Touristification, smartization, and social sustainability in European regions

Touristification and smartization processes are commonly associated with economic growth strategies. Here we emphasize and demonstrate the need to consider the implication of these processes on social sustainability. Initial results imply that: (1) regions not specialized in tourism are associated with a lower share of population in poverty;and (2) regions' ‘smartness' level is negatively associated with the share of poverty. However, in regions highly specialized in tourism, smartization demonstrated an opposite association of increased intra-regional poverty. As residents' quality of life is becoming a key policy consideration, understanding the effect of these socio-economic processes on socially sustainable growth has timely implications for regional planning, including for post-COVID-19 recovery strategies.

Crowd Monitoring in Smart Destinations Based on GDPR-Ready Opportunistic RF Scanning and Classification of WiFi Devices to Identify and Classify Visitors' Origins

Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination.