A Location Analytics Method for the Utilisation of Geotagged Photos in Travel Marketing Decision-Making

Location analytics offers statistical analysis of any geo- or spatial data concerning user location. Such analytics can produce useful insights into the attractions of interest to travellers or visitation patterns of a demographic group. Based on these insights, strategic decision-making by travel marketing agents, such as travel package design, may be improved. In this paper, we develop and evaluate an original method of location analytics to analyse travellers' social media data for improving managerial decision support. The method proposes an architectural framework that combines emerging pattern data mining techniques with image processing to identify and process appropriate data content. The design artefact is evaluated through a focus group and a detailed case study of Australian outbound travellers. The proposed method is generic, and can be applied to other specific locations or demographics to provide analytical outcomes useful for strategic decision support

A Location Analytics Method for the Utilisation of Geotagged Photos in Travel Marketing Decision-Making

© 2019 World Scientific Publishing Co. Location analytics offers statistical analysis of any geo-or spatial data concerning user location. Such analytics can produce useful insights into the attractions of interest to travellers or visitation patterns of a demographic group. Based on these insights, strategic decision-making by travel marketing agents, such as travel package design, may be improved. In this paper, we develop and evaluate an original method of location analytics to analyse travellers\u27 social media data for improving managerial decision support. The method proposes an architectural framework that combines emerging pattern data mining techniques with image processing to identify and process appropriate data content. The design artefact is evaluated through a focus group and a detailed case study of Australian outbound travellers. The proposed method is generic, and can be applied to other specific locations or demographics to provide analytical outcomes useful for strategic decision support

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

Measuring destination image : a novel approach based on visual data mining. A methodological proposal and an application to European islands

Availability of User Generated Content and the development of Big Data and machine learning algorithms have paved the way to collecting and analysing great volumes of data. We scan imagery data from traveling-related posts on Instagram to identify the key features of the destination image and of its dynamics. Specifically, we exploit a newly introduced Visual Object Recognition tool (Google Cloud Vision) to convert into textual labels the content of about 860,000 travel-related pictures posted on Instagram in Summer 2019 for several European islands. The output, a vector of labels’ frequencies on a very fine-grained scale, is used to proxy the destination image at different points in time. We then introduce the Index of Distance in Destination Image, a metric built on the pictures’ labels ranking, and aimed at providing a quantitative measure of (dis)similarity between destination images. We show that the analysis of labels and the index are fit to compare destinations cross-sectionally and over time, providing a useful tool for researchers, marketers and DMOs. We also deliver evidence on how external shocks (like extreme events linked to climate change) or the organization of events modify the cognitive sphere of the destination image, with repercussions on activities undertaken by tourists and relevant implications for local policies

On the use of multi-sensor digital traces to discover spatio-temporal human behavioral patterns

134 p.La tecnología ya es parte de nuestras vidas y cada vez que interactuamos con ella, ya sea en una llamada telefónica, al realizar un pago con tarjeta de crédito o nuestra actividad en redes sociales, se almacenan trazas digitales. En esta tesis nos interesan aquellas trazas digitales que también registran la geolocalización de las personas al momento de realizar sus actividades diarias. Esta información nos permite conocer cómo las personas interactúan con la ciudad, algo muy valioso en planificación urbana,gestión de tráfico, políticas publicas e incluso para tomar acciones preventivas frente a desastres naturales.Esta tesis tiene por objetivo estudiar patrones de comportamiento humano a partir de trazas digitales. Para ello se utilizan tres conjuntos de datos masivos que registran la actividad de usuarios anonimizados en cuanto a llamados telefónicos, compras en tarjetas de crédito y actividad en redes sociales (check-ins,imágenes, comentarios y tweets). Se propone una metodología que permite extraer patrones de comportamiento humano usando modelos de semántica latente, Latent Dirichlet Allocation y DynamicTopis Models. El primero para detectar patrones espaciales y el segundo para detectar patrones espaciotemporales. Adicionalmente, se propone un conjunto de métricas para contar con un métodoobjetivo de evaluación de patrones obtenidos

A Data Analytics Study for Adverse Reactions of Blood Donors by Age, Gender, and Donation Type

The blood donation process is usually very safe, and blood donors are comfortable during the blood donation procedure; however, blood donors occasionally experience various types of adverse reactions during or at the end of blood donation. Some of these reactions are very minor while blood donors sometimes experience serious reactions as well. This study aims to analyze the various types of adverse reactions experienced by the blood donors. The study conducts detailed analysis on a significant amount of real data collected through a blood organization in the southern part of the United States and provides the results regarding the frequency and types of adverse reactions based on multiple attributes such as age, gender, and donation type

A pilot investigation of a wildlife tourism experience using photographs shared to social media: Case study on the endangered Borneo Pygmy Elephant

The increasing popularity of social media in the last decade has led to a considerable amount of user-generated content being shared online, with personal photography representing a significant portion of this. At the same time, the application of social media data to scientific research has also gained momentum. This thesis presents a preliminary exploration of how tourist-generated photographs sourced from social media can be applied to the analysis of both wildlife and social based dimensions of wildlife tourism experiences. To demonstrate proof of concept and a framework for how this approach can be employed, a case study on the viewing of Borneo Pygmy Elephants during riverboat tours along the Lower Kinabatangan River in Sabah, Malaysia from August to October 2017 is provided. The wildlife-centred research presented in this study found that 73% of the reported elephant sightings occurred within 1 km of agricultural land adjacent to the river (predominantly being oil palm plantations). This finding was reflected in the results of the social analysis on tourist responses to elephant-viewing along the river, with 30% of photograph captions on Instagram making reference to conservation issues, including the loss of natural forest habitat. To ensure sustainability of elephant-viewing tourism at this destination, site specific management requires ongoing and real-time information, particularly relating to landscape level issues. The findings of this pilot study suggest that social media derived content can be used to supplement and enhance understanding of wildlife tourism experiences by providing up-to-date information pertaining to visitor experience and the location and conditions under which wildlife is observed. The study also highlights the benefit of adopting a multiple-platform approach to researching different aspects of wildlife tourism, reflecting the different ways that social media platforms are used. Further work is required to validate and assess the reliability of data sourced from social media against traditionally collected empirical data in order to extend this approach to larger datasets

Big data analytics and sustainable tourism: a comprehensive review and network based analysis for potential future research

The 2030 agenda for Sustainable Development is transforming the world. Sustainable tourism options are the forwarding steps for achieving that agenda by providing environmentally, socially, and economically sound tourism options. An increasingly important technology i.e., Big Data Analytics (BDA) can significantly benefit sustainable tourism regarding tourist destination selection, enhanced tourism experiences, and satisfaction. In this regard, the authors reviewed the extensive literature on BDA in sustainable tourism, traced the evolving trends, characterized the gaps, and provided recommendations for future research. The authors shortlisted 187 articles for in-depth analyses. A comprehensive literature review was developed, and a bibliometric study and network analyses were performed to analyse the extent and nature of BDA usage in the sustainable tourism area. The bibliometric study and network analyses helped the authors document the evolution and trends in applying BDA in sustainable tourism. Based on the literature review, a future research framework was developed by integrating BDA in sustainable tourism. The framework helped in envisioning relevant directions for future research. The article helps tourists, the tourism industry, the travel industry, and the hotel and hospitality service providers to gain insights into the benefits of using BDA in sustainable tourism and adopt BDA concepts and tools in tourism to help ensure improved tourist satisfaction

Visual Analytic Extraction of Meaning from Photo-Sharing Services for Leisure Pedestrian Routing

Present-day routing services are able to provide travel directions for users of all modes of transport. Most of them are focusing on functional journeys (i.e. journeys linking given origin and destination with minimum cost) and pay less attention to recreational trips, in particular leisure walks in an urban context. These walks have predefined time or distance and as their purpose is the process of walking itself, the attractiveness of chosen paths starts playing an important role in route selection. Conventional map data that are informing routing algorithms cannot be used for extracting street attractiveness as they do not contain a subjective component, or in other words, do not tell whether or not people enjoy their presence at a particular place. Recent research demonstrates that the crowd-sourced data available from the photo- sharing websites have a potential for being a good source of this measure, thus becoming a base for a routing system that suggests attractive leisure walks. This PhD research looks at existing projects, which aim to utilize user-generated photographic data for journey planning, and suggests new techniques that make the estimation of street attractiveness based on this source more reliable. First, we determine the artifacts in photo- graphic datasets that may negatively impact the resulting attractiveness scores. Based on the findings, we suggest filtering methods that improve the compliance of the spatial distributions of photographs with the chosen purpose. Second, we discuss several approaches of assigning attractiveness scores to street segments and make conclusions about their differences. Finally, we experiment with the routing itself and develop a prototype system that suggests leisure walks through attractive streets in an urban area. The experiments we perform cover Central London and involve four photographic sources: Flickr, Geograph, Panoramio and Picasa. A Visual analytic (VA) approach is used throughout the work to glean new insights. Being able to combine computation and the analytical capabilities of the human brain, this research method has proven to work well with complex data structures in a variety of tasks. The thesis contributes to VA as an example of what can be achieved by means of the visual exploration of data