Distribution of tourists within urban heritage destinations: a hot spot/cold spot analysis of TripAdvisor data as support for destination management

ABSTRACTThe emergence of social media and Web 2.0 has a notable impact upon the tasks of destination managers as these platforms have developed into influential mechanisms affecting tourist behavio..

Distribution of tourists within urban heritage destinations: a hot spot/cold spot analysis of TripAdvisor data as support for destination management

The emergence of social media and Web 2.0 has a notable impact upon the tasks of destination managers as these platforms have developed into influential mechanisms affecting tourist behaviour. This paper shows how Destination Management Organizations (DMOs) can reap the benefits of the Web 2.0 revolution as it serves as an important source of user-generated information, bringing novel opportunities for data-driven destination management. To test the applicability of user-generated content for destination management, this paper analyses restaurant reviews from five Flemish art cities which were retrieved from the Web 2.0 platform TripAdvisor. Getis-Ord hot spot analysis revealed spatial clusters of frequently (‘hot spots’) and rarely (‘cold spots’) reviewed restaurants in four out of the five art cities. By comparing these spatial patterns, the digital footprints of tourists were uncovered and discussed with DMO directors. Found patterns appeared to reflect local policies aimed either at concentrating tourism, as in Bruges, the city with the most prominent hot spot, or spreading tourism over time and space as seen in Antwerp and Ghent where less prominent hot spots were present. The visualization proved to be a valuable input when discussing tourism management and fuelled the sharing of knowledge between the destinations

Big (Geo)Data in Social Sciences: Challenges and Opportunities

Actualmente asistimos a una verdadera revolución en la producción y el tratamiento de datos masivos (Big Data). Aunque los principales usuarios de este tipo de datos son las empresas, el mundo de la investigación ha encontrado también interesantes posibilidades en el análisis de Big Data, con abordajes nuevos a viejos problemas o incluso con el planteamiento de cuestiones que no podían ser abordadas con datos tradicionales. El presente artículo constituye una revisión de trabajos de investigación que utilizan datos masivos geolocalizados, Big (Geo)Data, y muestra ejemplos de aplicación en la investigación, ordenando los trabajos revisados según fuentes de datos: registros de llamadas de teléfonos móviles, redes sociales, comunidades de fotografías geolocalizadas, registros de transacciones con tarjetas de crédito, tarjetas inteligentes de transporte, navegadores, etc. El trabajo concluye con unas reflexiones sobre las ventajas que ofrece el Big (Geo)Data para el investigador, como la alta resolución espacial y temporal de los datos y, en muchos casos, su cobertura global y su carácter gratuito, pero también resalta algunos de los principales inconvenientes que plantea su uso, como el sesgo y la dificultad de su proceso y, en muchos casos, de acceso a los mismos.Currently we are witnessing a revolution in the production and processing of massive data (Big Data). Although the main users of such data are companies, social researchers have also found interesting possibilities in the analysis of Big Data, with new approaches to old questions or even with the approach to issues that could not be addressed with traditional data. This article is a review of research papers using geolocated massive data, Big (Geo)Data, and shows examples of their application in research, grouping the papers according to data sources: mobile phone calls records, social networks, communities of geolocated photos, credit card transactions records, transport smart cards, car navigators, etc. The paper concludes with some reflections on the advantages of Big (Geo)Data in social sciences research (high temporal and spatial resoluction, and, in many cases, global coverage and free of charge), but it also highlights some of the main problems arising from their use, such as bias, processing capacity and access barriers

Religious Tourism Development Strategies in Qom Province: Using and Comparing QSPM and Best Worst Methods

After Mashhad county, Qom province has the most valuable religious, cultural, historical and natural potential as the second Iranian pilgrimage centre. This study was conducted to formulate strategies for the development of religious tourism in Qom province using the most influential view of the strategy-formation process named design school. This school normally uses External Factor Evaluation (EFE) Matrix, Internal Factor Evaluation (IFE) Matrix, SWOT Matrix, QSPM matrix, and some other tools. The strengths, weaknesses, opportunities, and threats were determined using IFE and EFE matrices. The SWOT matrix was prepared and then the proper strategies for the development of religious tourism in Qom province (hold and maintain strategies or ST strategies) were determined using the Internal-External (IE) Matrix in the next step. Extracted ST strategies were prioritised using the QSPM and five strategies were proposed respectively. This study used the Best-Worst Method (BWM) to prioritise the created strategies in addition to QSPM this aims at developing strategic planning methodology. The results of the BWM were compared to the QSPM and the priority of the second and third strategies were modified. The priority of the first, fourth and fifth strategies is the same in the two methods. Moreover, the correlation coefficient between the results of the two methods was calculated. This shows a similarity of approximately 95 percent. So, it seems using the BWM method is more cost-effective than QSPM, due to saving time and cost

Harnessing social media data to explore urban tourist patterns and the implications for retail location modelling

The tourism landscape in urban destinations has been spatially expanded in recent years due to the increasing prevalence of sharing economy accommodation and other tourism trends. Tourists now mix with locals to form increasingly intricate population geographies within urban neighbourhoods, bringing new demand into areas which are beyond the conventional tourist locations. How these dispersed tourist demands impact local communities has become an emerging issue in both urban and tourism studies. However, progress has been hampered by the lack of fine granular travel data which can be used for understanding urban tourist patterns at the small-area level. Paying special attention to tourist grocery demand in urban destinations, the thesis takes London as the example to present the various sources of LBSN datasets that can be used as valuable supplements to conventional surveys and statistics to produce novel tourist population estimates and new tourist grocery demand layers at the small area level. First, the work examines the potential of Weibo check-in data in London for offering greater insights into the spatial travel patterns of urban tourists from China. Then, AirDNA and Twitter datasets are used in conjunction with tourism surveys and statistics in London to model the small area tourist population maps of different tourist types and generate tourist demand estimates. Finally, Foursquare datasets are utilised to inform tourist grocery travel behaviour and help to calibrate the retail location model. The tourist travel patterns extracted from various LBSN data, at both individual and collective levels, offer tremendous value to assist the construction and calibration of spatial modelling techniques. In this case, the emphasis is on improving retail location spatial Interaction Models (SIMs) within grocery retailing. These models have seen much recent work to add non-residential demand, but demand from urban tourism has yet to be included. The additional tourist demand layer generated in this thesis is incorporated into a new custom-built SIM to assess the impacts of urban tourism on the local grocery sector and support current store operations and trading potential evaluations of future investments

Towards a National 3D Mapping Product for Great Britain

Knowing where something happens and where people are located can be critically important to understand issues ranging from climate change to road accidents, crime, schooling, transport and much more. To analyse these spatial problems, two-dimensional representations of the world, such as paper or digital maps, have traditionally been used. Geographic information systems (GIS) are the tools that enable capture, modelling, storage, retrieval, sharing, manipulation, analysis, and presentation of geographically referenced data. Three-dimensional geographic information (3D GI) is data that can represent real-world features as objects in 3D space. 3D GI offers additional functionality not possible in 2D, including analysing and querying volume, visibility, surface and sub-surface, and shadowing. This thesis contributes to the understanding of user requirements and other data related considerations in the production of 3D geographic information at a national level. The study promotes Ordnance Survey’s efforts in developing a 3D geographic product through: (1) identifying potential applications; (2) analysing existing 3D city modelling approaches; (3) eliciting and formalising user requirements; (4) developing metrics to describe the usefulness of 3D data and; (5) evaluating the commerciality of 3D GI. A review of current applications of 3D showed that visualisation dominated as the main use, allowing for better communication, and supporting decision-making processes. Reflecting this, an examination of existing 3D city models showed that, despite the varying modelling approaches, there was a general focus towards accurate and realistic geometric representation of the urban environment. Web-based questionnaires and semi-structured interviews revealed that while some applications (e.g. subsurface, photovoltaics, air and noise quality) lead the field with a high adoption of 3D, others were laggards due to organisational inertia (e.g. insurance, facilities management). Individuals expressed positive views on the use of 3D, but still struggled to justify the value and business case. Simple building geometry coupled with non-building thematic classes was perceived to be most useful by users. Several metrics were developed to quantify and compare the characteristics of thirty-three 3D datasets. Results showed that geometry-based metrics such as minimum feature length or Euler characteristic can be used to provide additional information as part of fitness-for-purpose evaluations. The metrics can also contribute to quality control during data production. An investigation into the commercial opportunities explored the economic value of 3D, the market size of 3D data in Great Britain, as well as proposed a number of opportunities within the wider business context of Ordnance Survey

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