Abstract:As cities become increasingly complex, Information and Communication Technologies (ICTs) bring smartness into organisations and communities, contributing to a more competitive tourism destination, i.e., smart tourism destinations. Enhanced information access coupled with a new kind of tourists avid for online content and predisposed to share information on social media, allows for a better understanding of tourist behaviour regarding their spatial distribution in urban destinations. Thus, smart tourism portrays individuals as information makers, refining the available alternatives for tracking their location. Big data analytics is a technology with the potential to develop Smart City services. From the analysis of the spatial distribution of tourists in the city of Lisbon based on data collected from the 'Panoramio' social network, we identify the most popular places in the city in a context of tourist visits. This new data largely contributes to understanding the consumption of space within urban tourist destinations and therefore enables us to differentiate the overcrowded places from the ones with potential to grow. This allows decision-makers to imagine new ways of planning and managing towards a sustainable 'smart' future.

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Agent-based models (ABMs) are becoming more relevant in social simulation due to the potential to model complex phenomena that emerge from individual interactions. In tourism research, complexity is a subject of growing interest and researchers start to analyse the tourism system as a complex phenomenon. However, there is little application of ABMs as a tool to explore and predict tourism patterns. The purpose of the paper is to develop an ABM that increases knowledge in tourism research by (i) considering the complexity of tourism phenomenon, (ii) providing tools to explore the complex relations between system components and (iii) giving insights on the functioning of the system and the tourist decision-making process. A theoretical ABM is developed to improve knowledge on tourist decision-making in the selection of a destination to vacation. Tourists' behaviour, such as individual motivation, and social network influence in the vacation decision-making process are hereby discussed.

This article provides an approach to the geographic and quantitative interpretation of tourism intensification, drawing on the concepts of fractals, and fractal dimension ( D). Exploring tourism intensification in Lisbon, we first present a geographic construct that represents the spatial layout of tourism based on crowd-contributed spatial signatures advocating a collective sense of the “tourist city.” Then, we assess the tourism-related intensification patterns, based on the estimation of D, for different years. Significant statistical associations can be found between D and tourism intensification across the urban space. Intensification on tourism cores is more homogeneously distributed, yet it evolves into a more compact form of spatial organization. On the other hand, there is a decline in the degree of homogeneity of tourism intensification from tourism cores to the periphery. This approach has also proved useful for exploring tourism intensification in destinations at different hierarchical levels, such as in Lisbon and Oporto metropolitan areas.

Augmented Reality (AR) is a pillar of the transition to Industry 4.0 and smart manufacturing. It can facilitate training, maintenance, assembly, quality control, remote collaboration and other tasks. AR has the potential to revolutionize the way information is accessed, used and exchanged, extending user’s perception and improving their performance. This work proposes a Pervasive AR tool, created with partners from the industry sector, to support the training of logistics operators on industrial shop floors. A Human-Centered Design (HCD) methodology was used to identify operators difficulties, challenges, and define requirements. After initial meetings with stakeholders, two distinct methods were considered to configure and visualize AR content on the shop floor: Head-Mounted Display (HMD) and Handheld Device (HHD). A first (preliminary) user study with 26 participants was conducted to collect qualitative data regarding the use of AR in logistics, from individuals with different levels of expertise. The feedback obtained was used to improve the proposed AR application. A second user study was realized, in which 10 participants used different conditions to fulfill distinct logistics tasks: C1 — paper; C2 — HMD; C3 — HHD. Results emphasize the potential of Pervasive AR in the operators’ workspace, in particular for training of operators not familiar with the tasks. Condition C2 was preferred by all participants and considered more useful and efficient in supporting the operators activities on the shop floor.