The importance of online tourism demand

In recent decades, the combination of tourism and Information and Communication Technologies (ICT), has originated considerable changes in tourists’ behaviour. The analysis of tourism demand resulting from the Internet is of growing importance, given the increasing number of online reservations observed in recent years. However, in order to analyse the new trends caused by online bookings, the availability of data enabling the measurement and characterization of this phenomenon is essential. This has, however, been a considerable limitation, given that either no data on key variables is available or the available data is sometimes of questionable quality. For professionals and researchers in the area of tourism, the high volume of tourists who use the Internet to make hotel and travel reservations is worth of consideration, given that it may potentiate the discovery of new source markets, the identification of clients with different characteristics and may help explain the dynamics between suppliers or countries. The existence of predictive studies to support decision-making and planning, by professionals of the tourism sector, is of great importance. Panel data models are a useful and appropriate method for the analysis and modelling of tourism demand. These models consider both the time series and the cross-sectional dimensions of the data and allow for the inclusion of social variables. The results of estimation of tourism demand, through panel data models, show that the Internet and the sharp technological development have encouraged the increasing demand for tourism. The growing number of tourism companies online will naturally promote or potentiate an increase of tourism demand.

Análise da procura turística através de modelação dinâmica em macro painel de dados

A procura turística efectuada através da Internet, reveste-se de uma importância cada vez maior em consequência do crescimento acentuado do número de reservas online observado nos últimos anos, originando inclusive, o surgimento de um novo tipo de viajante: o turista mais experiente, sofisticado e sábio, o qual procura valores excepcionais nas suas viagens. A análise da procura turística actual não pode negligenciar as características do turismo electrónico, uma vez que o volume de compras de produtos turísticos efectuado através da Internet é cada vez mais acentuado. Neste contexto, os modelos de dados em painel apresentam-se como abordagem indicada para a análise da procura turística. Devido às suas características, que permitem a utilização de dados de séries temporais e seccionais, estes modelos possibilitam a inclusão de variáveis sociais e de variáveis observadas ao longo de um período de tempo. Os resultados da modelação e da estimação da procura turística, através dos dados em painel de grandes dimensões, permitem concluir que o ambiente tecnológico que envolve a actividade turística tem incentivado o aumento da procura turística e que pode ser um dos factores que a determinam na conjuntura que caracteriza a sociedade actual.

A indústria hoteleira do Algarve: uma análise de dados em painel da estrutura de capital

Dissertação de Mestrado, Finanças Empresariais, Faculdade de Economia, Universidade do Algarve, 2015

Open source data sensing software for ultrasonic non-invasive temperature estimation

In this paper, an open source solution for measurement of temperature and ultrasonic signals (RF-lines) is proposed. This software is an alternative to the expensive commercial data acquisition software, enabling the user to tune applications to particular acquisition architectures. The collected ultrasonic and temperature signals were used for non-invasive temperature estimation using neural networks. The existence of precise temperature estimators is an essential point aiming at the secure and effective applica tion of thermal therapies in humans. If such estimators exist then effective controllers could be developed for the therapeutic instrumentation. In previous works the time-shift between RF-lines echoes were extracted, and used for creation of neural networks estimators. The obtained estimators successfully represent the temperature in the time-space domain, achieving a maximum absolute error inferior to the threshold value defined for hyperthermia/diathermia applications.