novel places, which can bring positive

emotions to tourists. It can be seen that night

tourism, as a new mode of tourism, has a fixed

scenery for tourists to enjoy, and the probability of

the occurrence of harm to to public health events is

almost zero.

Figure 1: Word cloud map of effective high-frequency

words.

3.2 Semantic Network Analysis

Semantic network analysis is a kind of high

frequency word as nodes, with high frequency word

combination common times for the relationship

between nodes, and then by building semantic

ICEMME 2022 - The International Conference on Economic Management and Model Engineering

762

network graph analysis of high frequency phrase

semantic method in the text, through semantic

network analysis can be more intuitive

understanding of tourists emotional cognition of the

ancient city of Lijiang, clear interaction between

high frequency words. In the semantic network

analysis, the 325 valid comments of tourists

attending the night tour of Lijiang Ancient City were

processed and analyzed by ROST CM6.0, and the

results are shown in Figure 2.

Figure 2: 325 valid comments semantic network diagram.

According to the results of Figure 2, the semantic

network presents a multicenter structure and is

dispersed into different networks. From the figure,

we can clearly see that "The Old Town of Lijiang"

and "night tour" are the core nodes, which are the

first-level words, and which are the key words in

these data, which is in line with the research object of

this paper. "Bar street" for the secondary vocabulary,

and the most closely related is the "experience",

"comfortable", "light", "song", reflects the bar

gorgeous lights and playing songs will attract tourists

to stop, and got the recognition of most tourists,

therefore, to strengthen the control of the bar street

helps to maintain public health, prevent the

occurrence of harm to to public health events. At the

same time, with the "wall" is "flower" most closely

"photo", "scenery" and "beautiful", which reflects the

tourists to flower wall motivation and purpose, you

can see that the ancient city beauty for tourists has a

strong attraction, therefore, the ancient city should be

in such attractions protective measures, and to

remind tourists slogan, prevent tourists because of

photos and step empty, trample and other hazards to

their own health events. And "Sifang Street",

"Wooden Street" and other buildings are also loved

by tourists because of their strong sense of history.

From here, we can see that the architecture,

recreation methods and beautiful scenery in the

ancient city of Lijiang are deeply loved by tourists.

In addition, "snack", "specialty" and other peripheral

words also just show that the food in the ancient city

will also bring a good sense of experience to tourists,

and the ancient city should also strengthen the

management of food health in the city.

To sum up, bars, ancient buildings and punching

points are the most popular places for tourists, and

the semantic network map is mainly positive or

neutral words, which express the tourists' overall

emotional cognition of the ancient city of Lijiang

during the night tour, indicating that the

management of the ancient city is in place, and there

are no incidents endangering public security.

3.3 Sentiment Analysis

Through emotional analysis, we can know the

Public Health Research on Night Tourism of Tourist Destinations Based on Big Data: Takes the Ancient City of Lijiang as an Example

763

emotional state of tourists during the night tour of

the ancient city of Lijiang. Using ROST CM6.0 for

tourists to participate in the ancient city of Lijiang

night tour of article 325 valid comments emotional

value calculation, then, according to the emotional

value of Lijiang when emotional state, when the

emotional value is positive, said tourists night tour

of Lijiang is with positive emotions, when the

emotional value is negative, said tourists night tour

of Lijiang is with negative emotions, when the

emotional value is 0 said tourists night tour of the

ancient city of Lijiang is with a neutral mood. The

results obtained are shown in Tables 1 and Table 2.

Table 1: Statistical results of emotional tendencies.

Emotional tendenc

(

article

)

Percenta

Positive moo

217 66.8%

Neutral moo

39 12%

ative emotions 69 21.2%

Amount to 325 100%

Table 2: Statistical mental results for positive and negative emotion.

Positive moo

Negative emotions

Level Qty P

General (0-10) 87 26.7% General (0-10) 49 15.1%

Moderate degree

(10-20)

69 21.2% Moderate

degree (10-20)

19 5.8%

Height (above 20) 61 18.9% Height (above

20)

1 0.3%

Amount to 217 66.8% Amount to 69 21.2%

According to the statistical results of emotional

tendency in Table 1, there are 217 articles, neutral

emotions and 69 articles of positive emotions, 39

articles of the tourists, accounting for 66.8%, 12%

and 21.2% of the valid comments, respectively. The

tourists are mainly positive when visiting the ancient

city of Lijiang. According to this result can get

tourists in the ancient city of Lijiang night is mainly

positive mood, this is due to the ancient city of good

experience project to meet the expectation of night

tour, bring tourists positive emotional cognition, and

tourists did not harm their own health events, shows

the ancient city management in place.

The statistical analysis of table 2 is respectively

in the positive emotions and negative mood related

comments, and table 2 of positive and negative

emotions segment statistical results show that

tourists in the ancient city of Lijiang is general,

moderate and high number of 87,69 and 61,

respectively, the percentage of 26.7%, 21.2% and

18.9%, respectively, overall the number of the three

degrees is little difference. According to this result,

we can get tourists' positive emotions during the

night tour of the ancient city of Lijiang. Since the

main tourist points are bars, ancient buildings and

artificial punching points, tourists can also have a

good emotional cognition of these places. When

tourists visit the ancient city of Lijiang, 49,19 and 1

were general, moderate and high, respectively,

accounting for 15.1%, 5.8% and 0.3%, respectively.

Overall, the number of general negative emotions,

the number of moderate negative emotions and the

number of high negative emotions vary greatly.

According to this result, it can be found that very

few tourists show extreme dissatisfaction with the

ancient city of Lijiang, and most tourists on the

negative feelings of the old city of Lijiang only stay

in a slight dissatisfaction. The analysis of tourist

comments on negative emotions found that the main

sources of negative emotions were expensive prices,

food quality, and unmaintained scenic spots.

To sum up, tourists are generally positive about

the ancient city, which shows that the ancient city is

in place and adequate measures have been taken to

maintain the public health in the ancient city; the

analysis of negative tourists shows that the ancient

city should also be improved in terms of food safety

and infrastructure maintenance.

4 CONCLUSIONS AND

RECOMMENDATIONS

In order to study the public health of the ancient city

of Lijiang, after the above data mining, processing

and analysis, the following conclusions and related

ICEMME 2022 - The International Conference on Economic Management and Model Engineering

764

suggestions are drawn: First, through the

high-frequency word analysis, there are three main

attractions in the ancient city: experience projects,

ancient buildings and beautiful scenery. Most

tourists will go to the bar, tourists will go to flower

wall, square street, sightseeing, therefore, the

management of the ancient city should be inclined to

these places, should arrange staff at these points, and

timely respond to the traffic attractions diversion,

traffic limit measures, prevent stampede, to protect

the life and health of tourists; second, the strong

sense of atmosphere point can cause tourists to

gather. The comment text data on Ctrip is the real

evaluation of the tourists after a night tour of the

ancient city, which can more intuitively reflect the

tourists' emotional cognition of the ancient city, and

will also make other tourists happy to go. Therefore,

the ancient city in the construction of relevant scenic

spots, should also do a good job of emergency plans,

such as improving emergency lighting equipment.

Third, generally speaking, the safety protection

measures in the ancient city are done well, but food

safety and scenic spot facilities are still the main

reasons of endangering public health. For the

tourists who travel at night, the lights can not fully

cover all the scenic spots. When the facilities in the

scenic spots are in trouble, the tourists are

vulnerable to injury. At the same time, when the

flow of people is too large, it will harm the public

health. When food safety problems can threaten the

health of tourists. Therefore, the ancient city should

strengthen the management of these two aspects.

REFERENCES

Bromley R, Tallon A. (1994) Living in the 24-hour cite:

city centre have been the focus of much leisure and

housing development [J]. Advances in Atomic

Molecular & Optical Physics, 34(18):85-123.

Dai B, Yang H. H.. (2019) Night tourism leads the new

trend of cultural tourism [J]. China Real Estate, (14):

73-75.

Huang X. T.. (2015) Takes the Ocean Park in Hong Kong

as an example [J]. Tourism Journal, 30 (06): 39-45.

Lemelin R, Jaramillo L. P., López O. N., Del V. E.. (2021)

In the still of the night: firefly tourism in Mexico [J].

Anatolia, 32(1):12-22.

Liu Y. J.. (2019) Economic development of night tourism

under the integration of Culture and tourism [N].

China Tourism News, 22 (003).

Liu Y, Bao J. G., Chen K. Q.. (2017) Study on the

Emotional Characteristics of Chinese Tourists to

Australia —— Text Analysis based on Big Data [J].

Tourism Journal, 32 (05): 46-58.

Public Health Research on Night Tourism of Tourist Destinations Based on Big Data: Takes the Ancient City of Lijiang as an Example

765