Supporting destination management through GPS based predictive modelling

In the last decennia, both tourism and leisure industries have created significant benefits to the economic, cultural and social development of destinations around the globe. The value of these benefits provide a clear motivation to establish both sustainable development and management of destinations. Nevertheless, practicalizing sustainable development and management presents a number of challenges ranging from national policies to the overall international tourism system. That being said, mankind has a history of using innovative technologies to counter current day challenges. Therefore, I have decided to write my BA thesis in an attempt to reveal the potential of predictive GPS-modelling for European destinations according to tourism professionals.

How is the research structured?

The first section of the research will be comprised of a literature review in which the following key-concepts will be addressed:

  • Requirements of GPS based predictive modelling
  • Benefits of GPS based predictive modelling
  • Limitations of GPS based predictive modelling
  • Implementations of GPS based predictive modelling
  • Existing strategies and methods for destination management.
  • The feasibility of GPS based predictive modelling in destination management
  • Law and regulations

Sequentially, the paper will apply a qualitative approach supported by expert interviews and/or focus groups. At last, the outcomes of the theoretical exploration will be presented to experts in the field of tourism destination management in order to explore potential synchronisation of strategies, methods and GPS based predictive modelling.

Advantages of combining GPS data with predictive models

The intended outcome is to inspire both public and private entities as well as to provide a documentation of specifically relevant literature based knowledge. By obtaining insights in GPS based predictive modelling an attempt is made to obtain more clarity in regards to predicting at least the following:

  • Scenarios of increased visitor pressure
  • Visitors’ means of transportation
  • Movement in and around locations of interest
  • Direction of movement
  • Potential pressure points

Using big data to design urban tourism policies

Derived results could serve as guidance in both smart management and potential formulation of new policies. This supports Europe’s endeavour to “develop a knowledge base, including the impact of tourism (economic, environmental and socio-cultural) in urban EU tourism destinations and support them in designing urban tourism policies through better use of data” (European commission, 2020).

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