Understanding Demographics and Experience of Tourists in Yellowstone National Park through Social Media

This study compared tourists’ demographic variables between survey data and Twitter data in Yellowstone National Park and explored tourists’ experience through Twitter data. First, there were significant differences in age groups of tourists between social media data and survey data. Compared to survey data, tourists who identified by Twitter data concentrated on middle age groups. Secondly, the spatial distribution of geotagged tweets reflected the road network and main attractions in Yellowstone National Park. The peak visitation season is from June to September in survey data, while, in social media data, the peak visitation season is slightly shorter. Finally, the sentiment analysis was conducted and only 6.7% of tweets were negative, indicating that most tourists in Yellowstone National Park had good experience. Therefore, analyzing Twitter data will be helpful for understanding tourists’ demographics, attitudes and experience in the national parks and improving customer service in the further

A Big Data Framework to Identify Tourist Interests Based on Geotagged Travel Photos

Understanding the interests of tourists is a key skill for attraction managers to prepare plans and make strategic decisions in tourism marketing. The rapid growth and spread of social media websites provide an information-rich channel from which tourism researchers and managers can collect a large amount of text-based reviews or comments and photos relating to the past travel experiences of users. The travel photos with geographic information are especially helpful in identifying the geographical location of the destinations. By analyzing these big data in various formats can help to understand the interests of tourists at destinations. In this paper, a framework is proposed to identify the interests of tourists by integrating information carried by the geotagged photos shared on social media websites. Such an approach is expected to provide sustainable tracking on popular places of interest (POIs) updated by tourists and pick the best representative photos taken by them. The performance of this model is evaluated by conducting a case study using the geotagged photos taken in Hong Kong. A case study proved this proposed framework could make a thriving tourism industry more efficient

Validating Volunteered Geographic Information: Can We Reliably Trace Visitors\u27 Digital Footprints?

The objective of this study is to evaluate the quality of Volunteered Geographic Information (VGI) data for the purpose of describing patterns of visitor movement within a tourism destination. Using a Southern United States tourism destination as a case study, this research quantifies visitor flow networks using both data mined from Instagram and data collected using a traditional online survey methodology and then conducts a series of statistical analyses to compare results. In doing so, this paper highlights the advantages of using VGI data for tourism research, but also draws attention to potential trappings. Keywords: Volunteered Geographic Information (VGI), Instagram, visitor network, digital footprin

Assessing the potential of social media for estimating recreational use of urban and peri-urban forests

Acknowledgements The research for this paper was financially supported through the Swiss Federal Office for the Environment (FOEN). The views and opinions expressed in this paper are those of the authors, and do not necessarily represent the policies or official positions of the FOEN or the institutions they work for. We thank Rahul Deb Das for his assistance in data collection and processing. We gratefully acknowledge the comments and feedback of two anonymous reviewers.Peer reviewedPublisher PD

EXPLORING THE EXPENDITURE-BASED PROFILE OF MACAO VISITORS: A CLUSTER ANALYSIS

Visitor profiling has been increasingly recognized as an important tourism marketing tool in the advent of smart tourism. There is a substantial body of literature relating to tourism market segmentation and visitor profiles. However, most of them focus on psychographic and behavioral factors. Seldom research has addressed the visitor profile based on actual expenditure during the visit. In this study, we explored the expenditure-based profile of Macao visitors using a randomly sampled dataset from a large-scale visitor profile survey supported by Macao Government Tourist Office. Utilizing self-reported actual expenditure data from 3577 visitors, we extracted six expenditure clusters of Macao visitors using a k-means clustering with silhouette analysis. The six clusters, i.e., entertainment, gambling, cuisine, shopping, both cuisine & shopping, and transit, have significant differences in expenditure preferences and expenditure levels. We also analyzed the demographic and behavior profile for each cluster. Our findings shed light on developing customized marketing strategies to the profile of each distinct expenditure cluster of Macao visitors

Exploring urban visitors' mobilities. A multi-method approach

Aquesta tesi doctoral sorgeix de la necessitat d’aprofundir en el coneixement de les mobilitats dels visitants, entendre les decisions que configuren el seu comportament espacio-temporal i identificar i explorar els efectes que les seves mobilitats tenen sobre les destinacions urbanes. La tesi es desenvolupa entorn a quatre objectius específics que s’emmarquen en l’àmbit de recerca relacionat amb el seguiment de l’activitat dels visitants en destinacions turístiques urbanes. Cadascun d’aquests objectius es desenvolupa en cadascun dels articles científics que conformen aquesta tesi doctoral, publicats tots ells en revistes de revisió per parells. El primer article es proposa com a objectiu identificar els factors, relacionats amb el perfil socioeconòmic dels turistes i amb les característiques de la seva estada, que determinen la selecció d’opcions de transport i mobilitat sostenible per moure’s per la destinació urbana. El segon article pretén analitzar i comprendre com afecta el comportament espacio-temporal dels turistes en els seus patrons de consum econòmic i, per tant, en la generació d’ingressos per a l’economia local. El tercer article es proposa analitzar la influència de l’espai urbà sobre la forma en què els visitants es desplacen per la destinació. I finalment, el quart article té per objectiu reconstruir trajectòries i/o fluxos espacio-temporals a partir de dades geolocalitzades de les xarxes socials per tal de detectar patrons de mobilitat dels visitants de destinacions urbanes. Les fonts de dades i els mètodes utilitzats per complir amb els objectius de partida són diverses. En aquest sentit, la tesi aporta també una àmplia radiografia dels pros i les contres de les diferents fonts de dades disponibles per a l’anàlisi de les mobilitats dels visitants en destinacions turístiques.Esta tesis doctoral surge de la necesidad de profundizar en el conocimiento de las movilidades de los visitantes,entender las decisiones que configuran su comportamiento espaciotemporal e identificar y explorar los efectos que sus movilidades tienen sobre los destinos urbanos. La tesis se desarrolla en torno a cuatro objetivos específicos que se enmarcan en el ámbito de investigación de seguimiento de visitantes, y que se desarrollan en cada uno de los artículos científicos, publicados todos ellos en revistas de revisión por pares, que conforman esta tesis. El primer artículo se propone como objetivo identificar los factores, relacionados con el perfil socioeconómicos de los turistas y con las características de su estancia, que determinan la selección de opciones de transporte y movilidad sostenible para moverse por el destino urbano. El segundo artículo pretende analizar y comprender cómo afecta el comportamiento espaciotemporal de los turistas en sus patrones de consumo económico y, por tanto, en la generación de ingresos para la economía local. El tercer artículo se propone analizar la influencia del espacio urbano sobre la forma en que los visitantes se desplazan por el destino. Y finalmente, el cuarto artículo tiene por objetivo reconstruir trayectorias y / o flujos espaciotemporales a partir de datos geolocalizados de las redes sociales para detectar patrones de movilidad de los visitantes de destinos urbanos. Las fuentes de datos y los métodos utilizados para cumplir con los objetivos de partida son diversos. En este sentido, la tesis aporta también una amplia radiografía de los pros y contras de las diferentes fuentes de datos disponibles para el análisis de las movilidades de los visitantes en destinos turísticos.This dissertation arises from the need to deepen the knowledge of the mobility of visitors, understand the decisions that shape their spatiotemporal behaviour and identify and explore the effects that their mobility has on urban destinations. The thesis is developed around four specific objectives that fall within the scope of visitor tracking research, and that are developed in each of the scientific articles, all of them published in peer-reviewed journals, that make up this thesis. The first article aims to identify the factors, related to the socioeconomic profile of tourists and the characteristics of their stay, that determine the selection of sustainable transport and mobility options to move within the urban destination. The second article aims to analyse and understand how the visitors’ spatiotemporal behaviour affects their patterns of economic consumption and, therefore, the generation of income for the local economy. The third article aims to analyse the influence of the built environment on the visitors’ mobilities at destination. And finally, the fourth article aims to reconstruct trajectories and / or spatiotemporal flows from geolocated data obtained from social networks in order to detect visitors’ mobility patterns at urban destinations. The data sources and methods used to meet the objectives are multiple. In this sense, the thesis also provides an extensive x-ray of the pros and cons of the different data sources available for the analysis of visitors’ mobilities in tourist destinations

Identifying tourist-functional relations of urban places through Foursquare from Barcelona

This is a post-peer-review, pre-copyedit version of an article published in GEOJOURNAL. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10708-019-10055-9Previous research has mainly focused on spatial relations among tourist attractions in urban areas. However, few studies have examined the functional relations between tourist attractions and other urban places (i.e. the flows of tourists between them). Therefore, this study focuses on quantification of the tourist-functional relations among Places of Interest (POIs) using Foursquare data from Barcelona. This represents an effort to highlight the important functional closeness between different types of POIs whose significance is not usually obvious from their spatial relationships. In order to quantify these functional relationships, this paper classifies Foursquare POIs into 22 categories according to their different usages and constructs a matrix of usage-flows to depict the connections among these different usages. A model of interaction values is introduced to describe the strength of relations and identify the dominant tourist usages. The results confirm that the functional centroids differ from these spatial distributions which only focused around tourist attractions. In addition to tourist attractions, places in the categories of Restaurants, Transport, and Hotels play important roles in functional relationship of tourism. The typical urban usages of tourists can be distinguished by the interaction values between these categories. Our model provides a practical method to quantify the interlinkage of usages of POIs based on tourist flows. In particular, LBSN data has potential as a method to observe the tourist-functional relations among places.Peer ReviewedPostprint (author's final draft

Development of Context-Aware Recommenders of Sequences of Touristic Activities

En els últims anys, els sistemes de recomanació s'han fet omnipresents a la xarxa. Molts serveis web, inclosa la transmissió de pel·lícules, la cerca web i el comerç electrònic, utilitzen sistemes de recomanació per facilitar la presa de decisions. El turisme és una indústria molt representada a la xarxa. Hi ha diversos serveis web (e.g. TripAdvisor, Yelp) que es beneficien de la integració de sistemes recomanadors per ajudar els turistes a explorar destinacions turístiques. Això ha augmentat la investigació centrada en la millora dels recomanadors turístics per resoldre els principals problemes als quals s'enfronten. Aquesta tesi proposa nous algorismes per a sistemes recomanadors turístics que aprenen les preferències dels turistes a partir dels seus missatges a les xarxes socials per suggerir una seqüència d'activitats turístiques que s'ajustin a diversos contextes i incloguin activitats afins. Per aconseguir-ho, proposem mètodes per identificar els turistes a partir de les seves publicacions a Twitter, identificant les activitats experimentades en aquestes publicacions i perfilant turistes similars en funció dels seus interessos, informació contextual i períodes d'activitat. Aleshores, els perfils d'usuari es combinen amb un algorisme de mineria de regles d'associació per capturar relacions implícites entre els punts d'interès de cada perfil. Finalment, es fa un rànquing de regles i un procés de selecció d'un conjunt d'activitats recomanables. Es va avaluar la precisió de les recomanacions i l'efecte del perfil d'usuari. A més, ordenem el conjunt d'activitats mitjançant un algorisme multi-objectiu per enriquir l'experiència turística. També realitzem una segona fase d'anàlisi dels fluxos turístics a les destinacions que és beneficiós per a les organitzacions de gestió de destinacions, que volen entendre la mobilitat turística. En general, els mètodes i algorismes proposats en aquesta tesi es mostren útils en diversos aspectes dels sistemes de recomanació turística.En los últimos años, los sistemas de recomendación se han vuelto omnipresentes en la web. Muchos servicios web, incluida la transmisión de películas, la búsqueda en la web y el comercio electrónico, utilizan sistemas de recomendación para ayudar a la toma de decisiones. El turismo es una industria altament representada en la web. Hay varios servicios web (e.g. TripAdvisor, Yelp) que se benefician de la inclusión de sistemas recomendadores para ayudar a los turistas a explorar destinos turísticos. Esto ha aumentado la investigación centrada en mejorar los recomendadores turísticos y resolver los principales problemas a los que se enfrentan. Esta tesis propone nuevos algoritmos para sistemas recomendadores turísticos que aprenden las preferencias de los turistas a partir de sus mensajes en redes sociales para sugerir una secuencia de actividades turísticas que se alinean con diversos contextos e incluyen actividades afines. Para lograr esto, proponemos métodos para identificar a los turistas a partir de sus publicaciones en Twitter, identificar las actividades experimentadas en estas publicaciones y perfilar turistas similares en función de sus intereses, contexto información y periodos de actividad. Luego, los perfiles de usuario se combinan con un algoritmo de minería de reglas de asociación para capturar relaciones entre los puntos de interés que aparecen en cada perfil. Finalmente, un proceso de clasificación de reglas y selección de actividades produce un conjunto de actividades recomendables. Se evaluó la precisión de las recomendaciones y el efecto de la elaboración de perfiles de usuario. Ordenamos además el conjunto de actividades utilizando un algoritmo multi-objetivo para enriquecer la experiencia turística. También llevamos a cabo un análisis de los flujos turísticos en los destinos, lo que es beneficioso para las organizaciones de gestión de destinos, que buscan entender la movilidad turística. En general, los métodos y algoritmos propuestos en esta tesis se muestran útiles en varios aspectos de los sistemas de recomendación turística.In recent years, recommender systems have become ubiquitous on the web. Many web services, including movie streaming, web search and e-commerce, use recommender systems to aid human decision-making. Tourism is one industry that is highly represented on the web. There are several web services (e.g. TripAdvisor, Yelp) that benefit from integrating recommender systems to aid tourists in exploring tourism destinations. This has increased research focused on improving tourism recommender systems and solving the main issues they face. This thesis proposes new algorithms for tourism recommender systems that learn tourist preferences from their social media data to suggest a sequence of touristic activities that align with various contexts and include affine activities. To accomplish this, we propose methods for identifying tourists from their frequent Twitter posts, identifying the activities experienced in these posts, and profiling similar tourists based on their interests, contextual information, and activity periods. User profiles are then combined with an association rule mining algorithm for capturing implicit relationships between points of interest apparent in each profile. Finally, a rule ranking and activity selection process produces a set of recommendable activities. The recommendations were evaluated for accuracy and the effect of user profiling. We further order the set of activities using a multi-objective algorithm to enrich the tourist experience. We also carry out a second-stage analysis of tourist flows at destinations which is beneficial to destination management organisations seeking to understand tourist mobility. Overall, the methods and algorithms proposed in this thesis are shown to be useful in various aspects of tourism recommender systems