=Paper=
{{Paper
|id=Vol-2056/p3
|storemode=property
|title=CityScope Andorra Data Observatory: A Case Study on Tourism Patterns
|pdfUrl=https://ceur-ws.org/Vol-2056/p3.pdf
|volume=Vol-2056
|authors=Arnaud Grignard,Luis Alonso,Núria Macìa,Marc Vilella,Kent Larson
}}
==CityScope Andorra Data Observatory: A Case Study on Tourism Patterns==
https://ceur-ws.org/Vol-2056/p3.pdf
CityScope Andorra Data Observatory: A Case
Study on Tourism Patterns
Arnaud Grignard1 , Luis Alonso1 , Núria Macià2 , Marc Vilella3 , Kent Larson1
1
MIT Media Lab - City Science - Cambridge, USA
2
Universitat d’Andorra - Sant Julià de Lòria, Andorra
3
Observatori de la Sostenibilitat d’Andorra - Sant Julià de Lòria, Andorra
Abstract. This paper presents a data-driven agent-based simulation of
individual mobility based on spatio-temporal data from mobile phones.
The model developed is embedded within the CityScope framework, a
platform used as decision support system for urban planning. This work
analyzes the Andorra visitors’ flow and traffic congestion through an
agent-based visualization using different representation and abstraction
features.
1 Introduction
Telecom data coupled with other data sources—such as social media—can help
us understand human behavior at spacial, temporal, and social level. These un-
precedented rich sources of data allow us to study how people move and, thus,
how our society behaves. Previous research [1] [2] shows that such insights can
be used to design interventions to improve our daily lives and even visitors’
experience in the scope of tourism strategies. However, the results of the analy-
ses are not always comprehensible for non-experts. CityScope is a visualization
framework, developed by City Science at MIT Media Lab, that serves both as an
urban data observatory and laboratory for decision making in urban planning.
CityScope is a next generation, tangible, augmented reality platform that helps
to (1) visualize and understand the meaning of complex urban data and inter-
relationships, (2) simulate the impact of multiple interventions, and (3) support
decision making in a dynamic, iterative, and evidence-based process. CityScope
helps non-experts to engage into conversation through visualizations that syn-
thesize the analyses in a coherent manner on the physical model of their cities.
An Agent-Based Model (ABM) has been used for simulating the actions and
interactions of autonomous agents in this work. The central idea of the model
is to show emerging patterns in visitors’ behavior during specific events and un-
derstand how this may affect and coexist with regular activity. This model leads
to insightful visualizations that show how visitors move across the country.
The remaining of this paper is organized as follows. Section 2 gives a general
overview of the framework. Section 3 describes the input data. Section 4 describes
the model implemented. Section 5 shows the visual results of the simulation and,
finally, Section 6 discusses further research.
2 Overview
Andorra, located between Spain and France in the middle of the Pyrenees, is a
country with a population around 78,000 people that welcomes more than eight
million visitors a year. According to the statistics provided by the Departament
d’Estadı́stica d’Andorra, the tourism sector accounts for 80% of the GDP of the
country. Andorra has two types of visitors: (1) tourists, which stay over at least
one night and (2) same-day visitors, which enter and leave the country the same
day. The presented model has mainly been developed to simulate the movement
of visitors across the territory and gain understanding on this industry. Modeling
people’s flow can help us assess the actual impact of visitors in terms of traffic
congestion, energy consumption, consumer spending, among others. The current
model focuses on visitors’ attendance at the events held in the country as well as
traffic congestion levels. The following two events in 2016 have been analyzed:
(1) Cirque du Soleil: VISION and (2) Le Tour de France.
Scalada by Cirque du Soleil is a series of indoor, summer shows specifically
designed for Andorra by 45 DEGREES—the global events company from Cirque
du Soleil. Since 2013, the company has combined art, technology, and Andorran
elements in their performances and attracted many visitors to the country. VI-
SION was a 60-minute event performed on Tuesdays, Wednesdays, Thursdays,
Fridays, and Saturdays from July, 2 to July, 30 2016 at 10:00pm. The venue
had a capacity for 5,000 people per performance. Le Tour de France is the an-
nual multiple stage bicycle race held in France. However, the event occasionally
makes passes through nearby countries. In 2016, Andorra hosted the arrival of
the Stage 9 up in the mountains (Arcalı́s, Ordino) and the departure of the Stage
10 in the city center (Escaldes, Escaldes-Engordany).
The model has been implemented using different environments: Processing
[3] and the GAMA platform [4]. Processing is both an open source program-
ming language and integrated development environment built for the visual
design community. It is well suited for accurate visualizations and provides a
seamless feature interaction integration. However, it has not been designed to
develop complex ABM. In this sense, GAMA is a modeling and simulation-
development environment for building spatially explicit agent-based simulations.
This multiple-application domain platform uses a high-level and intuitive agent-
based language that allows users to undertake most of the tasks related to mod-
eling, visualization, and simulation exploration with dedicated tools. GAMA
includes a uniform cost search pathfinding algorithm that allows to work out
custom pathfinding logics—helping overcome issues encountered with Process-
ing. The resulting model is projected on the Andorra CityScope table, which is
a 3D model of the two main cities of Andorra4 .
4
It has been presented at the Smart City Expo World Congress 2016 in Barcelona.
https://youtu.be/hdL0aundHL4
3 A Data-driven Model
Figure 1 represents the different elements obtained from the input datasets: (1)
telecom data, (2) amenities, and (3) road network. The ABM uses these different
types of data to characterize both static and dynamic agents.
Telecom data: We use cell phone communication data to understand human
mobility patterns. Andorra Telecom provided a three-year collection—from 2014
to 2016—of anonymized Call Detail Records (CDR), which represents a total of
450GB of data. Observations in these records have a spatial component and
are triggered by any kind of action with a mobile phone (i.e., phone call, text
message, cellular data). From the features, we obtain the location of the cell
towers involved in the action and, thus, compute the origin and destination of
each agent. We can also assign the country of residence to the agents.
Amenities: Amenities are places where agents may go, such as restaurants,
hotels, or points of interest. Their geolocation have been gathered from TripAd-
visor, Yelp, and the Andorra Turisme office.
Road Network: Agents do not move in a straight line; their trajectories are
constrained by the actual road network. Therefore, agents move along a graph
topology, which is provided by Open Street Maps. Roads can be of different type
(primary, secondary, residential, and pedestrian) allowing only certain behaviors.
Roads can be either one-way or bidirectional, but not all agents can go in both
directions. The congestion level is updated during the simulation according to
the number of agents present on the road and it can be modified to emphasize
specific patterns such as traffic congestion.
Fig. 1. Input data: cell towers (triangle), amenities (circle) and roads (line). The grey
area represents the scope of the Andorra CityScope table.
4 Model Description
Figure 2 corresponds to the simulation of a regular day—used as a benchmark
in the analyses. Every simulation represents a full day and runs until all the
observations of the day from the CDR data set are processed. People are repre-
sented by solid circles and vehicles by stroke circles; their color varies according
to the country of residence—red refers to people from Spain, blue refers to peo-
ple from France, and white refers to people from other countries. For the sake
of clarity, we follow the classification provided by the Departament d’Estadı́stica
d’Andorra: Spain, France, and Others. In 2016, Andorra received eight million
visitors approximately divided into 4.2 M Spanish people, 3.2 M French peo-
ple, and 0.6 M from other nationalities. However, the telecom data allows us to
provide more detailed classification. By extracting the origin of the SIM card,
we identified visitors mainly from Belgium, The Netherlands, United Kingdom,
Italy, Norway, United States, and Germany.
At city scale, the two main, central cities of the country—Andorra la Vella
and Escaldes-Engordany—are explicitly displayed using Geographic Information
Systems (GIS) data (see map in Fig. 2). The rest of the territory is conceptually
represented by clusters, which correspond to the two cities located near the
border (i.e., Sant Julià de Lòria near the Spanish border and Pas de la Casa
near the French border) and the parishes of Canillo, Encamp, Ordino, and La
Massana (see pie charts in Fig. 2). The emerging structures show people’s flow
from one city to another giving a general view of the activity at a country level.
ABMs have successfully been applied to study emergences from a wide range
of adaptive system made of individual entities, contributing to an easier and
deeper understanding on local interactions, variability among entities, adaptive
behaviors, and environmental states [5]. Lately, ABMs have also been used as a
data visualization tool since they give the possibility to interact with the rep-
resentation [6]. In the presented model, dynamic agents have a set of variables
assigned that influence their behavior whenever a change occurs, either in its
own state (e.g., when the agent arrives at its destination, it stops) or in the
external environment (e.g., when a road is full, the agent can take an alternative
path). The set of variables is composed of (1) country of residence, (2) origin
location—defined randomly or using telecom data—, (3) preferred destination—
generated by a decision making submodule—, (4) distance traveled, (5) speed
of movement, and (6) passable streets. Agent’s trajectory is determined by an
Origin-Destination (OD) matrix. The OD matrix is computed using the location
of the cell towers where the action with the mobile phone was originated and
terminated. The destination of the agent is set to the closest amenity to the cell
tower where the action terminated. Depending on its speed (time difference be-
tween origin and destination location), the agent will be considered as a walking
person (solid circle) or as a vehicle (stroke circle). The model is implemented
with an enriched GIS data that provides information to the dynamics agents in
order to adapt their movement such as amenities’ capacity and working hours,
events, and direction of roads. Agents adapt themselves to both (1) congestion
traffic and (2) amenity occupancy.
Fig. 2. Andorra CityScope overview. The map provides a detailed city view whereas
the clusters provide an abstract view of the country. Each cluster grows according to
the number of users. The temporal evolution of the simulation is displayed hourly on
the slider and the volume of people is displayed by the histogram at the bottom right
corner.
Congestion traffic. If congestion is too high, a pathfinding is called to recal-
culate an alternative route. If a road is busy, then the agent will recompute the
shortest path to its destination avoiding this road.
Amenity occupancy. Once agents reach their destination, they stay there for
a few iterations. The number of iterations is defined by the average time spent on
those places. The amenity size increases (or decreases) according to the number
of agents currently in the location. Depending on the amenity occupancy, the
agent might recompute its destination. If the amenity assigned as destination
is full, the agent will select another amenity close to its initial destination. The
chosen amenity also depends on the agent’s country of residence and the language
affinity of the amenities.
5 Results
The emerging patterns display the actual dynamics of the city providing a urban
planning tool that goes beyond the traditional ones that are usually focused
on land uses and sociological static data extracted from surveys. The ABM
visualization shows different patterns of movements from visitors revealing the
structure of the city as a complex system.
The following subsections describe (1) raw and (2) aggregated results, which
highlight helpful information regarding Cirque du Soleil and Le Tour de France,
and refer to (3) online results.
5.1 Raw Results
When running a simulation on the Andorra CityScope table, one can immedi-
ately identify three main elements: (1) city representation defined by buildings,
amenities, cell towers and roads, (2) people’s movement defined by dynamics
agents, and (3) amenities’ density.
As mention in Section 4, the number of people that are present in the ameni-
ties evolves during the simulation. The amenity size increases (or decreases)
according to the number of agents currently in the location. This helps identify
which and when places are popular or busy and isolate them. For instance, Fig.
4 shows the activity during the Cirque du Soleil on July, 16. The wide white
circle spots the location where the show was taking place. This was the dens-
est place at that time; ticketing for the event was 5,174 attendees. According
to the statistics from the Andorra Turisme report, the average attendance per
performance was 4,540 people in 2016. Overlapping layers from different days
and/or editions of the event—while dynamically running them—is a useful way
to display and discuss the numbers.
Fig. 3. Simulation for the Cirque du Soleil on July, 16.
(a) (b)
Fig. 4. Aggregated results. (a) Instant congestion heatmap for a specific time during
Le Tour de France and (b) heatmap that summarizes the congestion for the whole day.
Fig. 5. Traffic congestion during the Cirque du Soleil on July, 16. From green to red,
the traffic congestion increases.
5.2 Aggregated Results
Differently from the Cirque du Soleil, Le Tour de France is an outdoor event and
there is no ticketing process to assess attendance. To this end, aggregated data
can be visualized on the CityScope table resulting in heatmaps that summarize
global activity in the city and provide an attendance estimate. Figures 4(a) and
4(b) show occupancy levels for Le Tour de France on July, 12. The starting line
was in Escaldes-Engordany, which corresponds to the hottest area in Fig. 4(b)—
large red concentration on the left side of the image. Comparing this kind of
visualization to the activity of a regular day, one can understand which events
bring more people to the country. In addition, we can identify where these visitors
go and what they do. This could be used to efficiently plan events or find new
ones that help spread visitors across the territory. Figure 6 shows congestion
levels. Focusing on the roads only, the movement of agents representing vehicles
can be translated into another view based on traffic density.
5.3 Online Results
A video displaying the ABM visualization of the Andorra CityScope Data Ob-
servatory is available at: https://youtu.be/fLikAuFvVyg
6 Discussion and Further Research
Data collected from wearable could lead to more accurate human behavior stud-
ies. However, CDR data can cover larger groups for longer periods of time in a
non-invasive way and studies can be easily scaled/replicate. CDR data only al-
low to trace visitors movement based on the cell towers geolocation. To improve
the accuracy of the ABM, Andorra Telecom is collecting a new source of data
from the Radio Network Controller that can now provide the geolocation of the
devices. The geolocation has an error of 50-100 m in urban areas and up to 200
m in rural areas. Besides, observations for this data source are triggered (1) by
any kind of action with the phone (phone call, text message, cellular data)—like
CDR data, but also (2) when the user moves and the network detects the device
changes cells or technology (i.e., 2G, 3G, 4G), or (3) when the user is static and
the update network timer expires.
Further work includes integrating data coming from sensors deployed in stores
to study how visitors move and behave inside buildings. This model will also be
used to replay agents’ behavior in order to understand the city dynamics and
lead to more efficient urban designs.
Acknowledgment
This work has been developed within the framework of collaboration between
MIT Media Lab City Science and Fundació ActuaTech.
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