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Project

Andorra | Tourism

Creative Commons

Attribution 4.0 International

Arnaud Grignard

Groups

View the main City Science Andorra project profile.

With more than eight million visitors a year, tourism represents almost 30% of the economy of Andorra. By gathering and analyzing data from social media, call detail records, and wifi, we can understand the country's dynamics of tourism and commerce as well as design interventions that can improve the experience for tourists, encouraging them to visit Andorra more frequently, stay longer, and increase spending. 

Current Projects

  • Event Analysis
  • Social Network
  • Location Recommendation system

EVENT ANALYSIS

Based on the analysis of call detail records and social media, the goal of this project is to understand the tourist behaviors in Andorra. After mining those anonymized data, we have been able to learn different patterns and behaviors of the tourism in Andorra thanks to an agent-based model developed in order to represent the flow of people. This simulation is also coupled with an interactive table called CityMatrix.

Andorra CityScope Data Observatory
Contributors: Arnaud Grignard, Luis Alonso, Núria Macià (Fundació ActuaTech), Marc Vilella (OBSA), Kent Larson.

This work has been presented in 2016 at the Smart City Expo World at Barcelona.
Contributors: Arnaud Grignard,Luis Alonso, Ariel Noyman, Ryan Zang, Núria Macià (Fundació ActuaTech), Marc Vilella (OBSA), Kent Larson.

SOCIAL NETWORK

The goal of this project is to let users discover the different parishes of Andorra through social media and to allow professionals to uncover some areas of the tourism business in Andorra where improvements can be made.

Contributors: Naichun Chen, Arnaud Grignard, Claire Tsao, Martin Saveski, David Sukhin, Tanya Ivonchyk, Wei Hou Wu.

LOCATION RECOMMENDATION SYSTEM

Based on the analysis of call detail records and social media, the goal of this project is to predict the tourist behaviors and routes in Andorra and finally develop a better targeted tourist attraction recommendation system.

Contributors: Naichun Chen, Yan Leng, Alejandro Noriega, Joseph Cunningham, Reijul Sachdev.