tourism consumption patterns

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• v.2022; 2022

Study on Tourism Consumer Behavior and Countermeasures Based on Big Data

1 Jinzhong University, Jinzhong 030619, Shanxi, China

2 Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia

Associated Data

The experimental data used to support the findings of this study are available from the corresponding author upon request.

In our study, through consulting, summarizing, and analyzing a large number of related literature studies on tourism consumer behavior, tourism big data, text data analysis, and so on, a framework of research ideas on tourism consumption was constructed. The train browser, NLPIR, and other software packages are used to crawl, preprocess, and mine the travel sample data, and the word frequency analysis, co-occurrence analysis, content analysis, sentiment analysis, network analysis, and other methods are used to analyze the characteristics and decision-making behavior of tourists. Based on the results of behavioral analysis, we proposed tourism development strategies from three aspects: reforming and promoting tourism marketing strategies, improving tourism product and service quality, and improving tourism destination management methods. The results show that (1) for the tourist characteristics, taking into account the factors of climate and geographical location, the domestic market is divided into four grades of markets, and different marketing strategies are adopted according to different market characteristics; (2) for the tourism decision-making behavior, a “push-pull resistance” tourism decision-making model was established through word frequency analysis, co-occurrence analysis, and content analysis; (3) for the tourism consumption preferences, through network analysis of scenic spots, it is found that there are three tourist routes preferred by tourists; and (4) for the tourism perception evaluation behavior, based on the “cognitive-emotional” model, this study describes the tourism image from the two dimensions of the cognitive image and emotional image. Generally speaking, tourists show a positive perception state. The research on tourism consumer behavior based on UGC (user-generated content) data can help scenic spots and other tourism companies to understand the characteristics and rules of tourists' behavior, understand the consumption preferences of different tourism groups, develop diversified tourism products, improve the quality of tourism services, and further cater to market segments. This research provides a new idea for tourist attractions and tourism management departments to monitor tourist behavior through big data analysis.

1. Introduction

Tourism consumer behavior involves many disciplines such as marketing [ 1 ]. Tourism consumption behavior refers to the process that tourism consumers choose and purchase tourism products to meet the needs of tourism pleasure and other experiences. This process includes the generation of needs before travel, the decision-making process, consumption in scenic spots, and post-purchase evaluation. Influenced by many factors such as economy, society, and cultural environment, it is an experience activity with comprehensive, marginal, and extraordinary characteristics [ 2 ].

The American Marketing Association defines consumer behavior as follows: “The dynamic interaction process among emotional, cognitive, behavioral, and environmental factors is the behavioral basis for human beings to perform the exchange function in life” [ 3 ]. This definition regards consumer behavior as a process, including multiple stages of selection, purchase, and disposal, and the process is dynamic. Tourism consumption behavior is more complex because it involves the whole process from leaving home to returning home [ 4 ]. To reveal the rules of tourist behavior, the relationship between various behaviors, and the core influencing factors of behavior, academia has proposed many different models. Despite the differences among different models, these models collectively emphasized these psychological activities and behavioral manifestations, such as travel motivation, travel decision-making, choice preference, destination image, and satisfaction, before, during, and after travel [ 5 ].

Tourism motivation is an internal factor that prompts potential tourists to take a certain tourism action, and it is also a key factor for a series of tourism behaviors in the future. Unlike consumption demand, consumption motivation is related to products, so tourism motivation is the link between tourism demand and specific tourism destinations [ 6 ]. Scholars at home and abroad have made a lot of research on tourism motivation. The most popular theoretical models are the distribution center model, push-pull theory, optimal arousal theory, leisure incentive method, and travel career ladder (TCL) model, among which push-pull theory is the most widely used [ 7 ].

Based on scholars' cognition that “tourism behavior is a series of rational decision-making activities,” most of the tourism decision-making theories have evolved from the decision-making model of consumer behavior, which has led to the emergence of many tourism decision-making behavior models [ 8 ]. Sirakaya and Woodside, Smallman, Cohen, and other scholars have made a more detailed summary of the tourism decision-making model, mainly focusing on the model research of destination selection and various subdecisions (such as accommodation and tourism activities) [ 9 ]. Summarizing previous research on decision-making models, Sirakayaet al. believe that the final tourism consumption decision will depend on the interaction between the following variables: intrinsic variables, including travel attitude, motivation, information search behavior, and personality characteristics; extrinsic variables, including constraints, destination pull factors, the influence of family and reference groups, marketing mix, social class, and culture; the nature of the scheduled trip, including travel distance and date and playtime; and travel experience, including emotions and feelings during the trip and post-tour evaluation [ 10 ].

Motivation initiates action and guides desirable behavior, but policymaker preferences help filter choices precisely; preferences are more specific than motivation, revealed by visitor whereabouts and visitor behavior. Many scholars' research on tourism consumption preference focuses on destination choice preference (such as Tzu-Kuang Hsu), shopping preference (such as Samuel), accommodation preference (such as Isabel), food preference (such as Richard), etc. Destination image is “an attitude or psychological structure that reflects the sum total of tourists' personal views, trust and impressions of a tourist destination” [ 11 ]. During the entire tourism process, tourists' perception of the destination image is dynamic. Destination image theory is an important part of tourism research and is widely regarded as an important factor affecting tourists' decision-making, destination selection, post-tour evaluation, and future behavior [ 12 ]. The advent of the Travel 2.0 era (i.e., new ways for real travelers to interact and share information or content through Web 2.0) has changed the way travel information is searched, viewed, and evaluated. In this context, the tourism destination image has been reshaped and widely disseminated through Web 2.0 and UGC [ 13 ]. In the study of destination image perception, the most widely used theoretical model is the “cognitive-affective” model; that is, the destination image is divided into two interrelated components: cognitive image and affective image, where the cognitive image is an individual knowledge or belief about a destination. Emotional image refers to an individual's subjective feelings about the destination. The interaction of cognition and emotion forms a unique overall image of each destination, including positive or negative evaluations. Compared with emotional image, cognitive image has a stronger impact on the overall image [ 14 ].

At present, scholars at home and abroad have not reached a clear consensus on consumer satisfaction. Some scholars (such as Oliver) believe that satisfaction is an emotional state derived from tourism experience [ 15 ]. In this view, tourists' emotion has become an important indicator of satisfaction evaluation. Some scholars (such as Chon) believe that satisfaction is a function of goodness of fit between tourists' expectations of the destination before travel and the perceived evaluation of their visit experience after travel, and the expectation uncertainty model is the most commonly used measurement model.

With the rapid development of the Internet, structured and unstructured data are generated, recorded, stored, and accumulated, forming big data with the characteristics of volume, variety, and velocity, bringing amazing changes to tourism research, which has entered the big data era [ 16 ]. According to different data sources, tourism big data are mainly divided into three categories: UGC data, equipment data, and transaction data. Among the three types of data, UGC data have the most applications due to its low cost and easy access [ 17 ]. Device data applications have problems such as high cost, low accuracy, and small coverage. Since most of the transaction data are private information, only tourism organizations or government departments are used. Owned, its use level is the lowest.

In the digital age, the proliferation of the Internet and social media has dramatically changed travel patterns, providing a platform to share UGC data. User-generated content (UGC), also known as user-generated content (UCC) and consumption-generated media (CGM), was first proposed by KPCB chief analyst Mary Meeker in 2005, thinking that UGC is a new type of content in the Web 2.0 environment. There is no unified and clear definition of the creation, release, and organization mode of information resources. UGC data have been widely used in tourism research and mainly include two types: (1) online text data, such as online reviews, online travel notes, and blogs posted on social media; (2) online photo data, such as in photo sharing online photo data published by websites or online travel journals. However, due to various limitations, UGC data such as audio and video have not been widely used by researchers at home and abroad. The UGC data discussed in this study are mainly text data [ 18 ]. Online text data mainly include two types: one is review data, that is, the evaluation data of online tourism products or tourism destinations, expressing tourists' attitudes towards tourism products (such as accommodation facilities, restaurants, and scenic spots), mainly used to measure satisfaction, electronic word of mouth, etc. [ 19 ]. The second is online travel notes or blogs, that is, travel note modules on online travel websites or travel blogs on social media, which record travel feelings and experiences, mainly involving travel behavior, travel sentiment analysis, and travel recommendations. Research on UGC tourism data, at both home and abroad, focuses on empirical research, but the research focus is slightly different. Foreign research focuses on tourist satisfaction/service quality, destination image, tourism behavior, and tourist segmentation. In China, the purpose of land image and tourism behavior is the focus of research [ 20 ].

Co-occurrence refers to the phenomenon that specific keywords co-occur in a text collection. Co-occurrence analysis is a quantitative study of co-occurrence phenomena to reveal the content correlation of information and the knowledge implied by feature items [ 21 ]. The specific process of co-word analysis mainly has four stages: one is to determine the analysis data set; the other is to determine the analysis object. High-frequency words related to the research object are extracted from the text collection; the third is to construct a binary co-word matrix to obtain quantitative information such as co-occurrence frequency; the fourth is to conduct co-word analysis, which is generally combined with social network analysis for visual display [ 22 ].

Throughout the research on tourism consumer behavior at home and abroad, it is more inclined to conduct research on tourists' tourism motivation, tourism decision-making, spatial behavior, consumption preference, tourism experience, destination image, tourist satisfaction, and other subfields from an individual and micro-perspective [ 20 ]. However, they all ignore a problem: tourists are an organic whole, and their behavior runs through the entire tourism process before, during, and after tourism, but the relevant research literature seldom regards pre-tourism, tourism, and post-tourism as the study as a whole. Previous studies usually divide tourism consumer behavior into multiple subfields and use questionnaire surveys and other survey methods to conduct in-depth, detailed, and enlarged research on a subfield, but it is easily affected by questionnaire design structure, sample sampling, and investigators. Due to the limiting factors such as the subjectivity of the survey, it is impossible to objectively and completely understand the behavioral characteristics and laws of tourists. However, with the widespread of social media and Web 2.0, an online search for travel information, purchase and evaluation of travel products, and sharing travel experiences have become one of the important behavioral characteristics of tourists. UGC data with other characteristics have also become one of the important data sources for tourism behavior research [ 23 ]. In addition, in the research on tourism consumption behavior based on UGC data, domestic and foreign researchers have focused on empirical research on tourist satisfaction, destination image, tourism spatial behavior, tourism decision-making, etc., lacking the overall research on tourist behavior [ 24 ].

Therefore, this study combines the characteristics and content of online travel data to conduct an overall analysis of tourist behavior from the aspects of tourism motivation, tourism decision-making, consumption preference, destination image perception, and tourist satisfaction. This research proposes a research framework of tourism consumption behavior based on online travel notes, which is mainly divided into two levels: the first level is a three-stage dynamic model of tourism consumption behavior; the second level is to establish tourism decision-making, tourism consumption preference, and behavioral models such as perceptual evaluation after swimming.

In our study, we first conducted the data collection and mining and introduced the general situation of the tourist area and introduced the data collection and preprocessing of online travel notes, the selection basis of text mining tools, and the selection criteria of online travel note samples in Section 2 and then introduced the methods of word frequency analysis, co-occurrence analysis, network analysis, sentiment analysis, etc., and conducted mining and research on the characteristics of tourist consumption behavior from four aspects: tourist characteristics, decision-making behavior, consumption choice preference, and perception evaluation, which can be found in Section 2 ; in Section 3 , through in-depth research on the behavior characteristics extracted by text data mining, the influencing factors of tourism consumption behavior are summarized, and combined with the results of field research, the corresponding strategies for tourism development planning in tourist areas are put forward.

2. Methods and Data Collection

2.1. methods, 2.1.1. literature research.

The literature research method is a basic research method adopted in this article. A large number of related disciplines and professional books and relevant predecessors' research results have been consulted. On the basis of sorting out and summarizing previous research, the research direction and content of this study are determined; at this stage, by consulting a large number of related literature on summer tourism, consumer behavior, tourism big data, etc., the theoretical basis and methods of this research are summarized [ 3 ].

2.1.2. Content Analysis

Content analysis is a method that quantitatively analyzes the content of nonquantitative literature to find out the core content and laws contained in the literature. This research uses NLPIR big data semantic intelligent analysis software to analyze the content of the collected online travel text data and obtained valuable and meaningful data, then explores the behavioral characteristics and laws of summer tourism consumers, and finally explains the research phenomenon.

2.1.3. Sentiment Analysis

Text sentiment analysis refers to the process of analyzing, processing, summarizing, and reasoning on subjective texts with emotional color [ 20 ]. According to the different granularity of text, it can be divided into multiple levels such as word level, sentence level, and chapter level. This study uses sentiment analysis to extract emotional words or emotional information from the online travel text data set and uses the chapter as a unit to identify the emotional color of travel notes, so as to obtain tourists' emotional tendencies towards four places in southern Xinjiang.

2.1.4. Network Analysis [ 12 ]

Network analysis is a method to study the characteristics of the entire network and the individuals in the location-based network structure by describing the relationship structure between given entities (represented by nodes) and applying quantitative techniques to generate relevant indicators or results [ 15 ]. Based on the idea that the relationship between tourist attractions is not isolated but related to each other, this study analyzes the network structure between tourist attractions by calculating the co-occurrence frequency of different tourist attractions and proposes a proposal for tourism attraction cooperation strategy based on summarizing the spatial flow characteristics of tourists between tourist attractions.

2.1.5. Mathematical Statistical Analysis [ 5 ]

This study uses SPSS and Excel software to carry out statistical analysis on the crawled tourist sources, days of play, travel companions, and other indicators, as well as the number of articles and frequency of use of related subject words, to obtain the basic characteristics of tourists, and to study summer tourism consumption. Behavioral characteristics provide corresponding data support [ 25 ].

This study mainly uses NLPIR (natural language process and information retrieval) software to achieve co-occurrence analysis. The calculated quantitative information includes co-occurrence frequency, binary probability, and binary word pair information entropy. Co-occurrence frequency refers to the number of co-occurrences of two words before and after, which is used to reflect the strength of association between them. It is generally believed that the higher the co-occurrence frequency of a phrase, the closer the relationship between the phrases. Binary probability refers to the probability of occurrence of co-occurring word pairs, and the information entropy of a binary word pair represents the breadth of information contained in the phrase [ 6 ]. The calculation formula of the information entropy is as follows:

where H ( U ) is information entropy and P i is the probability of variable i .

To extract and utilize useful information hidden in online text data, methods such as text mining and content analysis are widely used in tourism research. Text mining mainly includes three typical stages: data collection, data mining, and result output, in which the data mining stage includes two sub-steps of data preprocessing and pattern discovery. The first step of data collection is as follows. There are two main ways to collect UGC data: open API access and Web crawler. The second step is data mining. Collected online text data are analyzed to extract useful information through two substages: data preprocessing and pattern discovery. Pattern discovery is another key stage of text mining, aiming to explore interesting information in documents, and typical techniques in existing tourism research are LDA analysis, sentiment analysis, statistical analysis, clustering and classification, text summarization, and dependency modeling. The third step results in output [ 8 ]. Interesting information extracted through data mining is transformed into useful knowledge to further serve tourism research. According to relevant research, valuable knowledge covers tourist satisfaction, consumption preference, tourist destination image, tourist route, review characteristics, etc., which is of great help in improving tourism management and providing tourism advice [ 19 ].

2.2. Data Collection

The four prefectures in southern Xinjiang include Aksu Region, Kizilsu Kirgiz Autonomous Prefecture, Kashgar Region, and Hotan Region. It is an important section of the central route of the Silk Road, the southern route, and the church. The two routes of the Silk Road pass through the four prefectures in southern Xinjiang. It is located on the northwestern border of the motherland, the western edge of the Taklimakan Desert, and the southwestern part of the autonomous region; it borders six countries including Kyrgyzstan and India. There are 5A and 4A tourist attractions such as the ancient city of Kashgar, the Populus euphratica scenic spot in Zepu County, the Stone City scenic spot, and the Daolang portrait scenic spot, the Honghai Bay scenic spot, and the Etigar Mosque. It is a first-class port in 5 countries including Kashgar International Airlines and has a rich border and ethnic characteristic tourism resources.

2.2.1. Sample Selection of Online Travel Data

This research mainly uses the travel notes related to the actual tour itinerary shared by tourists on the Internet after the trip. By searching for websites with tourism community functions (based on the comprehensive ranking of tourism websites on top.chinaz.com ), and according to the research object and research purpose of this study, Ctrip, Mafengwo, Baidu Travel, and Qunar were selected as the main data sources. In the crawling of online travel note text data, the Web crawler software (Train Browser 8.2 version) was mainly used to crawl the online travel note data related to summer travel in southern Xinjiang from four selected online travel websites, and the original online travel note database was formed, namely Database.mdb. Then, the crawled travel database according to the following criteria is filtered: online travel notes that do not take southern Xinjiang as a tourist destination are excluded; travel notes that only have pictures, less text (less than 500 words), incomplete travel information, and are not suitable for text content analysis are eliminated; travel notes that are not for the purpose of sharing travel experiences are eliminated, such as advertisements and propaganda; and duplicate travel notes and merge serial travel notes of the same author are removed. After screening, 500 online travel notes (including 250 on Mafengwo, 110 on Qunar, 90 on Baidu Travel, and 40 on Ctrip) were selected as the initial text database for this study.

Due to the limitation of HTML structure, some indicator data need to be further sorted out. For example, Excel's “replace” function is used to clean up redundant text information such as “departure time/” in indicators such as “departure date”; blank data are filled according to the content of travel notes; the CLEAN function is used to remove characters such as “newline” and “space” in the text of the travel note; and individual vacancies are manually extracted from the text to supplement and improve them. In addition, the body text data of the 500 travel notes are copied into an “original corpus.txt” document to form a travel note one-line format.

2.2.2. Selection of Chinese Text Mining Tools

In this study, the NLPIR big data semantic intelligent analysis platform developed by Zhang Huaping was selected for text mining analysis. The NLPIR analysis platform has the following advantages: (1) the word segmentation tagging technology based on the cascading hidden horse model has a word segmentation accuracy of close to 98.23% and has the advantages of high accuracy, and fast speed, and strong adaptability. (2) The entity extraction system based on role annotation can intelligently identify the names of people, places, institutions, media, authors, and the subject keywords of articles that appear in the text. (3) The word frequency statistics function based on the patented perfect double array TRIE tree algorithm has high efficiency and is more than ten times faster than the conventional algorithm. (4) The keyword extraction technology based on context conditional entropy can extract several words or phrases representing the semantic content of the article on the basis of comprehensively grasping the central idea of the article. In addition, it uses cross-information entropy to calculate the context of each candidate word. Conditional entropy identifies the most recent new words and assigns them weights. (5) Text sentiment analysis is based on a deep neural network, and NLPIR sentiment analysis provides two modes: sentiment discrimination of full text and sentiment discrimination of specified objects [ 26 , 27 ].

2.2.3. Preprocessing of Travel Note Text Data Set

In this study, the travel note text data are preprocessed as follows: (1) new words are discovered and user dictionaries are built. Using the new word discovery function of NLPIR, words with new connotations and new concepts are mined from the original corpus and added to the user dictionary to improve the accuracy of the word segmentation system. In addition, the user dictionary is manually checked to fill in the gaps, and the content of the scenic spots and tour routes in the research area in the user dictionary is further improved; (2) building stop word lists for data cleaning: data cleaning is used to detect and remove inaccurate or useless records in text data, such as misspellings and stop words, to leave valuable travel information; and (3) word segmentation and word frequency statistics are circularly performed, and the user dictionary is continuously improved, and word list is stopped until accurate word segmentation and word frequency statistics are achieved. Finally, the more accurate word segmentation results are analyzed by Chinese word frequency statistics, and the phrases that are irrelevant or meaningless to the research content are eliminated.

3. Results and Discussion

3.1. analysis of the basic characteristics of tourists.

For the consideration of personal privacy protection, online travel notes rarely show the demographic characteristics of tourists but can capture module information such as tourist source, travel time, travel days, travel partners, and travel methods and obtain basic characteristics of tourists through descriptive statistical analysis [ 20 ].

Distribution characteristics of customer sources: the distribution of source areas shows obvious geographical differences between east-west and north-south. North China, Northeast China, and East China accounted for 82.6% of the tourists [21-23]. Affected by the principle of travel distance attenuation and the difference in summer climate, Northeast China accounted for the largest proportion, followed by North China and East China. There are many high-temperature weather in summer and economic development factors, and there is a strong demand for going north to escape the summer heat. The tourism resources in the southwest and northwest regions are relatively concentrated, which can meet the tourism needs of tourists in the local and surrounding areas, so there is a big difference between the east and west source of tourists and affected by the travel distance, and southern Xinjiang is more attractive to northern tourists than southern tourists. Therefore, there are differences in the distribution of tourists from north to south. However, due to the law of regional differentiation, there are differences in tourism resources between the north and the south. For the motivation of seeking innovation, the central and southern regions and the southwest regions will become potential tourist sources ( Table 1 ).

Statistical results of customer source distribution.

Figure 1 shows the results of statistics of departure date data in 500 travel notes based on the year. The number of travel notes generally maintains a continuous upward trend, with a larger increase after 2015, which is not only closely related to the tourism development of the four southern Xinjiang regions but also related to the popularity of online travel notes. With the gradual improvement of tourism-sharing community-related websites, the creation of online travel journals has gradually become a part of the tourism process.

2015–2021 statistical statistics of online travel notes in southern Xinjiang.

Regarding the number of days to visit ( Figure 2 ), due to the abundant resources of scenic spots in southern Xinjiang (three tour routes have been developed), the tickets are valid for three days, and the summer vacation is long, so more tourists choose the itinerary within 1 week, accounting for 84%, of which 3-4 days of in-depth tours are the most popular, followed by 1-2 weeks of itineraries, accounting for 13.3%, and the least number of tourists who choose to play for more than 2 weeks. After calculation, the average playing days of the travel note sample is 5 days, which shows that tourists in southern Xinjiang spend a long time playing.

Statistics of tourist travel days.

The per capita consumption of tourists has a relatively large span, with a minimum per capita consumption of 200 yuan and a maximum of 10,000 yuan, but in general, the per capita consumption is concentrated in the range of 1,000–3,999 yuan (57.4%). According to the content of the travel notes, the reasons for the low consumption are as follows: first, the distance to the destination and the number of days of play are few, so the consumption is not high; second, there are friends or relatives in the local area; it is to choose low-cost group tours. Combined with the number of days of tourist travel, it is calculated that the per capita daily consumption is between 100 and 2550 yuan (see Figure 3 ).

Statistics on per capita consumption of tourists.

3.2. Analysis of Tourists' Decision-Making Behavior

Travel motivation is a key factor in all travel behaviors and an important driver of travel decisions [24-25]. 59 related entries were extracted through word frequency analysis articles, and entries were classified into three types of factors: push force, pull force, and resistance force according to the word meaning (see Table 2 ).

Word frequency statistics of tourist motivation.

Iso-Ahola divides push factors into two dimensions: escape (such as escape from everyday life or interpersonal circumstances) and seeking (such as adventure or friendship building), which explain why tourists travel and what type of destination or type of destination they need. Drawing on Iso-Ahola's division of dimensions, after content analysis, this study divides the thrust factors into two dimensions: escape and seek [26]. Through the co-occurrence analysis, tourists are not only eager to escape the high temperature in summer but also want to escape the hustle and bustle of the city, and they want to go on a trip. Seeking: through the cluster analysis of the words of seeking dimension, tourists' travel motives are mainly to accompany family members or friends, to seek novelty, to go on vacation, to pursue freedom and excitement, etc. Among them, accompanying family members and friends to experience different sceneries and perceive different worlds is an important factor driving tourists to travel abroad. Summer tourism is not only a leisure and vacation way for people to escape from high-temperature cities but also an important way for people to seek companionship and novelty.

Tourism decision-making is the external manifestation of tourism motivation and the core behavior in the process of tourism [ 27 ]. A decision-making process contains multiple subdecisions of information, and each subdecision exists in two stages of the tourism process: one is information search and decision-making before travel, such as which information acquisition channel to choose and what information to search for; the second is tourism instant decisions. According to the content analysis and word frequency analysis of travel notes, this section mainly analyzes the characteristics of tourism decision-making behavior from three aspects: tourism decision-making behavior, decision-making information needs, and information acquisition channels.

After keyword analysis and co-occurrence analysis (see Table 3 ), tourism information channels mainly come from the Internet, including travel notes, strategies, and reviews of travel websites such as Ctrip, official websites, or APPs of scenic spots and search engines such as Baidu, Taobao, and Dianping. Due to third-party platforms information needs, tourists have different preferences in choosing information channels. For example, tourists mainly obtain information on destinations and attractions, tourist routes, itineraries, and other information through the strategies, travel notes, and reviews of tourism community websites; book and buy tickets through the official website or APP of the scenic spot, online travel website, Taobao, etc.; through the official website of the scenic spot or Ctrip, etc.; booking accommodation on travel websites; and inquiring about recommended food through Dianping and other platforms.

Keyword extraction and analysis of tourism information channels.

3.3. Analysis of Tourists' Consumption Preference

Online travel notes are travel experiences and experiences recorded by tourists after their trips based on their memories. The sights and travel activities frequently mentioned by tourists in travel notes indicate that they have left a deep impression on tourists or unforgettable travel experiences. Such experiences are often positive. Therefore, the greater the frequency of tourist attractions or tourist activities, the higher the popularity of the tourist attractions or tourist activities, and the more tourists prefer them. Based on this, this study filters out the tourist attractions that tourists prefer based on the word frequency of the names of the attractions, as shown in Figure 4 .

Tourist attraction preference statistics based on word frequency and weight.

The preference of tourist attractions based on word frequency screening shows a typical long-tailed curve distribution from high to low. The trend of the curve changes can reflect the uniformity of the heat distribution of tourist attractions. The faster the curve changes and the greater the amplitude, the more uneven the heat distribution of tourist attractions and vice versa. It can be seen from Figure 4 that only a few high-fat spots are distributed in the head of the curve, and most of the low-fat spots are distributed in the long tail of the curve, of which Kashgar Old Town has the highest weight.

3.4. An Analysis of Tourists' Perception and Evaluation Behavior

Tourism image refers to an individual's overall perception or overall impression of a tourist destination, which not only affects the destination selection stage but also affects the behavior of tourists, including sharing behavior, revisit intention, and recommendation behavior. It is widely accepted that the image of a tourist destination is generalized into two interrelated dimensions: the cognitive dimension and the emotional dimension, of which the cognitive dimension is the premise of the emotional dimension, both of which have a direct impact on the overall image of the destination.

This section analyzes the perception of tourists' destination image from the dimensions of intuition, positive emotion, and negative emotion. The specific dimensions are shown in Table 4 .

Analysis of intuition dimension, positive affective dimension, and negative affective dimension.

Tourists' perception of the cognitive image of southern Xinjiang mainly focuses on natural tourism resources, special catering, climate environment, social environment, etc., and more of the praise of natural scenery and special food, which tends to be positive and emotional.

Through the extraction of emotional comment words and co-occurrence analysis, the positive and negative factors perceived by summer tourists are analyzed. Among the 30 emotional comment words extracted, high-frequency words such as “good,” “like,” “worthy,” “beautiful,” and “happy” reflect tourists' positive evaluation of the trip. “Enjoyment,” “stimulation,” “comfort,” and “comfortable” are the positive emotional expressions of tourists who escape from the usual environment to the tourist destination for vacation and leisure and enjoy the novel natural scenery and comfortable climate environment. However, due to weather factors, tourists are worried that they “cannot see what they want to see,” so they have emotional expressions of “regret,” “disappointment,” “missing,” and “gap.” In addition, due to the excessive passenger flow in the peak season, tourists also feel “dangerous,” “crowded,” and “tired.”

Tourism consumption is a typical hedonic consumption, experience consumption, the pursuit of feeling, fun, and pleasure, and tourism satisfaction mainly comes from experience, especially emotional experience. Rojas and Camarero believe that emotion is the determinant of satisfaction, and emotion has a moderating effect on satisfaction; Bosque believes that positive emotion and negative emotion are the antecedent variables of satisfaction, and loyalty is the dependent variable of satisfaction. Therefore, this study measures tourists' post-tour satisfaction evaluation from the two aspects of emotional tendency and behavioral willingness (i.e., loyalty).

To understand each tourist's sentiment evaluation of tourism, this section conducts sentiment analysis on text data based on document granularity and word granularity. First, the positive or negative sentiment words in each document are identified and scored through the sentiment dictionary, and then, the scores of all sentiment words in the document are aggregated and summed to obtain the final sentiment score. After the sentiment score statistics, the average sentiment score of the 500 travel notes is 46.73 points, of which the average positive (i.e., positive) sentiment score is 87.04 points, and the negative (i.e., negative) sentiment score is −40.31 points. Different emotional intensities are measured according to the sentiment score: positive affect (5, +∞), negative affect (−∞, −5), and neutral affect [−5, 5], where positive affect can be divided into high (25, +∞), moderate (15, 25], and moderate (5, 15); negative affect can be divided into high (−∞, −25), moderate [−25, −15), and moderate [−15, −5).

The statistical results in Figure 5 show that 91% of tourists have a positive emotional orientation towards the travel experience, of which 65% of tourists have the highest degree of positive emotion, 17% are moderate, and 18% are average. Only 1% of tourists have negative emotional inclinations, and all of them are highly negative, which shows that most tourists have positive and positive emotional inclinations towards the tourism experience in southern Xinjiang.

Statistical chart of emotional tendency analysis of travel notes.

3.5. Behavioral Analysis-Based Tourism Development Strategies

Based on the two factors of geographic location and climate, the domestic primary market is the Bohai Rim Region with Beijing-Tianjin-Hebei as the core. This region has a developed economy, long high-temperature period in summer, and strong demand for summer vacation. The secondary market is the provinces and cities around scenario spots, and the travel distance in this area is short and suitable for short-term self-driving tourism products; the tertiary market is the Pearl River Delta region with Shanghai as the core, and travel in this area is long distance, developed economy, long high-temperature period, suitable for multi-destination in-depth tourism, and “airplane + other” combined transportation products; the fourth-level market is other domestic provinces and cities.

Relying on geographical advantages, “geo-tourism” is developed. Geo-tourism is developed, and characteristic border tourism products are created. Infrastructure construction is improved, tourism products are innovated and developed, the tourism industry chain is extended, and the “trade zone + tourism zone” construction is used to reduce travel barriers for tourists and promote the development of tourism.

Unique attractions are relied to develop “themed tourism.” Relying on the unique attractive elements of tourist destinations, we should develop characteristic-themed tourism products that meet the needs of different tourist groups: first is relying on the advantages of climate and environment and the second is to develop “food tourism” routes based on local catering characteristics. Finding, experiencing, and enjoying food have increasingly become an important tourist motivation.

Local cultural resources are relied to develop “cultural tourism”. Cultural tourism resources should be developed in the development of tourism resources, and cultural tourism forms such as historical exploration and folk customs should be developed using the unique local Korean culture, Kanto culture, national culture, and cultural resources and landscape resources. While satisfying the basic needs of tourists for sightseeing and taking pictures of beautiful scenery, it also develops tourism products with high participation and strong experience of tourists such as folk experience and adventure exploration, prolonging the length of tourists' stay, and satisfying tourists for summer vacation and vacation leisure purposes.

We should focus on making up for the shortcomings of infrastructure services, improving transportation service facilities, and improving the level of tourist reception services during peak seasons. First of all, in terms of tourism transportation, the accessibility of transportation is improved, especially the transportation accessibility outside the scenic spots, and the management of the order of the tourism transportation market is strengthened. Secondly, in terms of tourism commodities, there are many types of special products in tourist areas, but only blueberries and ginseng are mentioned in the travel notes. This shows that other special products cannot attract tourists' attention and desire to buy. The development and packaging of special products should be strengthened, as well as publicity. Finally, a connection mechanism for tourists in peak seasons is established to improve the quality and level of reception services for tourists in peak seasons, thereby reducing the safety risk of crowd congestion and the negative emotions of tourists.

Cooperation between cities and counties in tourist attractions in tourist areas is strengthened. Scenic spots and their management departments should strengthen the cooperation between core scenic spots and surrounding scattered scenic spots, establish a scenic spot cooperation mechanism, integrate scenic spots in tourist areas, extend outward from the core scenic spots tour routes, and implement a combined ticket or pass system in large tourist areas. The integrated development of regional tourism is promoted. Tourism cooperation outside tourist areas is strengthened. The tourism management department should strengthen the tourism linkage and cooperation with the tourism departments of other cities and jointly establish summer escape and border tourism routes or tourism strategies across the three northeastern provinces, to achieve a win-win situation for all parties.

Classified Internet marketing based on tourist segmentation: according to the degree of network involvement, tourists can be divided into high involvement and low involvement; according to the degree of experience, tourists can be divided into deep experience, moderate experience, and shallow experience; and according to age, tourists can be divided into teenagers, youth, middle-aged, and elderly; and according to the place of residence, tourists can be divided into the urban type and rural type. Different market segments have different motivations for online consumption, online consumption preferences, and online consumption perceptions and evaluations. Classification of online marketing strategies should be formulated according to the consumption habits and travel attitudes of different groups. For example, travel groups with high Internet involvement should cooperate with well-known and popular travel websites or online opinion leaders to increase the popularity of scenic spots in this area.

The factors that cause tourists' “dissatisfaction” are the concentrated congestion of tourists in peak season, which not only brings adverse effects on tourists' travel experience but also on the ecological environment of scenic spots. The best way to solve the problem of congestion is to accurately predict the flow of tourists, understand the spatial behavior of tourists, and implement tourist diversion.

There are still many deficiencies in this study, which need to be improved and further studied in future research. It is in terms of data: due to the limitation of data acquisition, 500 travel notes were obtained after screening, and the amount of data is not particularly large. In the future, more channels and methods will be explored to collect data, so that the reference value of the data will be more perfect; due to the influence of the personal privacy protection policy, personal information such as the age and knowledge level of the travel journal publisher cannot be accurately obtained, so the representativeness of the travel journal sample cannot be accurately measured; the travel journal includes text and image data. This article only studies the form of text data. The next step is to conduct research on the form of image data. In the future, data mining algorithms such as cluster analysis, topic identification and classification, and dependency modeling can be further studied and applied to in-depth study of tourist behavior patterns and mechanisms, as well as the establishment of an intelligent analysis system for tourist behavior based on UGC data.

4. Conclusions

By consulting, summarizing, and analyzing a large number of related literature studies on tourism consumer behavior, tourism big data, text data analysis, etc., this study constructs a thinking framework for the research of tourism consumption behavior based on UGC text data and conducts research on the basis of this thinking framework. We use train browser, NLPIR, and other software packages to crawl, preprocess, and travel sample data and use the word frequency analysis, co-occurrence analysis, content analysis, sentiment analysis, network analysis, and other methods to analyze the characteristics of tourists and decision-making. According to the behavior analysis, tourism management departments, and tourism enterprises, the reform and promotion of tourism marketing strategies and the improvement of tourism product and service quality were done. This study proposes the development strategy of southern Xinjiang summer vacation tourism in three aspects to improve the management mode of tourism destination. Finally, the conclusions of this study are as follows:

• This study uses ten days as the unit to count the travel time and proposes to use ten days to months as the tourism cycle to develop tourism, refine product design, and adjust and allocate tourism resources.
• Based on the “push-pull” theory, this study establishes a “push-pull resistance” tourism decision-making model. Through word frequency analysis, co-occurrence analysis, and content analysis, it is concluded that “escape” and “seek” are the internal driving forces that affect tourists' travel decisions. Among them, companionship and experience are the main travel motives. Tourism enterprises should develop and design in-depth and diversified experience products to create unforgettable travel experiences, thereby stimulating tourists' travel motivation: “climate suitability,” “attraction of scenic spots,” and “scenic spots.” “Uniqueness” is the pulling factor for tourists to choose the four destinations in southern Xinjiang. Scenic spots can use the pulling factor to create tourism IP and create “selling points” to attract tourists; in addition, the changeable weather and inconvenience of transportation have become obstacles for tourists to travel, so scenic spots should focus on making up for shortcomings and improving the quality of tourism services.
• Through network analysis of scenic spots, it is found that there are three tourist routes preferred by tourists, namely short-distance travel and long-distance travel, and long-distance border travel. Tourism management departments and scenic spots should strengthen tourism cooperation between cities and scenic spots and develop theme tourism and diversified tourism routes such as geo-tourism and cultural tourism to achieve regional coordinated development.
• Based on the “cognitive-emotional” model, this study describes the tourism image from two dimensions, cognitive image and emotional image. Generally, tourists present a positive perception state. In addition, this study measures tourist satisfaction from the analysis of emotional tendencies and future behavior intentions (i.e., loyalty, including willingness to revisit and recommendation). Emotional state but the revisit rate is not high, indicating that the tendency of post-tour memories and evaluations to be effectively transformed into re-consumption behavior is weak. Scenic spots or research communities can conduct in-depth research on the effective needs of tourists, and at the same time, the positive electronic word-of-mouth effect is used to adopt “tourism”. + new media,” “tourism + UGC,” and other marketing methods.
• Based on the research on tourism consumer behavior based on UGC data, this study can help scenic spots and other tourism enterprises understand the consumption preferences of different tourism groups, optimize tourism routes, develop diversified tourism products, improve the quality of tourism services based on the characteristics and laws of tourists' behavior, and further cater to market segments.
• This research provides a new idea for tourism scenic spots and tourism management departments to monitor tourists' behavior through big data analysis. A tourism big data analysis platform based on UGC data can be established to help relevant management entities to trace back the characteristics, changes in tourists' consumption behavior, and the impact on scenic spots. It can also help tourism websites to establish a personalized scenic spot recommendation system according to tourists' consumption preferences.

Acknowledgments

This work was supported by the Key Project of Shanxi Federation of Social Sciences, Study on integrated development of rural tourism, SSKIZDKT2020166; Philosophy and Social Project of Higher Education Department of Shanxi Province, Study on the integration of rural tourism industry in resource-based areas, 2020W189; and Jinzhong University Maker Team Project, Study on the path of improving the quality and efficiency of rural tourism in Shanxi Province under rural revitalization strategy, jzxycktd2019015.

Data Availability

Conflicts of interest.

The authors declare that they have no conflicts of interest to report regarding this study.

Handbook of e-Tourism pp 1–22 Cite as

Consumer Behavior in e-Tourism

• S. Volo 5 &
• A. Irimiás 6  
• Living reference work entry
• First Online: 17 December 2021

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Tourism scholars have extensively investigated tourists’ behavior; from motivations to actual choices and consumption patterns, the way tourists behave has relevant implications for theory and practice. In e-Tourism, consumer behavior encompasses the wide range of tourists’ behaviors supported by technologies and happens at different stages: prior undertaking a vacation, during the experience itself, and after it, when tourists are engaged in post-vacation assessments. Research on these aspects is vast, encompassing both the supply and demand side, but it remains scattered. This chapter provides an informed overview of consumer behavior in the e-Tourism era. The core of the chapter focuses on three phases of consumer behavior that have significantly been reshaped by e-Tourism: pre-trip stage, on-site experience, and post-trip evaluation. These three relevant areas are herein analyzed, and considering the tourists and providers’ perspective, the most relevant changes enabled by the e-Tourism era are presented. The conclusion section discusses the relevance of behavioral changes induced by digitally mediated experiences, outlines advances, and presents future perspectives for tourism and hospitality.

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Volo, S., Irimiás, A. (2022). Consumer Behavior in e-Tourism. In: Xiang, Z., Fuchs, M., Gretzel, U., Höpken, W. (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_8-1

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• Published: 10 February 2022

Analysis of spatial patterns and driving factors of provincial tourism demand in China

• Xuankai Ma 1 , 2 , 3 ,
• Zhaoping Yang 2 &
• Jianghua Zheng 1  

Scientific Reports volume  12 , Article number:  2260 ( 2022 ) Cite this article

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• Environmental economics
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Modeling and forecasting tourism demand across destinations has become a priority in tourism research. Most tourism demand studies rely on annual statistics with small sample sizes and lack research on spatial heterogeneity and drivers of tourism demand. This study proposes a new framework for measuring inter-provincial tourism demand's spatiotemporal distribution using search engine indices based on a geographic perspective. A combination of spatial autocorrelation and Geodetector is utilized to recognize the spatiotemporal distribution patterns of tourism demand in 2011 and 2018 in 31 provinces of mainland China and detect its driving mechanisms. The results reveal that the spatial distribution of tourism demand manifests a vital stratification phenomenon with significant spatial aggregation in the southwest and northeast of China. Traffic conditions, social-economic development level, and physical conditions compose a constant and robust interaction network, which dominates the spatial distribution of tourism demand in different development stages through different interactions.

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Zhenjie Liao, Lijuan Zhang, … Shan Liang

Introduction

Tourism is an essential driver of world economic development. The world was affected by the COVID-19 outbreak in 2019, and according to a report by the World Tourism Organization 1 , the world's top ten consumer countries showed continued growth in tourism consumption against the backdrop of a global economic slowdown. According to the Ministry of Culture and Tourism of the People’s Republic of China ( https://www.mct.gov.cn/ ), China's 2020 annual domestic tourism numbered 2.879 billion trips, down 52.1% from a year earlier. The first quarter of 2021 saw 11.872700 billion domestic trips organized by national travel agencies, increasing 138.50% year-on-year. As the contribution of tourism to the regional economy is improving more and more significantly, the study of tourism demand has become a popular research topic 2 . Many scholars have carried out tourism demand forecasting through qualitative analysis, time series models, econometric models with artificial intelligence, and the accuracy of forecasting has gradually improved. However, tourism and tourists are closely correlated in terms of spatial mobility, and if spatial effects are ignored, a model estimation can be biased and produce misleading coefficient estimates 3 , 4 , 5 . Deng and Athanasopoulos were the first to incorporate spatiotemporal dynamics into an Australian domestic tourism demand model study 6 , and Yang and Zhang showed that spatiotemporal models have a significantly enhanced effect on the performance of tourism demand forecasting between domestic provinces in China 7 . Liu et al. noted that demographic factors, climate, key transportation modes, economic level, and other aspects of tourism demand were not investigated 8 . Therefore, it is imperative to understand the spatiotemporal patterns and driving mechanisms of tourism demand.

From the tourism supply side, the geographical and spatial clustering of tourism-related services produces spatial dependence and scale effects at the macro level, thus providing tourists with more acceptable prices and convenient services to achieve regional tourism growth. From the tourism demand side, tourists from the same region have more similar social psychology and tourism demand 9 , their tourism demand patterns are similar in terms of spatial preferences, and travel patterns show a more consistent cyclicality in time. Domestic tourism is an undisputed driver of economic development and poverty alleviation in less developed regions than international tourism 10 . Pompili et al. argued that choosing the provincial level as the geographic unit to study tourism flows yields more valuable results 11 . The results of the detection of spatial effects within a region can provide a scientific and empirical reference to local governments, tourism planners and administrative units regarding resource allocation and infrastructure development.

Several studies have explored the factors affecting tourism demand. For example, Priego et al. explored the impact of climate change on domestic tourism flows in Spain 12 . Massidda and Etzo studied the contribution of road infrastructure to tourism demand in domestic tourism in Italy 9 . Priego et al. emphasize the importance of meteorological factors on domestic travel destinations in Spain 12 . Technological innovation 13 and knowledge spillovers 14 cannot be ignored in driving tourism productivity and making tourism demand grow. Alvarez‐Diaz et al. 15 , Marrocu and Paci 16 , Massidda and Etzo 9 confirmed that the size of the population is also one of the drivers of tourism demand. Despite the large number of studies exploring the factors affecting tourism demand, most researchers have focused more on the impact of single aspects of socio-economic or natural factors on tourism demand, and there are no studies based on a geographic perspective that integrate the various dominant factors into a comprehensive mechanism of impact on tourism demand.

We found from the early literature that the number of tourists and tourism income served as the main proxies for tourism demand modeling. With the development of the Internet, some researchers 8 , 17 found that tourists' search engines for tourism information retrieval are the starting point and an essential part of tourism decision and travel. Li et al. summarized relevant 2012–2019 in their latest review 18 . We know about search engine data primarily based on empirical studies investigating the eximious contribution to tourism demand observation and forecasting. With Google Trends being widely used for tourism demand forecasting at multiple spatial scales worldwide 19 , Baidu Index performs even better in the Greater China region 20 . Song et al. demonstrated that Internet data has a significant driving effect on tourism demand research, with search engine data being the most common Internet data source used by researchers 19 . It is now well established from various literature that analytical methods have been implemented to address the single driving mechanisms of tourism demand. In their paper, Marrocu and Paci indicated that the application of spatial autoregressive models gave the spatial dependence patterns of tourism flows access to be effectively presented 16 . Yang and Fik investigated tourism growth change in 342 cities in China using spatial growth regression models 21 . Deng et al. used a spatial econometric analysis framework to analyze the impact of air pollution on inbound tourism in China 22 . In general, tourism demand is not affected by any individual factor, and the interactions among the factors affect the distribution of tourism demand. Therefore, it is crucial to detect the interrelated effects of tourism demand drivers. However, most existing studies ignore the interaction among the drivers of tourism demand. In addition, most existing models used in the literature make assumptions about the data and fail to reveal the interaction among the factors.

To fill these gaps, this study aims to address the spatial heterogeneity and drivers of tourism demand by using 678,900 Origin–Destination flows (OD flows) of tourism demand data from 31 Chinese provinces at the years 2011 and 2018, which helps gain insight into the spatial heterogeneity of tourism demand exhibited in the period of rapid economic development. Second, to our knowledge, this might be the first attempt to present a theoretical framework for a multi-factor driving mechanism of tourism demand, which incorporates social-economic development, population size, urban ecological conditions, tourism resources, physical conditions, traffic conditions, and technological innovation. Third, from the perspective of spatially stratified heterogeneity, this study taps the influence of the main driving factors and the interaction between different potential factors on the spatial heterogeneity of tourism demand. In addition, the study of tourism demand should not only focus on the influence of local economic activities and the natural environment but also the influence of inter-regional spatial correlation. Therefore, this study uses spatial autocorrelation and geographic detector models to analyze the spatial variation of tourism demand and its drivers at the provincial level.

The rest of the study is organized as follows: “ Materials and methodology ” section provides an overview of the proxies affecting tourism demand and the data and methods used in this paper. “ Results ” section analyzes the drivers and spatial characteristics of tourism demand. Finally, “ Discussion ” and “ Conclusions ” sections are the discussion and conclusion of the findings, respectively.

Materials and methodology

Study areas.

After the world economic crisis in 2008, China started its economic recovery in 2011, followed by an average annual growth of 9.48% in GDP and 14.99% in total domestic tourism consumption until 2018 (Fig.  1 b,c). This paper investigates the factors driving the changes in the spatial distribution of tourism demand at a provincial level in China in 2011 and 2018 to provide a basis for planning decision-makers in developing countries and regions. This study regarded the provinces as the primary geographical unit, and 31 administrative provinces in mainland China were selected as the study area. Due to the unavailability of data, Hong Kong, Macao, and Taiwan are not included in the study area (Fig.  1 ).

An overview map of the study area: ( a ) 31 provinces in mainland China; ( b ) economic conditions in the study area from 2011–2018; ( c ) tourism in the study area from 2011 to 2018. Data on China's economy are from the National Bureau of Statistics of China ( http://www.stats.gov.cn/ ) and the Chinese Academy of Social Sciences ( http://english.cssn.cn/ ). Standard map services are provided by the Ministry of Natural Resources of China ( http://bzdt.ch.mnr.gov.cn/ ), GS (2020)4619.

Dominant factors and proxy variables of tourism demand

From a systemic perspective, the underlying driving mechanism of tourism demand is constituted by the tourist travel intention of the source and the destination's attractiveness. It is influenced by the resistance of temporal distance, spatial distance, and social distance 23 , 24 , 25 . Natural conditions and human factors determine tourism demand (Fig.  2 ). In this study, social-economic development (Z1), population (Z2), urban ecology (Z3), tourism resources (Z4), physical conditions (Z5), traffic conditions (Z6), and technology innovation (Z7) are used as factors that directly affect tourism demand. Considering the availability of data, GDP per capita, value-added of tertiary industry, and the average wage of employees is used to characterize the level of socio-economic development. The total population and nighttime light index measure the population scale. Urban ecological condition is indicated by Urban park green area. Museums and A-class scenic spot index represent the richness of tourism resources. The physical environmental conditions consist of altitude, average daily hours of sunshine, average daily temperature, green space coverage index. Transportation conditions are reflected by the urban road area, highway mileage, and railroad mileage. The number of enterprises in the high-tech industry represents the region's scientific and technological innovation capability. The search intensity of the Baidu index was employed to quantify tourism demand.

Determinants and their geographical proxy variables concerning the spatial distribution of tourism demand.

Tourism demand of inter-province

Search engine big data are one of the data sources that can accurately quantify tourism demand. Search engines collect records of Internet users retrieving information on the Internet to form search engine indices with high timeliness. Baidu index ( https://index.baidu.com/ ) has better accuracy in the Greater China region for measuring tourism demand, and keywords query it. The keyword database consisted of the combinations of destination provinces name + "tourism". The Baidu index of each keyword could be decomposed according to the region and time, to obtain the daily search intensity of internet users in province A for tourism information in province B. We constructed an origin–destination (OD) spatiotemporal matrix in 2011 and 2018, which contains the intensity of travel information retrieved by residents of one province on the Internet for another province for each day. The correlation matrix of tourism demand among provinces visualized in Fig.  3 was obtained by summing up the daily tourism demand flows by year and accumulated in terms of destination provinces to obtain the tourism retrieval index of each province for a year. It characterized the total tourism demand of that province. Additionally, the annual Baidu Index of province-A Internet users query for tourism information about province-B is defined as a tourism demand flow for this OD.

Correlation matrix of tourism demand between provinces: Based on the daily Baidu indexes accumulated throughout the year in ( a ) 2011; ( b ) 2018. The Y-axis is the origin, and the X-axis is the abbreviation that replaces the destination, the name of each province. The color of each grid represents the total annual amount of tourism demand from the source province to the destination province. The grid color From lighter to darker represents the strength of tourism demand flow.

Indicators for influencing factors

We collected statistical panel data released in 2012 and 2019 from the China City Statistical Yearbook ( http://www.stats.gov.cn/tjsj/tjcbw/ ), including official statistics on the economic development level, population, urban ecological conditions, transportation conditions, and science and technology innovation. A-class tourism resources lists were extracted from the summary of government documents published on each local government website, and the tourism resources were numerically mapped according to A-1, AA-2, AAA-3, AAAA-4, and AAAA-5 remapping. The tourism resource addresses were geocoded into spatial point data, and kriging interpolation was implemented for spatial interpolation to generate the A-class tourism resources index raster. In addition, we used remote sensing data as a geographic proxy variable for physical conditions and population distribution. The NPP-VIIRS-like NTL Data from Harvard Dataverse ( https://library.harvard.edu/services-tools/harvard-dataverse/ ), which represents the intensity of human activity at night, the elevation data is from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences ( https://www.resdc.cn/ ), the climate data is from the National Meteorological Science Data Center of China ( http://data.cma.cn/ ), and the green space coverage index is from the USGS ( https://www.usgs.gov/ ). All proxy variables were free of being counted according to provincial administrative boundaries, and the raw data were in a GeoDetector model where sampling points would capture the values of different variables.

Methodology

Exploratory spatial data analysis.

Exploratory spatial data analysis is a series of spatial data statistical analyses applied to describe and visualize the spatial and temporal distribution patterns of tourism demand. Global spatial autocorrelation 26 is adopted to determine whether the spatial distribution pattern of tourism demand is clustered, dispersed, or random 27 . The local spatial autocorrelation 28 is practiced in identifying areas where spatial clustering and outliers occur to explore their spatial effects 29 . Considering the spatial data of provinces are polygons and checked by topology, Queen contiguity is utilized to indicate the spatial weight matrix between provinces 30 .

Spatial stratification heterogeneity analysis

GeoDetector is an advanced spatial statistical analysis model used to study factors' impact on diseases at a specific geographical area early 31 . Furthermore, it gradually developed into various research fields with spatial characteristics, such as ecological security, food production, urban land use, carbon emissions. It is a hypothesis that if the independent variable directly influences the dependent variable in space, then the spatial distribution of the dependent variable should converge with the spatial distribution of the independent variable 32 . The model detects the similarity of two variables in spatial distribution patterns from the perspective of spatially stratified heterogeneity 33 .

In this paper, GeoDetector was adopted to analyze the factors affecting the spatial distribution of tourism demand in 31 provinces of China (Fig.  4 ). Factor detection, ecological detection, and interactive detection submodules were applied to quantify their spatial heterogeneity and interactions between factors on the spatial distribution of tourism demand. The Q statistic measured and explained the influence of the independent variable X on the dependent variable Y on spatial heterogeneity. The expressions are as follows.

where: \(Q\) -statistic is a measure of the explanatory power of the influence of factor X on tourism demand Y; M represents the number of strata (subdivisions); \(N\) represents the number of provincial geographical units in the study area; \({N}_{j}\) represents the number of provinces in subdivision \(j\) ; \({\sigma }^{2}\) and \({\sigma }_{j}^{2}\) indicate the variance of tourism demand in the whole study area and the variance of tourism demand in each subdivision, respectively. The greater the value of Q, the stronger the influence of factor X on tourism demand Y.

Principle of geodetector.

Stratification of geographic proxy variables has a significant impact on the accuracy of factor detection. The optimization algorithm for stratifying geographic proxy variables parameters proposed by Song et al. offered optimizing spatial discretization 34 . The optimization algorithm assumes that each variable is stratified using different unsupervised discretization methods to form different stratification schemes. If one alternative scheme obtains an enormous Q-statistic based on factor detection calculations, this stratification scheme captures the most significant driving force between that variable and the observed variables.

The study area was spaced at 50 km intervals, and 3795 sampling points were generated to sample 16 continuous-type variables. Quantile method, natural break method, geometric break method, standard deviation break method, and equivalence breakpoint method were used as statistical stratification methods with intervals of 3–6, and the Q-statistic of proxy variables and observations under different stratification schemes were probed. Finally, the scheme with the enormous Q-statistic was selected as the stratification and interval parameters for this proxy variable.

Spatial patterns of tourism demand in provinces

Spatial distribution.

Figure  5 illustrates the spatial distribution of tourism demand and flows between provinces in 2011 and 2018. In 2011, tourism demand was mainly distributed in China's first-tier cities with Beijing and Shanghai and the border provinces from southwest to northwest, with Hainan, Yunnan, Tibet, and Xinjiang being the main tourism demand destinations 35.33% of tourism demand in the study area. In 2018, tourism demand showed a trend of migration to first-tier cities, with Yunnan, Tibet, Shanghai, Chongqing, and Guizhou accounting for 36.46% of the total tourism demand.

Spatial distribution of tourism demand and tourism demand flow: ( a ) tourism demand in 2011, and ( e ) is in 2018; ( b – d ) are the inter-provincial directional tourism demand flows in 2011, which are high-intensity flow, medium intensity flow, and low-intensity flow respectively, and ( f – h ) is in 2018.

From the spatial perspective of tourism flow, there are 930 tourism flows between provinces with a minimum Euclidean distance of 115 km, a maximum of 3600 km, and a median of 1286 km. Long-distance travel 35 is a characteristic of tourism between domestic provinces. In 2011 the tourists' origins were concentrated in eastern China, and there were two clusters of high-intensity tourism flows distributed from Beijing and the ring-Beijing area to Yunnan and Hainan; this phenomenon altered significantly in 2018, with high-intensity tourism flows concentrated on one cluster of tourism flows from eastern China to Yunnan Province. Medium-intensity tourism flows presented a complex network with a stochastic pattern in 2011; in 2018, the complexity of the tourism network decreased, with steady clusters of tourism flows originating from the eastern provinces to Xinjiang and Tibet, while the northeastern region became a complete exporter of tourists. Comparing the low-intensity tourism flows in 2011 and 2018, it can be attended that the overall origin and destination were practically completely connected, i.e., there were tourism flows from both border provinces and the central region. The central provinces also energetically export tourists to the peripheral provinces, diverting the overall tourism flow network to a more luxuriant state. In summary, we can catch that the domestic tourism network in China during the period of rapid economic development showed a remarkable complex pattern, with the origins of tourists consolidated in the densely populated and economically developed areas in the east and the destinations distributed in the first-tier cities, and remote areas in the central and western regions.

From 2011 to 2018, the northeastern provinces, Beijing-Tianjin-Hebei region (Beijing, Tianjin, Hebei), Yangtze River Delta region (Shanghai, Jiangsu, Zhejiang, Anhui), and Pearl River Delta region (Guangzhou) are stable tourist sources; the average value of tourism demand in each province rose from 642,515 to 1,529,387, an increase of 2.38 times (Fig.  6 ). Yunnan and Guizhou in the southwest and Gansu in the west grew at a much higher rate than the national average; Heilongjiang, Jilin, and Liaoning in the northeast grew at a much lower rate than the average; and Hainan in the south became the only province in the country where tourism demand decreased.

Spatial distribution of the ratio of tourism demand in 2018 to 2011.

Spatial dependency

The spatial distribution pattern of tourism demand shifted from medium to high clustering in 2011 and 2018, and the positive Moran’s I revealed the existence of high-value to high-value clustering or low-value to low-value clustering of tourism demand in the study area, and the spatial pattern and spatial dependence of tourism demand with evident clustering. The z-score increased by 177.58% during this period, and the p-value decreased by 88.96%. The probability of rejecting the null hypothesis increased from 90 to 95%. Thus, the spatial clustering trend of tourism demand strengthened.

The global Moran’s I identified the overall spatial dependence of tourism demand in each province within the study area, and Local spatial autocorrelation analysis was applied to uncover the local spatial association patterns. As can be seen from Fig.  7 , significant stratification of tourism demand in 2011 and 2018 on a local spatial basis in China (absolute value of Z-score > 2.56, p-value < 0.01), consisting mainly of high-high value clusters (H–H) and low-low value clusters (L-L). In 2011, the H-H cluster was in Yunnan Province in southwestern China, the high values surrounded by low values cluster (H-L) appeared in Beijing, and the L-L cluster was in Heilongjiang Province in northeastern China. In 2018, the H-H cluster was still in Yunnan province, and the L-L clusters were distributed in Heilongjiang, Jilin, and Liaoning province, covering the whole northeastern region. From the perspective of spatial and temporal distribution, tourism demand formed a growth pole in southwestern China centered on Yunnan from 2011 to 2018, and tourism demand in Guizhou, adjacent to Yunnan, grew significantly and showed a spatial diffusion effect the region (Fig.  7 ). While in northeast China, the number of L-L clusters increased, and the provincial growth rate of tourism demand in low-value agglomeration was much lower than the national average during the study period. The existence of the H-L cluster in Beijing in 2011 and the disappearance of this cluster in 2018 indicated that Beijing had strong competitiveness in the region in the early stage, and the weakening polarization effect and the increasing diffusion effect diminished in the later stage when tourism demand was gradually distributed in a balanced manner in the Beijing-ring region. It suggested that tourism demand was more stable in China spatially in high-value clustering, and low-value clustering had increased, forming an increasingly stable high-value area in the southwest and low-value area in the northeast. Therefore, tourism demand in one province largely influences tourism demand in the adjacent provinces.

Local indicators of spatial association (LISA) for tourism demand in ( a ) 2011 and ( b ) 2018. The numerical marks on the maps represent P values, ** 5% level of significance (P < 0.05); *** 1% level of significance (P < 0.01).

Driving forces of tourism demand

Influencing factors of tourism demand.

Figure  8 showed the explanatory power of the driving factors for tourism demand in 2011 vs. 2018. In 2011, The number of enterprises in the high-tech industry (0.5622) had the highest explanatory power, implying that GDP had a remarkably noticeable impact on tourism demand. Average daily hours of sunshine (0.4934), Urban park green space area (0.4928), Urban road area (0.4763), GDP per capita (0.4473), Railroad mileage (0.4229) had the same level of high explanatory power, which meant that these three drivers had the most noticeable impact on tourism demand. The number of museums (0.3932), value-added of tertiary industry (0.3892), Average wage of employees (0.3479), Total population (0.3429), Highway mileage (0.3024) were also significant drivers of tourism demand. Average daily temperature (0.2757) and altitude (0.2028) also influenced tourism demand. Green space coverage index (0.0616), A-class scenic spot index (0.0321). The nighttime light index (0.0062) had minimal explanatory power on tourism demand.

Power of determinant Q-statistic value for each driving factor in 2011 and 2018.

In 2018, the average daily hours of sunshine (0.5848) significantly affected tourism demand, expressing the strongest association with tourism demand: urban park green space area (0.4084), Average daily temperature (0.407). GDP per capita (0.4058) had a significant explanatory power on the spatial distribution of tourism demand. Urban road area (0.3924), Railroad mileage (0.3863), Average wage of employees (0.3753), The number of museums (0.37), Total population (0.3347), Highway mileage (0.3299), The number of enterprises in the high-tech industry (0.3191) were also significant factors influencing tourism demand. Tourism demand was limitedly influenced by the value-added of tertiary industry (0.2148), Altitude (0.1443). Green space coverage index (0.0198), A-class scenic spot index (0.0062). The nighttime light index (0.0054) had minimal effects on tourism demand.

Interaction of driving factors

One hundred twenty couple of interactions were generated yearly between the 16 factors in 2011 and 2018, the bulk of which had an enhancing effect on tourism demand, with the primary interaction type being nonlinear enhancement (55.83% in 2011 and 75.83% in 2018), followed by bi-variable enhancement (43.33% in 2011 and 23.33% in 2018). The explanatory power of the interaction on tourism demand was greater than that of the single factor with the maximum explanatory power.

As shown in Fig.  9 a,c, in 2011, Average wage of employees-Average daily hours of sunshine (0.9935), Average wage of employees-Urban road area, GDP per capita-Railroad mileage, were two-factor non-linearly enhanced interactions with Q-statistic for the interactions greater than 0.99, showing a tourism demand with extreme explanatory power. Urban park green space area-Highway mileage, Average daily hours of sunshine-Highway mileage, Average daily hours of sunshine-Railroad mileage, Average wage of employees-Highway mileage, The number of museums-Average daily hours of sunshine, Average wage of employees-Urban park green space area, Urban park green space area-The number of museums were two-factor none-linearly enhanced interactions with interaction Q-statistic values greater than 0.98, a significant increase in the influence of synergy on tourism demand.

GeoDetector results: Power of determinants in interaction in ( a ) 2011 and ( b ) 2018; the difference of the impacts between two explanatory variables in ( c ) 2011 and ( d ) 2018.

In 2018 (Fig.  9 b,d), Average daily temperature-Urban road area (0.9949), Urban park green space area-Average daily temperature, Average daily temperature-Highway mileage, Total population-Average daily temperature, GDP per capita-Highway mileage, Average wage of employees-Highway mileage, GDP per capita-Total population, The number of museums-Average daily temperature, were two-factor non-linearly augmented interaction patterns with interaction Q-statistic greater than 0.99, which almost wholly control the spatial distribution of tourism demand. Value-added of tertiary industry-Average daily temperature, GDP per capita-Average daily hours of sunshine, GDP per capita-Value added of tertiary industry, GDP per capita-Urban road area, GDP per capita-The number of museums, GDP per capita-Urban park green space area, were two-factor none-linearly enhanced and GDP per capita-Average daily hours of sunshine was two-factor enhanced with interaction Q-statistic values greater than 0.98.

The regional economic development and construction were the main drivers, followed by the size of the population and the base of tourism services, and again by the traffic conditions, with the influence of natural factors and tourism resources being minimal in 2011. Moreover, by 2018, the influence of tourism comfort factors began to rise, such as average daily hours of sunshine and average daily temperature, representing a significant increase. The level of social and personal economic development and transportation conditions also increased influence to 2011. It indicated that in the aftermath of the world economic crisis and during the economic recovery, the driving force affecting tourism demand is the city's economy and level of development. However, after high economic growth, tourists have more requirements for the comfort of the experience during tourism, and the economy is no longer the main driving force directly affecting the spatial distribution of tourism demand.

Interaction mechanism

We filtered the five combinations with the maximum Q-statistic values from each of the 120 interaction combinations for 2011 and 2018, respectively, with an average explanatory power greater than 0.99 and two-factor nonlinear enhancement. It indicates that these combinations play a decisive role in the spatial distribution of tourism demand. The dominant factors to which each proxy variable belongs are also shown in Table 1 . The ten combinations generated the interaction networks with the most explanatory power. The proxy variables and the determinants are mapped as nodes; the cumulative value of the Q statistics for the interactions between the node and other nodes determines the node's size. The interactions between the factors are edges, and the Q-statistic values for interaction measured the weight of the edges. Different hierarchical interaction networks are visualized in Fig.  10 revealed the interactive mechanism.

Diagram of Interactive Network: the interactive network of proxy variables in 2011 ( a ) and 2018 ( b ); interactive network of dominants in ( c ) 2011 and ( d ) 2018; ( e ) global interactive network based on proxy variables; ( f ) global interactive network based on dominants.

From the perspective of the interaction of proxy variables, a strong triangular network community was formed in 2011 by average wages of employees—average daily hours of sunshine-highway mileage. In 2018, the interaction network shaped a significant polarization with the average daily temperature at the center, and average daily temperature and highway mileage formed a chain community. From the perspective of the interaction of dominants, Fig.  10 c,d illustrate a substantial triangular network community formed by traffic conditions dominated by socio-economic development conditions and complemented by physical conditions in 2011. This community continued to persist in 2018, with the difference that the roles of physical and traffic conditions were switched.

Notably, the most significant interaction results in 2011 and 2018 were integrated to reveal the driving mechanisms impacting the distribution of tourism demand. Physical conditions existed at the core of the interaction network, mainly in the form of average daily temperature, which interacted extensively with other factors and was the central driver influencing the distribution of tourism demand, indicating that tourism comfort is the basis of the natural scenery tourism attraction. Socio-economic development followed closely behind in physical conditions, with the average wage of employees representing the general level of economic development in the region and the prosperity of the tertiary sector, characterizing the level of tourism services, as well as being the foundation for driving the city to be a tourist attraction. The importance of traffic conditions was uncovered in tourism accessibility and the compression of the time distance. The three formed a concrete network of interactions that influenced the spatial distribution of tourism demand.

Tourism is one of the critical engines of local economic development and serves as a regulatory tool for coordinated development within and between countries and regions. Tourism is extraordinarily vital and dynamic, and according to the latest report of the World Tourism Organization, tourism worldwide has shown a rapid recovery after the impact of COVID-19. This paper suggested a theoretical framework to explore the spatial distribution of driving tourism demand based on a spatially stratified heterogeneity perspective, obtained the dominant drivers to shape the spatial distribution of tourism demand, and discussed the interaction mechanisms among the drivers.

Internet Big data have derived a tremendous amount of Internet operation records of individual Internet users, which provide us with new means of observation. Early observations of tourism demand relied on management statistics of scenic spots and cities. After 2008 Internet search engines, big data were widely used as indicators to quantify tourism demand, and they proved to have good reliability at different geographic scales to accurately reflect the amount of tourism demand. For example, Yang et al. and Xin et al. predicted tourism demand in Hainan Province 36 , China, and Beijing Forbidden City 37 , Beijing, China. The results proved that the Baidu index could more accurately reflect tourism demand's spatial and temporal characteristics. This paper uncovered the spatial distribution of tourism demand and flow network patterns reflected by the Baidu index on a national scale (Figs.  3 , 6 ). It demonstrated that the Baidu index could characterize tourism demand dynamically and build tourism flow networks.

Notably, there were regional differences in the distribution of inter-provincial tourism demand in China. The study results showed that tourism demand increased significantly from 2011 to 2018, the spatial clustering pattern of tourism demand was not randomly distributed (Fig.  5 ); there were two types of spatial effects in regional tourism growth, namely spatial spillover and spatial heterogeneity 21 . The high tourism demand cluster was shaped in the southwest, and the low tourism demand cluster was rendered in the northeast (Fig.  7 ). The spatial competitive effect of high and low imbalance in the capital ring gradually vanishes, and the tourism demand in the central region tends to be homogeneous.

By investigating the spatial distribution pattern of domestic inter-provincial tourism demand in China, we recognized heterogeneity in the sensitivity of long-distance tourism flows to the distance in different intensities (Fig.  11 ). There was an explicit reversal at 1900 km for high-intensity tourism flows, i.e., the distance between origin and destination was shorter than 1900 km, and tourism demand positively corresponded with distance; conversely, whereas the was over 1900 km, tourism demand declined with increasing distance. Medium-intensity tourism flows were not sharp with distance. Low-intensity tourism flows obeyed the distance decay law.

Results of locally weighted regressions of distance and travel demand: ( a ) 2011 ( b ) 2018. 2011 and 2018 tourism demand flow intensities were normalized separately. Locally weighted regression 38 of distance and normalized tourism demand intensity was performed according to the stratification of tourism demand flows in Fig.  5 , where the Euclidean distances of flows were calculated in the Beijing_1954_3_Degree_GK_Zone_35 coordinate system.

The dominants that have a decisive influence on tourism demand were physical conditions, socio-economic development, and traffic conditions; the proxy variables are the average daily temperature, the average wage of employees, and highway mileage.

Physical conditions had high explanatory power for the spatial distribution of tourism demand, proving that natural scenery tourism was more prevalent in China than urban humanistic tourism. Tourist attraction and comfort of the natural scenery type were determined physical conditions; for instance, world natural heritage sites have a stronger role in promoting tourism 39 . Murphy et al. analyzed daily time-scale park visitation and weather data for Pinery Provincial Park, Canada, from 2000 to 2009, demonstrating the high sensitivity of tourism demand to average daily temperatures 40 .

While socio-economic development furnishes the foundation for breeding urban humanistic tourism, the average wage of employees is an efficient indicator of the region's economic development 41 , where high-quality tourist reception, available public information, travel safety, and diversified recreational convenience services are important factors attracting tourists. Meanwhile, efficient administrative supervision services in economically developed regions directly impact public information, recreational convenience, safety protection, and recreational convenience 42 .

Traffic conditions make it possible to connect tourists to tourist attractions. Wang et al. (2020) uncovered a well-coupled relationship between tourism efficiency and traffic accessibility in Hubei Province 43 , China, from 2011 to 2017. Highway mileage enhanced the coverage of inter-regional connections and compressed tourists' time costs to their destinations; on the other hand, it also increased the polarization of intra-regional connections, thus benefiting the central regions rather than the peripheral ones from the traffic 44 . This finding was consistent with the results that Southwest China forms a high tourism demand cluster, while Northeast China is a low tourism demand cluster in “ Spatial dependency ” section and Fig.  7 . Another recent study 45 has observed that less-developed central and western regions attract more visitors than developed eastern regions by improving transportation conditions in China.

Several aspects need to be considered in related follow-up studies. First, this study analyzed the drivers of tourism demand at the provincial level in China, with prominent medium- and long-distance tourism characteristics. In contrast, complete tourism demand occurs between prefecture-level cities, which should be considered the primary research unit in the future. However, writing a crawler program to request raw data from the Baidu index to obtain the daily tourism demand O-D flow has limitations. Therefore, moderately reducing the scope of the study may be helpful. Secondly, direct dominants in this study's theoretical framework of the driving mechanism are economic development level, population size, urban ecological conditions, tourism resources, natural environment, transportation conditions, and science and technology innovation. The results showed that the geographical variables represented by the tourism resources index, night lighting index, and green space coverage index have little impact on tourism demand, it might be caused by the difference between the scale of rasters and spatial panel data, these rasters may have more efficiently representation on the scale of urban. Therefore, more representative ones should be selected in future research as proxy variables. Finally, the COVID-19 global pandemic significant public safety event on tourism demand is also very impacting. The effects of severe public contingencies and the government's immediate response policies on tourism demand should be added to the tourism demand driving mechanism in the future.

Conclusions

This study adopted Baidu index data spatialized into flow space, and multi-source data to investigate domestic tourism demand's spatial pattern and drivers during China's rapid economic development (2011–2018). The results show that (1) China's domestic tourism demand has significantly increased, shaping a spatial pattern in which first-tier cities and western regions where the core tourism destinations and the tourism attractiveness of northeastern regions gradually disappeared. The tourism demand network is increasingly prosperous and gradually develops from disorderly to orderly, with eastern regions as the main source of tourists. (2) From the single driving factor, the factor with the strongest and increasing control over the spatial distribution of tourism demand is sunshine hours > the average wage of employees > highway mileage. (3) In terms of the composite factor interaction results, the interaction network formed by physical conditions-economic development level-transportation conditions steadily and strongly determines the spatial distribution pattern of tourism demand.

The novelty of this study is the flow-based spatialization of the search engine index (Baidu index), which efficiently mapped the spatial mode of tourism demand and unearthed the network formed by domestic tourism flows, domestic long-distance travel in China is positively correlated with distance in terms of travel demand between the source and destination within 1900 km and vice versa. Additionally, the factors affecting the spatial distribution of tourism demand were interpreted from spatial heterogeneity, and the significant impact of the interaction between factors on tourism demand was resolved and captured the complex network. The findings of this paper can provide a reference for regional tourism planning decision-makers. Simultaneously, it can also provide a systematic tourism demand driving mechanism for tourism demand forecasting researchers and promote modeling accuracy.

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Acknowledgements

The authors acknowledge the help of Dr.Yongze Song and reviewers on improving and commenting on the manuscript. This work was supported by The Second Tibetan Plateau Scientific Expedition and Research Program (STEP) [Grant No. 2019QZKK1004].

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Xuankai Ma & Jianghua Zheng

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X.M. conceived and designed the study, conducted the data analysis and visualization, and wrote the manuscript. Z.Y. provided project administration, supervision, validation. J.Z. gave guidance to improve the results in revision. All authors reviewed and commented on the manuscript.

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Correspondence to Zhaoping Yang .

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Ma, X., Yang, Z. & Zheng, J. Analysis of spatial patterns and driving factors of provincial tourism demand in China. Sci Rep 12 , 2260 (2022). https://doi.org/10.1038/s41598-022-04895-8

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Received : 13 November 2021

Accepted : 04 January 2022

Published : 10 February 2022

DOI : https://doi.org/10.1038/s41598-022-04895-8

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