Geo Data Science for Tourism

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.

Using Semantic-Based User Profile Modeling for Context-Aware Personalised Place Recommendations

Place Recommendation Systems (PRS's) are used to recommend places to visit to World Wide Web users. Existing PRS's are still limited by several problems, some of which are the problem of recommending similar set of places to different users (Lack of Personalization) and no diversity in the set of recommended items (Content Overspecialization). One of the main objectives in the PRS's or Contextual suggestion systems is to fill the semantic gap among the queries and suggestions and going beyond keywords matching. To address these issues, in this study we attempt to build a personalized context-aware place recommender system using semantic-based user profile modeling to address the limitations of current user profile building techniques and to improve the retrieval performance of personalized place recommender system. This approach consists of building a place ontology based on the Open Directory Project (ODP), a hierarchical ontology scheme for organizing websites. We model a semantic user profile from the place concepts extracted from place ontology and weighted according to their semantic relatedness to user interests. The semantic user profile is then exploited to devise a personalized recommendation by re-ranking process of initial search results for improving retrieval performance. We evaluate this approach on dataset obtained using Google Paces API. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based place recommendation model

Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach

Promoting a destination is a major task for Destination Marketing Organizations (DMOs). Although DMOs control, to some extent, the information presented to travelers (controlled sources), there are other different sources of information (uncontrolled sources) that could project an unfavorable image of the destination. Measuring differences between information sources would help design strategies to mitigate negative factors. In this way, we propose a deep learning-based approach to automatically measure the changes between images from controlled and uncontrolled information sources. Our approach exempts experts from the time-consuming task of assessing enormous quantities of pictures to track changes. To our best knowledge, this work is the first work that focuses on this issue using technological paradigms. Notwithstanding this, our approach paves novel pathways to acquire strategic insights that can be harnessed for the augmentation of destination development, the refinement of recommendation systems, the analysis of online travel reviews, and myriad other pertinent domains

Discovering visiting behaviors and city perceptions by mining semantic trajectory

Tourism is a crucial industry for many cities, necessitating the development of unique attractions to draw in more visitors. Understanding the visiting behaviors and perceptions of visitors helps to uncover the city’s distinctive characteristics, thereby aiding in the further growth of its tourism industry. It’s important to note that different population groups may exhibit varying visiting behaviors depending on the time of their visit, which in turn can shape their impressions of the city. This study explores the dynamic visiting behaviors and city perceptions of locals and tourists throughout different times of the day and week. The study area is London, one of the world’s most famous tourist cities. To conduct this study, User-Generated Content (UGC) is utilized, specifically data from Foursquare check-ins and Flickr tags from April 3, 2012, to September 16, 2013. The study first identifies the spatiotemporal distribution of hotspots for each population group based on their Foursquare check-ins. The relative concentration of locals and tourists is then examined through the difference ratio to understand their unique visiting preferences. Next, the spatiotemporal movements of locals and tourists and their city descriptions during their trips are analyzed by constructing semantic trajectories. The place is the fundamental element of a semantic trajectory, and places are constructed by clustering Foursquare check-ins. The property of the place is defined by three dimensions: location (represented by borough names), locale (represented by place categories), and sense of place (represented by topics generated in topic modeling based on Flickr tags). Semantic trajectories are then clustered based on their semantic dimensions, and typical trajectories are mined for each cluster. The distribution of trajectories and their semantic dimensions are compared between locals and tourists at different time spans to explore how London’s impressions evolve over time. The results suggest distinct visiting behaviors and city perceptions over time for locals and tourists. Both groups primarily concentrate in the city center, with small hotspots around the airport. However, locals tend to visit more suburban areas than tourists. Locals show higher preferences for business districts during the daytime and on weekdays, while tourists consistently show interest in shopping in the city center. In terms of city perceptions, the city center is associated with descriptions of cityscapes and transport during the daytime. At night, people tend to associate the same area with nightlife activities. Furthermore, locals are interested in leisure activities and fitness, while tourists tend to focus on tourist attractions and the Olympics

Introducing semantic variables in mixed distance measures: Impact on hierarchical clustering

Today, it is well known that taking into account the semantic information available for categorical variables sensibly improves the meaningfulness of the final results of any analysis. The paper presents a generalization of mixed Gibert's metrics, which originally handled numerical and categorical variables, to include also semantic variables. Semantic variables are defined as categorical variables related to a reference ontology (ontologies are formal structures to model semantic relationships between the concepts of a certain domain). The superconcept-based distance (SCD) is introduced to compare semantic variables taking into account the information provided by the reference ontology. A benchmark shows the good performance of SCD with respect to other proposals, taken from the literature, to compare semantic features. Mixed Gibert's metrics is generalized incorporating SCD. Finally, two real applications based on touristic data show the impact of the generalized Gibert's metrics in clustering procedures and, in consequence, the impact of taking into account the reference ontology in clustering. The main conclusion is that the reference ontology, when available, can sensibly improve the meaningfulness of the final clusters.Peer ReviewedPostprint (published version

Digital and Strategic Innovation for Alpine Health Tourism

This open access book presents a set of practical tools and collaborative solutions in multi-disciplinary settings to foster the Alpine Space health tourism industry’s innovation and competitiveness. The proposed solutions emerge as the result of the synergy among health, environment, tourism, digital, policy and strategy professionals. The approach underlines the pivotal role of a sustainable and ecomedical use of Alpine natural resources for health tourism destinations, and highlights the need of integrating aspects of natural resources’ healing effects, a shared knowledge of Alpine assets through digital solutions, and frames strategic approaches for the long-term development of the sector. The volume exploits the results of the three-years long EU research project HEALPS 2, which involved several stakeholders from the health tourism, healthcare and sustainable tourism industries. This book is relevant for health tourism destinations and facilities (hotels, clinics, wellness and spa companies), regional and local authorities (policy makers), business support organizations, researchers involved in digital healthcare and geoinformatics

Digital and Strategic Innovation for Alpine Health Tourism

This open access book presents a set of practical tools and collaborative solutions in multi-disciplinary settings to foster the Alpine Space health tourism industry’s innovation and competitiveness. The proposed solutions emerge as the result of the synergy among health, environment, tourism, digital, policy and strategy professionals. The approach underlines the pivotal role of a sustainable and ecomedical use of Alpine natural resources for health tourism destinations, and highlights the need of integrating aspects of natural resources’ healing effects, a shared knowledge of Alpine assets through digital solutions, and frames strategic approaches for the long-term development of the sector. The volume exploits the results of the three-years long EU research project HEALPS 2, which involved several stakeholders from the health tourism, healthcare and sustainable tourism industries. This book is relevant for health tourism destinations and facilities (hotels, clinics, wellness and spa companies), regional and local authorities (policy makers), business support organizations, researchers involved in digital healthcare and geoinformatics

The Influence of Electronic Word of Mouth in an Online Travel Community on Travel Decisions: A Case Study

As a result of embracing the Internet, online travel communities have become an important information source for travelers. The members of these communities communicate through postings called electronic word-of-mouth (eWOM) the act of sharing information on a particular topic. Electronic word-of-mouth (eWOM) is informal communications among consumers regarding the usage or characteristics of goods and services on the Internet (Litvin, Goldsmith, and Pan, 2008). Furthermore, the influence of eWOM has been found to be influential on consumer purchasing behavior (Guernsey, 2000). Thus, an understanding of the potential of eWOM in online travel communities on travel decisions has implications for tourism marketers as well as researchers. The purpose of this research is to examine a single online travel community in order to conduct an in depth analysis of the influence of eWOM on travel decisions. The study uses online travel community postings (eWOM) to explore the types of travel decisions that are discussed, influence of eWOM on these decisions, the types of members and their specific influence on types of travel decisions, the information types provided by the members, the activity level of members and their influence on travel decisions of other members. Thorn Tree Forum, part of Lonely Planet website is the online travel community studied for this research. In an effort to select a sample that would yield maximum variation, treemaps, and purposeful sampling is used to select eight country forums to use as the framework for collecting community member postings. Postings are collected for an eight month period. Data collection and analysis used a multistep process that included thematic networks, coding for influence and details of information shared among members. The results suggest that eWOM in this online travel community influence travel decisions including accommodation choice, food and beverage recommendations, transportation options, safety of the destination, monetary issues, destination information, and itinerary refinements. Residents were influential in accommodations, food and beverages, and destination information, whereas experienced travelers influenced all types of travel decisions except accommodations. Information types identified include warnings, advice/tips, recommendations, and clarifications. Clarifications were the most influential postings, followed by recommendations and advice/tips. The members were categorized into three types low, medium, and high activity level members. Medium activity level members were the most influential members followed by low and high activity level members. The results of this study provide direction for theoretical development of using online travel communities for travel decision making and provide managerial guidance for utilization of online travel communities for enhancing travel products and destination