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Geo Data Science for Tourism
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Geo Data Science for Tourism

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 29298

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Andrea Marchetti

E-Mail Website
Guest Editor
Institute of Informatics and Telematics – National Research Council (IIT-CNR), 56124 Pisa, Italy
Interests: open data; data visualization; data science; web applications; cartographic mapping techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Angelica Lo Duca

E-Mail Website
Guest Editor
Institute of Informatics and Telematics – National Research Council (IIT-CNR), 56124 Pisa, Italy
Interests: data science; data narrative; web applications; machine learning; cultural heritage; tourism
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The hospitality and tourism industries are considered as critical parts of the economy of a Country. Thanks to the availability of a huge quantity of geographical data, such industries can offer high-quality Web applications, which improve the attractiveness of tourism destinations as well as all the businesses related to tourism. In addition, the use of Web applications for tourism helps to understand tourists behavior, by managing, analyzing and visualizing huge quantities of geographical data, such as those contained in reviews about accommodations. However, Web applications applied to the tourism domain present several issues and challenges, such as their durability and copyright related to collected data. The use of open data for tourism (e.g. accommodations, restaurants, attractions, events, transportations) and open source maps to represent geographical data can be an answer to all these challenges. 

This Special Issue calls for research articles related to novel approaches to geographical data collection, analysis and visualization related to the design and implementation of Web applications in the tourism domain. Original contributions that report on real experiences and use cases in the usage of any kind of geo data in the tourism domain are also encouraged. 

While not excluding the possibility of presenting works on other topics regarding tourism, the specific topics of interest of this Special Issue are the following:

  1. Issues, challenges and solutions related to geographical data in the tourism domain
  2. Data Collection, Cleaning, Enrichment, Integration and Visualization of tourism geographical datasets
  3. Copyright issues related to the publication and use of collected geographical datasets of tourism entities
  4. Data Matching techniques for tourism entities based on geographical information (address, coordinates, geographical areas) for integrating two or more datasets
  5. Text Analysis of tourism entities reviews based on customer nationality
  6. Time Series analysis and forecasting related to tourism data of different geographical areas
  7. Machine Learning and Deep Learning Analysis of geographical datasets related to the tourism domain
  8. Web applications for tourism based on geographical maps
  9. Comparison among different open map services for tourism
  10. Analysis and Impact of the COVID-19 pandemic on tourism

Dr. Andrea Marchetti
Dr. Angelica Lo Duca
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Tourism
  • Geographical Data
  • Data Science
  • Data Collection
  • Data Analysis
  • Data Visualization
  • Web Applications
  • Open Data
  • Open Map Services

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Published Papers (8 papers)

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Research

29 pages, 7288 KiB  
Article
Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model
by Xiao Zhou, Jiangpeng Tian and Mingzhan Su
ISPRS Int. J. Geo-Inf. 2022, 11(2), 118; https://doi.org/10.3390/ijgi11020118 - 7 Feb 2022
Cited by 6 | Viewed by 2806
Abstract
This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters [...] Read more.
This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs. Full article
(This article belongs to the Special Issue Geo Data Science for Tourism)
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20 pages, 8734 KiB  
Article
Identifying the Relatedness between Tourism Attractions from Online Reviews with Heterogeneous Information Network Embedding
by Peiyuan Qiu, Jialiang Gao and Feng Lu
ISPRS Int. J. Geo-Inf. 2021, 10(12), 797; https://doi.org/10.3390/ijgi10120797 - 29 Nov 2021
Cited by 1 | Viewed by 2213
Abstract
The relatedness between tourism attractions can be used in a variety of tourism applications, such as destination collaboration, commercial marketing, travel recommendations, and so on. Existing studies have identified the relatedness between attractions through measuring their co-occurrence—these attractions are mentioned in a text [...] Read more.
The relatedness between tourism attractions can be used in a variety of tourism applications, such as destination collaboration, commercial marketing, travel recommendations, and so on. Existing studies have identified the relatedness between attractions through measuring their co-occurrence—these attractions are mentioned in a text at the same time—extracted from online tourism reviews. However, the implicit semantic information in these reviews, which definitely contributes to modelling the relatedness from a more comprehensive perspective, is ignored due to the difficulty of quantifying the importance of different dimensions of information and fusing them. In this study, we considered both the co-occurrence and images of attractions and introduce a heterogeneous information network (HIN) to reorganize the online reviews representing this information, and then used HIN embedding to comprehensively identify the relatedness between attractions. First, an online review-oriented HIN was designed to form the different types of elements in the reviews. Second, a topic model was employed to extract the nodes of the HIN from the review texts. Third, an HIN embedding model was used to capture the semantics in the HIN, which comprehensively represents the attractions with low-dimensional vectors. Finally, the relatedness between attractions was identified by calculating the similarity of their vectors. The method was validated with mass tourism reviews from the popular online platform MaFengWo. It is argued that the proposed HIN effectively expresses the semantics of attraction co-occurrences and attraction images in reviews, and the HIN embedding captures the differences in these semantics, which facilitates the identification of the relatedness between attractions. Full article
(This article belongs to the Special Issue Geo Data Science for Tourism)
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26 pages, 6584 KiB  
Article
Nanjing’s Intracity Tourism Flow Network Using Cellular Signaling Data: A Comparative Analysis of Residents and Non-Local Tourists
by Lingjin Wang, Xiao Wu and Yan He
ISPRS Int. J. Geo-Inf. 2021, 10(10), 674; https://doi.org/10.3390/ijgi10100674 - 4 Oct 2021
Cited by 6 | Viewed by 2735
Abstract
With the rapid development of transportation and modern communication technology, “tourism flow” plays an important role in shaping tourism’s spatial structure. In order to explore the impact of an urban tourism flow network on tourism’s spatial structure, this study summarizes the structural characteristics [...] Read more.
With the rapid development of transportation and modern communication technology, “tourism flow” plays an important role in shaping tourism’s spatial structure. In order to explore the impact of an urban tourism flow network on tourism’s spatial structure, this study summarizes the structural characteristics of the tourism flow networks of 43 scenic spots in Nanjing from three aspects—tourism flow network connection, node centrality, and communities—using cellular signaling data and the social network analysis method. A comparative analysis revealed the tourism flow network structures of residents and non-local tourists. Our findings indicated four points. Firstly, the overall network connectivity was relatively good. Core city nodes displayed high spatial concentration and connection strength. However, suburban nodes delivered poor performance. Secondly, popular nodes were intimately connected, although there were no “bridging” nodes. Lesser-known nodes were marginalized, resulting in severe node polarization. Thirdly, regarding the network community structure, the spatial boundary between communities was relatively clear; the communities within the core city were more closely connected, with some parts encompassing suburban nodes. Most suburban communities were attached to the communities in the core area, with individual nodes existing independently. Fourthly, the primary difference in the tourism flow network structures between residents and non-local tourists was that the nodes for residents manifested a more balanced connection strength and node centrality. Core communities encompassed more nodes with more extensive coverage. Conversely, the nodes for non-local tourists showed wide discrepancies in connection strength and node centrality. Furthermore, core communities were small in scale with clear boundaries. Full article
(This article belongs to the Special Issue Geo Data Science for Tourism)
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21 pages, 19711 KiB  
Article
Spatiotemporal Evolution and Trend Prediction of Tourism Economic Vulnerability in China’s Major Tourist Cities
by Chengkun Huang, Feiyang Lin, Deping Chu, Lanlan Wang, Jiawei Liao and Junqian Wu
ISPRS Int. J. Geo-Inf. 2021, 10(10), 644; https://doi.org/10.3390/ijgi10100644 - 25 Sep 2021
Cited by 10 | Viewed by 3343
Abstract
The evaluation and trend prediction of tourism economic vulnerability (TEV) in major tourist cities are necessary for formulating tourism economic strategies scientifically and promoting the sustainable development of regional tourism. In this study, 58 major tourist cities in China were taken as the [...] Read more.
The evaluation and trend prediction of tourism economic vulnerability (TEV) in major tourist cities are necessary for formulating tourism economic strategies scientifically and promoting the sustainable development of regional tourism. In this study, 58 major tourist cities in China were taken as the research object, and an evaluation index system of TEV was constructed from two aspects of sensitivity and adaptive capacity. On the basis of the entropy weight method, TOPSIS model, obstacle diagnosis model, and BP neural network model, this study analyzed the spatiotemporal patterns, obstacle factors, and future trends of TEV in major tourist cities in China from 2004 to 2019. The results show three key findings: (1) In terms of spatiotemporal patterns, the TEV index of most of China’s tourist cities has been on the rise from 2004 to 2019. Cities throughout the coast of China’s Yangtze River Delta and the Pearl River Delta urban agglomeration show high vulnerability, whereas low vulnerability has a scattered distribution in China’s northeast, central, and western regions. (2) The proportion of international tourists out of total tourists, tourism output density, urban industrial sulfur dioxide emissions per unit area, urban industrial smoke and dust emission per unit area, and discharge of urban industrial wastewater per unit area are the five major obstacles affecting the vulnerability degree of the tourism economy. (3) According to the prediction results of TEV from 2021 to 2030, although the TEV of many tourist cities in China is increasing year by year, cities with low TEV levels occupy the dominant position. Research results can provide reference for tourist cities to prevent tourism crises from occurring and to reasonably improve the resilience of the tourism economic system. Full article
(This article belongs to the Special Issue Geo Data Science for Tourism)
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21 pages, 6996 KiB  
Article
Spatiotemporal Dynamic Analysis of A-Level Scenic Spots in Guizhou Province, China
by Yuanhong Qiu, Jian Yin, Ting Zhang, Yiming Du and Bin Zhang
ISPRS Int. J. Geo-Inf. 2021, 10(8), 568; https://doi.org/10.3390/ijgi10080568 - 23 Aug 2021
Cited by 17 | Viewed by 3561
Abstract
A-level scenic spots are a unique evaluation form of tourist attractions in China, which have an important impact on regional tourism development. Guizhou is a key tourist province in China. In recent years, the number of A-level scenic spots in Guizhou Province has [...] Read more.
A-level scenic spots are a unique evaluation form of tourist attractions in China, which have an important impact on regional tourism development. Guizhou is a key tourist province in China. In recent years, the number of A-level scenic spots in Guizhou Province has been increasing, and the regional tourist economy has improved rapidly. The spatial distribution evolution characteristics and influencing factors of A-level scenic spots in Guizhou Province from 2005 to 2019 were measured using spatial data analysis methods, trend analysis methods, and geographical detector methods. The results elaborated that the number of A-level scenic spots in all counties of Guizhou Province increased, while in the south it developed slowly. From 2005 to 2019, the spatial distribution in A-level scenic spots were characterized by spatial agglomeration. The spatial distribution equilibrium degree of scenic spots in nine cities in Guizhou Province was gradually developed to reach the “relatively average” level. By 2019, the kernel density distribution of A-level scenic spots had formed the “two-axis, multi-core” layout. One axis was located in the north central part of Guizhou Province, and the other axis ran across the central part. The multi-core areas were mainly located in Nanming District, Yunyan District, Honghuagang District, and Xixiu District. From 2005 to 2007, the standard deviation ellipses of the scenic spots distribution changed greatly in direction and size. After 2007, the long-axis direction of the ellipses gradually formed a southwest to northeast direction. We chose elevation, population density, river density, road network density, tourism income, and GDP as factors, to discuss the spatiotemporal evolution of the scenic spots’ distribution with coupling and attribution analysis. It was found that the river, population distribution, road network density, and the A-level scenic spots’ distribution had a relatively high coupling phenomenon. Highway network density and tourist income have a higher influence on A-level tourist resorts distribution. Finally, on account of the spatiotemporal pattern characteristics of A-level scenic spots in Guizhou Province and the detection results of influencing factors, we put forward suggestions to strengthen the development of scenic spots in southern Guizhou Province and upgrade the development model of “point-axis network surface” to the current “two-axis multi-core” pattern of tourism development. This study can explain the current situation of the spatial development of tourist attractions in Guizhou Province, formulate a regulation mechanism of tourism development, and provide a reference for decision-making to boost the high-quality development of the tourist industry. Full article
(This article belongs to the Special Issue Geo Data Science for Tourism)
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21 pages, 20172 KiB  
Article
Spatial Distribution Pattern and Influencing Factors of Sports Tourism Resources in China
by Yifan Zuo, Huan Chen, Jincheng Pan, Yuqi Si, Rob Law and Mu Zhang
ISPRS Int. J. Geo-Inf. 2021, 10(7), 428; https://doi.org/10.3390/ijgi10070428 - 23 Jun 2021
Cited by 48 | Viewed by 5441
Abstract
Sports tourism is an emerging tourism product. In the sports and tourism industry, resource mining is the foundation that provides positive significance for theoretical support. This study takes China’s sports tourism boutique projects as the study object, exploring its spatial distribution pattern through [...] Read more.
Sports tourism is an emerging tourism product. In the sports and tourism industry, resource mining is the foundation that provides positive significance for theoretical support. This study takes China’s sports tourism boutique projects as the study object, exploring its spatial distribution pattern through the average nearest neighbor index, kernel density, and spatial autocorrelation. On the strength of the wuli–shili–renli system approach, the entropy value method and geographic detector probe model are used to identify the driving factors affecting the spatial distribution pattern. Findings reveal the following: (1) From 2013 to 2014, the sports tourism resources in China present a distribution pattern with the Yangtze River Delta urban agglomeration as the high-density core area and the Guizhou–Guangxi border area and the western Hubei ecological circle as the sub-density core areas. (2) From 2014 to 2018, China’s sports tourism boutique projects increased by 381, and the regional differences among various provinces tended to converge. The high-density core area remained unchanged. The sub-density cores are now the Yunqian border area of the Karst Plateau, the Qinglong border area of the Qilian Mountains, and the Jinji border area of the Taihang Mountains, shaping the distribution trends of “depending on the city, near the scenery” and “large concentration, small dispersion”. (3) The proportion of provincial sports tourism development classified as being in the coordinated stage is 61.29%. (4) The explanatory power of the factors affecting the spatial layout in descending order is natural resource endowment, sports resource endowment, transportation capacity, industrial support and guidance, market cultivation and development, people’s living standards, software and hardware services, and economic benefit effects. The explanatory power of the interaction of two different factors is higher than that of the single factor. Full article
(This article belongs to the Special Issue Geo Data Science for Tourism)
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18 pages, 18653 KiB  
Article
Socioeconomic and Environmental Impacts on Regional Tourism across Chinese Cities: A Spatiotemporal Heterogeneous Perspective
by Xu Zhang, Chao Song, Chengwu Wang, Yili Yang, Zhoupeng Ren, Mingyu Xie, Zhangying Tang and Honghu Tang
ISPRS Int. J. Geo-Inf. 2021, 10(6), 410; https://doi.org/10.3390/ijgi10060410 - 14 Jun 2021
Cited by 7 | Viewed by 3704
Abstract
Understanding geospatial impacts of multi-sourced drivers on the tourism industry is of great significance for formulating tourism development policies tailored to regional-specific needs. To date, no research in China has explored the combined impacts of socioeconomic and environmental drivers on city-level tourism from [...] Read more.
Understanding geospatial impacts of multi-sourced drivers on the tourism industry is of great significance for formulating tourism development policies tailored to regional-specific needs. To date, no research in China has explored the combined impacts of socioeconomic and environmental drivers on city-level tourism from a spatiotemporal heterogeneous perspective. We collected the total tourism revenue indicator and 30 potential influencing factors from 343 cities across China during 2008–2017. Three mainstream regressions and an emerging local spatiotemporal regression named the Bayesian spatiotemporally varying coefficients (Bayesian STVC) model were constructed to investigate the global-scale stationary and local-scale spatiotemporal nonstationary relationships between city-level tourism and various vital drivers. The Bayesian STVC model achieved the best model performance. Globally, eight socioeconomic and environmental factors, average wage (coefficient: 0.47, 95% credible intervals: 0.43–0.51), employed population (−0.14, −0.17–−0.11), GDP per capita (0.47, 0.42–0.52), population density (0.21, 0.16–0.27), night-time light index (−0.01, −0.08–0.05), slope (0.10, 0.06–0.14), vegetation index (0.66, 0.63–0.70), and road network density (0.34, 0.29–0.38), were identified to have nonlinear effects on tourism. Temporally, the main drivers might have gradually changed from the local macro-economic level, population density, and natural environment conditions to the individual economic level over the last decade. Spatially, city-specific dynamic maps of tourism development and geographically clustered influencing maps for eight drivers were produced. In 2017, China formed four significant city-level tourism industry clusters (hot spots, 90% confidence), the locations of which coincide with China’s top four urban agglomerations. Our local spatiotemporal analysis framework for geographical tourism data is expected to provide insights into adjusting regional measures to local conditions and temporal variations in broader social and natural sciences. Full article
(This article belongs to the Special Issue Geo Data Science for Tourism)
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16 pages, 710 KiB  
Article
Why Is Green Hotel Certification Unpopular in Taiwan? An Analytic Hierarchy Process (AHP) Approach
by Yen-Cheng Chen, Ching-Sung Lee, Ya-Chuan Hsu and Yin-Jui Chen
ISPRS Int. J. Geo-Inf. 2021, 10(4), 255; https://doi.org/10.3390/ijgi10040255 - 10 Apr 2021
Cited by 9 | Viewed by 3510
Abstract
The main purpose of this study was to investigate the factors that discouraged Taiwan hoteliers from applying for green hotel certification. The analytic hierarchy process (AHP) method was used to perform a weighted analysis that comprehensively identified important hindering factors based on information [...] Read more.
The main purpose of this study was to investigate the factors that discouraged Taiwan hoteliers from applying for green hotel certification. The analytic hierarchy process (AHP) method was used to perform a weighted analysis that comprehensively identified important hindering factors based on information from hotel industry, government, academic, and consumer representatives. Overall, in order of importance, the five dimensions of hindering factors identified by these experts and scholars were hotel internal environment, consumers’ environmental protection awareness, environmental protection incentive policy, hotel laws and regulations policy, and hotel external environment. Among the 26 examined hindering factor indices, the three highest-weighted indices overall for hoteliers applying for green hotel certification were as follows: environmental protection is not the main consideration of consumers seeking accommodations, lack of support by investment owners (shareholders), and lack of relevant subsidy incentives. The major contribution of this study is that hoteliers can understand important hindering factors associated with applying for green hotel certification; therefore, strategies that can encourage or enhance the green certification of hotels can be proposed to improve corporate image in the hotel industry, implement social responsibility in this industry, and obtain consumers’ approval of and accommodation-willingness for green hotels. Full article
(This article belongs to the Special Issue Geo Data Science for Tourism)
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