This document summarizes research analyzing visitor flows in the canton of Fribourg, Switzerland using mobile phone data. The objectives are to demonstrate how mobile data can capture tourist movement patterns and how network analysis can help solve tourism problems. The researchers analyze network metrics like density and modularity to identify clusters of attractions. Bow-tie network structures show core, in/out, and transit areas. Modules reveal nuclear-mix patterns of attractions. While focused on one area, the methodology could provide insights into visitor flows if applied to other destinations.

1. ENTER 2017 Research Track Slide Number 1
Strategic Visitor Flows (SVF)
analysis using mobile data
Rodolfo Baggioa, and Miriam Scaglioneb
a Master in Economics and Tourism
Bocconi University, Italy
rodolfo.baggio@unibocconi.it
b Institute of Tourism, University of Applied Sciences
and Arts Western Switzerland Valais
miriam.scaglione@hevs.ch

2. ENTER 2017 Research Track Slide Number 2
Agenda
• Relevance of the research
• Objective of this rearch
• Literature review
• Data & Methodology
• Results
• Conclusion
• Limits and future research

3. ENTER 2017 Research Track Slide Number 3
Relevance of the research
• ‘Visitor flows’ (VF) is defined as the generalized
spatial movement patterns of travellers.
• VFs are important for understanding travel
networks which go beyond the specific spatial
dimension to include informational or virtual
dimensions such as travellers’ experiences for
– Improving marketing strategies i.e. increasing value in the supply
chain
– Challenging traditional organisation of DMOs

4. ENTER 2017 Research Track Slide Number 4
Stienmetz, J. L., & Fesenmaier, D. R. (2013). Traveling the Network: A Proposal for Destination
Performance Metrics. International Journal of Tourism Sciences, 13(2), 57-75.
doi:10.1080/15980634.2013.11434673

5. ENTER 2017 Research Track Slide Number 5
Objective of this research
• This research focuses on the aspects of travel
networks and the spatial dimension. The main
aims are twofold Managerial and Methodological.
1. Prove the utility of mobile data in grasping
generalized patterns of tourist movements in the
canton of Fribourg, Switzerland
2. Show how appropriate approach to Big Data
environment helps solving problems based on
network metrics

6. ENTER 2017 Research Track Slide Number 6
Literature review
• Travel networks updated and enriched the concept of
tourism attraction system, as was proposed in the
‘90s.
– travel itinerary by Lew &McKercher (2002),
• single destinations /hub / tour patterns (Gunn, 1994; Lue et al.,
1993)
• gateway /egress destination or attraction (Lew &McKercher,
2002)
• Seminal research last century has shown the
relevance of the spatial dimension in market
segmentation (Dredge, 1999; Gunn, 1994; Lue et al.,
1993) and confirmed the network nature of this
approach (Leiper, 1990).

7. ENTER 2017 Research Track Slide Number 7
Data
• Swisscom has anonymized the users using Hashing-
Algorithm techniques and shifting of the date; no
characteristics of the users are given.
• 18,138 anonymized and aggregated mobile users
(AMU) belonging to one of the top European
incoming countries in Fribourg canton tourism, from
17 and 28 August 2014
– 2G A Interface data, 2G IuPS Interface data, 3G IuCS data
and 3G IuPS data.
• This anonymization process does not affect the
results of this research.
• The position of the cells (namely antennas) as proxy
for the geo-localization of AMU.
• There are approximately 1,500 cells.

8. ENTER 2017 Research Track Slide Number 8
Data

9. ENTER 2017 Research Track Slide Number 9
Network analysis
• Global topological structure:
– global metrics
• density
• average path length, diameter,
• degree heterogeneity: Gini index
– weighted degree distribution
• Mesoscopic structure
– bow-tie structure
– modularity structure

10. ENTER 2017 Research Track Slide Number 10
Bow-tie structure
• Many directed networks
have:
– a core of strongly connected
nodes (SCC),
– two sets of nodes connected
one-way (IN and OUT) SCC
– a set of nodes connecting IN
and OUT components without
passing through the core
(TUBES, TENDRILS)
– and some disconnected groups
– (NB: a bow-tie shape)

11. ENTER 2017 Research Track Slide Number 11
• Clustering of the nodes in groups
more connected within a group
than between groups
– uncovered via stochastic
algorithms that maximize a quality
function Q, called modularity index
• intuitively the ratio between
densities inside and outside the
groups
– Q is normalized:
• 0 = no structure, 1 = completely
disjointed groups
• Signals self-organizing structures
in the network
Modularity

12. ENTER 2017 Research Track Slide Number 12
Bow-tie structures by total trajectory
duration

13. ENTER 2017 Research Track Slide Number 13
Bow-tie structures comparison of SCC by total trajectory
1 hour vs 3 days and more than 3 days duration

14. ENTER 2017 Research Track Slide Number 14
Modules & attractions

15. ENTER 2017 Research Track Slide Number 15
Remarks
• Trajectories represents different levels of
geographical scale and represent generalized
sequential patterns (Orellana et al., 2012)
• Network analysis reresents trajectories in paths
weighted by popularity
• Modularity allows showing the cluster of
attractions and identify Leiper (1990) nuclear-mix
patterns of the tourism attraction system
• Bow-tie structure is in line with the node itinerary
classification by Lew and McKercher (2002)
allowing to identify getaway and egress ones

16. ENTER 2017 Research Track Slide Number 16
Remarks
• Data used in this research belong to one specific
example
• Replication of this methodology on other
destinations and cross comparisons will be useful
in finer tuning the methods and gaining wider
knowledge of the phenomenon
• Also, improvements in general network science
methods can allow future more sophisticated
analyses

17. ENTER 2017 Research Track Slide Number 17
Conclusion
• Application of network analysis can help in better
grasping an important aspect, the spatial one
• Then, a good knowledge of the destination and its
peculiarities helps improving interpretation in
order to provide useful insights into the
understanding of the real movements of people
• This will enable more effectiveness in planning
products and services with a better connection
with the travelers' preferences and needs

18. ENTER 2017 Research Track Slide Number 18
Thank very much for the attention
Any questions?