How Can Big Data Support Smart Scenic Area Management? An Analysis of Travel Blogs on Huashan

Abstract

Data from travel blogs represent important travel behavior and destination resource information. Moreover, technological innovations and increasing use of social media are providing accessible ‘big data’ at a low cost. Despite this, there is still limited big data analysis for scenic tourism areas. This research on Huashan (Mount Hua, China) data-mined user-contributed travel logs on the Mafengwo and Ctrip websites. Semantic analysis explored tourist movement patterns and preferences within the scenic area. GIS provided a visual distribution of blogger origins. The relationship between Huashan and adjoining tourism areas revealed a multi-destination pattern of tourist movements. Emotional analysis indicated tourist satisfaction levels, while content analysis explored more deeply into dissatisfying aspects of tourist experiences. The results should provide guidance for scenic areas in destination planning and design.

1. Introduction

Travel behavioral pattern analysis is important for the planning and management of tourism destinations and attractions, allowing managers to more effectively develop strategies, map out travel routes, recommend products and experiences, and manage visitor impacts [1]. Travel blogs on social media are an excellent information source for analyzing tourist movements, activities, preferences, and satisfaction levels [2]. However, these data are not often being applied in scenic area planning in China. There is a tendency to focus just on entry tickets sold, revenue generation, and volumes of tourists, rather than on gathering and analyzing robust data on travel patterns and tourist behavior. Tourism planning in most Chinese scenic areas focuses on the levels of financial investment and GDP increases, but generally ignores tourist services and smart management [3]. The Jiuzhai Valley National Park in Sichuan was the first scenic area with a smart management system in China [1,4,5]. However, despite being an innovator, it was forced to launch a tourist flow forecasting system in 2014 to prevent a repeat of an overcrowding crisis during China’s Golden Week in 2013 [6]. Even very well-known attractions in China such as Huashan are lacking basic data, including detailed statistics on the origins of tourists. Smart tourism strategies based on big-data analysis will undoubtedly contribute to solving these information deficiencies.

Huashan (simplified Chinese：华山) in Shaanxi Province is a popular scenic area and was selected as the case study for this research. Huashan is a mountain situated in Huayin in the Weinan region of Shaanxi Province, which is 120 km from Xi’an [7]. It is located near the southeast corner of the Ordos Loop of the Yellow River Basin, south of the Wei River Valley, at the eastern end of the Qin Mountains, in southern Shaanxi Province. It is the most western of the Five Great Mountains of China, and has a long history of religious significance. Huashan has five main peaks, the highest being the South Peak at 2155 m (7070 feet). Huashan can be classified as a mature and world-class tourist destination [8,9]. The number of tourists visiting Huashan has been growing steadily; for example, in 2016, total tourist arrivals were 26.2 million and ticket income was RMB 339 million Yuan, representing increases of 5% and 8%, respectively, over 2015 [10]. During the field study at Huashan in May 2016, although it is a leader in Weinan’s tourism sector, the Huashan Tourism Group could not provide detailed statistical data on tourists other than tourist arrivals based on ticket counts and information provided by local travel agents and hotels in Weinan. Huashan’s smart management system has existed since 2014 [11]; however, the focus is on providing online travel information and generating e-commerce. The system is lacking fine-grained statistics on tourists’ behavior, so that it cannot provide data support for intelligent services and further planning. Due to the steep terrain of Huashan, overcrowding during peak attendance periods leads to falls and trampling. Huashan needs to invest great care, time, and effort to ensure the safety of its visitors. As with most of China’s state-run scenic areas, Huashan does not feel compelled to significantly enhance visitor services while ticket revenues and profits are high. Most critically, the administration team at Huashan lacks research on tourist satisfaction. The absence of these data constrains intelligent management, marketing, and the sustainable development of Huashan and the surrounding region of Weinan. Paradoxically, due to the popularity of Huashan among tourists, there are many travel blogs on Huashan in Chinese social media, which are waiting to be mined to profile tourist behavior patterns and satisfaction levels. Recognizing the potential for user-generated content, travel blogs uploaded by Huashan tourists were analyzed to document travel movements, site linkages, and satisfaction levels. The principal research questions were:

• How do Huashan visitors describe their travel experiences in blogs?
• What sites are visited within the Huashan scenic area?
• What are the patterns of movement within Huashan and adjoining destinations?
• Are people satisfied with their experiences at Huashan? If tourists are dissatisfied, what are the reasons?
• What are the geographic origins of Huashan tourists?
• What are the monthly distributions of visits, expenditures, and lengths of stay for visitors to Huashan?

Answering these questions by analyzing travel blogs for Huashan is potentially a smart tourism solution leading to more effective scenic area planning and management. It may also contribute ideas and solutions to enhancing the sustainability of the scenic area.

2. Literature Review

2.1. Travel Blog Data and Tourist Behavior

Data from travel blogs have five potentially beneficial features (five Vs), which are large scale (volume), content diversity (variety), quickly changing (velocity), authenticity (veracity), and application value (value) [12,13]. Additionally, spatial–temporal data from such blogs is multi-sourced, objective, dynamic, realistic, and fine-grained [1]. The data are free as people publish for personal reasons, such as recording and sharing experiences [14,15]. Blogs contain text, photos, videos, and other data forms. Tourists record some aspects of their actual behavior in blogs, which can be numerous, available at low cost, and contain rich information. Blogs are uploaded on sophisticated platforms for sharing through smartphones, pads, laptops, etc. [16,17]. Many online platforms also contain data tagged by users or by websites using geographic and time references [18]. Consumers are in control of the text, geographic references, access times, images, and other information, rather than the destinations or attractions [16]. The geographic- (spatial) and time- (temporal) tagged data reveal travel behaviors and site and resource use [19,20]. They constitute the tourists’ digital footprints, depicting movement within destinations and surrounding areas [16,21]. In addition, the data can be used to better comprehend tourist preferences for specific products, sites, and experiences, and satisfaction with the management and service quality of attractions [1].

Since travel blogs have rich information for investigating tourist movements and other behaviors, they are being increasingly used in tourism research. However, there is a danger of generalizing results from data that are not representative or have been poorly gathered [22]. Furthermore, as suggested by Hall [23,24,25] and Shoval [26], qualitative and quantitative analyses should be integrated to explore tourist behavior and traditional approaches should not be disregarded. These data collection approaches include face-to-face interviews [27], survey questionnaires [28,29,30], secondary data published by government or tourism organizations [27,31,32], onsite observations [14], and data collected by mobile tracking equipment [33,34,35].

Social media data may reveal tourists’ impressions and experiences of the resources and environment of special scenic areas [1]. However, researchers must recognize that these do not reveal actual behavior. Notwithstanding this limitation, the data are less expensive than other sources of mobility big data, such as the Global Positioning System (GPS) logs on mobile phones that are controlled by mobile service providers. Therefore, travel blog data are appropriate in the case of Huashan, especially since it is a Chinese scenic area that has not implemented systems to track and mine tourist behavior data to support smart management.

2.2. Analysis of Tourist-Generated Big Data

The analysis of blogs and traveler reviews represents a relatively new research method for tourism scholars. Tourist behavioral patterns and behaviors can be observed, recorded, and analyzed [36] through these data. Landmarks, travel routes, and places frequently used by tourists can be identified. Research using data uploaded by tourists on social media is at different geographic levels. At a global level, Hawelka et al. (2014) used Twitter data to explore international tourist travel behavior [37]. According to the nearly one billion tweets in 2012, these researchers investigated the mobility of tourists in different countries, characteristics of tourist flows, radii of rotation, diversity of destinations, and balance of capital inflows and outflows. At a country level, Li and Yang (2017) studied Sina Weibo data to explore travel patterns during China’s Golden Week in 2014 [38]. Other studies have focused on urban areas [14,26,34,39] and attractions.

Thanks to the structured comparisons of images published on Flickr, scholars can readily obtain and analyze those photographic data. Some of the urban-scale research focuses on using Flickr’s photo and text information [14,39]. As well as exploring the most popular tourist attractions, these studies compare travel patterns and behaviors of tourists from different origins [34]. Researchers also are using big data with geographic labels to analyze tourist preferences and activities [14,40]. For example, Guo et al. (2015) applied compact and sequential mode mining methods to collect and analyze geographical information from blogs on Qunar.com to analyze the interests, tourism activities, and use of specific tourism services [2].

This research analyzes data at the tourist attraction level, specifically for a scenic area. It focuses on tourist movements within and outside of the scenic area, visualizing the results with Geographical Information System (GIS) software.

3. Methods

3.1. Data Collection

There is a great quantity of user-generated content on Chinese mainstream online travel websites such as Mafengwo, Baidu Tourism, and Ctrip. The data include not only text and photos in travel blogs, but also tagged data such as travel dates, travel expenses, lengths of stay, associated destinations, and author residences. These data are gradually becoming easier to obtain with steady improvements of data structure in the travel websites. Several commercial software programs for web information acquisition are available, such as GooSeeker, Enthone, and the Locomotive and the Octopus web crawlers. These programs generally have the advantages of rapid iteration and ease of operation, making UGC acquisition from travel websites more convenient.

The Octopus web crawler tool was used to capture data from the Travel Guide Channels of Mafengwo and Ctrip on 20 May 2016. Two new tasks were created in the capture software and then a complete and clear capture process was established. First, all the lists of travel blogs related to Huashan were obtained by searching the home pages of the Travel Guide Channels using “Huashan” as the keyword. Next, a circular crawl list was created to catch the detailed pages of each travel blog. Then, in each detailed page, different grabbing positions were set up according to the page structure to obtain the corresponding contents, such as title, full text, release time, and tourist behavior. Finally, with the automatic page-turning function of the software, all relevant travel materials were obtained.

A total of 1468 travel blogs (over 840,000 words in Chinese) were captured. Among them, 768 (over 58,000 words) were retrieved from Mafengwo, and 700 (over 265,000 words) were retrieved from Ctrip.

3.2. Data Cleaning

The data were saved in a structured format, that is, importing the basic trip elements, including the blog title, author, and full text as well as tourist behavioral information, including travel dates, travel expenditures, lengths of stay, other destinations visited, and author residences into an Excel file to form a database of travel blogs. Data missing basic information (3977 articles with more than 1,900,000 words), travel website template data extracted by the regular expression function of the software (3341 articles, >2,700,000 words), and advertising text data (1305 articles, >800,000 words) were deleted. The pure-text process was conducted on the full text of each travel blog and contents were sorted by sentences. Duplicate and blank content (>20,000 words), short articles with 10 characters and less or meaningless content such as “I am here!”, “This picture is beautiful... “(more than >50,000 words in total) were removed. After sorting and screening, a total of 1080 high-quality Huashan travel blogs (>700,000 words) were obtained. Among them, 549 articles were from Mafengwo (>439,000 words) and 531 were from Ctrip (>265,000 words).

3.3. Data Analysis

To address the first four research questions, content analysis of blogs was conducted. The semantic analysis of blog contents applied ROST CM, NetDraw and other tools for word segmentation and frequency statistics, and semantic structure drawings. A customized lexical pool was created based on the unique vocabulary associated with Huashan, and then integrated with the built-in Chinese word library of ROST CM. For the former, words were included such as “Huashan (华山),” “West Peak (西峰),” “cliff (绝壁),” “sunrise (日出),” “plank walk (栈道),” “Weinan (渭南),” “lamb liver soup (杂肝泡),” and “spicy Chinese food (香椿辣子).” Figure 1 shows the plank walk at the cliff near the South Peak of Huashan. Then, word segmentation processing on the full-text content of the travel blogs was done. Meaningless words were filtered out and word frequency statistics calculated. Using the highest frequency words, the Word Extraction feature was applied to each sentence line of the travel blogs. A co-occurrence matrix was derived by calculating the total frequencies of all the feature words. This matrix was visualized by the topological graph process using NetDraw to represent the semantic structure.

The emotional analysis of visitor satisfaction and dissatisfaction assessed inclinations within blog text using an emotional word library. After word segmentation, the text was separated into lines according to the ending punctuation marks such as periods, question marks, exclamations, ellipses, etc. The researchers ensured that each line expressed independent and complete meanings. Next, a dictionary of Chinese commendatory and derogatory terms written by Professor Li Jun of Tsinghua University and sentiment words from the China National Knowledge Infrastructure was selected as the basis for the emotional analysis. Common negative Chinese words were used as negative emotional expressions, and common Chinese adverbs represented the emotional judgments. The words in each line of the travel blogs were compared to those in this emotional lexicon. Also, emotional indications were judged according to multiple negation rules in Chinese language habits. A score was assigned according to the degree of emotion expressed by the adverbs. Positive and negative points indicated positive and negative emotions, respectively; zero point scores were neutral. The higher the absolute score, the greater the degree of emotion being expressed by the tourist.

The data analysis produced descriptive statistics on tourist characteristics. Microsoft Office Excel was used to classify, aggregate, cross-analyze, and visualize charts on structural tag data including tourist origin regions, places visited within Huashan scenic area, travel dates and expenditures, lengths of stay, and visits to adjoining destinations. These results satisfied the requirements for research objectives 5 and 6.

4. Results

4.1. Content Analysis of Huashan Travel Blogs

A total of 4561 keywords were found after the word segmentation. Some 224 keywords in the Huashan travel blogs appeared more than 10 times (Appendix A), while the top 35 keywords are shown in

Table 1. Occurrence of keywords in Huashan travel blogs.

Number	Keyword	Occurrence Number	Number	Keyword	Occurrence Number
1	Huashan 华山	580	19	Accommodation 住宿	78
2	Xi’an 西安	464	20	Downhill 下山	74
3	North Peak 北峰	125	21	Snack 小吃	74
4	Train 火车	103	22	Route 路线	74
5	West Peak 西峰	95	23	Yan Pagoda 雁塔	72
6	Wall 城墙	132	24	Terracotta Warriors 兵马俑	71
7	Cableway 索道	131	25	Shanxi 陕西	69
8	East Peak 东峰	122	26	South Peak 南峰	69
9	History 历史	108	27	Bell Tower 钟楼	68
10	Yuquan Yard 玉泉院	107	28	Climbing 登山	66
11	Huis 回民	105	29	Delicacy 美食	65
12	Train station 火车站	104	30	Square 广场	65
13	Airport 机场	102	31	Plank walk 栈道	62
14	Hotel 酒店	101	32	Metro 地铁	60
15	Sunrise 日出	93	33	Culture 文化	57
16	Tourist 游客	92	34	Rest 休息	57
17	Admission ticket 门票	84	35	Chang’an 长安	57
18	Drum Tower 鼓楼	78	...	...	...

. The semantic network diagram is illustrated in Figure 2. The scenic spots within Huashan that tourists visited most or found of greatest interest were comprised of four peaks (the South, North, East, and West Peaks). The Middle Peaks were less attractive and not included. Figure 2 indicates that East and West Peaks are associated with sunrise views and viewing. Climbing the West Peak was perceived as requiring greater physical exertion than the other peaks. The ropeway (or plank walk) was mentioned most frequently, along with the North and West Peaks. The main attractions within Huashan were Yuquan Yard, Gold Lock, and Cang Long Ling. The distribution of tourists within Huashan seemed in accordance with natural conditions, and ropeway and tourism product design of the scenic area.

As can be seen from the semantic network diagram (Figure 2), Xi’an (西安) has a great impact on Huashan tourism. The Terracotta Warriors (兵马俑), Huaqing Pool (华清池), Hukou Waterfall (壶口瀑布) on the Yellow River (黄河), and downtown Xi’an were the most frequently visited adjoining attractions for Huashan tourists. Non-local tourists arrived in Huashan mainly by flights (飞机) through Xi’an Xianyang Airport (咸阳机场) or by train (火车).

4.2. Travel Pattern Analysis

The co-occurrence numbers of the place names appearing in Huashan blogs were calculated to represent the association degree between destinations. Then the association degree of Huashan and its adjoining destinations were obtained; only the elements whose co-occurrence numbers were greater than 10 were listed in

Table 2. Association degree of destinations visited by Huashan tourists.

No.	Destination	Destination	Co-Occurrence	No.	Destination	Destination	Co-Occurrence
1	Huashan 华山	Xi’an 西安	353	10	Huashan 华山	Qinghai Lake 青海湖	10
2	Huashan 华山	Terracotta Warriors 兵马俑	50	11	Xi’an 西安	Terracotta Warriors 兵马俑	62
3	Huashan 华山	Dunhuang 敦煌	22	12	Huaqing Pool 华清池	Terracotta Warriors 兵马俑	30
4	Huashan 华山	Huaqing Pool 华清池	22	13	Xi’an 西安	Xianyang 咸阳	29
5	Huashan 华山	Luoyang 洛阳	21	14	Xi’an 西安	Dunhuang 敦煌	26
6	Huashan 华山	Huayin 华阴	19	15	Xi’an 西安	Hukou Waterfall 壶口瀑布	25
7	Huashan 华山	Hukou Waterfall 壶口瀑布	16	16	Yan’an 延安	Hukou Waterfall 壶口瀑布	23
8	Huashan 华山	Yan’an 延安	13	17	Xi’an 西安	Huaqing Pool 华清池	22
9	Huashan 华山	Xianyang 咸阳	11	18	Xi’an 西安	Luoyang 洛阳	11

.

Using a batch acquisition tool named xGeocoding, the coordinate information of these destinations, the latitude and longitude data, was derived. Taking the correlation degree between two destinations as input variables into GIS, a map showing the association degrees between Huashan and its adjoining destinations was obtained. Results illustrated at Figure 3 were exported from GIS.

Only 17.4% of visitors had Huashan as their sole destination. The attractions jointly visited with Huashan included Xi’an, Terracotta Warriors, Huaqing Pool, Lishan, Xianyang, Huayin, Yan’an, Hukou Waterfall, Huangdi Mausoleum, Lishan, and some other popular places in Shaanxi Province. Visitors also had other joint destinations with Huashan, such as Luoyang, Songshan Shaolin Temple, Longmen Grottoes, Qinghai Lake, and other top attractions in neighboring provinces. However, Xi’an and Huashan was the most popular multi-destination itinerary found in the travel blogs.

4.3. Satisfaction or Dissatisfaction with Huashan Trips

Table 3. Emotional analysis of travel blogs.

Category	Absolute Value > 20		10< Absolute Value < 20		Absolute Value < 10
	Number	Proportion	Number	Proportion	Number	Proportion
Positive	1157	5.60%	2653	12.85%	7876	38.15%
Negative	22	0.11%	375	1.82%	2362	11.44%

shows the emotional analysis results. There were 11,686 positive (56.6%), 6126 neutral (29.7%), and 2833 negative evaluation items (13.7%). The significant level of negative comments should be of concern to the Huashan Management Committee.

A thick data analysis of the unsatisfactory evaluations was conducted, selecting the top 100 blogs with the lowest scores for negative emotions, and reflecting the most unsatisfactory experiences with Huashan visits. Content analysis on each unsatisfactory evaluation was carried out, including in-depth analysis of different tourism experience elements, infrastructure, and services. Dissatisfaction with Huashan was focused on service facilities, congestion and garbage at scenic spots, and self-driving navigation difficulties (

Table 4. Dissatisfaction evaluation of Huashan visits.

Issues	Existing Problems	Travel Blogs with Translation in English
Accommodation	Poor conditions, difficulty in booking at the height of the tourist season	Accommodation conditions are rudimentary; no bathing was possible because there is a lack of water in the mountains. 住宿条件简陋，因山上缺水，无洗澡条件。
		Go inside a hundred meters, you can see the hotel; too many young people, sound insulation is not good. 往里走一百米就是这个酒店，年轻人较多，隔音不好。
		Accommodation during National Day must be booked half a month in advance, or it is difficult to choose a cheap hotel or youth hostel. 国庆住宿必须提前半个月预定，不然很难选到便宜的酒店或青年旅社。
		I called the police; the hotel name is Jinxin Hotel. 我就打报警，看了一下宾馆的名字是金鑫宾馆。
Catering	Management confusion, high prices, bad service attitude	A chicken is so expensive, WTF; in the off-season, you can choose to kill fresh chickens or have the chickens from last night, but now you can only have the latter. 一只鸡卖这么贵，太坑爹了，而且淡季的话可以选择昨夜杀的鸡和现杀的鸡，现在是旺季只有隔夜杀的鸡了。
		At night, I went to the platform below the north peak, which was super-crowded. There was not a private shop selling bottled water, only the official one, very very expensive. 晚上，我到了北峰下面平台，人山人海。平台没有私人开得卖水小店，都是官方的售价，死贵死贵的。
		I was cold and hungry, I had no choices and had to eat the most expensive instant noodles, which I have never had before. 我又冷又饿，没办法吃了自己史上最贵的一桶方便面。
		Food is expensive but not delicious; even frozen dumplings are better than that. 饭菜又贵又不好吃，还不如速冻饺子呢。
		The boss is very snobbish, you are not allowed to sit without ordering; Gansu boys had breakfast there, only porridge, bread, pickles, but it was so expensive. 老板很势力，不吃饭不许坐，甘肃男生在那里吃了早餐只有粥、馒头、咸菜，还卖那么贵。
		I was very hungry in the mountains, so I ate a bowl of instant noodles. I had the waitress bring me some boiled water, which I had to pay for that. 在山上饿的不行，吃了碗泡面。我让阿姨给我加点白开水，还得另外收费。
Toilets	Small quantity, heavy smell	The hillside is full of people; it’s very difficult to find a toilet. 山坡上全是人，上个厕所何其困难。
		Looking around for a toilet, no mood for sightseeing. 四处寻找厕所，无心游览了。
		There is a small temple on the middle peak, but there is nothing inside. There’s a public toilet in the back and it smells bad. 中峰上有个小庙，什么东西都没有，后面是个公共厕所，发出难闻的异味。
Passenger ropeway	Long wait time	There are too many people waiting for the ropeway, mostly occupied by tour groups. 坐索道的人太多了，都是旅行团，带团去的能等死。
		After waiting for the ropeway for nearly 40 min, when getting down I felt sick. 索道等候近四十分钟，下行时觉得恶心想吐。
Capacity	Full of tourists	It so happened that it rained during May Day holiday, it took nearly three hours to buy tickets, and I was wet, cold, and sad. 五一刚好赶上下雨，排队买票花了近三个小时，被雨淋湿湿的，更冷的难过。
		Huashan has too many drawbacks. It’s a tragedy that I encountered too many tourists. 华山的弊端太多，碰到游客一多真的会悲剧。
		The pedestrians in front are not moving, so the traffic jams; I had to tilt my head stupidly just looking at the sea of clouds. 前面的行人也不动，交通堵车，只能侧头傻呆呆地望着云海。
Sanitary	Litter everywhere	It is chaotic; there is garbage on both sides of the tourist queue. 秩序有些混乱，队伍两边到处是随手丢弃的垃圾。
		I saw very annoying things on Sun-watching Platform; there is so much garbage left behind by tourists. 观日台看到了让人很气愤的事情，有很多游客留下的垃圾。
Transportation	Illegal taxis, rip off, old railway station	These minibus drivers do not compete with the illegal taxi drivers. But when you get on the minibus, the driver said the illegal taxi drivers were actually the hotel’s - confederates, who would tell tourists to have a rest at hotels first before climbing the mountain at night. They would then take you to - tourist traps that would rip you off. 这些小巴司机也不会主动跟黑车司机抢客，但是上了小巴，小巴司机就说了，这些黑车司机竟是旅馆的托，骗着游客说，晚上登山，现在可以去旅馆休息休息。然后，就把你拉去黑旅馆继续宰你
		The train station is a bit old, which is reasonable. But the road to the tourist center is also old, full of potholes. Fortunately, I have a seat. Halfway there, the driver was afraid of being checked, so he asked the standing tourists to squat; there were many large bags, so this was very embarrassing. 火车站有点老旧情有可原，去游客中心的路居然一样老旧，坑坑洼洼。好在还有个座位，中途怕检查，司机让站着的游客蹲下，都是大包小包的也够难为情的。
		In the train station I did not want to stay longer than it takes to take a few photos, so I left quickly. There were always black car drivers around, as well as pimping, very annoying. 在火车站不愿多做停留拍几张照片，赶紧离开，时时刻刻都有黑车司机，还有拉皮条的来骚扰烦死人了。
		High-speed rail tickets from Huashan North to Xi’an North are quite difficult to buy; this time we had a loss. 从华山北到西安北的高铁票很难买，这次就吃了亏。
		Rushing out of the trap of the black car drivers surrounded, you will see only a few regular taxis穿过黑车司机的包围，会看到不多的几辆正规出租车。
		Do not believe any taxi drivers who say they will charge you legally; when detouring, they won’t tell you. 不要相信任何说打表的出租车司机，绕路的时候，他们是不会告诉你的。
		It took us four hours to reach the foot of Huashan; we stopped frequently. 用了四个小时到达华山脚下，中途经常停下。

).

4.4. Regions of Origin of Huashan Tourists

Table 5. Cities of origin of Huashan tourists.

Number	Origin	Proportion	Number	Origin	Proportion
1	Beijing 北京	16%	16	Shenyang 沈阳	2%
2	Shanghai 上海	12%	17	Qingdao 青岛	1%
3	Guangzhou 广州	9%	18	Luoyang 洛阳	1%
4	Zhengzhou 郑州	6%	19	Shijiazhuang 石家庄	1%
5	Xi’an 西安	5%	20	Hangzhou 杭州	1%
6	Chengdu 成都	4%	21	Wuxi 无锡	1%
7	Chongqing 重庆	3%	22	Xingtai 邢台	1%
8	Taiyuan 太原	3%	23	Ningbo 宁波	1%
9	Weinan 渭南	3%	24	Lanzhou 兰州	1%
10	Nanjing 南京	3%	25	Haerbin 哈尔滨	1%
11	Changsha 长沙	2%	26	Xianyang 咸阳	1%
12	Wuhan 武汉	2%	27	Suzhou 苏州	1%
13	Dalian 大连	2%	28	Yuncheng 运城	1%
14	Tianjin 天津	2%	29	Xuzhou 徐州	1%
15	Jinan 济南	2%	30	……	……

shows the blog authors’ residence cities, retrieved from tagged data in blog websites. Using GIS software, the visual pattern of origin distribution is shown in Figure 4. Huashan tourists were mainly from cities outside of Shaanxi Province. The first tier of origins according to volumes of tourists were Beijing, Shanghai, and Guangzhou; the surrounding region composed by Zhengzhou, Taiyuan, etc.; Southwest China consisting of Chengdu, Chongqing, etc. The next tier was comprised of Central China with Wuhan, Changsha, etc.; Northeast China including Shenyang, Dalian, etc.; and other parts of China. Spatial distance determined the distribution of Huashan tourist markets, which is influenced by economic development levels and convenience of transportation. The closer, the higher the level of economic development, and the more convenient the transportation, the greater the volumes of tourists are to Huashan.

Figure 5, Figure 6 and Figure 7 provide the travel date, expenditure, and length of stay results respectively. These statistics were calculated using the tagged data in Huashan travel blogs, which was structured by the blog websites. Most people visited Huashan in the months of April to October. This seasonal pattern for Huashan tourism is not as limited as for many other destinations in Northern China. The per capita travel expenditure of Huashan tourists was in the range of RMB 1000 to 3000 Yuan, which is slightly higher than for other scenic areas in China. The length of stay was three to five days. It bears noting that the length of stay of tourists who only visited Huashan was not as long as for people who also visited the Terracotta Warriors, Huaqing Pool, Yellow River (Hukou Waterfall), and other attractions in downtown Xi’an. This implies that Huashan needs to add to and upgrade its tourism products to increase length of stay and expenditure, and to enhance the holding power of the surrounding Weinan region.

5. Conclusions, Management Implications, and Research Limitations

This research is among the first to data-mine scenic area travel blogs by incorporating semantic analysis along with GIS visualizations. It demonstrates the value of these user-generated contents for market and satisfaction analysis of scenic area attractions. It is an exploratory analysis on travel blog data about scenic area attractions and there is considerable scope for future studies. Suggestions include analyzing the photographic content of travel blogs; conducting preference analyses among different tourist market segments; and cross-validation analysis with data from traditional research methods.

The results show that the tourist experience at Huashan is based on climbing and especially associated with the iconic “plank walk.” Xi’an and Huashan are linked as destinations in the minds and actions of tourists. Specifically, downtown Xi’an, Terracotta Warriors, Huaqing Pool, and Yan’an are often grouped with Huashan in multi-destination trips.

The multi-destination tendency of Huashan tourists underlines the potential for cooperative marketing by the Huashan Management Committee along with the neighboring provinces of Henan, Qinghai, and Gansu. The other closest sites and attractions within Weinan did not appear in multi-destination patterns, which suggests that Huashan is overshadowing neighbors through its much greater destination image and market popularity. The Huashan Management Committee must, therefore, strengthen its role as a tourism development agent for the Weinan region. Greater attention must be focused on regional tourism development and marketing, integrating the tourism resources in Eastern Shaanxi and along the Yellow River.

There is a significant level of dissatisfaction with the facilities, services, and operational management of Huashan, which requires immediate attention. Overcrowding and littering are already serious issues, and will worsen as tourist numbers continue to increase. The sustainability of the Huashan experience is under threat. Visitor monitoring and management are insufficient at the current time; however, smart data-gathering and analysis such as demonstrated in this research can help point to solutions that will improve resource and experience sustainability.

Many attraction administration teams in China still have a narrow “ticket revenue and GDP” mindset and need to broaden their perspectives to operate more professionally as destination managers while assuring the sustainability of precious natural and cultural resources. The Huashan Management Committee should gather and use contemporary information sources, including smartphone ‘footprint’ data, to obtain real-time, spatial data on tourist and personnel movements within the scenic area that impact on the natural resources and environment, traffic flows and convenience of navigation, and visitor safety, experiences, and enjoyment. Managers should be accessing real-time data from big-data centers and cloud computing platforms, as well as analyzing tourist preferences and requirements.

As Gretzel et al. (2015) claimed, the lifeblood of smart tourism is big data, and the final purpose of smart tourism planning is extracting intelligence from big data [41]. Smart scenic area management will be assisted by technological approaches to gathering, analyzing, and interpreting big data [41,42], along with taking care of the human side by providing the types and quality of experiences that visitors are seeking [3,42]. This research verified that the results from travel blog data could help reveal tourists’ opinions on services offered [42] at the area level, although there is a risk of bias by under-representing Huashan visitors who do not post online. Through the development of a smart scenic area system, the administration will be able to monitor tourist flow distribution, traffic conditions, and service facility use in real time. Timely diversion measures can be adopted to ensure the safety, comfort, and enjoyment of tourists. Moreover, service and facility quality must be continuously evaluated and improved based on visitor survey results and observation on usage of facilities and service encounters. Capacity measurement of most popular sites needs immediate attention as overcrowding is spoiling the tourist experience at Huashan.

It is recognized that this is only one example of a famous scenic area in China and the results may not be generalizable to other countries, let alone to other similar destinations in China. However, the research and its analysis can be helpful to protected area managers for smart destination management and promoting sustainability. The combination of qualitative and quantitative techniques applied to a scenic area using traveler blogs is rather unique. It has the potential of providing protected area managers with visitor monitoring and management data that can enhance resource sustainability and visitor satisfaction.

There are some limitations to this research that must be recognized. The research data were all from social media sources and there is a danger that they may be biased in under-representing Huashan visitors who do not post online. Additionally, all tourists were treated alike, and differences in demographics, travel group composition, and arrangements (e.g., independent vs. group tours) were not investigated. It is very important to stress that big data processing methods should be combined with other approaches, rather than being considered an independent method.