Demand networks: from smart city to tourism intelligence

In the last years tourism has seen an incredible growth. However, this has resulted in a high diversity in the ways destinations have experienced the phenomenon and its effects, mainly for what concerns the concentration of tourist flows to specific areas. Overtourism, undertourism and other similar buzzwords have been used to highlight the different problems seen in many locations and situations. Much is, probably, just a subjective perception due to the high variability and strong seasonality of the phenomenon during a typical year. A growing strand of literature has examined the issues. From these, one element seems to emerge: it is a complex and multi-layered phenomenon that manifests differently in different locations and needs multidimensional efforts to address associated challenges in terms of policies, organizations, institutions, and behavior. In the end, as many maintain, the issue can be reduced to a destination management issue, or, better, to a failure in correctly managing a destination and the flows of people visiting it.

One of the most important points, for a rational and efficient management of a destination, is the adoption of an integrated set of strategies that combine tourism, transport and land-use related measures. In this, the control of accessibility and mobility to and within a tourism destination is a crucial tool to regulate visitor flows (VF), reduce traffic congestion and pollution and meet tourists’ and residents’ requirements. Therefore, understanding tourist mobility becomes a central activity for planning of on-site movement and marketing of attractions and services. In the past years, many have increasingly studied the movement patterns of tourists and how to guide practice based on movement patterns.

The recent development and diffusion of information and communication technologies (ICT), mainly since internet could be embarked in mobile phones and the increase number and adoption of social networking platforms have brought the availability of extensive geolocated datasets related to human movement, enabling researchers to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. Thanks to the social network multimedia broadcast capacities, the spatiotemporal information is enriched by contents such as photos, videos, text which add a second analysis layer namely, virtual interactivity. This virtual interactivity allows the users to be in touch with their family and friends reporting in real time and geotagged way their experiences. For the research, this virtual interactivity adds a social level to spatiotemporal aspects of VF. Therefore, this conception of VF locates the methodological tools in the crossroads of quantitative and qualitative methods. Moreover, assessing and monitoring VF and using the outcomes for reshaping the destination organization’s governance model, moving it from a static-central model to a dynamic network approach requires good multidisciplinary competences and the choice of the data to collect and of the methods for gaining the needed insights. Bridging the gap from smart city technologies to tourism intelligence enrichment is the main aim of this project.


HES-SO Valais Wallis:

Tomsk Polytechnic University: