Smart destinations as open data generators: barriers and opportunities

A Smart Tourism Destination (ITD) is based on a management model based on innovation and the use of advanced technology to optimise the visitor experience and improve the sustainability of the destination, while strengthening the quality of life of residents. The DTI model is based on a series of indicators that allow the state of the tourism destination to be analysed, areas for improvement to be diagnosed and strategic action plans to be developed. This approach, promoted by SEGITTUR (Sociedad Estatal para la Gestión de la Innovación y las Tecnologías Turísticas) and other regional public entities (e.g. the DTI-CV model of the Comunitat Valenciana defined by INVATTUR - Instituto Valenciano de Tecnologías Turísticas), has been consolidated as a key pillar in the digital transformation of tourism. This intensive use of technologies in ITDs has transformed them into true data-generating centres, which - combined with external sources - can be used to optimise decision-making and improve destination management.

Data provenance in an ITD and its use

In an ITD, data are basically generated from two main areas:

1. Data generated by visitors or tourists: they create a digital footprint as they interact with different technologies. This footprint includes comments, ratings, images, spending records, locations and preferences, which are reflected in mobile apps, social media or booking platforms. In addition, data is generated passively through electronic payment systems or urban mobility systems, as well as traffic measurement devices, among others.
2. Data generated by the tourist destination: thanks to the sensorisation and implementation of IoT networks (Internet of Things ), destinations collect real-time information on traffic management, energy consumption, environmental quality and use of services (public or private). In addition, the destination generates essential data on its tourism offer, such as updated lists of accommodation or hospitality establishments, places or events of tourist interest and complementary services.

The combination of these data sources in a Intelligent Destination Platform (IDP) such as the one proposed by SEGITTUR, allows ITDs to use them to facilitate a more innovative and experience-oriented management.

Title: Areas of data generation in an ITD

Source: own elaboration

There are numerous examples and good practices in the use of these tourism data, implemented by various European destinations, whose description is documented in the report Study on mastering data for tourism by EU destinations. This report provides a detailed insight into the opportunities that the use of data offers to improve the competitiveness of the tourism ecosystem. Furthermore, this report does not ignore the importance of tourist destinations as data generators, formulating a series of recommendations for public administrations, including the development of a cross-cutting data management plan - i.e. involving not only the area of tourism, but also other areas such as urban planning and the environment-, guaranteeing an integrated approach. This plan should promote the availability of open data, with a special focus on data related to sustainability, accessibility and specialised tourism offer.

Smart destination models and open data

SEGITTUR's DTI model (recently described in its practical guide) establishes as a requirement the creation of an open data portal in tourist destinations to facilitate the publication of data in the place where tourism activity takes place and its access in reusable formats, enabling the development of different products and services. No specific themes are established, but information of interest such as public transport, shops, job offers, cultural agenda or environmental sensors are highlighted. Interesting is the definition of indicators to assess the quality of the portal such as compliance with open data standards, the existence of systems to automate the publication of data or the number of datasets available per 100,000 inhabitants. It is also indicated that new datasets should be added progressively as their usefulness is identified.

It should be noted that in other DTI models, such as INVATTUR's DTI-CV model mentioned above, it is also proposed that destinations should have a tourism open data portal in order to promote tourism innovation.

High-value tourism data

The European Union, through Directive (EU) 2019/1024 on open data and re-use of public sector information and Implementing Regulation (EU) 2023/138, has defined high value datasets in various areas, including tourism within the category of statistical data. These are data on tourism flows in Europe:

• Overnight stays in tourist accommodation establishments, at national level, at NUTS 2 level (autonomous communities and cities), NUTS 3 level (provinces) and for some specific cities.
• Tourist arrivals and departures, tourist expenditure, hotel occupancy, demand for tourist services, at national level.

It is interesting to note that these and other data have been collected in Dataestur, the data platform for tourism in Spain. Dataestur organises its data in different categories:

• Travel and leisure: statistics on tourist arrivals, attraction ratings, museum visits and leisure spending.
• Economy: data on employment in the tourism sector, active businesses and spending by international visitors.
• Transport: data on mobility, including air traffic, bus and rail transport, roads and ports.
• Accommodation: information on hotel occupancy, rural tourism, campsites and tourist accommodation, as well as prices, profitability and hotel satisfaction.
• Sustainability: indicators on air quality, water and nature conservation in tourist destinations.
• Knowledge: analysis of visitor perception, security, digital connectivity, tourism studies and reports.

Most of these data are collected at provincial level (NUTS 3) and are therefore not published at destination level. In this sense, the Spanish Federation of Municipalities and Provinces (FEMP) proposes 80 datasets to be published openly by the local administration which, in addition, take into account high-value data, bringing them down to the local level. Among all these data sets, the following are explicitly defined as data within the tourism category: cultural agenda, tourist accommodation, census of commercial and leisure premises, tourist flows, tourist attractions and monuments.

Barriers and opportunities in the publication of open data by ITDs

After analysing the current state of data management in the field of tourism, a series of opportunities for tourism destinations as generators of open data are proposed:

• Provision of data for internal consumption: tourism data covers multiple themes and is generated in different departments within the local administration, such as tourism, urban planning, mobility, environment or economy. Given this scenario of diversity of sources and decision-makers, working on the publication of data in reusable formats not only facilitates its reuse by external agents, but also optimises its use within the local administration itself, allowing for a more efficient and data-based management.

• Fostering innovation in tourism: open data from tourism destinations is an excellent raw material on which to develop intelligent products and services with added value for the sector. This facilitates public-private collaboration, promoting the creation of a technology industry around tourism destinations and the open data they generate.

• Facilitating the participation of tourism destinations in data spaces: the publication of open data allows the managing bodies of tourism destinations to join data spaces in a more robust way. On the one hand, having open data facilitates interoperability between actors in the sector. On the other hand, tourism open data initiatives increase the data culture in tourism destinations, boosting the perception of the usefulness of data-driven tourism management.

Despite these clear opportunities, there are a number of barriers that make it difficult for tourism destinations to publish data in open format effectively:

• Necessity of sufficient budget and technical resources: the publication of open data requires investments in technological platforms and in the training of specialised teams. This is even more important in the field of tourism, where data are heterogeneous, subject-matter diverse and fragmented, requiring additional efforts in their collection, standardisation and coordinated publication.
• Small business dominance in tourism destinations: tourism businesses need to be supported to incorporate the use of open destination data, as well as the development of data-driven solutions tailored to the needs of the destination.
• Awareness of the usefulness of open data: there is a risk that open data will be seen as a trend rather than a strategic resource that enables tangible benefits. In this sense, data is perceived as an internal resource rather than an asset that can be shared to multiply its value. There is a lack of clear references and examples of the impact of the reuse of open data in tourist destinations that would allow for a deeper incorporation of a data culture at the tourist destination level.
• Difficulty in operationalising data strategies: Tourism destinations have incorporated the publication of open data in their strategic plans, but it is necessary to push for its effective implementation. One of the key issues in this regard is the fear of loss of competitive advantage, as the publication of open data related to a destination's tourism activity could reduce its differentiation from other destinations. Another concern relates to legal and personal data protection aspects, especially in areas such as mobility and tourism consumption.

Conclusions: the future of open data in ITD models

In relation to data management, it is necessary to address aspects that are still not sufficiently developed in the DTI models, such as data exchange at the destination, rather than the mere purchase of information; the transversal integration of data on a local scale, allowing the cross-referencing of information from different areas (urban planning, environment, tourism, etc.).); obtaining a greater level of detail in the data, both in terms of time (specific events) and space (areas or points of interest within destinations), safeguarding privacy; and the development of an effective open data strategy.

Focusing on this last point, ITD strategies should include the publication of open data. To this end, it is a priority to define a data management plan that allows each destination to determine what data is produced, how it can be shared and under what conditions, ensuring that the opening of data does not negatively affect the competitiveness of the destination or conflict with current data protection and privacy legislation.

A key tool in this process is the definition of a catalogue, which makes it possible to organise, prioritise and classify the available data (and their metadata) according to their value and usefulness for the different actors in the tourism ecosystem. This catalogue should enable ITD data to comply with the FAIR principles (Findable, Accessible, Interoperable, Reusable), facilitating their open publication or allowing their integration in data spaces (such as the European tourism data space developed in the DeployTour project). In this context, each identified and catalogued dataset should have two versions:

1. An open version, accessible to any user and in a reusable format, with an unrestricted licence (i.e. an open dataset).
2. A version that allows specific agreements for use in data spaces, where the sovereignty and control of the destination is maintained, establishing access restrictions and conditions of use.

Regardless of the approach taken, all published data should comply with the FAIR principles, ensuring that it is findable, accessible, interoperable and reusable, promoting its use in both the public and private sectors and facilitating the development of innovative data-driven solutions in the field of tourism.

Jose Norberto Mazón, Professor of Computer Languages and Systems at the University of Alicante