Influence-Time-Proximity Driven Locations Recommendation Model: An Integrated Approach

Modeling Seasonality in Tourism Forecasting

Abstract: Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, RIMA14 and ARIMA1. ARIMA14 is used for modeling stochastic nonstationary seasonality and requires first and fourth differences to achieve stationarity. ARIMA1 considers the series only in first differences, and seasonality is modeled with a constant and three seasonal dummies. The selection of either model depends on the nature of seasonality. Conventional unit root tests determine the nature of seasonality and the order of integration and, therefore, the series' choice of forecasting model. To determine whether the test correctly identifies the forecasting model for tourism demand, out-of-sample forecasting performance of ARIMA1 and ARIMA14 is compared with HEGY unit root model selection method. Comparing forecasting performance of both models with HEGY unit root model selection shows that the outcome of HEGY test procedure may not be useful in the selection of a univariate time-series model for quarterly tourism demand series.

Analysing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources

Abstract: Available statistics on tourism from official European sources are limited in terms of both the spatial and temporal resolutions, curbing potential analyses and applications relevant for tourism management and policy. In this study, we produced a novel, complete and consistent dataset describing tourist density at high spatial resolution with monthly breakdown for the whole of the European Union. This is achieved thanks to the integration of data from conventional statistical sources with big data from emerging sources, namely two major online booking services containing the precise location and capacity of tourism accommodation establishments. The produced dataset allowed us to uncover key spatiotemporal patterns of tourism in Europe at unprecedented detail, showcasing the usefulness of complementing official statistical data with emerging big data sources.

Where to go and what to do: Extracting leisure activity potentials from Web data on urban space

Abstract: Web data is the most prominent source of information for deciding where to go and what to do. Exploiting this source for geographic analysis, however, does not come without difficulties. First, in recent years, the amount and diversity of available Web information about urban space have exploded, and it is therefore increasingly difficult to overview and exploit. Second, the bulk of information is in an unstructured form which is difficult to process and interpret by computers. Third, semi-structured sources, such as Web rankings, geolocated tags, check-ins, or mobile sensor data, do not fully reflect the more subtle qualities of a place, including the particular functions that make it attractive. In this article, we explore a method to capture leisure activity potentials from Web data on urban space using semantic topic models. We test three supervised multi-label machine learning strategies exploiting geolocated webtexts and place tags to estimate whether a given type of leisure activity is afforded or not. We train and validate these models on a manually curated dataset labeled with leisure ontology classes for the city of Zwolle, and discuss their potential for urban leisure and tourism research and related city policies and planning. We found that multi-label affordance estimation is not straightforward but can be made to work using both official webtexts and user-generated content on a medium semantic level. This opens up new opportunities for data-driven approaches to urban leisure and tourism studies.

Location-Time-Sociality Aware Personalized Tourist Attraction Recommendation in LBSN

Abstract: With the development of the tourism, vast amount of tourist attraction information makes it complex and time-consuming for users to obtain satisfactory travel destination Rich topological, temporal and spatial information in Location-Based Social Network (LBSN) helps to mine user preference deeply and evokes effectiveness of tourist attraction recommendation In this paper, a personalized tourist attraction recommendation mechanism is designed in terms of different factors, including user preference, social relationship, location distance and location popularity Firstly, a large number of check-ins in LBSN are filtered for further geographical space clustering to get real tourist attraction check-ins by DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm Then, a location-aware LDA model is used to excavate user's latent preference topic distribution and region's latent topic distribution on tourist attractions After that, these two attributes together with social relationship are used to make user interest scoring function Furthermore, another two scoring functions are established respectively based on tourist attraction location distance and popularity At last, a personalized tourist attraction recommendation algorithm is designed to recommend tourist attractions to users, which ranks tourist attractions based on weighted average by combining the above three scores Experiments are carried out on Foursquare data sets and results show that our method can perform well on recommendation in home city and new city

Measuring Seasonality of Tourism Demand in Petra, Jordan (2006-2017)

Abstract: Petra is a mature tourism destination in the south of Jordan with a degree of seasonality over the last 10 years. Despite the recognized importance of Petra for the tourism industry in Jordan, there has been a lack of studies that discuss seasonal demand variations and its impacts on other related industries in the region. Yet, this study aims at analyzing patterns and effects of seasonality of tourism demand in Petra for the period 2006-2017. The required data was obtained from Ministry of Tourism and Antiquities (MoTA). Four methods were used to measure tourism seasonality in Petra. These are: Seasonality indicator; Seasonality ratio; Gini coefficient; and Seasonality index. The results of the study showed a modest level of tourism seasonality in the study area. Among methods, Seasonality index appeared to be the appropriate and simple way to calculate seasonality patterns at tourism destinations. The results showed that there are two peaks of seasonality in tourism demand of Petra. The first and the highest one was in April and the second took place in the months of October and November. In addition, seven months represented the low season of tourism demand in Petra. These are December, January and February as well as June, July, August and September. The tourism seasonality in Petra based on that is mainly due to the weather in these months which represent the coldest and warmest months in the year respectively.