ARTICLES
(85—102) | CZECH JOURNAL OF TOURISM 02 / 2019 | 85
Big Data: a Source of Mobility Behaviour
and a Strategic Tool for Destination Management
Filip Emmer / Andrea Holešinská
e-mail: holesinska@econ.muni.cz
Institute of Tourism, Faculty of Economics and Administration, Masaryk University, Brno, Czech Republic
Emmer, F., & Holešinská, A. (2020). Big Data: a Source of Mobility Behaviour and a Strategic Tool for Destination Management.
Czech Journal of Tourism, 8(2), 85–102. DOI: 10.2478/cjot-2019–0006.
Abstract
The abundant use of the Internet and mobile technologies while traveling leaves a digital footprint in the form of
big data that can be tracked. Big data bring information about spatial visitor behaviour that is valuable for strategic
destination management. Big data enrich not only scientific fields (e. g. management, marketing, or geography)
with their knowledge, but also represent the invention of new tools for their actual processing. Generally, big data
are considered as a strategic tool enhancing the competitiveness of a destination. The paper presents the basic
characteristics of big data and reviews research focused on big data in tourism. Moreover, it identifies its potential
for tourism from both the theoretical and methodological point of view. The final part deals with current trends in
using the big data in tourism and its application in destination management. The future trends of big data in the
context of destination management are implied as well.
Keywords
tourism, big data, mobility behaviour, destination management
JEL classification: C81, M15, Z32
ARTICLES
86 | CZECH JOURNAL OF TOURISM 02 / 2019 | (85—102)
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
Introduction
The long-term economic growth of most countries in the world and the associated trend
of increasing population wealth have enabled the development of mass tourism, in which
hundreds of millions of people participate every year. In combination with the massive
development of information and communication technologies (ICT) as described by
Kambatla, Kollias, Kumar, and Grama (2014), there have been significant changes in
the behaviour of tourism entities, not just the Business to customer segment, but also
Business to business and customer to customer (Kietzmann, Hermkens, McCarthy, &
Silvestre, 2011). The emergence of the Internet, which has been evident since the beginning
of the new millennium, has created a platform that facilitated communication and
accelerated a number of tourism-related activities (Li & Wang, 2011; Thevenot, 2007).
A good example is the recently created social networks that allow sharing of photo experiences,
as well as opinions and feedback, all in near real time. By their importance,
Xiang and Gretzel (2010) identify them as one of two megatrends having a strong impact
on tourism (the other being search engines). Support and information services are
equally important for tourism. Today, it is no longer necessary to leave the comfort of
your home or office when planning a vacation, travel agencies are not required to get
the best prices, and a person no longer has to rely on oral recommendations when booking
a hotel. Everything can be found in just a few minutes thanks to the Internet on the
website, whether destinations or comparison sites. Such a way of searching, interacting,
and subsequent decision-making processes leaves a considerable digital footprint that
provides a significant amount of data. Heerschap, Ortega, Priem, and Offermans (2014)
speak of the transition from “data scarcity to data abundance” to the growing volume
of data. Kambatla, Kollias Kumar, and Grama (2014) point to the volume of processed
information, which in the private business sector alone amounted to 9.57 × 1021
bytes
of data, and which was estimated to double every two years. It is this, if not the only,
characteristic that gave such data a name. These are big data.
Information is of great value on the market. They represent a competitive advantage.
Today, the source of information is big data that are generated by the fact that
“customers leave electronic traces during all travel-related activities” (Fuchs, Höpken, &
Lexhagen, 2014). This creates data on spatial tourist behaviour (i.e. mobility and tourist
decision-making process), which are important for the strategic management of the
destination, see Baggio and Scaglione (2018).
The aim of this paper is to identify the existing trends in the use of big data with an
emphasis on their use in the destination management area and to outline the possible
future trends in the use of big data for the purpose of destination management.
The questions are as follows:
ARTICLES
(85—102) | CZECH JOURNAL OF TOURISM 02 / 2019 | 87
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
Big data
The concept of big data may appear to be a relatively new term, unknown to the general
public. However, the essence of big data has been perceived indirectly since the origin
of the first computers and their generated outputs (Press, 2013). In the coming years,
the increasing capacity of the generated data began to be reflected in the effort of both
scientists and commercial companies, increasingly storing this data electronically, for
example, in the form of powerful compressors (Marron & Maine, 1967). At the beginning
of the millennium, it was clear that entities with large volumes of data faced new
challenges. Laney (2001) emphasized in particular the role of data management. However,
as Snijders, Matzat, and Reips (2012) point out, big data are defined very vaguely,
most often as “datasets so large and complex that they become awkward to work with
standard statistical software”. A narrower specification is offered by Laney (2001), who
characterized the properties of these emerging data sets and summarized them into the
3V concept (volume, velocity, variety), which is still the starting point for defining big
data. In the follow-up studies, several modifications of this concept appear, for example,
the 7V concept (Yu, Choi, Shin, & Ahn, 2014). For the purpose of this text, it is essential
to define big data using the 3V concept and selected sub-characteristics.
Big data characteristics
Volume
The basic feature of big data is its volume. It is created by the continuous generation
of data, mainly through various platforms operating on the Internet - the so-called
Internet of Things (Botta, de Donato, Persico, & Pescapé, 2016), which nowadays interconnects
an increasing number of devices of everyday life and thus forms a connected
network, through which it exponentially increases the data obtained (Gubbi, Buya,
Marusic, & Palaniswani, 2013). The volumes of the data generated are doubled every
two years and their capacity is estimated to reach 44 zettabytes, equivalent to 1 billion
terabytes (EMC, 2014). Such an increase in data was predicted at the beginning of the
millennium, as data storage costs gradually decreased, while communication through
electronic channels was made easier and cheaper, thus reducing transaction costs for
data forwarding as such (Laney, 2001). The creation of increasingly large amounts of
data is also related to the behaviour of businesses that have identified the importance
of information collected from the data that can significantly affect their market behaviour
and maximize profit, leading to ongoing data retention, including legacy data.
However, this long-term trend of data retention shows a declining marginal benefit
from each additional data point stored (Laney, 2001). As the marginal utility decreases,
the so-called Digital Universe Paradox can be observed. Although the cost of the data
ARTICLES
88 | CZECH JOURNAL OF TOURISM 02 / 2019 | (85—102)
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
storage and transmission has fallen sharply over time, companies are investing more
and more in data management tools.
In the current research of big data in the field of tourism one can observe the irreplaceable
role of this factor. Vu, Li, Law, and Ye (2015) work with a sample of 29,443
photos from Hong Kong, Phillander and Zhong (2016) in its sentimental analysis based
on a dataset of 31,550 samples covering more than 30 hotels, Olmedo, Goméz, Palomares,
and Gutiérrez (2018) are based on a comparison of big data sources from a sample
of 307,062 geo-located photos and 234,159 tweets from Twitter related to landmarks
in Madrid.
The challenge for data management, which destination management has to cope with
limited resources and ever-increasing volumes, may be another characteristic of big data.
This second V is variety. The very nature of big data, consisting of a large volume, also
makes it impossible to use some standard or classical statistical methods for calculations,
which can make it more difficult to use them.
Variety
The second characteristic of big data can be understood in several levels. The first is the
variety of big data sources. This article is based on the distribution reported by Li, Xu,
Tang, Wang, and Li (2018), which differentiates data according to the following three
sources of their origin. The first are the data generated by users themselves (typically
Twitter reviews, blogs, Instagram photos), otherwise referred to as user-generated data
(UGC data), data generated by devices. For the purpose of tourism, the important are
especially the data from mobile phones or sim cards, data from navigation and other
devices using GPS data and transaction data, whether on-line booking, credit or debit
card payments and cash withdrawals, all of which are the source of the so-called transaction
data (Xiang, Du, Ma, & Fan, 2017). The second level of perception of the variety
of the data obtained is based on their sources; it is the very form in which the data is
organized. The UGC data have probably the greatest variety from these sources. Photographs,
which offer a source of information both from the place of acquisition as well as
from their description or information about the author, can be processed, reviews and
tourists’ evaluation have a textual form, which is usually subjected to a sentimental or
lexical analysis. A lot of useful information and data are also increasingly obtained from
Youtube videos.
However, the variety of data also entails a variety of difficulties in obtaining it. East,
Osborne, Kemp and Woodfine (2017) used GPS locators to map the movement of visitors
to the Marwell Zoo in the UK. They state that “the accuracy of the GPS units was
affected by the buildings that the participants entered and by the dense tree cover. Accuracy
in the more open areas of the park was typically ± 5 m directly adjacent to the
woodland accuracy in the region of ± 8e10 m”. Despite these and other problems with
GPS data, research has made it possible to identify very precisely the density of the visitor
stream, which also varied over time. As the authors mention, “This bunching and
ARTICLES
(85—102) | CZECH JOURNAL OF TOURISM 02 / 2019 | 89
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
peaking in visitor numbers can be of benefit to the institution as it allows visitor presentations
to be timed to coincide with the peak in visitor numbers at a given location”.
Philander and Zhong (2016) as one of the main limits of social network data (specifically
Twitter) report that their “Analysis is limited to the extent that social media platform
owners are willing to share their data, or that data can be legally mined”. At the same
time, “Twitter API provides limited search capacity regarding returned data and time
frame”. Such output cannot be reliably provided by a classical questionnaire survey. The
vast majority of this information and data is generated in hours and sometimes minutes,
thus defining the third basic characteristic of big data, which is velocity.
Velocity
The velocity of data primarily refers to the rate at which data are generated. This velocity
is determined by the source platform, i.e. online data sources and sensors, but for some
types of data also by the immediacy of their profit and the absence of interaction with the
people who directly produce the data. This principle can be presented on the difference
of collecting data about the movement of tourists in the destination, for which the data
from mobile phones can be used, but the same problem can be investigated by a questionnaire
survey. While a significant amount of time is needed to collect data from individual
respondents in terms of data generation velocity, the data from mobile phones, GPS and
sim cards are generated almost immediately. Heerschap, Ortega Priem, and Offermans
(2014) point out the advantages of mobile data more specifically: “It is possible to, for example,
zoom in on small specific tourist areas and very short time frames, such as certain
events, coastal areas, or national holidays; to distinguish data per country of residence; or
determine the number of transit passengers. Kitchin (2013) highlights the automatically
generated data and its velocity:” Such systems include immigration passport control where
passenger details are collected and checked against various databases in real-time, and
new data are generated such as CCTV, photographs, fingerprints or iris scans”. Especially
the real-time data creation and processing are often crucial for economic operators. The
ability to respond flexibly to market developments makes it possible to better exploit opportunities
or respond to the changes in demand trends (Che, Safran, & Peng, 2013).
Veracity
The findings of research studies indicate the “uncertainty” of the truth of the output
from the big data analysis (Lukoianova & Rubin, 2014). This uncertainty is determined
by the origin, analytical tools and methodology used to process big data (Lukoianova &
Rubin, 2014). The reliability of the results may also be distorted by the incompleteness of
the data in terms of population representation, as the demographically older part of the
population may not be sufficiently represented, if at all, in the data. Vaynerchuk (2013)
points out that big data obtained from social networks may not be complete because
“twitter users skew towards younger and urban groups”.
ARTICLES
90 | CZECH JOURNAL OF TOURISM 02 / 2019 | (85—102)
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
Vulnerability
Many big data sources are based on personal information, typically the data from mobile
applications, sim cards and payment cards. There have been a number of cyber-attacks
in the recent times aimed at stealing email addresses and accounts on Linkedln (The
Guardian, 2012). The issue of the security of such data appears to be one of the key
issues in tourism, given the recent events, such as the terrorist attacks in France and
Belgium, which could otherwise be targeted with more serious consequences if sensitive
data were used in the future. A double-edged weapon may also be the ignorance of
tourists themselves about the collection and evaluation of these data. The existing data
is “most of the time based on information recalled by the interviewees” (Baggio & Scaglione,
2017), allowing the respondent the right not to answer. On the other hand, some
forms of big data are obtained entirely independently of tourist awareness and, although
the data are anonymised in the vast majority of cases, they potentially pose a sensitive
personal and security risk.
Big data resources
In relation to the variety of big data, a diversity of potential data sources is essential
for destination management as well as research on big data. Li, Xu, Tang, Wang, and
Li (2018) divide them into three categories, the data generated by the user himself, the
data automatically generated by machines or chips, and finally, the data generated by the
transactions (consult Figure 1 below).
Figure 1 Big data sources used for tourism research purposes and their representation
Source: Li, Xu, Tang, Wang, & Li (2018)
ARTICLES
(85—102) | CZECH JOURNAL OF TOURISM 02 / 2019 | 91
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
UGC data
The user-generated content (UGC) data have gained in importance due to the boom
of social networks. These enable content to be distributed at high speed and with wide
reach. This source can be divided into the online text data, which consist mainly of opinions
and reviews, and the online photo data.
On-line text data
Opinions and reviews of tourism participants form a valuable source for quality management,
identification of opportunities in terms of implementation prerequisites, but also
in identifying the key stakeholders in the destination. Research on the online text data
is conducted by Olmedo, Gómez, Palomares, and Gutiérrez (2018), who compare different
sources of big data in tracking and monitoring the activities of Madrid travellers,
one of which is geo-localized social networking on Twitter. This social network is suitable
for a data collection and analysis because of its relative openness in providing access to
APIs (application programming interfaces). This is also used by Phillander and Zhong
(2016), who, using a sentimental analysis, quantify and evaluate the customer experience
of “tweeting” tourists staying in hotel establishments in Las Vegas. The benefit of this
method is that the sentimental analysis “can provide a quantifiable measure of real-time
marketing campaign impacts complementary to other traditional research methods”. Ye,
Law, Gu, and Chen (2011) reviewed the impact of reviews on travel sales to a destination.
By analysing contributions to the Chinese reservation portal ctrip.com, where only
those customers who have made and paid an order can write their experience of quality
of service.
On-line photo data
With massive web development 2.0 and the emergence of online photo albums, blogs
and social networks, the reach and potential audience that can see these photos has
also increased (Boyd, 2007). This audience then also creates an image, preferences and
motivation about the destination to visit or not to visit. Many potential tourists even
consider such sources of information more relevant than the officially published documents
and leaflets that are usually used by the Tourism Headquarters and DMOs (Mack,
Blose, & Pan, 2008; Xiang & Gretzel, 2010). Currently, the most used platform for big
data is Flickr. Flickr is a video and photo hosting service that has over 75 million active
users worldwide (www.flickr.com/jobs) who upload over 1.6 million photos daily (www.
flickr.com/photos). Lo, McKercher, Lo, Cheung, and Law (2011) have shown that 89%
of Hong Kong residents take photos on their recreational trips, with 41% sharing them
online and 40% posting them directly to Flickr or a similar website. In research, however,
they encounter one of the typical big data problems when they point out that “At
present, a large demographic gap exists in both the profile and importance of travel
between those who do and do not post their photographs online. The gap will likely
become more acute as online photo-sharing media continue to gain in popularity with
a larger share of the population.” The Flickr data allow to accurately locate where the
ARTICLES
92 | CZECH JOURNAL OF TOURISM 02 / 2019 | (85—102)
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
tourist was. If s/he uploaded more than one photo, it is possible to determine what
her/his movement in the destination looked like based on time delays and geolocation.
Kurashima, Iwata, Irie, and Fujimura (2010), for example, have developed a system of referrals
for places to visit, based only on tracking and evaluating photos from Flickr. This
system calculates the likelihood of a site visit, taking into account the user’s preferences
and current location. Olmedo, Gómez, Palomares, and Gutiérrez (2018) use another
Panoramio photo platform in their comparison of big data sources, but this is no longer
available today. Instagram, currently the largest photo and video sharing platform, also
embodies high potential, but it provides almost no data files for its security policy.
Device data
This kind of data originated with the massive development of the so-called Internet of
Things, i.e. the devices that are constantly connected to the Internet. In combination
with many types of sensors and sensors available to these devices, the devices belonging
to the Internet of Things category offer potential, especially in mapping movement
and spatial habits. The main technologies used to make such data types include GPS,
bluetooth and WIFI, as well as the RFID technologies, working on the principle of radio
frequency waves (Derakhshan, Orlowska, & Li, 2007).
GPS data
These data are significant compared to the previous types, especially the number of
users who generate them, knowingly or unknowingly, and because they are obtained directly
in the destination and given space. In particular, two approaches are used for the
collection of such data types. The first is the data collection directly from mobile phone
applications that use location services. This approach is particularly suitable for larger
areas and for analysing a larger number of tourists. The second approach mentioned by
Orellana, Bregt, Ligtenberg, and Wachowicz (2012) is the distribution of GPS tracking
devices, for example, at the entrances of fun parks or monuments. Using these monitoring
devices, the movements of tourists in the area are subsequently monitored and
evaluated. Zheng, Huang, and Li (2017) use the GPS technology to collect and evaluate
data in the Beijing Summer Palace area. These data are used to predict spatial behaviour
and distribution of tourists in the destination. Tchetchik, Fleischer, and Shoval (2009)
use the GPS data in combination with a questionnaire survey to segment tourists in the
historic Acre area.
Mobile data
Similar to the GPS data, information about the movement of sim cards and mobile
devices allows very precise observation of the movement of tourists. Compared to the
GPS data, however, they can originate in two, very different ways. These data are divided
into two types. The first is the so-called Log data, i.e. the data obtained directly from
the subject’s mobile phone through an application installed on the device. At 5-minute
ARTICLES
(85—102) | CZECH JOURNAL OF TOURISM 02 / 2019 | 93
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
intervals, such an application sends information about the location and time and all the
operations that the mobile phone has performed during this time period (Heerschap,
Ortega, Priem, & Offermans, 2014). The second type of mobile data are the anonymous
data based on call details (Call detail records). These data are generated by telecommunications
service providers. Every time a mobile device contacts an operator (SMS,
phone, or data download), a record of the location and time where the interaction
took place is created. The applicability of the analysed data has been demonstrated by
Heerschap, Ortega, Priem, and Offermans (2014), while highlighting the limits and challenges
for further development, for example, “results of measurements based on the big
data sources also require new methods of data visualisation, such as geo-location maps
with time indicators. The challenge here is to produce visualisations that can be clearly
underwood and interpreted by users, such as policy-makers.”
Bluetooth data
A wide subset of mobile data are the data related to signal transmission. These include
Wifi data and Bluetooth data. The visitor behaviour in Ghent using the bluetooth data
was examined by Versichele, de Groote, Bouuaert, Neutens, Moerman, and Van de
Weghe (2014).
Transaction data
The transaction data can be divided into the data used for online payment and search
strategies and the data generated by using credit and gift cards and payments directly
at the destination. As reported by Jansen and Molina (2006), a search query analysis
(the case of the first data source) makes it possible to determine the user’s strategy for
obtaining information, typically search intent, search depth, or keywords used by the
user. Wöber (2007) divided the metropolis into two types by analysing searches related
to European cities. The first group (Madrid, Budapest, Prague, Nice, or Rotterdam) was
dominated by searches for guided tours, operas, museums, and art. While in the second
group (Heidelberg, Talinn, Copenhagen, Lyon), questions related to the attractions and
events prevailed. Similar data sources include the data from reservation systems used
by Saito, Takahashi, and Tsuda (2016) to analyse customer behaviour based on the data
obtained from the reservation system. The default dates are the booking data for four
hotels in Kyoto. The data include the number of bookings, the prices of individual bookings
and their booking dates. The results of this research were used in the following
years to predict the long-term development of consumer behaviour. In contrast, Shih,
Nicholls, and Holecek (2009), for example, used data from eight consecutive years,
based on sales of ski passports in the Michigan area (physical sales data) to examine the
effect of weather changes on sales.
On the contrary, Weaver (2008) demonstrates usefulness of loyalty and gift cards:
“The portability of these cards makes them a convenient companion to mobile consumers.
For example, reward cards enable a hotel chain to collect data from consumers even
ARTICLES
94 | CZECH JOURNAL OF TOURISM 02 / 2019 | (85—102)
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
if they stay at a number of its properties across an entire country or in several countries.
Preferences expressed by hotel customers can be recorded and stored within a centralized
database.”
Big data in tourism
Baggio and Scaglione (2018) clearly describe a shift from the traditional data to big data.
At the beginning, there were traditional methods of data collection (surveys or opinion
polls). Baggio (2016) called them small data approaches. With the development of social
media, the data generated via geo-tagged photos, the so-called volunteered geographical
information started to be widely used (Baggio & Scaglione, 2018). Another data source
that has increased its importance due to business intelligence (Höpken & Fuchs, 2016),
are the transaction data linked to destination guest cards. Many researchers use the data
generated by global positioning systems (GPS). Nowadays, a frequently used data source
include passive mobile phone positioning data.
Table 1 Big data sources and research cases
Data source categories
by Li, Xu, Tang, Wang,
and Li (2018)
Data source categories
by Baggio and
Scaglione (2018)
Cases
- Small data approaches
(surveys/opinion polls)
Hwang, Gretzel, and
Fesenmaier (2006)
Fuchs, Höpken, and Lexhagen
(2014)
Haarshap, Ortega, Priem, and
Offermans (2014)
Sabou, Onder, Brasoveanu,
and Scharl (2016)
User-generated
content data
— On-line photo data
Volunteered geographical
information (geo-tagged
photos)
Zach and Gretzel (2011)
Kádár and Gede (2013)
Vu, Li, Law, and Ye (2015)
Stienmetz and Fesenmaier
(2016)
— On-line text data Philander and Zhong (2014)
Ye, Law, Gu, and Chen (2011)
Transaction data Destination guest cards Fuch, Abadzhiev, Svensson,
Höpken, and Lexhagen (2013)
Höpken, Fuchs, and Lexhagen
(2013)
Zoltan and McKercher (2015)
Holland, Jacobs, and Klein
(2016)
ARTICLES
(85—102) | CZECH JOURNAL OF TOURISM 02 / 2019 | 95
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
Device data
GPS data
GPS Shovel and Isaacson (2007)
Zheng, Huang, and Li (2017)
East, Osborne, Kemp, and
Woodfine (2017)
Mobile data Passive mobile phone
positioning data
Haarschap, Orlega, Priem, and
Offermans (2014)
Baggio and Scaglione (2018)
Saluveer, Ruan, Tiru, Altin,
Kroon, Snitsarenko, Aasa, and
Silm (2020)
Source: own processing
Big data offer potentially great benefits. Höpken, Ernesti, Fuchs, Kronenberg, and
Lexhagen (2017) see it in the prediction of arrivals. Therefore, it is necessary to examine
the spatial behaviour of tourists (Olmedo, Gómez, Palomares, & Gutiérrez, 2018).
It is possible to avoid (overtourism), a large concentration of demand to a few points
of interest in the destination. Big data also allow the issue of shared housing, which has
significant socio-economic impacts on residents in the destination (Zheng, Huang, & Li,
2017).
Baggio and Scaglione (2018) highlight the use of big data to interpret visitor flows and
use this information for strategic destination management. Destination management information
systems (DMIS) are important for effective planning, leading to a reduction in
information asymmetry among tourism stakeholders (Fuchs, Abadzhiev, Svensson, Höpken,
& Lexhagen, 2013). Knowledge of information about customer behaviour thus contributes
to the competitiveness of the destination. Big data are important for destination
management organizations (DMOs), but also for tourism stakeholders from both private
and public sectors as they enter DMIS. According to Fuchs, Abadzhiev, Svensson, Höpken,
and Lexhagen (2013) “individual stakeholders may use data for improving their own
activities somewhat independently of others, their relationship is more interdependent
at the strategic destination level, what calls for more integrated and coordinated knowledge
application processes”. Baggio and Scaglione (2017) point out that “from the point
of governance, …. the mobile data network analysis would be perceived by stakeholders
as more objective than experts opinion panels”.
The benefit of using big data is undoubtedly knowledge about the visitor’s mobility
and decision-making behaviour, and thus the possibility to better influence or (adapt)
to her/his needs, i.e. to better (more effectively) target and compose the offer, and at
the same time the possibility to affect the visitor’s behaviour in the destination with respect
to the use of recreational resources. Thus, the benefits can be seen both in terms
of competitiveness (access to information and more detailed visitor knowledge) and
sustainability (more targeted visitor management), see the model of the competitive destination
Ritchie and Crouch (2006). In general, big data have the potential to streamline
the planning process.
ARTICLES
96 | CZECH JOURNAL OF TOURISM 02 / 2019 | (85—102)
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
Big data and methodology
Most authors (e.g. Philander & Zhong, 2016; Heerschap, Ortega, Priem, & Offermans,
2014) agree that big data make it possible to improve or refine classic statistical outputs,
enriching them with information that can be specific to DMO and decision-making
processes, such as information on consumer preferences, more detailed tourist flows, or
potential stakeholders based on user reviews.
This knowledge contributes to strategic planning as a part of the management mechanism.
The implication of big data extends the background of knowledge-based destination,
business intelligence, organizational learning (networks), destination management
(mobility behaviour), visitor management (spatial distribution), or marketing (decisionmaking
process). For instance, Beritelli (2019) uses big data for the description of visitor
flows and consequently, for the inference of the lifecycle of destination.
From the methodology point of view, the review of quoted studies about big data
shows that the most frequently used research methods for data processing are coding,
classification, and clustering (see Table 2). It is mostly processing of the integrated
(both qualitative and quantitative) data from different sources, e.g. external data (web
content), unstructured data (customer reviews), and data in real-time. Therefore, it is
necessary to use research tools such as data mining software.
Table 2 An overview of frequently used tools and methods
Author Tools Research methods Object of research
Önder (2017) Classification Multi-destination trips
Sabou, Onder,
Brasoveanu, and
Scharl (2016)
ETIHQ Dashboard (data
analytics system)
semantic web
technologies
ontologies applied
encoding
organizational learning at
tourism destinations
Fuchs, Abadzhiev,
Svensson, Höpken,
and Lexhagen (2013)
business intelligence
system - data mining
software
(DMIS)
Classification,
Clustering
Customer behaviour
in all trip phases (new
knowledge)
Francalanci and
Hussain (2015)
Clustering,
Network analysis
(visualization)
multi-layered clusters of
nodes surrounding hub
nodes
Sun, Wei, Tsui, and
Wang (2019)
Search query data;
Kernel-based extreme
learning machines
Indexing,
Generalization,
Forecast tourism
demand (arrivals)
Source: own processing
No matter how rich big data are, there are certain limits. Big data cannot replace
classic statistical sources and cannot provide results that would satisfy DMO in broadspectrum
decision-making in the light of the classic DMO roles (Philander & Zhong,
ARTICLES
(85—102) | CZECH JOURNAL OF TOURISM 02 / 2019 | 97
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
2016; Heerschap, Ortega, Priem, & Offermans, 2014). One of the reasons is the cost of
obtaining these data and the amount of big data sources that would be necessary for
such a comprehensive analysis.
Phillander and Zhong (2016) describe other limits of the use of big data. One of them
is a sample of the population that generates the data. Although modern technologies
have spread across the whole of society, their normal use is not evenly distributed across
the age spectrum. An obvious example is those in the old-age pensioner category who
travel frequently, but their footprint may not be recorded at all. Another important
problem mentioned by these authors is the ethical problem since big data allow the collection
of highly sensitive data that could be misused very easily in case of leakage.
Conclusion
Using big data to visualize visitor behaviour in a destination is crucial for the strategic
development of the destination. They provide information about visitor mobility within
the destination and at the same time identify the most visited sites.
Probably the most widespread source of big data are the Mobile Positioning Data,
which enable to obtain better cross-border statistics on the movement of tourists. Their
main asset, however, is the excellent coverage in the collection of data on multi-day stays,
as they make it possible to describe ‘non-registered’ or ‘non-paid’ overnight stays. This
can also be used to map the Airbnb phenomenon and, in combination with the data on
housing price developments, this information can serve to better target measures and
possibly regulate the phenomenon of shared housing.
The potential of big data may conceal the facilitation of solving many problems in relation
to the supply and demand of tourism, but it should be considered that “progress
and discoveries eliminate many obstacles, but at the same time put new obstacles and
threats” (Bastiat, 2015). In the case of big data, this is particularly the question of privacy
and the potential misuse of this, often very sensitive, information.
The massive use of big data in the IT industry has only begun in recent years. For the
time being, these alternative data sources are used only marginally in tourism. There
is still a strong dependence on the collection and evaluation of data from national or
international institutions. However, this does not mean that current research neglects
this area, on the contrary. As this article shows, the medium-term trend of exploring big
data is growing.
Although the use of big data for destination management has been researched only
by a small number of authors, the potential in this area is quite evident. The variety of
big data, which is one of the basic characteristics of these data, allows the data to form
a kind of complementary relationship with one data source to quantify a certain phenomenon
in a destination (for example, travel around tourist attractions) and another
source enables to explore the qualitative aspects of the same (for instance, feelings of
destination through the sentimental analysis of social data). Such a comprehensive picture
of all activities in the destination would give destination management organisations
ARTICLES
98 | CZECH JOURNAL OF TOURISM 02 / 2019 | (85—102)
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
(DMOs) a whole new dimension of interaction with stakeholders in the destination in
the context of the 3C (cooperation-coordination-communication) principle (Holešinská,
2012).
Although big data is of great value for regional and local DMOs because they enable
a high level of accuracy of information, at present big data are used rather at macroregional
level. This is due to the lack of staffing and financial capacity required for data
mining or data purchasing and analysis. In such an environment, it is therefore natural
that there is a trend to use big data as a refinement of one particular monitored phenomenon
in the destination.
The future development of big data use can be illustrated by the example of the
portal of the Swiss national project Grandtour (https://grandtour.myswitzerland.com/
en/), which among other things allows users to share experiences and connects them
on social networks with a widely used hashtag. The set of photos and their descriptions
marked with this hashtag allows to analyse the places visited, the visitor’s opinion, their
emotions, but also the extent that such a contribution may have.
From the economic point of view, however, the current trend of using big data is
evident. Due to the high purchase price compared to other data sources, big data are
nowadays a luxury property for many stakeholders in tourism. But what is the luxury of
today, is the necessity of tomorrow (Von Mises, 1998). Big data were generated as a result
of advances in the field of profit, processing and evaluation of data from the most
modern devices using the internet, mobile or other kind of signal and it will take some
time, if any, to establish themselves in the tourism environment. However, if their value
is proven, they can be widely used.
In this respect, tourism policy should be involved by supporting digitization activities
while creating the right conditions for using big data for strategic destination management.
For DMO, working with big data in the future is crucial, and even strategic.
Acknowledgment
This article was supported by the project New Mobility – High-Speed Transport Systems and Transport-Related
Human Behaviour, reg. number CZ.02.1.01/0.0/0.0/16_026/0008430, code for CEP (EF16_026/0008430) at
Masaryk University in Brno.
References
Baggio, R., & Scaglione, M. (2017). Strategic Visitor Flows (SVF) Analysis Using Mobile Data. In
Schegg, R., Stangl, B. (Eds.), Information and Communication Technologies in Tourism 2017 (pp.
145–157). Cham: Springer. DOI: 10.1007/978–3-319–51168–9_11.
Baggio, R., & Scaglione, M. (2018). Strategic visitor flows and destination management organization.
Information Technology & Tourism, 18, 29–42. DOI: 10.1007/s40558–017–0096–1.
Baggio, R. (2016). Big data, business intelligence and tourism: a brief analysis of the literature.
In Fuchs, M., Lexhagen, M., Höpken, W. (Eds.), IFITT workshop on the big data and business
ARTICLES
(85—102) | CZECH JOURNAL OF TOURISM 02 / 2019 | 99
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
intelligence in the travel and tourism domain. European Tourism Research Institute (ETOUR).
Östersund: Mid-Sweden University.
Bastiat, C. F. (2015). Petice za zákaz slunce a jiné absurdity ekonomie [Petitions for the prohibition of
the sun and other absurdities of economics]. Prague, Czech Republic: Mises.cz.
Beritelli, P. (2019). Transferring concepts and tools from other fields to the tourist destination:
A critical viewpoint focusing on the lifecycle concept. Journal of Destination Marketing &
Management, 14. DOI: 10.1016/j.jdmm.2019.100384.
Botta, A., de Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and
internet of things: a survey. Future Generation Computer System, 56(C), 684–700. DOI: 10.1016/j.
future.2015.09.021.
Boyd, D. (2007). Why Youth Social Network Sites: The Role of Networked Publics in Teenage
Social Life. In Buckingham, D. (Ed.), Youth, Identity, and Digital Media, Beckman Center Research
Publication No. 2007–16. Cambridge, USA: The MIT Press. Retrieved from https://ssrn.com/
abstract=1518924.
Che, D., Safran, M., & Peng, Z. (2013). From Big Data to Big Data Mining: Challenges, Issues,
and Opportunities. In Hong, B., Meng, X., Chen, L., Winiwarter, W., Song, W. (Eds.), DASFAA
2013: Database Systems for Advanced Applications, vol. 7827 (pp. 1–15). Verlag/Berlin/Heidelberg:
Springer. DOI: 10.1007/978–3-642–40270–8_1.
Derakhshan, R., Orlowska, M. E., & Li, X. (2007). RFID Data Management: Challenges and
Opportunities. In 2007 IEEE International Conference on RFID, March 26–28 (pp. 175–182).
Grapevine, USA: IEEE. DOI: 10.1109/RFID.2007.346166.
East, D., Osborne, P., Kemp, S., & Woodfine, T. (2017). Combining GPS & survey data improves
understanding of visitor behaviour. Tourism Management, 61, 307–320. DOI: 10.1016/j.
tourman.2017.02.021.
EMC Digital Universe with Research & Analysis. (2014). Executive Summary: Data Growth,
Business Opportunities, and the IT Imperatives. The Digital Universe of Opportunities: Rich Data
and the Increasing Value of the Internet of Things [online]. Retrieved from https://www.emc.com/
leadership/digital-universe/2014iview/executive-summary.htm.
Francalanci, C., & Hussain, A. (2015). Discovering social influencers with network visualization:
evidence from the tourism domain. Information and Communication Technologies in Tourism, 16,
103–125. DOI: 10.1007/s40558–015–0030–3.
Fuchs, M., Abadzhiev, A., Svensson, B., Höpken, W., & Lexhagen, M. (2013). A knowledge
destination framework for tourism sustainability: A business intelligence application from
Sweden. Tourism, 61(2), 121–148.
Fuchs, M., Höpken, W., & Lexhagen, M. (2014). Big data analytics for knowledge generation in
tourism destinations – A case from Sweden. Journal of Destination Marketing & Management, 3,
198–209. DOI: 10.1016/j.jdmm.2014.08.002.
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision,
architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–
1660. DOI: 10.1016/j.future.2013.01.010.
Heerschap, N., Ortega, S., Priem, A., & Offermans, M. (2014). Innovation of tourism statistics
through the use of new big data sources. In 12th World Telecommunication/ICT Indicators Symposium
(WTIS-14), Tbilisi, Georgia, 24–26 November 2014. ITU: Geneva, Switzerland. 12p. Retrieved from
https://www.itu.int/en/ITU-D/Statistics/Documents/events/wtis2014/002INF-E.pdf.
Holešinská, A. (2012). Destinační management jako nástroj regionální politiky cestovního ruchu (Destination
management as a tool for regional tourism policy). Brno, Czech Republic: Masaryk University.
ARTICLES
100 | CZECH JOURNAL OF TOURISM 02 / 2019 | (85—102)
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
Holland, C. P., Jacobs, J. A., & Klein, S. (2016). The role and impact of comparison websites on
the consumer search process in the US and German airline markets. Information Technology and
Communication in Tourism, 16, 127–148. DOI: 10.1007/s40558–015–0037–9.
Höpken W., Ernesti D., Fuchs M., Kronenberg K., & Lexhagen M. (2017). Big Data as Input
for Predicting Tourist Arrivals. In Schegg R., Stangl B. (Eds.), Information and Communication
Technologies in Tourism 2017 (pp. 187–199). Cham: Springer. DOI: 10.1007/978–3-319–51168–
9_14.
Höpken, W., & Fuchs (2016). Introduction: Special Issue on Business intelligence and big data in
the travel and tourism domain. Information and Communication Technologies in Tourism, 16, 1–4.
DOI: 10.1007/s40558–016–0054–3.
Höpken, W., Fuchs, M., & Lexhagen, M. (2013). The knowledge destination – applying methods
of business intelligence to tourism. In Wang, J. (Ed.), Encyclopedia of Business Analytics and
Optimization. Pennsylvania: IGI Global Publisher.
Hwang, Y. H., Gretzel, U., & Fesenmaier, D. R. (2006). Multicity trip patterns: Tourists to the
United States. Annals of Tourism Research, 33(4), 1057–1078. DOI: 10.1016/j.annals.2006.04.004.
Jansen, B. J., & Molina, P. R. (2006). The effectiveness of Web search engines for retrieving
relevant ecommerce links. Information Processing & Management, 42, 1075–1098. DOI: 10.1016/j.
ipm.2005.09.003.
distribution of geotagged photography. The International Journal for Geographic Information and
Geovisualization, 48(2), 78–88.
Kambatla, K., Kollias, G., Kumar, V., & Grama. A. (2014). Trends in big data analytics. Journal of
Parallel and Distributed Computing, 74(7), 2561–2573 DOI: 10.1016/j.jpdc.2014.01.003.
serious! Understanding the functional building blocks of social media. Business Horizons, 54(3),
241–251. DOI: 10.1016/j.bushor.2011.01.005.
Kitchin, R. (2013). Big data and human geography: Opportunities, challenges and risks. Dialogues
in Human Geography, 3(3), 262–267. DOI: 10.1177/2043820613513388.
Kurashima, T., Iwata, T., Irie, G., & Fujimura, K. (2010). Travel route recommendation using
geotags in photo sharing sites. In CIKM ’10: Proceedings of the 19th ACM international konference
on Information and knowledge management, October 26–30 (pp. 579–588). Toronto, Canada:
ACM. DOI: 10.1145/1871437.1871513.
Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. In Application
Delivery Strategies, File 949. Stamford, USA: META Group Inc. Retrieved from https://
blogs.gartner.com/doug-laney/files/2012/01/ad949–3D-Data-Management-Controlling-Data-
Volume-Velocity-and-Variety.pdf.
Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review.
Tourism Management, 68(October), 301–323. DOI: 10.1016/j.tourman.2018.03.009.
Li, X., & Wang, Y. C. (2011). China in the eyes of western travelers as represented in travel blogs.
Journal o Travel & Tourism Marketing, 28(7), 689–719. DOI: 10.1080/10548408.2011.615245.
Lo, I. S., McKercher, B., Lo, A., Cheung, C., & Law, R. (2011). Tourism and online photography.
Tourism Management, 32(4), 725–731. DOI: 10.1016/j.tourman.2010.06.001.
Lukoianova, T., & Rubin, V. L. (2014). Veracity Roadmap: Is Big Data Objective, Truthful and
Advances In Classification Research Online, 1(1), 1–13. DOI: 10.7152/acro.v24i1.14671.
Mack, R. W., Blose, J. E., & Pan, B. (2008). Believe it or not: Credibility of blogs in tourism. Journal
of Vacation Marketing, 14(2), 133–144. DOI: 10.1177/1356766707087521.
ARTICLES
(85—102) | CZECH JOURNAL OF TOURISM 02 / 2019 | 101
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
Marron, B. A., & de Maine, P. A. D. (1967). Automatic data compression. Communications of the
ACM, 10(11), 711–715. DOI: 10.1145/363790.363813.
Olmedo, M. H. S., Gómez, B. M., Palomares, J. C. G., & Gutiérrez, J. (2018). Tourists’ digital
footprint in cities: Comparing Big Data sources. Tourism Management, 66, 13–25. DOI: 10.1016/j.
tourman.2017.11.001.
Önder, I. (2017). Classifying multi-destination trips in Austria with big data. Tourism Management
Perspectives, 21, 54–58. DOI: 10.1016/j.tmp.2016.11.002.
Orellana, D., Bregt, A. K., Ligtenberg, A., & Wachowicz, M. (2012). Exploring visitor movement
patterns in natural recreational areas. Tourism Management, 33(3), 672–682. DOI: 10.1016/j.
tourman.2011.07.010.
Philander, K., & Zhong, Y. Y. (2016). Twitter sentiment analysis: Capturing sentiment from
integrated resort tweets. International Journal of Hospitality Management, 55, 16–24. DOI:
10.1016/j.ijhm.2016.02.001.
Press, G. (2013). A Very Short History of Big Data [online]. Forbes, May. Retrieved from https://
www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/#2549c2f865a1.
Ritchie, J. B., & Crouch, G. I. (2006). The competitive destination: a sustainable tourism perspective.
Wallingford: CABI Publishing.
Sabou, M., Onder, I., Brasoveanu, A. M. P., & Scharl, A. (2016). Towards cross-domain data
analytics in tourism: a linked data based approach. Information and Communication Technologies
in Tourism, 16, 71–101. DOI: 10.1007/s40558–015–0049–5.
Saluveer, E., Raun, J., Tiru, M., Altin, L., Kroon, J., Snitsarenko, T., Aasa, A., & Silm, S. (2020).
Methodological framework for producing national tourism statistics from mobile positioning
data. Annals of Tourism Research, 81, 102895. DOI: 10.1016/j.annals.2020.102895.
Shih, C., Nicholls, S., & Holecek, D. F. (2008). Impact of Weather on Downhill Ski Lift Ticket Sales.
Journal of Travel Research, 47(3), 359–372. DOI: 10.1177/0047287508321207.
Shoval, N., & Isaacson, M. (2007). Tracking tourists in the digital age. Annals of Tourism Research,
34(1), 141–159. DOI: 10.1016/j.annals.2006.07.007.
Siato, T., Takahashi, A., & Tsuda, H. (2016). Optimal room charge and expected sales under
discrete choice models with limited capacity. International Journal of Hospitality Management, 57,
116–131. DOI: 10.1016/j.ijhm.2016.06.006.
Snijders, C., Matzat, U., & Reips, U. D. (2012). Big Data: Big Gaps of Knowledge in the Field of
Internet Science. International Journal of Internet Science, 7(1), 1–5.
Stienmetz, J. L., & Fesenmaier, D. R. (2016). Validating Volunteered Geographic Information:
Travel and Tourism Research Association:
Advancing Tourism Research Globally [online]. Retrieved from https://scholarworks.umass.edu/
ttra/2016/Academic_Papers_Visual/24a.
Sun, S., Wei, Y., Tsui, K. L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning
and internet search index. Tourism Management, 70, 1–10. DOI: 10.1016/j.tourman.2018.07.010.
Tchetchik, A., Fleischer, A., & Shoval, N. (2009). Segmentation of Visitors to a Heritage Site
Using High-resolution Time-space Data. Journal of Travel Research, 48(2), 216–229. DOI:
10.1177/0047287509332307.
The Guardian. (2012). Hacker advertises details of 117 million LinkedIn users on darknet [online].
Retrieved from https://www.theguardian.com/technology/2016/may/18/hacker-advertises-
details-of-117-million-linkedin-users-on-darknet.
Thevenot, G. (2007). Blogging as a Social Media. Tourism and Hospitality Research, 7(3/4), 287–289.
DOI: 10.1057/palgrave.thr.6050062.
ARTICLES
102 | CZECH JOURNAL OF TOURISM 02 / 2019 | (85—102)
Big Data: a Source of Mobility Behaviour and a Strategic Tool for ...
Vaynerchuk, G. (2013). Jab, Jab, Jab, Right Hook: How to tell your story in a noisy social world. New
York, USA: HarperCollins Publishers.
Versichele, M., de Groote, L., Bouuaert, M. C., Neutans, T., Moerman, I., & Van de Weghe, N.
(2014). Pattern mining in tourist attraction visits through association rule learning on Bluetooth
tracking data: A case study of Ghent, Belgium. Tourism Management, 44, 67–81. DOI: 10.1016/j.
tourman.2014.02.009.
Von Mises, L. (1998). Liberalismus (Liberalism). Prague, Czech Republic: Ekopress.
Vu, H. Q., Li, G., Law, R., & Ye, B. H. (2015). Exploring the travel behaviors of inbound tourists
to Hong Kong using geotagged photos. Tourism Management, 46, 222–232. DOI: 10.1016/j.
tourman.2014.07.003.
Weaver, A. (2008). When Tourists Become Data: Consumption, Surveillance, and Commerce.
Current Issues in Tourism, 11(1), 1–23.
Wöber, K. (2007). Similarities in Information Search of City Break Travelers — A Web Usage Mining
Exercise. In Sigala, M., Mich, L., Murphy, J. (Eds.), Information and Communication Technologies in
Tourism 2007 (pp. 77–86). Vienna: Springer. DOI: 10.1007/978–3-211–69566–1_8.
Xiang, Z., & Gretzel, U. (2010). Role of social media in online travel information search. Tourism
Management, 31(2), 179–188. DOI: 10.1016/j.tourman.2009.02.016.
Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms:
Implications for social media analytics in hospitality and tourism. Tourism Management, 58,
51–65. DOI: 10.1016/j.tourman.2016.10.001.
Ye, Q., Law, R., Gu, B., & Chen, W. (2011). The influence of user-generated content on traveler
behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings.
Computers in Human Behavior, 27(2), 634–639. DOI: 10.1016/j.chb.2010.04.014.
Yu, S. C., Choi, W. W., Shin, D. B., & Ahn, J. W. (2014). A Study on Concept and Services
Framework of Geo-Spatial Big Data. Journal of Korea Spatial Information Society, 22(6), 13–21.
DOI: 10.12672/ksis.2014.22.6.013.
Zach, F., & Gretzel, U. (2011). Tourist-Activated Networks: Implications for Dynamic Bundling and
EN Route Recommendations. Information Technology & Tourism, 13(3), 229–238.
Zheng, W., Huang, X., & Li, Y. (2017). Understanding the tourist mobility using GPS: Where is the
Tourism Management, 59, 267–280. DOI: 10.1016/j.tourman.2016.08.009.
Zoltan J., & McKercher, B. (2015). Analysing intra-destination movements and activity participation
of tourists through destination card consumption. Tourism Geographies, 17(1), 19–35. DOI:
10.1080/14616688.2014.927523.