Bagged fuzzy clustering for fuzzy data: An application to a tourism market.

Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely used to understand consumer behaviour. In this study, we propose a strategy of analysis, by combining the Bagged Clustering (BC) method and the fuzzy C-means clustering method for fuzzy data (FCM-FD), i.e., the Bagged fuzzy C-means clustering method for fuzzy data (BFCM-FD). The method inherits the advantages of stability and reproducibility from BC and the flexibility from FCM-FD. The method is applied on a sample of 328 Chinese consumers revealing the existence of four segments (Admirers, Enthusiasts, Moderates, and Apathetics) of the perceived images of Western Europe as a tourist destination. The results highlight the heterogeneity in Chinese consumers' place preferences and implications for place marketing are offered

Visitor profiling for cable car mountain destinations as a basis for protected area management : a case study of the summer season in the Tatra Mountains at Kasprowy Wierch (Poland) and Skalnaté Pleso (Slovakia)

Protected areas play a crucial role in the conservation of vulnerable mountain ecosystems, but at the same time they may serve as tourist destinations and attract large numbers of visitors. Areas located in close proximity to cable cars belong to some of the most challenging sites for mountain protected area management. This study focuses on two cable car areas: Kasprowy Wierch (Tatra National Park, Poland) and Skalnaté Pleso (Tatra National Park, Slovakia). Both sites belong to the most heavily used leisure destinations in the Tatra Mountains. The study focused on the summer, snow-free tourist peak-season, for which there is an ongoing discussion concerning the development of cable car services. In 2014 and 2015, on-site interviews were conducted in the two study areas (n = 3 304). In order to better understand visitors’ needs and goals, visitor profiling using K-means clustering was performed. Four distinct segments based on visitor motivations were identified: nature oriented (32 %), family / friends & well-being oriented (23 %), sports oriented (14 %), and a mixed segment with multiple motivations (31 %). The results show that two tourist segments were not particularly interested in nature experience, although they visited protected areas. A significant relationship between motivational segments and trip characteristics was identified. The visitor segments defined can be used practically in the management of cable car destinations located within protected areas

Why Wine Tourists Visit Cellar Doors: Segmenting Motivation and Destination Image.

This study examines the relationship between the motivation of wine tourists to visit cellar doors and destination image perception. A survey of tourists resulted in 676 useable questionnaires. Using a novel segmentation method, self organizing maps (SOM) and bagged clustering (BC), the study identified five distinct motivation clusters. These clusters were different on only gender and previous visit to the wine region. Three clusters of destination image were identified using the same segmentation method. Significant relationships were found between the motivation and destination image clusters. Implications for destination marketing and managing the tourist experience at the winery cellar door are discussed

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Pedestrian level of service for sidewalks in Tangier City

The pedestrian level of service (PLOS) is a measure that quantifies walkway comfort levels. PLOS defined into six categories (A, B, C, D, E, and F) each level defines the range of values, for example, a good level (best traffic condition) is defined with the letter A until reaching the worst level, F (high congestion). This article aims to define the PLOS on sidewalks considering walking conditions in Tangier City (Morocco). Sidewalks are analyzed using video recording in the urban center of Tangier City. The collected data are pedestrian flow and effective sidewalk width. Each level contains a range of values that corresponds to the pedestrian flow that defines the level of service. Clustering techniques are used to identify the threshold of each level using a self-organizing map (SOM). The results are different from those obtained with other methods because pedestrian traffic differs from country to country

A Conceptual Model of Exploration Wayfinding: An Integrated Theoretical Framework and Computational Methodology

This thesis is an attempt to integrate contending cognitive approaches to modeling wayfinding behavior. The primary goal is to create a plausible model for exploration tasks within indoor environments. This conceptual model can be extended for practical applications in the design, planning, and Social sciences. Using empirical evidence a cognitive schema is designed that accounts for perceptual and behavioral preferences in pedestrian navigation. Using this created schema, as a guiding framework, the use of network analysis and space syntax act as a computational methods to simulate human exploration wayfinding in unfamiliar indoor environments. The conceptual model provided is then implemented in two ways. First of which is by updating an existing agent-based modeling software directly. The second means of deploying the model is using a spatial interaction model that distributed visual attraction and movement permeability across a graph-representation of building floor plans

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