Tourism Push & Pull Factors: Ontology-Based Clustering

Accepted Manuscript
Title: Using Ontology-based Clustering to understand the push
and pull factors for British tourists visiting a Mediterranean
coastal destination
Authors: Aida Valls, Karina Gibert, Alı́cia Orellana, Salvador
Antón-Clavé
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DOI:
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http://dx.doi.org/doi:10.1016/j.im.2017.05.002
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6-3-2017
2-5-2017
Please cite this article as: Aida Valls, Karina Gibert, Alı́cia Orellana, Salvador AntónClavé, Using Ontology-based Clustering to understand the push and pull factors
for British tourists visiting a Mediterranean coastal destination, Information and
Managementhttp://dx.doi.org/10.1016/j.im.2017.05.002
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Using Ontology-based Clustering to understand the Push
and Pull factors for British tourists visiting a
Mediterranean coastal destination
Aida Valls(1), Karina Gibert(2), Alícia Orellana(3), Salvador Antón-Clavé(4)
(1)
Department of Computer Science and Mathematics.
Universitat Rovira i Virgili. Tarragona, Catalonia (Spain)
[email protected]
(2)
Department of Statistics and Operations Research
Knowledge Engineering and Machine Learning group
Universitat Politècnica de Catalunya-BarcelonaTech, Barcelona. Catalonia (Spain)
[email protected]
(3)
Costa Daurada Observatory of Tourism.
Tourism and Leisure Science & Technology Park
[email protected]
(4)
Research Group on Territorial Analysis and Tourism Studies.
Universitat Rovira i Virgili. Vila-seca, Catalonia (Spain)
[email protected]
Corresponding author: Aida Valls [email protected]
Highlights
 Motivation and destination meaning are crucial to understand tourist’s choices
 Destination managers should include motivation and meaning as open questions
in surveys
 Semantic interpretation of open questions can be performed with ontologies
 Clustering based on Ontologies can discover more meaningful tourist segments
Abstract
This paper studies why British tourists decide to travel to a particular destination in a
Catalan region. The analysis is based on a survey that includes open-ended questions.
First, we propose the operationalization of the concepts of motivation and meaning as
push–pull factors when choosing a destination. Second, an ontology-based clustering
method is presented, which makes it possible to analyse these qualitative factors from a
semantic perspective to obtain tourist segments. A benchmark confirms that the
segmentation obtained is better than that generated using classic clustering methods. The
results show that different meanings can be associated with any single place.
Keywords:
Data Mining; Tourism Motivations; Destination Meaning; Ontologies; Qualitative data Tourism
Geography.
1. Introduction, aims and objectives
There is a great need to understand how travellers choose their holiday destinations
(Jang et al., 2009; Guillet et al., 2011), and there is therefore an increasing amount of
research being performed to analyse the roles played by many factors that affect the
decision-making of tourists travelling to a single destination (Krippendorf, 1987; Dann,
2014; Prayag and Hosany, 2014; Wang et al, 2015).
It has long been acknowledged that tourists are influenced by psychological aspects
when choosing a destination (Gee et al., 1984; Gardiner et al., 2013; Pearce and Parker,
2013; French et al., 2000; Pearce and Lee 2005) and that motivations are one of the
main determinants of their destination choice (Iso-Ahola, 1982; Sirakava et al., 2001;
Pearce, 1993; Crompton, 1979; Dann, 1981; Pearce and Lee, 2005; Um and Cromptom
1990; Zhao, 2014; Nyaupane et al., 2011). It has also been recognised that decisionmaking is linked to factors related to the characteristics of each single destination,
mainly to its attractions and attributes (Grimm and Needham, 2012). However, despite
this general recognition, there is still limited empirical evidence on the scope and
characteristics of the relationship between the roles played by motivations and
destination attributes in the decision-making process. Indeed, it can even be assumed
that this may be very specific to each person for each destination (Jang et al., 2009).
Attempting to relate the psychosocial needs that predispose a person towards travelling
and the attributes and characteristics that attract a person to one specific destination
rather than another, the classic push–pull theory (Dann, 1977), widely accepted in the
tourist literature (Jang and Cai, 2002; Barkhordari et al, 2014; Ward, 2014; Mody et al,
2014), is a valid framework for trying to understand the decision-making behind
tourism travel (Jang and Cai, 2002; Klenosky, 2002; Uysal et al, 2008; Dann, 2014).
Research related to this aspect has mainly been performed using data from some of the
periodic surveys conducted elsewhere in the world (Baloglu, 2000). A significant
number of investigations have also designed their own data collection procedures.
However, in most cases, the available data do not make it possible to really understand
the central issue of why tourists select a specific destination in which to spend their
holidays. Interestingly, a common feature of many surveys, as recommended by
Eurostat and the United Nations World Tourism Organization (see Massieu, 2007), is
the use of closed-ended categories when asking about reasons for going to the
destination. Investigations usually also apply conventional statistical analysis to process
the resulting available data. Bearing this in mind and considering the extent and utility
of the results obtained so far, it can be stated that another kind of approach might be
worth exploring.
Aware of the importance of having extensive knowledge of tourists and tourist decisionmaking, the Costa Daurada Tourism Observatory in Southern Catalonia has since 2001
been conducting a continuous survey through the observation of tourist activity in the
local destinations of the two main regional tourism brands of the province of Tarragona:
the Costa Daurada and the Terres de l’Ebre. A report is issued periodically and includes
basic statistics on tourist characteristics, demographic profiles, types of trip, relations
with the destination, and other issues relating to destination choice.
It is interesting to note that after working for 10 years using questions with closedended responses (as do most of the surveys in other destinations when asking about the
reasons behind the tourist’s decision to come to the destination), the Observatory
decided some years ago to change and start using some open-ended type questions. The
reason for this was that the final users of the survey results, mainly tourist firms,
operators and destination managers, were unable to draw relevant insights about
destination choice using closed-ended responses. As a result, since 2011, the
Observatory has included two open-ended questions when asking tourists about their
motivations and the meaning of the destination, which also relates to their reason for
selecting it.
All the responses are given spontaneously by tourists and have no vocabulary
restrictions. These data are clearly of a different type to those obtained from classic
closed-ended responses where the respondent has to choose from an a priori closed set
of predefined possible answers. They also provide a myriad of new opportunities for
analysis including the chance to explore new ways of analysing the tourist’s specific
destination choice and to disentangle the roles of motivation and destination factors in
this decision.
The main purpose of this paper is therefore to introduce specially designed advanced
methods of processing the open-ended spontaneous responses given by travellers when
asked about the motivations and meanings of a certain destination and extracting
relevant knowledge as regards why they decided to visit that destination. To do this, we
operationalise the Push–Pull theory (Dann, 1977; Dann, 1981; Dann, 2014; Crompton,
1979; Klenosky, 2002) by using motivations as the push factor and meanings as the pull
factor.
Analysing openly expressed non-numerical information obviously requires more
sophisticated techniques than the descriptive statistics conventionally used up to now in
this context. Therefore, we apply recent advances in the field of Artificial Intelligence
(AI), and indeed we aim to show how methods derived from the field of AI may be
really suitable for solving this problem and, in particular, for exploring the segmentation
of tourists according to the relationship existing between the motivation and meaning of
the tourist destination.
To conduct a proper analysis, the tourists’ spontaneous responses had to be processed
(semantic analysis), and therefore segmentation techniques were appropriately
combined with the interpretation of the terms used by tourists from a semantic point of
view. Segmentation was first used in marketing in the last century (Smith, 1956).
Automatic clustering techniques were introduced in the seminal paper by Haley (1968)
and became a popular and useful market segmentation tool based on survey data
(Dolnicar, 2002). Punj (1983) discussed the advisability of using clustering methods to
support the automatic construction of segments. Some limitations of classic clustering
algorithms are outlined in a more recent survey (Dolnicar, 2013). In the present paper,
automatic clustering methods will be used in a proposal of general methodology that
can be easily extended to other scenarios and replicated for other languages. A new
clustering technique known asClustering based on Ontologies (CbO) (Batet et al., 2010)
that addresses the problem in these terms has been used to find clusters that could
automatically consider the interpretation of the terms.
Thus, the analysis presented here uses ontology-based clustering to exploit the data
from the Costa Daurada Tourism Observatory database relating to the open-ended
questions concerning motivations and meanings for British tourists visiting the province
the Tarragona. The results show that the method can coherently, appropriately and
effectively identify segments of tourists by using qualitative descriptions of the
motivation for choosing the destination, openly expressed, and qualitative words related
to the general meaning that the destination has for these tourists. The method can
generate meaningful, useful and distinguishable tourist profiles that fulfil the standard
requirements of effectiveness already referred to by Bull (1995).
The paper is structured as follows: Section 2 sets out the state of the art in the research
areas of both tourists’ decision-making regarding travel to a destination and AI
applications to tourism. Section 3 proposes a new methodological framework to make
the segmentation of tourists based on the push–pull factor model. Section 4 presents the
results obtained in a case study of British tourists visiting the Costa Daurada and the
Terres de l’Ebre (regional tourism brands for the province of Tarragona in southern
Catalonia, Spain) and finally Section 5 presents the conclusions of this case study along
with conclusions regarding the methodological framework proposed.
2. State of the art
This section discusses the state of the art of relevant topics for the analysis relating to
the fields of tourism research and computational analysis.
2.1. Introducing the meaning of destinations as a pull factor for understanding
traveller choices
Widely used in tourism research, the push–pull theory (Dann 2014) is a valid
framework for understanding tourism travel and decision-making (Nikjoo and Ketabi,
2015; Barkhordari et al, 2014; Ward, 2014; Mody et al, 2014; Uysal et al, 2008; Prayag
and Hosany, 2014). Most importantly for the analysis performed in this paper, it is also
useful for framing the operationalization of the two types of open-ended question
(motivations and meanings) available from the Costa Daurada Tourism Observatory.
In conventional Push–Pull theory, push factors are those considered to be psychosocial
needs that predispose a person to travel, while pull factors are those that attract a person
to a specific destination rather than another once the push processes have been
activated. In early studies on this classic theory, Dann (1977) and Crompton (1979)
provided empirical evidence by reporting nine factors—seven psychosocial push factors
and two pull factors—classified as cultural by the authors. The push factors considered
by Crompton (1979) were the need to escape from the everyday environment,
exploration and evaluation of the self, relaxation, prestige, regression due to moments of
less responsibility, improved family relationships (as travel is an opportunity to bring
the members of the family together) and the promotion of social relationships (as
holidays are a suitable time for this). According to Dann (1977), these are factors that
stimulate the desire to travel and are related to the commonly accepted concept of
motivation. Additionally, Crompton considered that the pull or drag factors emerging
from the tourist destination were novelty and education. According to this theory, the
specific content of the pull or drag factors could be very specific to each person and
especially to each destination. Overall, factors are generally related to the characteristics
of the destination, its attractions and attributes (Klenosky, 2002).
When reviewing the academic tourism literature, it emerges clearly that motivation—
the push factor in most analyses using the Push–Pull theoretical framework—is a
relevant factor. Indeed, even though the oldest theories on motivation were merely
classifications and typologies of tourists (Cohen, 1972; Plog, 1971; 1990; 1991),
motivation has been extensively used to divide the tourist market into meaningful
segments (Beh and Bruyère 2007; Chang et al., 2006; Formica and Uysak 1998; Sung et
al., 2016; Park and Yoon 2009; Brida et al., 2012; Lee et al., 2013; Hsu et al., 2010).
However, as stated in the introduction, the tourist’s decision-making process derives not
only from motivation but also from other factors—the pull factors—related to the
destination, such as the images, perceptions, previous experience and available
information that tourists have (Brown and Chalmers, 2003; Lin et al, 2007). On the
other hand, unlike the push factor, which is usually analysed through motivations, the
pull factor has been operationalised through a number of different indicators including
destination attributes and the perceived images of the destination. The latter is the case,
for instance, of a recent study (Prayag and Hosany, 2014) analysing the reasons that
why young tourists from the United Arab Emirates come to Europe, and, along similar
lines, it is also the case of studies such as those by Baloglu and McCleary (1999), Beerli
and Martin (2004), Prayag, (2010), Kim and Perdue (2011), Prayag and Ryan (2011)
and Mariné-Roig and Anton-Clavé (2017).
Unlike papers that use perceived images, perceptions or destination attributes as pull
factors, our paper seeks to take as a pull factor the ‘meaning that a destination evokes to
tourists’. This indicator has not been commonly used, probably because of the type of
data available from regular surveys on destinations or because of the various means
usually used by researchers when trying to quantitatively measure the meaning of a
single destination rather than the usefulness of the concept. In this regard, it is common
in studies that analyse the meaning of destination, for example, to measure it indirectly
using stay data, services consumed or the interaction among tourists (Dolnicar and
Grün, 2013; Snepenger et al., 2004) instead of obtaining results directly from the
spontaneous responses of tourists as we have through the survey used by the Costa
Daurada Tourism Observatory.
To introduce the meaning of a destination in a Push–Pull model of segmentation of
tourists according to their decision to travel while at the same time including their
motivations, it is essential that these can be measured and analysed. In this paper, we
propose a simultaneous measurement of the motivations and meanings of tourists that,
as far as the authors are aware, has not been explored before. Thus, our contribution
takes a step forward by defining a push–pull operationalization relating to motivations
and meaning that introduces two different novelties. First, the introduction of the
meaning of the destination as a pull factor when operationalizing the model, and second,
the use of open-ended responses when analysing both motivations and meanings.
Meaning is a complex term with many competing and complementary definitions and
interpretations that can be studied from various disciplines (Gergen, 1994 p.19;
MacCannell, 2003) and is related to the geographical expression of sense of place
(Cresswell, 2005). Some papers propose emotional approaches to the concept of
meaning (Hirschman and Stern 1999; Prayag et al., 2017; Dickinger and Lalicic, 2016;
Gaski and Etzel 1986), while a few deal with meaning within the field of leisure and
tourism services, practices and places (Ekinci et al. 2013). In our analysis, the meaning
of destination can be understood as the summary of everything coming to tourists from
the sphere of emotions associated with the destination. Meaning is relevant because
emotions, personal feelings, perceptions and attitudes related to the destination can be
summarised through it. It can also provide precise market segmentation, grouping
homogeneous customers together (Wedel and Kamakura, 2000) and helping to
understand the dynamic and changing nature of human motivation (Pearce and Packer,
2013).
Meaning is obviously not an objective factor, and one destination can have different
meanings for different people. Nevertheless, this does not necessarily constitute a
constraint as regards introducing it into the Push Pull modelling; in fact, it will be useful
in helping to better understand why tourists with different motivations decide to go to
the same destination. What really matters here is not whether the meaning given to a
destination is real, but whether the transformation of the destination into an idea (the
meaning that it has for each single tourist) determines any process of decision-making
related to a willingness to travel there. This transformation, far from being objective,
clearly states the difference between the destination and the meaning of the destination,
as can be explained in accordance with the system of social values (MacCannell, 1976;
MacCannell, 2003) that frames the decision of each individual. It is known that the
value of a destination directly depends on the meaning it has for each tourist (Bourdieu,
1979) and, in the same vein, it is also well known that, from a sociological perspective,
some authors (Urry, 1990; Hankinson, 2004) argue that destinations, as part of their
functional attributes, have symbolic attributes which are reflected in the meaning they
have for tourists.
Thus, it can be interpreted that meaning can consistently act as a pull factor for each
specific destination and, combined with motivations, can make it possible to discover
why tourists visit them. The challenge is, then, how to measure both motivations and
meanings and how to handle the data in order build an explanation of why a destination
is selected. We decided to solve these last issues through the use of advanced techniques
of tourist segmentation able to deal with open-ended survey answers and the application
of semantic clustering procedures.
2.2. On the techniques used for tourist segmentation
The segmentation process is fundamental in tourism marketing. According to the
American Marketing Association, different customer segments can be chosen as market
targets needing different marketing strategies. Useful segments should be effective,
identifiable, distinguishable, accessible, measurable, substantial, operative and stable to
make sustainable predictions for the future (Pires et al., 2001).
Market segmentation in the field of tourism has a long tradition. There are two
fundamental approaches: a priori segmentation (where the relevant criteria for
distinguishing tourists are known in advance, such as archetypes or demographic
profiles) and post hoc segmentation (derived from data analysis usually from surveys).
This second approach has attracted greater interest since the 1980s, as shown in the
review (Dolnicar, 2002). Tourist segmentation is mainly approached using statistical
techniques such as correspondence analysis (Diana and Pronello, 2010), exploratory
factor analysis, discriminant techniques, chi-squared automatic interaction detection
(Chen 2003) and different cluster analysis methods such as k-means (Pronello and
Camusso, 2011; Pesonen 2012) and Ward’s method (Bigné and Andreu, 2004; Park and
Yoon, 2009; Hosany and Prayag, 2013). Most of these expect a dataset containing
strictly numerical variables. Recently, Brida et al. (2013) made a proposal for binary
variables.
The data types considered in the literature in the context of tourism typically combine
numerical variables with nominal variables (Diana and Pronello, 2010), binary variables
(Brida et al., 2013; D’Urso et al., 2013) or variables with a numerical Likert-type scale
(with five or seven values) (Chen, 2003; Bigne and Andreu, 2004; Hosany and Prayag
2013). This makes it appropriate to combine cluster analysis with factor analysis (Wedel
and Kamakura, 2000; Park and Yoon, 2009; Pronelo and Camusso, 2011; Pesonen,
2012). However, this approach leads to clusters identified over the factorial space,
which might cause interpretation problems, since the interpretation of the factors is not
always trivial.
Despite the apparent relevance of tourist segmentation for business, the generation of
homogeneous and useful market segments with heterogeneous datasets is still an open
problem (Pires et al., 2011). The benefits of innovative technologies for cluster
identification continue to be poorly exploited in the field (Chen, 2003). Following this
direction, the latest developments in data mining and artificial intelligence offer new
possibilities for analysing heterogeneous data matrices without the need to move to a
fictitious space, such as the factorial space, thereby avoiding interpretation drawbacks.
In addition, AI introduces the possibility of analysing qualitative information from a
semantic point of view, as well as open-ended questions, which can fit well into the
framework of trying to obtain tourist segments based on the motivation of the tourists
and taking into account the meaning that the destination evokes for them. In this work,
both the motivations and the meanings are modelled through words directly expressed
by the tourists. Special attention is paid to taking into account the semantics of those
words in the process of identifying tourists with similar motivations or meanings. This
can be performed by using the semantic clustering method described in Section 2.3.
2.3 Semantic Clustering
The importance of including domain knowledge in the data mining procedure is crucial
in many information systems (Feelders et al., 2000; Chen et al., 2013). Some works
show the benefits of managing the semantics of the terms to improve data analysis
(Chen, 2006; Gibert et al., 2010; Al-Hassan et al., 2013). Several approaches can be
found in the literature, such as fuzzy sets (Zadeh, 1975; Martínez et al., 2009) and the
Bayesian model (García-Alonso et al., 2013).
To date, clustering techniques applied to crisp heterogeneous data matrices have not
considered the semantics of the terms. This is currently widely accepted, probably due
to the lack of proper tools for representing and managing conceptual values together
with their semantics. However, AI provides some well-developed tools, which may be
of interest when it comes to improving clustering performance by considering
semantics.
In the approach presented in this paper (Section 3), we make the most of ontology-based
semantic techniques. Ontologies are a knowledge representation model that enables
relationships between concepts to be expressed under the logic paradigm (Gómez-Pérez
et al., 2004). They are a suitable model for expressing semantics because they are highly
flexible and provide sufficient expressiveness for the purpose of this work. This avoids
the need for quantitative models for expertise and domain knowledge, indirect
measurements through indicators that must be previously validated, direct expert-based
quantification of similarities between terms, the definition of membership functions for
fuzzy sets and the definition of a priori probability distributions, as in more classic
models.
The authors are not aware of other clustering algorithms using ontologies for
segmentation purposes. Ontology-based data mining methods have been proposed for
other tasks, such as classification (Bernstein et al., 2005; Ceccaroni et al, 2004; García
et al, 2011; Moreno et al. 2014; Moreno et al., 2015, Zucon et al, 2013), rule-based
decision systems (Esposito and De Pietro, 2011; Zhang et al, 2002), neural networks
(Breen et al, 2002; Trappey et al., 2013), Bayesian causal networks (Helsper and van
der Gaag, 2002) and even case-based reasoning (Yang et al., 2005). It is worth
highlighting the increasing interest in ontology-based analysis for text mining and
document classification (Steyvers et al., 2001; Hotho et al., 2003; Thangamani and
Thangaraj, 2010; Song et al., 2011; Gu et al., 2009; Vadivelou and Ilavarsan, 2014).
Some works combine word interpretation with some structural information, such as
blog articles (Feng et al., 2011) and lexicons (Pluempitiwiriyaweja and Cerconeb,
2009).
Most of these approaches (Vadivelou and Ilavarsan, 2014; Steyvers et al., 2001; Hotho
et al., 2003; Thangamani and Thangaraj, 2010; Song et al., 2011) do not use classic
two-dimensional data matrices, in which objects are described by several variables, as in
the approach presented in this paper.
Our approach uses data matrices with the individuals (tourists in the application
presented here) in rows and the variables in columns. The columns can be
heterogeneous, including numerical and qualitative variables (where the values are
words), some with multiple responses. A generalization of Ward’s method is proposed.
The calculation of distances is refined using the semantics of the terms for those
qualitative variables for which semantic information is available in the reference
ontology. Pre-processing methods (Gibert et al., 2017) are used to appropriately
transform the multiple responses to be introduced into the clustering with no loss of
relevant information. Sections 3.3 and 3.4 provide details on how ontologies are
introduced into the clustering algorithm to better measure similarities between objects.
3. Materials and methods
3.1 Methodological framework
The methodology used in this paper to find tourist segments according to their decision
to visit a certain tourist destination provides several contributions with regard to the
traditional procedure for analysing tourists’ answers to questionnaires:
1. From the tourism analysis point of view:
a. The operationalisation of the push and pull factors through motivation
and meaning,
b. The use of qualitative open-ended questions to measure tourists’
motivations and meanings.
2. From the computational analysis point of view:
a. The use of reference ontologies to obtain semantic information about
the terms used by users in the open-ended questions,
b. The use of semantic clustering techniques to perform multivariate
analysis of tourists taking into account both the qualitative answers
and other variables to provide a semantically interpretable tourist
segmentation.
The main novelty resides in introducing the meaning of the destination as a pull factor
instead of the perceived image of the destination. As previously stated, the second of
these options has the greater presence in the related literature, probably for two main
reasons: the scarcity of data measuring meaning directly from the tourists’ spontaneous
responses and the difficulties in analysing it using conventional statistical systems. In
addition to this, the contribution is supported by the idea that meaning represents a
variable that concentrates greater decision-making influence in the destination selection
process.
An additional contribution of this work consists of obtaining motivation and meaning
from open and free responses, in which the vocabulary considered by users finds no
constraint other than their own terminological ability instead of using the closed and
preconceived lists of terms classically used for qualitative variables in Statistics and
Artificial intelligence. Thus, in the approach presented here, the tourist can use any
word they consider appropriate to indicate the motivation and meaning of a destination.
The study is thereby not reduced to a limited range of pre-established motivations or
fixed meanings, which, even when including the classic “other(s), please specify”
option common in closed questions today, might bias the expression of the user. Indeed,
the simple vision of the terms suggested in the closed lists can even limit the free
association of ideas with the destination. The authors in fact point out that this might be
precisely the reason why the classic formulation on closed lists has brought about little
advancement in improving knowledge of the destination selection process beyond what
it is already known in the sector. The proposed approach goes beyond the inherent
restrictions of questions with predefined lists of responses and those deriving from the
use of indirect measures through scales or indicators (whose validity is widely
controversial in most fields, including psychometrics, market research, etc.).
Finally, another innovation of our method consists of allowing respondents to give more
than one answer to the questions. The use of such a multiple response open-ended
question is also a new field that has not been explored in tourism, as far as we know.
The proposed model for tourism formalisation, including open qualitative variables for
obtaining the meaning and motivation of the destination, requires the most up-to-date
generation analysis tools that go beyond the econometric models traditionally used in
this area. Ontology-based clustering (Batet et al., 2010) is proposed as a means to
segment tourists according to motivation and meaning expressed through open,
unrestricted vocabulary. This methodology represents a step forward in the analysis of
data containing qualitative information because:
 it enables the simultaneous analysis of numerical, categorical and semantic data
 semantics is expressed through ontologies
 the sense of the terms is considered so as to compare tourists’ answers
 multi-valued open questions are processed for intelligent analysis.
The main consequence of taking into account the semantics of the terms used is that the
resulting clusters are more compact and consistent from a conceptual point of view and
are therefore more easily understandable by the end user, in this case the tourism
professional (Gibert et al., 2014; Gibert and Conti 2014; Moreno et al., 2015).
Thus, the proposed methodological framework materialises the link between the
ontology and the classifier through the introduction of an appropriate distance between
individuals (in this case tourists), as will be seen in the following sections.
3.2. Ontology-based clustering
The CbO method
(Batet et al., 2010) is an extension of the classic hierarchical
agglomerative clustering algorithms (Jain et al., 1999; Bouguettaya et al, 2015), in
which a nested set of classes is constructed in a bottom-up approach by merging a pair
of objects or classes at each step. Two basic parameters guide the procedure, giving rise
to different clustering algorithms:
 An aggregation criterion to prioritise which pair of classes or individuals are to
be merged at each iteration
 The function used to measure distances between pairs of objects. When dealing
with different types of variables, this distance function must have particular
characteristics (discussed in the following sections).
CbO is an ascendant hierarchical method based on a classical baseline of the reciprocal
neighbours algorithm (deRham, 1980). For an aggregation criterion, it uses Ward’s
criterion (Ward, 1963). This method first merges the pairs, with minimisation of the loss
of interclass inertia being its main criterion because inertia is a function of the squared
Euclidean distance between the terms compared. As an underlying distance, it uses a
Generalized Gibert’s mixed metrics (GGMM) (described in Section 3.3), which makes
it possible to cluster individuals described by numerical, categorical and semantic
variables simultaneously while taking into account the semantics of the terms, when
available, in the comparisons. To be more precise, our proposal uses a generalisation of
Ward’s criterion that in the classical expression substitutes the classical Euclidean
distance with a GGMM.
This clustering method has been implemented in a software system called KLASS
(Gibert and Nonell, 2008), which was used for the study presented in this paper.
3.3. Generalized Gibert’s mixed metrics
Given the structure of the problem to be dealt with in this research, a means of
comparing two individuals described by numerical, categorical and semantic variables
simultaneously is required. The literature is rich in semantic distances (Pedersen et al.,
2007; Lee et al., 2008). There are also proposals for comparing objects with numerical
and categorical attributes, such as those made by Gower (1971) and Ichino and Yaguchi
(1994). The authors are not aware of any proposals in the literature that allow
comparisons in a general case that includes all the numerical, categorical and semantic
variables together, except for the GGMM previously developed by the authors
themselves (Gibert et al., 2014). Ontology-based clustering CbO, therefore, uses
GGMM as the underlying distance of the algorithm.
Although categorical variables are the classical representation for qualitative variables,
those for which additional background knowledge is available to help interpret the
semantics of the terms are known as semantic variables, the meaning of their values can
be interpreted and become concepts rather than simple modalities. In our approach, the
background knowledge providing this additional semantics will be formalised by means
of ontologies.
GGMM is a compatibility measure that combines different distances according to the
type of variable. It has been shown to perform well in other applications related to
clustering tasks.
Given a set U={ Xk, k = 1 : K } of variables of three different types
= {k : Xk numerical
variable, k = 1 : K},
= {k : Xk semantic
= {k :Xk categorical variable, k = 1 : K},
variable, k = 1 : K}, such that U=   , the GGMM is defined as follows:
(2)
, where:

the normalised Euclidean metrics used with numerical variables

is the χ2 metrics to be computed with the categorical variable (see
Gibert and Cortés, 1997) and

is the superconcept-based distance (SCD) to be used for semantic
variables (see Section 3.4 and Gibert et al., 2014).
The GGMM is, in fact, a family of metrics indexed by three parameters
In
Gibert Cortés (1997), it is shown that the weights given below provide good results
associated with clustering processes:
(3)
where
(4)
with
,
=
and
, while
,
and
are the truncated maximums of the different subdistances. They balance the
contribution of all types of variable in the final distance and provide robustness in the
presence of outliers.
3.4 SuperConcept-based Distance
The SuperConcept-based Distance (SCD) enables comparisons to be made between two
terms of a qualitative variable for which semantics is available. Hence, it would be
expected to give a lower distance between church and cathedral than between church
and theatre or restaurant. A common approach in the literature is to use ontologies to
express relationships between terms and to define semantic distances between terms that
exploit the ontology’s geometry. Most of these are based on the minimum path between
the terms compared (Pedersen et al., 2007) and thereby lose considerable valuable
information. Therefore, the ontology-based clustering presented in Section 3.2 uses a
recent proposal made by the authors (Gibert et al., 2014)—the SCD—as the third
component in the GGMM,
. This distance is based on the existence of a
reference ontology, in which the terms of the semantic variables appear and which is
able to provide more accurate distances between terms taking into account all the
taxonomic evidence available (i.e., all the superconcepts) and not only the minimum
path.
Given a set of concepts C, an ontology HC containing a set of taxonomies over the
elements in C, and a concept ci ∈ HC, we consider the set of super-concepts (ancestors
in the taxonomy) of ci as the subset of all the concepts in C preceding ci in some of the
taxonomies of HC, including ci itself:
A(ci ) = cj ∈ C such that cj = ci ∨ cj is the ancestor or super-concept of ci , ci ∈ HC
Concepts ci and cj are compared on the basis of the ratio between the non-overlapping
taxonomic knowledge and the total number of ancestors of both terms.
Definition 1. SuperConcept-based distance (SCD)
SCD(ci , c j ) 
card ci   c j  card ci   c j 
card ci   c j 
(1)
SCD fulfils the properties of a metric (i.e. positivity, symmetry and triangular
inequality), which enables its use in hierarchical clustering algorithms (Gibert et al.,
2014; Batet et al., 2010) as it is equivalent to computing the Euclidean distance between
the binary vectors resulting in a flattening of the ontology in a vector of terms and a
description of a concept as its path towards the root.
3.5 The reference ontology
As mentioned earlier, a reference ontology is used by the SCD to improve comparisons
between the terms used in semantic variables. In turn, the SCD becomes a component of
the GGMM to permit global distances to be calculated between pairs of individuals
partially described by numerical, categorical and semantic variables. This is the distance
used by ontology-based clustering to find segments of individuals in this research. Thus,
in this section, the characteristics of the reference ontology used here are presented.
Ontologies formalise and represent domain knowledge (Gómez-Pérez et al., 2004),
providing a graphic model in which semantic interrelations are modelled as links
between concepts. They are highly flexible and provide sufficient expressiveness for our
purposes. Research into the structure of existing ontologies through the Swoogle
ontology search engine (Ding et al., 2004) has shown that domain ontologies usually
model only taxonomic relationships. Although the proposed methodology is general for
any reference ontology, in this work, WordNet (Fellbaum 1998) is used as it is a general
purpose ontology commonly used for semantic analysis with the English language. In
other domains, specific ontologies (like SNOMED in medicine) of reduced terminology
might also be used with the same methodology. WordNet offers a lexicon, a thesaurus
and semantic linkage between a large number of English terms. WordNet contains
around 120,000 nouns and 11,500 verbs. Hypernymy is by far the most common
relation, accounting for over 80% of all the semantic links modelled. The maximum
depth of the noun hierarchy is 16 nodes. WordNet is a general purpose ontology that
provides a sound background for any application.
Topics related through the management of semantics are language-dependent most of
the time. In the case of non-English applications, terms can be previously translated into
English and WordNet kept as the reference ontology or any ontology originally
designed in the native language might also be used if available. In the case of
descriptions of motivation and meaning, it is best to avoid translating the terms used by
the tourists, as several authors point out the biases that translations can introduce in this
particular, given that different languages have different words to describe landscapes
and emotions according to different values, interests and goals that are characteristic of
the different cultures (Picard and Robinson 2012). EuroWordNet is a general-purpose
multilingual ontology that includes seven languages that might be considered for
applications: Spanish (Vossen, 1999), French, Italian, Dutch, German, Czech and
Estonian.
3.6 Dealing with multivalued semantic variables
One of the characteristics of this research is that the open-ended questions allow the
tourist to provide more than one word for the both the reason and meaning questions.
This not only produces a richer view of the tourist but also makes the structure of the
problem more complex, since the data matrix contains cells with values which are no
longer single words (with or without semantics associated) but vectors of words, known
as multivalued variables. There are a number of different solutions available. One way
would be to pre-process the data by identifying the most frequent lists of words used by
the tourists and substitute them with a more global concept that represents the complete
list (e.g. sand, sunbathe, sun… would be substituted by beach). This can be done by
hand, provided that the lists of combinations are not long, but it can also be done by
using the reference ontology, automatically determining the Least Common Subsumer.
Another way would be to analyse the distribution of the number of words given in each
response and determine the length of lists covering more than a certain threshold of
individuals, and then using as many columns for the variable as determined. For
example, in our application, over 90% of the respondents only used one word, thus only
one variable has been used. Should a relevant percentage provide a second word, two
columns for the meaning could be considered in the analysis as the main meaning and
secondary meaning. In the first approach, the order in which the words are spoken is
dismissed. For those contexts in which the order of the words becomes important, the
second approach might be more suitable.
3.7. Obtaining segmentation using a hierarchical method
The result of a hierarchical method is a dendrogram (see Fig. 2) with the sequence of
nested classes built throughout the process. The leaves are the original individuals (in
our case, the tourists). The internal nodes represent the intermediate classes created. The
level of an internal node is proportional to the class’s homogeneity. The dendrogram
provides a visualisation of the inner structure of the dataset, which can be analysed a
posteriori, and the number of final classes (i.e. segments) determined accordingly,
contrary to what happens in partitioning clustering methods such as K-means, where the
number of classes to be found is an input parameter (Aparna and Nair, 2015). This is a
major advantage of hierarchical methods over partitioning methods, since in the general
case the number of interesting and relevant tourist segments is not known in advance
and has to be discovered during the data mining process.
The suggested number of classes is usually identified in the dendrogram as the
horizontal cut with the longest branches. This often coincides with the optimisation of
certain quality indexes such as the indexes of Calinski–Harabasz (Calinski and
Harabasz, 1974) or the Davies–Bouldin (Davies and Bouldin, 1979). In this paper, the
Calinski–Harabasz criterion is used. Each horizontal cut of the tree generates a partition
with a number k of clusters, k ϵ {2:n}. The Calinski–Harabasz index is evaluated at
every horizontal cut CH k . The best cluster is the one maximising the Calinski–
Harabasz index. For a given number of classes k, it is computed as:
Bk
CH k 
Wk
(k  1)
(n  k )
, where n is the sample size, k is the number of clusters, W(k) is the within-cluster
variability and B(k) is the between-cluster variability. The Calinski–Harabasz index is
higher as the clusters are more compact and distinguishable from each other. Therefore,
it is considered that the best clustering among all those suggested by a dendrogram is
the one that maximises the Calinski–Harabasz index.
3.8. Interpretation-oriented post-processing tools
In the area of data mining, much of the research in the last decade has focused on the
development of better and quicker algorithms to capture the underlying structure of the
dataset analysed, covering the data exploitation step itself. However, the other steps in
the knowledge-discovery process are of considerable importance for the completion of
the process (Fayyad et al., 1996). One of the most critical issues for the real impact of
data mining results on the target domain is to consider a post-processing step oriented at
the understandability of those results (Gibert and Conti, 2014). The current gap between
data mining results and effective decision-making has been argued in previous works
(Gibert et al., 2013), and several tools have been developed to post-process clustering
results to understand the clusters. These include annotated Traffic Lights Panels (Gibert
and Conti, 2015), Class Panel Graphs (CPGs) (Gibert et al., 2008), or direct concept
induction from dendrograms (Gibert, 2014). In this paper, CPGs are used to understand
the meaning of the clusters (see Fig. 3).
The CPG is actually a way of identifying relevant variables in the classes based on a
compact visualisation of the conditional distributions of many variables on a single
page, thus providing an overall perspective of the behaviours of variables in classes.
3.9. Data collection and sampling
To apply the proposed methodology to ascertain the role of push–pull factors in tourists’
decision-making, empirical research into the motivations and meanings of British
tourists on holiday was conducted in the province of Tarragona, a Spanish coastal
tourist region in Southern Catalonia with two main regional tourism brands (Costa
Daurada and Terres de l’Ebre) and many local tourism destinations. The study focused
on the British tourist market for two main reasons. First, English is the language most
studied in Computational Linguistics research, and therefore large, detailed ontologies
for general purposes (like WordNet) already exist in the language. Second, the British
market has a long tradition and a large magnitude in this particular region of Spain
(Sanz Ibáñez and Anton Clavé, 2016). Additionally, because of its numerical relevance,
focusing on the British visitor market also facilitates the design of a focused analysis of
the utmost interest to destination managers in the area.
3.9.1 Survey instrument
Since 2001, the Costa Daurada Tourism Observatory has conducted a regular omnibus
survey of tourists who visit the region and its main local destinations. This omnibus
survey is the major existing data gathering tool providing information about the
characteristics and profiles of tourists travelling to the Costa Daurada and the Terres de
l’Ebre. The survey measures socio-demographic characteristics, types of trip and tourist
perceptions and contains a set of questions aimed at ascertaining the reasons for
choosing any single local destination in the region. This is the concept on which this
work focuses.
The questionnaire, which is implemented by means of a direct survey of tourists, is
structured in accordance with the following groups of questions:
1. Characteristics of visitors by age, origin, social class, level of studies and
activity profile of the person who provides the main source of the household’s
economic income.
2. Characteristics of the stay and motivations for travelling to the destination.
3. Activities performed by visitors in the destination and the places visited or
planned to be visited during the stay, among other things.
4. Satisfaction related to several issues and the features of the tourism
destination.
The two key questions for the analysis undertaken in this paper are two open-ended
short answer questions, spontaneous and multiple, that are located in Sections 2 and 4 of
the questionnaire described above. If more than one response is given, interviewees are
asked to rank them from most to least important. Using open-ended questions instead of
categorical choices, the questionnaire solves the problem of any anchoring effect. The
questions are formulated as follows:
 What is/are the reason(s) behind this destination choice of yours to spend your
holidays?
 Could you tell me, with the first word that comes to mind, what this place means
to you?
The way the questions are formulated may be taken into account when interpreting the
results of the analysis.
3.9.2. Sampling
The sample consists of 460 British tourists over 15 years old who visited any Costa
Daurada and Terres de l’Ebre local destination during the summer of 2011. The survey
was conducted from April to September 2011. Only 20 of the 480 British tourists
contacted refused to participate, resulting in a response rate of 95%.
As regards the sampling technique applied, the survey was conducted on a sample of
visitors selected using a multi-stage sampling procedure. This procedure was
established to guarantee statistical representativeness. Since the survey is implemented
in open public spaces distributed throughout the many local destinations where tourists
take holidays, a key aspect of the sampling process is the selection of the individuals to
interview and the definition of the points of interaction with them.
The first stage of the sampling design consists of determining the points of the survey
and their area of influence. On the basis of the expertise of a committee of experts on
tourist activities in the area, a set of 16 key survey points covering the most visited local
destinations and all the varied typology of the tourists was selected. The committee of
experts comprised a geographer, two market research analysts, the directors of the local
destination management organisations involved in the study and a representative from
the hotel association. These points were subsequently validated by means of field
observation performed by members of the research team. The first sampling stage
determines the geographical coverage of the sample, guaranteeing the possibility that a
visitor may be interviewed independently of where their accommodation is located.
The second stage is the configuration of the survey fieldwork as regards both time and
geography. This means determining in which places the survey will be activated, and on
which days and at what time. The final sample contained all the time slots and all the
places selected during both weekends and workdays. The survey was implemented from
9 a.m. to 9 p.m. This second stage guarantees the possibility of selecting tourists that are
out of their accommodation only during the morning or only during the
afternoon/evening. It also ensures that the selection will randomly include both
weekend tourists and tourists that are in the destination only during the other days of the
week.
The third stage consisted of a systematic sampling selection of the individuals to
interview. They were chosen from the flow on the basis of ‘1 group or individual from
every 3 groups or individuals’ among tourists walking to the point of the survey, taking
into account the whole section of promenade or equivalent previously determined by the
research director. When a group was selected, the member interviewed was whichever
individual in the group over 15 years old would soonest reach their birthday. The
random selection of the individual to answer the questionnaire guarantees the selection
of people of all age groups, genders and origins.
In the case of areas in the province of Tarragona with lower numbers of visitors, the
tourists were interviewed in the reception area of their accommodation. In this case, the
first stage was to select those places with available tourist accommodation and then
randomly select the destinations to be part of the sample. This ensures that all
accommodation facilities chosen by tourists will have an equal probability of being in
the sample. The second stage was to assign the days to carry out the survey during both
weekends and weekdays. This made it possible to select not only tourists who visited
the place during the weekend but also tourists who visited it on weekdays. Interviews
were conducted in the morning from breakfast until the end of check-out time.
Mornings are the part of the day with the highest probability of finding all the people
hosted at a particular place of accommodation. To choose the person to be interviewed,
the same procedure of random selection as above was applied.
The questionnaire was implemented in open spaces in the case of the most important
tourism destinations and in accommodation establishments in the case of destinations
with fewer visitors. Since accuracy is important, an experienced interviewer conducted
the questionnaire to ensure that the respondent was answering it seriously. Although
when asked to participate in the survey, people are going about their own business, it
should be noted that the selected individuals need to collaborate properly during the
interview process. As they are on holiday, they have enough time to spend answering
the questionnaire, and therefore the time used to answer it and how this could affect the
quality of the responses is not a concern in the case of this survey. Usually the
individuals participating in the survey took approximately 7 minutes to answer the
questionnaire. Questions about motivations and meanings are to be found during the
second minute of the interview. The answer is totally spontaneous and not directed. The
interviewer notes down verbatim the exact expression used by the respondent. The
proportion of no answers to these two specific questions is almost zero. Uncompleted
questionnaires or cases in which the respondent did not behave normally are not
included in the sample.
4. Results
In this section, we first explain in detail the processes for preparing data for the analysis
and the subsequent application of the clustering algorithm. The segments obtained are
then identified and described.
4.1. Data selection and preparation
This study aims to understand the reasons why British tourists decide to go to a
particular local destination within the Costa Daurada and Terres de l’Ebre regions. A
tourist segmentation will be carried out taking into account both the location of the
tourist and the Push–Pull factors explained earlier (for a detailed analysis of the
projected and perceived image of Spain as a tourism destination for British travellers,
see Andre, Bigné and Cooper, 2001). The analysis may help discover whether there is
any relationship between the specific destination chosen, the tourists’ motivations and
their meanings so as to identify several profiles of tourists according to this relationship.
Four variables were therefore selected and prepared using the information provided
from the corresponding survey questions.
In an initial stage, some preliminary data preparation was done when required. The
main characteristics and processing of the four variables in this study are summarised
below:

Place: this variable refers to where the interview was held. There are 28
different locations on the Costa Daurada and in the Terres de l’Ebre,
including those in the capital city, Tarragona, coastal tourism destinations
such as Salou, Cambrils and La Pineda and other small towns within the
province of Tarragona.

Zone: the geographical area of the province is divided into nine different
zones: North, Tarragona, La Pineda, Salou, Cambrils, Terres de l’Ebre, inland
Costa Daurada, other Central areas and South. The areas of Tarragona,
Cambrils, Salou and La Pineda account for 87% of the interviews. This is
because they are the most tourist-oriented places in the province.

Motivation-Reason (push factor): this variable contains the textual answers
given by the tourists to question Q1. People were allowed to answer with a
maximum of three words. Each word should correspond to a different motive
for their visit. All answers were in English. To interpret their meaning using
the WordNet ontology, the words used in the answers have to appear in
WordNet. Minimal changes were made to preserve the original answers as
much as possible. The first transformation is to change adjectives and verbs
into nouns (for instance, Enjoy is changed to Enjoyment). Second, some
specific names or expressions were changed to the most similar and general
concept (for instance, PortAventura, which is the name of an amusement
park, is changed to Entertainment; Gaudí is changed to Culture (see Table
1)). Most of the tourists gave just one answer, so in the end only the first
word has been considered in this study. There are 43 different motivationsreasons, but only 10 motivations are given by more than 5 respondents.
Hence, there are 33 answers that are rather infrequent, but they may include
words indicating quite similar motivations, for example, heritage and history,
beauty and niceness, museum and culture. These semantic relations are then
exploited by the clustering method to correctly identify these similar reasons
to construct the segments.

Meaning (pull factor): this variable contains the textual answers given by
the tourists to question Q2. People were allowed to answer with a maximum
of three words. Each word should correspond to a different motive for their
visit. All answers were in English. The same recoding procedure was applied
to ensure that all the answers appear in the WordNet ontology. Some minimal
changes were made to preserve the original answers as far as possible (see
Table 2). In this variable, only 1.8% of respondents used two words to
indicate the meaning of the place, while the rest gave a one-word answer. The
answers were much more diverse than with motivations, with 69 different
words. The most frequent meaning is Goodness (15% of the tourists),
whereas Holiday, Beach, Sun, Niceness and Relaxation have a frequency of
approximately 8–10%.
4.2. Semantic clustering
Clustering based on Ontologies (CbO) is performed on these data with the following
parameters:

Aggregation method: Ward criterion (Ward, 1963)

Distance: Gibert Generalized Mixed Metrics (Gibert et al., 2014), with α=0 as
no numerical variables are used in this particular application, and β and γ as
suggested in eq. 3

Categorical variables: Place and Zone

Semantic variables: Motivation-Reason and Meaning

Ontology: WordNet
The execution of the CbO method with this configuration led to the dendrogram
shown in Fig. 1. At a higher level, we can see two classes: C457 and C454. Then, for a
3-class cut, C457 is divided into C456 and C455 and so on. To obtain a partition of the
individuals into a set of non-overlapping clusters, a horizontal cut at a certain level of
the dendrogram must be made. Following the classic Calinski–Harabaz criterion (see
Section 3.7 for details), the cut level has been established at the point indicated in Fig.
1, giving a partition into six classes.
Figure 2 shows the number of tourists belonging to each segment (nc) and the CPG for
those six segments. Two classes can be observed with approximately 150 tourists, three
classes with 40–50 individuals and another with 27. Because of the high number of
values given in some of the variables, it is impossible to show the names of all the terms
on the X-axis of the bar charts. Some of them were therefore deleted for the sake of
clarity. The CPG clearly shows different distributions of values in each of the segments,
which indicates that they represent different tourist profiles. In particular, differences in
the push–pull factors through different segments can be analysed. The Reason (or
motivation) dubbed ‘Holiday’ displays a high frequency in four of the segments.
However, this main reason is found in combination with other different concepts in each
segment. Meanwhile, Meaning is different in all segments, with three clear values that
appear exclusively in three different classes.
The information provided by the distribution of the values in each class has been used to
extract a definition for each of the segments. In this definition, the geographic
information provided by the variables ‘Place’ and ‘Area’ have also been included so as
to facilitate the subsequent analysis of the territorial distribution of the different British
tourist profiles. For a better understanding, the places were classified according to the
type of places they are: iconic city sites, central places in the city, beaches with a unique
landscape, inland regions and quiet places in natural surroundings.
 C453 Visiting. Group of 151 people whose fundamental push factor is
accessing culture, sightseeing and shopping. These are people who travel to go
to museums, see the sights and get to know places. The pull factor that leads to
the place is the sense that it is associated with achievable culture and wellness
and considered to be a suitable place for tourism. All the tourists who have
come to the destination to visit family and friends are in this segment.
This group differs from the others because it largely corresponds to tourists
coming to Tarragona city, mainly interviewed at an iconic place located within
the city, although it should be noted that tourists surveyed in inland places are
also located in this segment. The segment also includes tourists surveyed in
quiet southern areas of natural environment, although in lower proportions than
in the “Enjoying” class. Some tourists with this profile are distributed without
too much incidence among the different locations of Salou (18 persons), La
Pineda (14 people) and Cambrils (13 people), with those surveyed in an iconic
place of La Pineda and an iconic place of Cambrils being a group of only
secondary importance.
 C455. Enjoying. Group of 149 people who conjure up a single push factor
which is holidays, with the main pull factor for reaching this destination being
the goodness and niceness of the place, although this group also includes those
who valued relaxation and the possibilities of entertainment offered by the
PortAventura theme park. A significant proportion of these tourists were
interviewed in Salou, La Pineda and Cambrils, with a particular presence of
those who were interviewed in the iconic place of La Pineda and at its spot point
and at the iconic places and spot points of Salou and Cambrils. It can also be
observed that this segment contains a certain number of tourists who have
chosen inland areas and Tarragona city, although this is not the main class of
visitor to these areas.
 C446. Holidaying. Group of 52 people whose push factor is to choose a beach
holiday destination. The pull factor, which attracts to the destination of choice
and not to another, is the fact of considering it their ‘holiday destination’. The
sense of place is clearly associated with their holidays. These tourists were
mainly surveyed in the area of Salou and La Pineda, and the biggest group was
interviewed on unique beaches in Salou, with a notable incidence of interviews
also occurring in Cambrils, both at its iconic place and in a central point.
 C449. Beaching. Group of 42 people whose push factor is to take a beach
holiday. The pull factor that leads them to choose the destination is the beach.
These are tourists that are centrally motivated by the beach and who therefore
choose the destination because of the characteristics of its beaches. They are
mostly found in Salou, mainly in the central points of the town and then at the
iconic place and another central point of La Pineda. Some isolated tourists
aligned with this profile might also be found outside these areas, but only
exceptionally.
 C438. Sunbathing. Group of 39 people whose push factors are mainly holidays
and the beach and whose pull factor is the sun. Basically, this territory evokes
in them the sun. They are found in central parts of Salou and on one of its
unique beaches, although also on the beaches of La Pineda, in the iconic place of
La Pineda and in smaller groups throughout all locations. There is a certain
incidence of this segment among interviewees in Tarragona and marginally in
the quiet southern areas of natural environment.
 C414. Relaxing. Very homogeneous group of 27 people for whom the main
motivation or push factor is the beach. They differ from the previous groups in
that the meaning of the destination is associated with the beauty of the place
and the landscape, wellness and relaxation. This result hints at a conception
of the beach in the sense of its being a natural element. In general, they are
located primarily in a central point of Salou and at an iconic place of La Pineda,
but they tend to be more numerous on the unique beaches. Outside Salou and La
Pineda, only a small proportion of this profile is observed at an iconic place of
Cambrils and very marginally in the quiet southern areas of natural environment.
The following table summarises the push and pull factors of each segment and their
location in the different tourist zones of the area studied.
5. Discussion
The results show the existence of clearly different profiles of tourists coming from
Britain to different local destinations in the region of the Costa Daurada and the Terres
de l’Ebre according to their motivations and the meaning they give to their selected
destination. Identifying different types of tourists following this Push–Pull model opens
up an opportunity to better understand the reasons behind the tourist destination
selection process and, from a practitioner perspective, a chance to design new marketing
strategies. Some important findings of general interest for other Mediterranean coastal
tourism destinations are the following:

Sun and sand appear separately as pull factors and not together as a single
concept. One observed meaning of the destination is based exclusively on the
beach concept and a different one is based on sunbathing, though these are
two concepts that are traditionally considered together by the tourism
literature. The results show that they can operate as two different pull factors
to a destination.

Not all the tourists for whom Beach is their main push factor have the same
types of interests. Out of 128 tourists for whom Beach is a push factor, only
40% are mainly attracted by Beach as the main pull factor, whereas the rest
are found in two other segments with different views. Another group of
approximately 40% of these tourists associates the destination with the
concept of Holidays, which is much broader than the concept of Beach.
Finally, a group of 20% of the tourists chose this destination for the beauty of
the place, the niceness and good feeling and for the image of relaxation that
the destination offers them.

There is a set of British tourists whose motivation is basically the Sun but
linked to Holidays rather than Beach. Although the concept of Beach in the
previous segments probably also implicitly includes the concept of Sun, this is
not the meaning that popped into the tourists’ minds when answering the
questionnaire. Therefore, it is important for both academics and decisionmakers to know that destinations with sun but without beach also have a
segment of British tourists who would potentially be interested in visiting this
kind of place. This would apply to several inland towns in the area of the
Costa Daurada and the Terres de l’Ebre.

Finally, a segment of British tourists that is not associated with either beach or
sun has also been identified. They mainly chose this destination for its cultural
heritage, shopping and the other kinds of tourism activities available. It is
worth noting that this is the predominant profile in the capital city of
Tarragona.
6. Comparison with traditional approaches
The same dataset has been analysed using some traditional approaches to evaluate
the performance of our solution. We have focused on clustering methods that require no
previous hypothesis about the number of existing clusters (as happens with some
partitioning algorithms like C-means) because we do not want to introduce this
constraint into the model a priori.
Being the partition P our proposal (obtained by using the Ward’s hierarchical method
with the GGMM and optimizing the Calinski–Harabaz index to determine the number
of clusters over de resulting dendrogram), another seven approaches have been used to
compare the results. Ward’s method has also been used with other metrics that do not
take into account the semantic conditions of the variables Meaning and Motivation (e.g.
Chi-squared and Hamming, both assuming all variables are categorical). For the case
using Chi-squared, two possible cut levels appear on the dendrogram and both were
considered. Density-based methods (well known for their capacity to detect clusters of
any general shape) are also used. Thus, both OPTICS and DBSCAN are used with the
Chi-squared metrics, assuming all variables to be qualitative. Each was also tested with
a second metrics: Hamming metrics with OPTICS and DBSCAN with GGMM.
Although validation in non-supervised settings is still an open issue, to compare and
quantify the goodness of the clusters discovered, we considered 15 indexes available in
the literature. They are as follows (+ indicates that the index must be maximised, indicates that it must be minimised):

AvBet (+) is the between-clusters average distance.

AvWit (-) is the within-clusters average distance.

MaxDiam (-) gives the diameter of the biggest class.

MinSep (+) gives the minimum separation between clusters.

wSS (-) is the within-clusters sum of squares.

AvSilWidth (+) averages the silhouette (Rousseeuw, 1987) of all the elements
in each cluster, which quantifies how well an object lies in its cluster.

G2 (+) corresponds to Goodman and Kruskal’s Gamma coefficient (Gordon,
1999).

Pearson’s Gamma (+) is a normalised Hubert Gamma coefficient (Halkidi et
al., 2002), which correlates the distances between objects with a binary matrix
indicating whether two objects are in the same cluster.

Dunn Index, D (+) attempts to identify compact and well-separated clusters
(Halkidi et al., 2002).

Dunn-like, D2 (+) is a robust generalisation of the Dunn index proposed by
Bezdek and Pal (1998), substituting point-to-point distances by average
distances in both numerator and denominator.

Entropy (-) of the distribution of cluster memberships.

wbRatio (-) is the ratio between the within-average distance and the betweenaverage distance.

CH (+) is the Calinski–Harabaz index, based on reaching a compromise
between both the between-cluster and the within-cluster distances.

Widest Gap (-) gives the length of the biggest gap inside a cluster.

Sindex (+) measures the Davies–Bouldin Index (Davies and Bouldin, 1979),
averaging one index for each cluster, which measures the maximum pairwise
comparison involving the cluster and the other clusters in the solution.
These indexes provide several metrics to quantify the structural properties of the
clusters, which are indirectly related with the distinguishability of clusters (like AvBet)
or with their compactness (like AvWit or wS) or which even sometimes evaluate the
relationship between both characteristics (like CH or Dunn). They provide a quantitative
reference regarding the structural strength of clusters. However, in the end and on top of
structural validity, a further step is always required to verify the meaning and usefulness
of the classes obtained (see 3.8).
Finally, we introduce a new index, RankSum, which is the sum of ranks of all the
indexes, as a new means of summarising the information provided by them all and
enabling the best cluster from all points of view to be identified. In RankSum, the tests
were ranked increasingly or decreasingly according to the index interpretation (a higher
rank is always assigned to the best partition, and this corresponds to the maximum or
minimum value depending on the index, e.g. the higher MinSep the better, whereas the
lower widestGap the better). The sum of ranks is maximal if the cluster is the best
according to all indexes, which basically means some clusters are more compact and
distinguishable than others.
Table 4 shows the values of these validity indexes. Our proposed GG-Ward is among
the top positions in most of the indicators. For example, it is the best in the MaxDiam,
wSS and widestGap indexes (which indicates that the clusters are compact and without
internal gaps); it is the second in MinSep, the Dunn and Dunn2 indexes (which shows
that the clusters are also well separated); and it is third in AvBet, avSilWidth, G2,
Pearson, CH and Sindex. The score position of our partition is 4 (out of 8) for Entropy
and wbRatio.
According to the RankSum value, our proposal is the best solution (scoring 93.5
points), outperforming all the other partitions obtained by a large margin except for the
one obtained using the Hamming-Ward-Mode method, which is in second position with
a slightly lower score (91 points). Although the final scores are quite similar, once the
meaning of the proposed classes in this experiment is analysed, the partition obtained
with Hamming is dominated by the Location and Zone variables. It therefore fails in the
semantic interpretation of the other variables, mixing tourists with overlapping
motivations and meanings in each single clustering, and this does not fit with the aims
of the study. In addition, the terms associated with classes are quite generic (only beach,
beauty, culture, goodness and holiday show an important presence in the clusters and
most of these appear in several classes), providing a result which is good from a
structural point of view but not good as a decision support for destination managers.
7. Conclusions and future work
This paper shows that, by using semantic data mining methods to analyse the terms used
by respondents when answering two open-ended questions related to their motivations
for choosing the destination and the personal meaning of the destination, we can go one
step forward from the type of results obtained when conventionally grouping tourists
according to their characteristics, perceptions or motivations. Using the ontology-based
clustering method proposed, it has been possible to identify common structures among
groups of travellers and to establish relevant relationships between motivations and
meanings that might contribute to a better understanding of the decision-making process
followed by tourists and their selection of particular places for their holidays.
Undoubtedly, the application of this method to other destinations will also enable the
specific reasons for holidaying in a certain place to be obtained. The study validates a
new and useful method of analysis for both academics and practitioners. One relevant
conclusion of the study is that, if tourists with different motivations decide to go to the
same destination, this is because the destination has different meanings for each group
of tourists. Thus, having a tool for understanding the relationship between motivations
and meanings such as the one introduced in this paper becomes a first step towards a
new way of understanding tourists’ decisions that could be implemented in any type of
destination.
From a methodological point of view, this study denotes a shift in the way information
on tourist demand is usually collected using surveys and analysed using conventional
statistical tools. It leads us first to consider the greater importance that qualitative, openended questions must acquire to characterise tourist demand and find out about their
decisions. Second, the ontology-based clustering methodology as proposed in this study
can take advantage of the existence of reference information regarding the semantics of
the terms used to describe the elements to be segmented (in this case the tourists),
thereby guiding the classification process. With this method, it is possible to analyse
qualitative responses from a semantic point of view by considering an automatic
interpretation of what words mean based on the use of a reference ontology that
provides a formal framework for describing the conceptual relationships between the
words used by the tourists. With ontology-based clustering, the information directly
expressed by the user can be analysed, thus avoiding, on the one hand, the use of
indirect measures which, even when they can be validated are always controversial, and
on the other hand, the need to apply prior transformations to the data for the purposes of
analysis, maintaining the analysis within the space of the original variables. In addition,
it enables multiple variables of different types to be taken into account, thereby
providing a segmentation of the tourists that considers the various factors in a
comprehensive manner. Hence, the clusters obtained better represent the distinct
segments of tourists, identifying different profiles that can be explained through the
semantic information provided by the qualitative variables.
In this particular application, WordNet ontology has been used as the reference
ontology because it is standard, free-distributed, available, general purpose and provides
support to the English language, which is that used in the questionnaires. Because of
introducing the reference ontology into the clustering process, it has also been required
for introducing specific means for comparing individuals, able to take into account the
reference knowledge provided in the ontology itself. In particular, the SCD can evaluate
the semantic proximity between two words, taking into account their conceptual
relationship expressed in the reference ontology. Thus, this distance is suitable in the
clustering process for evaluating the semantic proximity between the words used by the
tourists and for building the clusters accordingly.
From the point of view of the results obtained, important findings also arise. The
results show that tourists travelling to the same destination do not form a single and
homogeneous group. In the case of the Costa Daurada and Terres de l’Ebre analysis,
this can be seen particularly as regards the sun and sea motivation. In fact, although
traditional analysis leads us to think that there is a pure tourist who spends their holiday
on the beach, which is the main attraction asset for the British tourist in the Costa
Daurada and Terres de l’Ebre, the analysis performed shows that in addition to this
segment, which does indeed exist, there is another segment looking for a beach holiday
whose sense of the place is that the area is their holiday destination, a tourist destination
typical of beach holidays. There is yet another segment motivated by the beach but
which chooses the destination taking into account its ability to offer relaxation, beautiful
landscapes and wellness, where the concept of beach appears as being more attached to
its values of wellness and relaxation.
Among British tourists, there is a segment of tourism motivated to spend a holiday in
the sun, and that is the main focus of their motivation and of the meaning the destination
has for such tourists. We should point out the emergence of beach tourism as being
separate from sun tourism. This concept breaks with the usual association of the two
words sun and sand. Although the beach will require sun in almost all cases, it is
interesting that destinations or areas that have sun but not beach can be made aware that
the meaning of sun has a segment of potential demand in itself, at least insofar as the
British tourism is concerned. Moreover, a segment appears in the British tourism that is
not associated with the beach or coastal destinations per se, but who choose the
destination because of its cultural wealth, its shopping, and because it is a tourism
destination in itself.
The results also have implications related to the Push–Pull theoretical framework. It
certainly appears useful and appropriate to have destination meaning values as pull
factors. This is a framework that, when used as proposed in this paper, allows a
complex understanding of the decision taken by different segments of tourists visiting
the same place. Indeed, the pull factor based on the meaning of the destination has
proved more useful in defining segments than the push factor itself. In other words,
including the meaning of the destination to identify different segments and define the
motivation behind the choice of destination has shown that tourists who manifested
similar push factors have been understood as being different based on the meaning
given to the chosen destination. The study has also shown that semantic data mining
allows a much richer analysis because it works with the meaning (semantics) of every
response, opening the door to combined studies in which the numerical information
associated with a more classical approach can be analysed alongside the information
obtained in conceptual terms without losing the richness that an open qualitative
response can offer and providing a more integral view of the tourists as a whole.
Finally, the paper has several practical implications for destination managers because
it proves that destinations can be segmented on the basis of what they mean to the
tourists as places. Destination managers can exploit the tourist segments discovered to
improve the effectiveness of their marketing decisions. For example, they can design
different advertising campaigns or strategically address messages to any relevant
segment by taking into account the reasons that explain the decision-making process of
the individuals it comprises. Additionally, the proposed methodology could also be
applied to other fields that require working with surveys with open questions and
geographical information. Pull and push factors can also be recognised in other
domains. Their interpretation will then be different, but the Ontology-based Clustering
method could still be a good tool to use to discover interesting profiles.
As a future work, we plan to study other international tourist markets that also have a
significant number of visitors to the Costa Daurada and the Terres de l’Ebre. In this
case, the language will have to be changed, which means the appropriate ontologies to
be used during the clustering process will have to be found. A comparison of the
segments identified from different countries would be also interesting to detect the
similar and unique factors of each when selecting the destination. This would help the
managers of these tourist destinations to devise more appropriate marketing campaigns
and define appropriate policies to promote the tourist attractions of the different places
involved.
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Aida Valls is a Lecturer at the Department of Computer Science and Mathematics in
Universitat Rovira i Virgili (URV). She received her PhD in Computer Science from the
Technical University of Catalonia in 2002. Her research interests include multiple
criteria decision-making, recommender systems, data mining and privacy preserving.
Her work is mainly focused on the treatment of linguistic and semantic information.
She has participated in several Spanish and EU research projects, with applications in
Tourism, Environment Risk Management and Health Care. She is the author of more
than 80 papers published in international journals and conferences. She is currently
the Vice-president of the Catalan Association for Artificial Intelligence and the Head of
the PhD Program in Computer Science and Mathematics of Security at URV.
Karina Gibert is an Associate Professor at the Universitat Politècnica de CatalunyaBarcelonaTech (UPC). She received her PhD in Computer Science. She was a Pioneer
member (1986) and current Head of the Knowledge Engineering and Machine Learning
research Group (UPC), with excellence certificate from the Catalan government. Her
areas of interest include hybrid AI and Statistics Data Mining, AI, intelligent data
analysis, pre and post-processing, knowledge discovery and data mining, intelligent
decision support, and data science. She was particularly interested in multidisciplinar
and applied research as well as technology transfer to productive sector. She has long
experience in health and environmental applications. She has participated in several
Spanish and EU research projects. She was a Technical consultant of Catalan
Government for Spanish Dependency Law deployment (2006–2008); Consultant of the
Mental Health Department at WHO (2008–2010); Head of Plant-Guesser project
(Aquology, AGBAR group, 2014); IP of Diet4You project (2015-, Spanish Government);
EU-best e-Health European project for K4Care (2010). She was a Winner of 1rst
HackingBullipedia contest (Telefonica and elBulliFoundation from Ferran Adrià, nov
2013). She organizes special workshops in Data Mining and Intelligent decision support
systems in the International conferences from 2000, in particular, in the Int’l Congress
on Environmental Modelling and Software from 2002. She has conducted intensive
scientific research (more than 300 papers, 37 SCI-papers, H-index:17), with
uninterrupted excellence certification from the Spanish government since 1990.
Alicia Orellana is the Director of the Tourism Observatory at the Science and Technology Park for Tourism and Leisure of
Catalonia. She holds a large experience in tourism market research. She is in charge of the measurement of the
performance of the tourism accommodation industry in several tourism brands and local destinations in Catalonia since
2001. As a director of the Tourism Observatory, she has also been in charge of ad hoc market and destination survey
operations, data analysis and in company statistical research studies. She is a Social Pyschologist from the Rovira I Virgili
University (1996).
Salvador Antón-Clavé is a Full Professor of Regional Geographical Analysis at the
Rovira i Virgili University (URV) and serves as the Director of Research at the Science &
Technology Park for Tourism and Leisure. He is currently Head of the Phd Program in
Tourism and Leisure at the URV and Research Scholar at the International Institute of
Tourism Studies at the George Washington University. He has served as the
Director/Dean of the Tourism and Leisure School/Faculty of Tourism and Geography at
the Rovira i Virgili University between 2002 and 2012. His has extensively published on
the analysis of the evolution of tourism destinations, urban and regional tourism
planning and development, the globalization of leisure facilities and issues concerning
tourism and ICT.
Fig. 1. Dendrogram representing the hierarchical clustering of British tourists.
Fig. 2. Class Panel Graph of British tourists segments.
Table 1. Recoding of original answers to Motivation-Reason into WordNet concepts.
Original answer
Port Aventura
WordNet term
Freq
1.
Entertainme
nt
2.
5
3.
Entertainme
nt
4.
1
5.
Friend
6.
5
7.
Holiday
8.
4
9.
Property
10. 2
11. Property
12. 1
13. Family
14. 2
15. Culture
16. 1
17. Culture
18. 1
19. Beauty
20. 1
21. History
22. 1
23. Return
24. 1
25. Transition
26. 1
27. Permanence
28. 1
29. People
+
30. 1
31. Attractivene
32. 1
Aquopolis
Visiting friends
Vacances
Second home
Owned house
Visit parents
Gaudí
Tarraco Viva
Love it
Too see historical things
Been before
Our way to Morocco
Stay here
Spanish people
location
Good-looking
ss
Party
33. Festivity
34. 1
35. Concerts
36. 1
37. Sportfishing
38. 1
39. Job
40. 1
41. Typicality
42. 1
43. Calmness
44. 1
45. Sea
46. 1
School music tour
Holidays fishing
Work
Very Spanish
No commercial
Near the sea
Table 2. Recoding of original answers to Meaning into WordNet concepts.
Original answer
WordNet term
Freq
Port Aventura
47. Entertainment
48. 1
2
Good-looking
49. Attractivenes
50. 8
s
Very-good
51. Goodness
52. 2
53. Holiday
54. 1
55. Sea
56. 1
57. Distinctivene
58. 1
Vacances
Beach water
Different
ss
Restful
59. Relaxation
60. 1
61. Landscape
62. 1
63. Kindness
64. 1
65. Cheapness
66. 1
67. Sky
68. 1
Balcó Mediterrani
Friendly
Good price
Blue sky
Table 3. Segmentation of the British tourism in the Costa Daurada and Terres de l’Ebre.
Push factors
MotivationReason
C453
Visiting
N=151
Pull factor
Meanings
69.
Location
70.
Typology of places
72. Iconic
71.
(51)
City spots (24)
Inland (12)
Quiet southern natural areas (11)
Tourism (21)
Shopping (20)
Tourism (19)
Culture (16)
Cambrils (13)
Niceness (11)
Salou (18)
La Pineda (14)
Terres de l’Ebre (11)
C455
Enjoying
N=149
73.
74.
76. Iconic
75.
(36)
City spots (34)
Quiet southern natural areas (23)
Iconic (9)
Inland (5)
La Pineda (33)
Cambrils (22)
Tarragona (23)
Niceness (21)
South (19)
Relaxation (16)
Inland TE (9)
Entertainment (9)
C446
Holidaying
N=52
77.
78.
80. Iconic (7)
79.
Unique Beaches (6)
City spots (6)
Cambrils (12)
Pineda (13)
Beach (14)
C449
Beaching
N=42
81.
82.
84. City spots
83.
(22)
Iconic (5)
La Pineda (14)
Beach (8)
C438
Sunbathing
N=39
85.
86.
88. City spots
87.
(9)
Unique beaches (6)
Iconic (9)
La Pineda (10)
Tarragona (5)
Beach (8)
C414
Relaxing
N=27
89.
90.
92. City
91.
spots(5)
Iconic (5)
Unique beaches (5)
Quiet southern natural areas (3)
Goodness (5)
La Pineda (6)
Relaxation (4)
South (3)
Table 4. Cluster validity indexes for several clustering methods.
Metric
s
Partitio
n
AvB
et
AvW
it
MaxDia
m
MinSe
p
wSS
avSilWid
th
GG
0.82
0.65
1.72
0.26
99.39
0.20
0.71
0.41
3.33
0.05
116.06
0.05
0.74
0.46
4.11
0.05
3.68
2.57
4.00
2.00
165.30
1634.6
0
0.03
Hamm
Ward
Wardcut1.7
Wardcut2.2
WardMode
chi2
Optics
0.38
0.67
4.34
0.03
-0.45
Hamm
Optics
DBSCA
N
DBSCA
N
3.62
2.50
4.00
0.00
174.71
1573.8
6
0.79
0.74
1.72
0.00
130.82
-0.05
0.63
0.61
4.34
0.00
188.81
-0.03
chi2
chi2
GG
chi2
0.30
0.23
G2
0.4
5
0.3
5
0.3
0
0.8
6
0.4
1
0.8
1
0.1
7
0.1
6
Pearso
n
0.39
0.22
0.23
0.61
-0.17
0.55
0.09
0.01
D
0.1
5
0.0
2
0.0
1
0.5
0
D2
0.8
6
0.2
1
0.2
0
1.0
8
0.0
1
0.0
0
0.0
0
0.0
0
0.1
6
0.8
5
0.7
1
0.2
3
Entrop
y
wbRati
o
CH
widestG
ap
Sinde
x
RankSu
m
1.57
0.80
42.96
0.95
0.31
93.50
1.56
0.58
14.31
2.07
0.06
81.00
1.08
0.62
2.75
0.06
69.00
1.37
0.70
3.77
108.3
1
3.00
2.00
91.00
0.34
1.75
-3.32
2.21
0.05
40.50
1.69
0.69
70.70
3.00
0.96
70.00
1.69
0.95
10.82
1.05
0.02
55.50
1.69
0.98
-8.00
2.23
0.05
39.50