Abstract and Figures

The tourism sector has an essential role in the sustainable development of a country. Therefore, in this research we propose a methodology to identify tourist routes that integrate the most important Points Of Interest in a region taking up as criteria profile characteristics in common between the sites evaluated using clustering techniques. To attain this goal, firstly, a literature review focused on compiled information used for location selection and evaluation in attraction potential sites. Then, clustering techniques are applied to identify similarities between sites, and finally, a layout of tourist routes is presented. We applied this methodology using data from a Region in Colombia. As a result, eight factors are proposed: Natural, Cultural, Tourist Plant, Infrastructure, Superstructure, Accessibility, Human and Tourist Capital and Security. From the second phase, three tourist groups were identified with three tourism factors for each of them; and then, two examples of tourist routes are proposed.
Content may be subject to copyright.
Research article
Methodological proposal for the identication of tourist routes in a
particular region through clustering techniques
Juan B. Duarte-Duarte, Leonardo H. Talero-Sarmiento, Diana C. Rodríguez-Padilla
Universidad Industrial de Santander, Facultad de Ingenierías Físico Mec
anicas, Escuela de Estudios Industriales y Empresariales, Bucaramanga, Colombia
Clustering techniques
Sustainable development
Tourism factors
Tourist clusters
Tourist routes
The tourism sector has an essential role in the sustainable development of a country. Therefore, in this research
we propose a methodology to identify tourist routes that integrate the most important Points Of Interest in a
region taking up as criteria prole characteristics in common between the sites evaluated using clustering
techniques. To attain this goal, rstly, a literature review focused on compiled information used for location
selection and evaluation in attraction potential sites. Then, clustering techniques are applied to identify simi-
larities between sites, and nally, a layout of tourist routes is presented. We applied this methodology using data
from a Region in Colombia. As a result, eight factors are proposed: Natural, Cultural, Tourist Plant, Infrastructure,
Superstructure, Accessibility, Human and Tourist Capital and Security. From the second phase, three tourist
groups were identied with three tourism factors for each of them; and then, two examples of tourist routes are
1. Introduction
Tourism site integration using curated routes is a strategy that could
improve sustainable tourism development and, at the same time, increase
the quality of life of local people. Different countries often regard tourism
as the preferred alternative to development (Briedenhann and Wickens,
2004) because of its economic and social value at the global scale.
Tourism plays a key role in sustainable development and impacts the
climate (UNWTO, 2008); tourism has also been identied as a tool to
raise the economic growth of underdeveloped regions and to improve the
standard of living of local communities (R
atz and Puczk
o, 1998)(Kom-
bol, 2000). Additionally, tourism presents sociocultural benets, im-
proves education, and reinforces the maintenance of culture and heritage
(Jafari, 2005).
Taking advantage of the benets of tourism, over the years, its eco-
nomic impact has been studied using different methodologies. For
example, Deng et al. (2002) have used multicriteria analysis techniques
to categorize Victoria Park in Australia as a tourism resource, and the
same technique have been used by Triantaphyllou (2000) in Bosnia,
Laguna Marín-Yaseli and Nogu
es Bravo (2003) in Iberian Mountain
Ranges, Infante S
anchez (2014) in Colombia, Egül &Ik (2014) in Turkey,
Rubio et al. (2016) in Argentina, Camarena (2016) in M
exico, and
Morteza et al. (2016) in Qeshm Island, Iran, among others. On the other
hand, Zimmer and Grassman (1996),Padín and Pardellas (2004),Blanco
es Cabello and Pascual Bellido (2015) have used the
LEADER method, a guide to evaluate the tourism potential of a territory,
in different regions of Spain and Mexico. Furthermore, Eagles et al.
(2001) and Mishra (2009) in different areas of Asia; Mart et al. (2013) in
Mexico; and Alam et al. (2015) in Malaysia, have used statistical methods
to evaluate tourism sites. Many other authors have employed other
Most methodologies usually use qualitative techniques that require
expert analysis (Laitamaki et al., 2016). However, there are different
quantitative techniques based on the structural information of tourist
sites, such as clustering techniques (CTs). There is evidence that the
application of CTs can create cooperative relations in high-tech sectors,
where the most valuable capital is knowledge and cooperation occurs
between companies and academia (Porter, 2000)(Rocha, 2004). How-
ever, it is possible to apply these techniques in more traditional sectors,
such as tourism: for instance, Pitchayadejanant and Nakpathom (2018)
clustered the agrotourism activities in an orchard using machine
learning, Gosal et al. (2019) processed and clustered the attractiveness of
different tourist locations, Nilashi et al. (2019) used clusters to predict
touristic choice preferences in ecofriendly tourism, and Guo et al. (2020)
clustered tourism attractions to predict the competitiveness of a city,
* Corresponding author.
E-mail address: diana.rodriguez15@correo.uis.edu.co (D.C. Rodríguez-Padilla).
Contents lists available at ScienceDirect
journal homepage: www.cell.com/heliyon
Received 24 September 2019; Received in revised form 26 February 2020; Accepted 29 March 2021
2405-8440/©2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
Heliyon 7 (2021) e06655
among others. These clustering techniques can create a promising new
development direction for the tourism industry (Estev~
ao et al., 2009).
We propose the use of CTs to design tourist routes (TRs) in a region to
maximize the potential benets of tourism. These routes are important
because TRs are tools that highlight the cultural heritage of sites, pro-
moting and beneting those who are part of it (Jeambey, 2016)(Brie-
denhann and Wickens, 2004). Additionally, TRs integrate several
attractions that have not been studied independently, allowing an equi-
table distribution of their benets (Meyer, 2004). Consequently, tourist
route implementation is a strategy to optimize job creation and tourism
and conserve the historical, cultural, and folkloric heritage of any site
(Open Africa, 2002).
Therefore, the research question is, How can we identify a meth-
odology to propose tourist routes in a particular region?In this paper,
we propose a methodology to identify theoretical tourist routes in such a
way that different tourist attractions can be integrated into a region. In
Phase 1, a literature review is performed to collect characteristics or at-
tributes to describe the tourism cluster of a site. In Phase 2, we apply a
clustering technique among sites to nd patterns and similarities among
them, identifying specic clusters. Finally, Phase 3 designs tourist routes
by integrating these clusters.
2. Literature review
To allow tourism on a site, different organizations and researchers
have proposed diverse methodologies to identify and categorize touristic
factors (UNWTO, 2013). These factors are an elemental guide in the
tourism process (Knezevic, 2008). Consequently, this investigation uses
predened characteristics and proposes to group them into a hierarchical
structure, with eight factors, covering 27 subfactors (Figure 1) described
in Table 1.
Next, we describe the methodologies used in different studies due to
their essential contributions to the classication of tourist factors.
2.1. Pierre defert
Defert (1972) proposes the categorization of tourist resources as fol-
lows: Hidrom (water); Litom (elements built by man); Antrop
om (so-
cioeconomic structure); Phitom (Natural terrestrial elements); and
enome (elements that generate memory). This categorization of re-
sources has been used by authors such as Camara and Morcate Labrada
(2014) to evaluate the city of Fort de France. Additionally, the consulting
company Auren Consultores (2014), directed by the School of Industrial
organization of Spain, compared Defert's classication to those of other
authors to choose the most suitable classication for Andalusia, Spain.
This comparison was also performed by researchers at the University of
San Luis Potosí in Mexico (2015) and by Rubio et al. (2016), a Ph.D. in
Geography of the Universidad Nacional del Surin Argentina.
The Secretary of Tourism of Mexico designs and implements policies
aimed at strengthening tourism activity (SECTUR, 1975).These policies
have been applied by various authors, such as Reyez and S
anchez (2005).
Reyes has used three variables to calculate the potentiality index of
natural tourism: 1) natural variables (geomorphologic, vegetal associa-
tions, and natural elements); 2) accessibility variables (terrestrial, mari-
time and air transport, gas station, and road density); and 3) equipment
variables (hotels, food establishments, tourist promotion units, banks,
and commercial establishments). Other authors who have used the
SECTUR information are Martínez (2010)), Mikery Guti
errez and
azquez (2014),Camara and Morcate Labrada (2014) and Cort
(2015); these authors used SECTUR information to analyze the tourism
potential of their respective case studies.
2.3. OAS
The Organization of American States (1987) proposed a resource
categorization divided into the following categories: 1) natural sites
(landscape-based resources); 2) museums and cultural traditions (art,
history, and monuments); 3) folklore (related to the traditions of the
resident population); 4) contemporary technical, scientic and artistic
realizations (elements that by their singularity have tourist interest and
are more current than historical characters); and 5) programmed events
(organized events, current or traditional). This categorization is easy to
replicate; several authors such as Blanco (2008),Conti et al. (2012),Solís
et al. (2013),Mikery Guti
errez and P
azquez (2014),Castellanos
Human and Tourist
Cultural Factor
Natural Factor
Touristic Facilities
Price and Quiality
Touris tic Activi ties
Touristic Attractions
Information and Communication Channels
Local Community (guides, atention in
another language, etc)
Folklore (music y dance, gastronomy,
ethnic groups, handicrafts, etc)
Natural Sites (number of: mountains,
coastlines, thermal baths, rivers, nature
conservation parks, etc)
Basic Services (water services, electricity,
garbage collection, etc)
Food Facilities.
Accommodation Facilities.
Supporting Services
Transport System
Environmental Safety
Economic Safety
Health Safety
Recreation and Events Safety
Medical Sa fety.
Public Safety.
Social Safety.
Tour ist Serv ices S afety
Transportation Safety
Private (tourism service providers, user
associations, etc)
Transport Systems (massive transport)
Festivities ans Festivals
Road system (highways, roads, pedestrian
zones, etc)
Recreation Facilities
Supporting Services
Public (tourism organizations, support
Transport System (depots, airports, buses,
taxis, etc)
Natural Sites (number of mountains,
coastl ines, ther mal baths, rivers, nature
conservation parks, etc)
Museums and Cultural Events (number of
museums, and archaeological sites)
Native Community (guides, attention in
another language, etc)
Figure 1. Tourism Factors and sub-factors.
J.B. Duarte-Duarte et al. Heliyon 7 (2021) e06655
Menjura and Ariza Cortes (2015), and Navarro (2015) have applied this
methodology to their tourist resources inventory.
2.4. LEADER guide
Zimmer and Grassman (1996) created the LEADER guide to evaluate
the tourist potential of a territory. He took into account the offer (natural
factors, socioeconomic factors, infrastructures and services available,
cultural factors, sports and leisure offerings, health offerings, accom-
modation, restaurants, and sites with the possibility of organizing con-
ferences and seminars) and the demand (dened from customer surveys).
Then, Peter carried out an analysis of the competition and, nally, per-
formed a study of the market trends to anticipate the opportunities and
risks linked to the new expectations of clients. Authors such as Blanco
azquez (2014),Andr
es Cabello and Pascual Bellido (2015), and
Vanegas (2017) have used the guide to apply tourism research method-
ologies in different parts of Europe and Latin America.
2.5. Gunn
Gunn (2002) has proposed factors such as market preferences,
advocacy, information systems, restrictions and benets (social envi-
ronment, governmental disposition, among others), and physical factors,
composed of potential destination areas, by geographic and competitive
contexts, to identify new and improved attractions and threats to the
environment. The authors who have used this categorization are Cha and
Uysal (1995),Smith (1987) and Smith (2013).
3. Methodology
3.1. Data
First, a region is selected (taking into account a region could be
divided into micro regions or sites to apply the following methodology
where the identied characteristics in the literature review are consulted
from primary or secondary sources such as: interviews from the
inhabitants of the region, going to each site of interest, looking for in-
formation in the regional ofcial website where you can nd its general
tourist characteristics; ofcial website of general and specic statistics of
the sites of the region, newsletters, articles, housing census, citizenship,
development plans, tourism development plans, inventories of the
region's assets, among others.
The data must be considered in terms of different types of variables
(e.g., the scale of measurement, reason variables, quantity variables, and
dichotomous variables). These variables have to be stored and classied;
furthermore, each evaluated site should have a numerical value for each
of these variables; that is, no missing data are allowed.
3.2. Touristic clusters
Clustering techniques can recognize patterns and similarities between
the characteristics of entities (Instituto Valenciano de Tecnologías
Turísticas, 2015). CTs organize studied entities from the information
provided by the decision maker; in this case, the information represents
the characteristics collected in the literature review section. Moreover,
the hierarchical clustering technique has excellent performance (Everitt
et al., 2011), allowing classications of greater accuracy than permuta-
tion methods such as K-means (Karypis et al., 2000)(Li et al., 2002).
Nevertheless, to apply this technique, there are two requirements: 1) the
selection of a link method and 2) the implementation of a distance
function (Wilks, 2011).
However, considering that, the proposed factors and their corre-
sponding characteristics have different types of variables, such as the
scale of measurement, according to reason, quantity and dichotomous, it
is necessary to transform them to the same scale. To do this, we use the
Gower coefcient of similarity because it allows the integration of
quantitative and qualitative variables (Lopez and Villa, 2011)(Podani,
2012). Furthermore, we can estimate the Euclidean distance Dij between
each pair of townships ij using the Gower similarity (Sij ) according to the
following relation D2
ij¼2ð1SijÞ. The coefcient of similarity between
the townships ij is explained by Eq. (1):
Table 1. Tourism Factors proposed.
Factor Description Author
Natural Any geomorphologic or biophysical element that allows the visit, appreciation,
and satisfaction of tourists. Zimmer states that a natural resource is one
that people can use to satisfy their needs to be alive (Zimmer and Grassman, 1996).
Vegetal associations, hydrography, ora, fauna, and climate, among others.
(SECTUR, 1975)(OEA, 1987)
Cultural Keeping historical traditions, which, according to the International Council of ICOMOS,
are dened as cultural heritage, historical sites, past and present traditions,
and monuments (ICOMOS, 1999).
Folklore, art, programmed events, and gastronomy, among others.
(OEA, 1987)(Clawson and Knetsch, 1968)
Tourist Plant Related to establishments managed by the public or private sector. These establishments
provide tourist services (Camara, 2014), attending the needs and desires of the tourist
(Quesada Castro, 2006). Hotels, restaurants, and touristic agencies, among others.
(SECTUR, 1975)(Zimmer and Grassman, 1996 )(Boull
on, 1997)
Infrastructure Concerning the set of elements, equipment or services necessary for the proper functioning
of a country, of a city or any organization (RAE, 2015). Transport systems, sanitation, public
services, signage, and medical facilities, among others.
(Zimmer and Grassman, 1996)(Boull
on, 1997)
Superstructure All the specialized agencies, public and private, of the local community responsible
for optimizing and
changing the functioning of each part of the tourist system (Labrada, 2014)(Quesada Castro, 2006).
(Zimmer and Grassman, 1996)(Boull
on, 1997)(Almeida, 2009)
Human and
Tourist Capital
Dened as the number of people with technical knowledge, skills,
singularity, and qualications (Lillo, 2006).
Attention in another language, native community, education, health conditions, and social
characteristics of the population, among others.
(Maass et al., 2010)
Accessibility The characteristic that allows environments, products, and services to be used
seamlessly by all people, achieving
the objectives they are designed for, as well as the relationship with the three primary forms
of human activity: mobility,
and understanding. These three forms of human activity are limited by barriers (L
opez, 2002)
(SECTUR, 1975)(Padín Fabeiro and Pardellas de Blas, 2004)
Security Interpreted as entities that allow us to perceive that we move in a space
without real or potential risks (MINCIT, 2003).
Fire prevention, food sanitation, and police services, among others.
(Solís et al., 2013)(Maass et al., 2010)
J.B. Duarte-Duarte et al. Heliyon 7 (2021) e06655
Sij ¼Pn
where Wijk ¼0 if entities jand kcannot be compared for variable i
because xij or xik is unknown.
Additionally, Gower denes:
For binary variables: Wijk ¼1yGijk ¼0ifxij xik;Wijk ¼Gijk ¼1if
xij ¼xik ¼1orifxij ¼xik ¼0 and double zeros (mutual absences) are
included; Wijk ¼Gijk ¼0ifxij ¼xik ¼0 and double zeros are excluded
from the comparison.
For nominal variables: Wijk ¼1ifxij and xik are known; then let, Gijk ¼
0ifxij xik;Gijk ¼1ifxij ¼xik.
Furthermore, for variables measured on the Interval and ratio scale: Wijk ¼
1ifxij yxik are both known and Gijk ¼1f
xij xik
=ðrange of variable iÞg
(Gower and Legendre, 1986).
Finally, we study the link method in each case; however, considering
the distance functions estimated in this investigation and its character-
istics, we suggest applying methods that use squared distances.
3.3. Tourist routes: related sites integration
For the design of the routes, we consider the recommendations of
Aravena Paillalef et al. (2013),Cadena et al. (2013) and Garrido et al.
(2015). The design process begins by identifying tourist points of interest
to integrate using routes. These points are subsequently located on a map
to determine their geographical distribution and to analyze the possible
integrations to make between them.
The design of these routes must consider the location of the sites and
their geographical proximity to one another, without taking into account
the road infrastructure between them. That is, routes can have two kinds
of sections: real and hypothetical (a.k.a. theoretical) roads. The real
routes link the sites without taking into account the road type (i.e.,
whether the road is paved or not). The hypothetical routes, on the other
hand, are unbuilt connections that will be required. We use this meth-
odology to identify possible infrastructure improvements that could be
helpful in connecting tourist sites.
4. Results
The methodology proposed was applied in Santander, which is one of
the 32 departments in Colombia. Santander is in the northwest part of the
country (Figure 2). Santander is composed politically of 87 townships
and is considered the fourth most important economy in Colombia,
contributing 7.8% of the total national gross domestic product (GDP). We
select Santander in this study due to its diversied economy, which
causes heterogeneity in the distribution of wealth among its inhabitants
(de Santander, 2016), as represented in the indices of monetary poverty,
the Gini coefcient (indicator of the distribution of income of individuals
within an economy) and the index of unmet basic needs (Departamento
Administrativo Nacional de estadística, 2018). By applying this meth-
odology and taking into account the benets of tourism, the economic
disparity in Santander may be able to be reduced.
4.1. Data
According to the information available from secondary sources (the
ofcial website of Santander governorate, the ofcial website of the
National Administrative Department of Statistic (DANE), among others),
we collect 79 characteristics for every township (Annex 1. 79 character-
istics for each Townships) 12 belong to the Accessibility factor, 10 belong
to the Human and Tourist Capital factor, 11 belong to the Cultural factor,
11 belong to the Natural factor, 11 belong to the Infrastructure factor, 16
belong to Tourist Plant, 5 belong to Security and 3 belong to Super-
structure. Applying the hierarchical clustering technique, we identied
that 6 of the 79 characteristics are dichotomous variables, 50 are vari-
ables of quantity, 21 are expressed as a percentage, and one is a reason
variable (Annex 1. 79 characteristics for each Townships).
4.2. Touristic clusters
According to the characteristics identied, to apply the clustering
techniques, we selected the Ward link method and the squared Euclidean
distance due to the ease of identifying atypical values between entities in
the same group; as a result, three tourist groups are proposed considering
the potential touristic identication. The Natural, Cultural, and Human
and Tourist Capital factor data are used to form the clusters; additional
data (Accessibility, Infrastructure, Tourist Plant, Security, and Super-
structure) are used to analyze the general infrastructure of each town-
ship. The Similarity Matrix and the Distance Matrix are in Annex 2.
Similarity Matrix and Annex 3. Distance Matrix, respectively.
We choose three groups because fewer groups would lead to negative
similarity (atypical relationships) and more groups would not signi-
cantly increase the similarity. We highlight that the groups are hetero-
geneous in size, as indicated in the dendrogram, a diagram showing the
similarity of attributes between each pair of entities grouped sequentially
(Minitab 18, 2018). We use yellow, blue, and red (Figure 3) to differ-
entiate groups; the geographical distribution of each group is given on
Santander's political map (Figure 4).
Touristic Cluster 1 (Blue): This cluster has the most signicant
raizalpopulation (he native population of San Andr
es, Providencia and
Santa Catalina due to crossbreeding between indigenous, Spanish,
French, English, Dutch and Africans (Ministerio de Cultura de Colombia,
2004)) located in the El Pe~
on, Coromoro, Guadalupe and Oiba town-
ships. This cluster also includes some of the most representative cultural
events in the region, such as "Las Ferias Bonitas" (the beautiful fairs), the
Tiple national week celebrating the traditional musical instrument, the
Luis A. Calvo festival in Bucaramanga, the Guabina and Tiple festival in
Figure 2. Representation of Santander-Colombia.
J.B. Duarte-Duarte et al. Heliyon 7 (2021) e06655
elez, the music Festival in San Vicente de Chucurí, and the Guascarrilera
festival in Matanza. Additionally, most of the townships around the
Chicamocha canyon (Aratoca, Cepit
a, Los Santos, and Villanueva) are in
this group.
Touristic Cluster 2 (Red): This group of townships stand out because
they have large indigenous populations, mulattos and Afro-Colombians
(referring to Colombian citizens of African descent), mostly distributed
in the towns of Cimitarra (with 3326 inhabitants of this type of popu-
lation), Puerto Parra (with 1454), Palmas de Socorro (with 1369) and
Puerto Wilches (with 1337) according to DANE's census and projections
for the population of each township in 2015. This group also has the
fewest cultural components compared to the other groups, with only 5 of
the 40 townships having representative aspects in this area. These
components are found in Socorro, widely known due to the insurrection
of the comuneroscommunity, where the basilica "Nuestra Se~
nora del
Socorrois located; in Surat
a, where the Bambuco National Festival
(traditional dance) is celebrated; in Pinchote, where the Trios National
Festival is celebrated; in Güepsa, where the Panelafestival is held,
featuring traditional brown sugar food; and in Chipat
a, the location of the
song festival. Finally, this group includes the P
aramo of Santurb
an, which
includes the townships of California, Santa Barbara, Surat
a, and Vetas.
Touristic Cluster 3 (Yellow): Represented by the most signicant
number of the ROMpopulation, or Gypsy ethnic communities, estab-
lished in Colombia (Ministerio de Cultura, 2010). Located in Gir
Floridablanca and Lebrija, these townships have several museums, such
as the Guane Archaeological Museum located in Floridablanca, "Mansi
del Frailemuseum in Gir
on, the Quijote de la Manchahouse museum in
Zapatoca, and Petroleum National Museum in Barrancabermeja. This
group is also recognized for its fairs and festivities and its handicrafts as
well as because there are some renowned typical food delicacies in these
townships, such as "Obleas" in Floridablanca, Culonasants in Bar-
ichara, artisan wine in Zapatoca and "Panuchas" (traditional candy) in
Malaga. These townships have several well-known caves, including "La
Pintada" cave in Lebrija, Caverne in Malaga, Nitro cave in Zapatoca, and
Guane cave in Gir
on, among others.
4.3. Tourist routes: related sites integration
The routes are shown on a political map of Santander and have been
added to Google Earth, a web-based mapping service platform. The
routes can be designed considering each cluster; for example, from
Cluster 1, a route was designed considering the proximity of each site of
interest to the townships without taking into account the existing routes
(Figure 5 and Figure 6) (this route is in Google Earth in Annex 4. Cluster 1
Route 1)
Route: Chicamocha canyon and more.
This route focuses on a scenic tourism tour, and it begins at Los
Santos Township, Cepit
a, Aratoca; continues to Curití, San Gil; and
ends at Villanueva. These locations contain the extensive Chicamocha
canyon, and it is also possible to appreciate other touristic sites, such
Figure 3. Dendrogram showing the three clusters selected.
Figure 4. Geographic clusters on Santander map.
J.B. Duarte-Duarte et al. Heliyon 7 (2021) e06655
as el Salto del Duendein Los Santos, Salto del Mono Aulladorin
Aratoca, the Yesocave and the La Vacacave in Curití. In addition,
tourists can enjoy the famous Fonce River in San Gil, where foreigners
can practice sports such as rafting or canoeing and, nally, the Indio
cave in Villanueva.
Analyzing the general infrastructure of these townships, considering
their geographical information and other data factors (Accessibility,
Infrastructure, Tourist Plant, Security, Superstructure), the canyon of
Chicamocha provides opportunities for landscape and scenery sight-
seeing, extreme sports, and historic tours. Accordingly, it will be
Figure 5. Touristic Cluster 1 Route 1 sites.
Figure 6. Touristic Cluster 1 Route 1. Figure 7. Touristic Cluster 2. Route 1.
J.B. Duarte-Duarte et al. Heliyon 7 (2021) e06655
necessary to build access roads between Los Santos and Aratoca due to
the lack of existing connections between them. Furthermore, most of the
townships on this route must improve and expand their internet
coverage. Additionally, the pavement of the roads in this region should
increase the number of tourist agencies as well as the options to practice
different kinds of sports.
Another tourist route we can nd in Cluster 2 is route 1 (Figure 7 and
Figure 8) named:
Route: Biodiversity of the Santurb
an P
This route focuses on landscape sightseeing tourism that begins with
a, continues to California, and ends in Vetas Township to develop
tourism around the Santurb
an P
aramo. Here, tourists can see the Cattleya
Mendelii and Befaria owers, the Frailejon, 15 species of mosses,and
several species of animals, including the Chirrador, the Pato Zambulli-
dor,and the Curí and the Condor, among others. Additionally, tourists
can visit La Lagunaand buffaloes in Surat
a; the P
aez,Pico,Quelpa and
Toro lagoons; hot springs; the San Antonio de Padua sanctuary, which is
located in the foothills of the eastern Colombian Cordillera in the Andean
Mountain system in California; and the La negra Lagoon in Vetas.
Analyzing the infrastructures of these three townships, we note that
although California is geographically next to Vetas, these townships are
not connected in terms of roadway infrastructure; therefore, it is pro-
posed that these townships be connected to take advantage of the tourist
activities provided by the P
5. Discussion
This article proposed to answer the following research question: How
can a methodology be identied to proposed touristic routes in a
particular region? To accomplish this aim, articial intelligence tech-
niques that allow us to systematically analyze all the characteristics that
can dene a possible tourist site are proposed. However, considering the
literature review, we found that there are multiple variables to determine
potential tourists of a site that can vary depending on the site analyzed
and the methodology used to evaluate the site. For example, there are
discrepancies in SECTUR (1975) methods; Zimmer and Grassman
(1996), among others. Additionally, it should be noted that not all sites
have the same characteristics, and therefore, as a preliminary step, the
elds must be ltered. In this study, since we evaluated the department of
Santander, we eliminated some characteristics considering its
geographical location (e.g., number of beaches, and seasons).
This process is essential, as the characteristics found can also change
according to the data available at the time of the study. Variable selection
is a critical process because if there are different variables, it could be
possible to develop different tourist proles than those found in the
present study. However, if in the future, different characteristics are
considered, these will not invalidate the proposed routes; instead, com-
plementary routes could be designed.
On the other hand, in the data collection, we used secondary sources;
more specically, the ofcial national, departmental, and township
websites, on which it is possible to nd reliable information. However,
according to the publication and updating policies, some information
required the prior permission of the owner, and some had outdated
publications, which means that although the characteristics selected are
appropriate for the site, their gures may vary.
In the last methodological section, the purpose of designing tourist
routes was to integrate potential tourist sites considering the position and
geographical proximity between townships. In Santander, some of this
integration was made taking into account the roadway infrastructure
available; however, we found nearby townships that do not have a direct
connection; for these cases, we propose theoretical roads so that, in the
future, stakeholders could propose infrastructure projects.
The results of this study are aligned with those of Pitchayadejanant &
Nakpathom (2018), who use data mining to identify associations and
patterns of activities in tourism, as we take advantage of the association
of patterns to create touristic clusters that are linked by routes. On the
other hand, Gosal et al. (2019) perform clustering that includes tourist
factors, including such as nature, ornithology, and religious pilgrimage,
among others, that complement our proposed factors; also, Nilashi et al.
(2019) conclude that big data and machine learning are essential factors
with enormous growth potential in the tourism industry. Finally, Guo
et al. (2020) compared different methods to identify economic clusters
that are essential to improve the tourism competitiveness of a city.
Taking into account the proposed data hierarchy, as well as the pro-
posed method, for future work, it could be necessary to update the in-
formation with data in situ, using deep techniques to analyze the
construction of building roads, bridges, and other infrastructures. This
research could be proposed to government entities as an alternative
method to growth and to improve the social, cultural, and economic
situation of each township of Santander. The proposed clusters could be
part of the departmental development plans around Colombia, taking
into account that this methodology could be replicable anywhere. In
addition, the tourism agencies could offer different attractions in
Santander using the proposed routes that include the preference of
tourists in terms of nature, culture, history or adventure; nally, these
routes can be promoted on platforms such as social networks and ofcial
websites of the department's cities.
6. Conclusions
In this study, we introduce a novel approach to speed up the decision-
making process related to designing and creating routes by identifying
tourism clusters that consider the similarity between their characteristics
Figure 8. Touristic Cluster 2 Route 1 sites.
J.B. Duarte-Duarte et al. Heliyon 7 (2021) e06655
based on a hierarchical structure. These clusters are geographical sites
that offer a particular type of tourism; however, the cluster groups
depend on the attributes that describe each location. That is, if we use
different characteristics, it is possible to nd a diverse cluster group. For
this, it is essential to justify the selection of attributes, the data collection
process and the sources. We also highlight that this methodology is based
on quantitative methods. At the route creation phase, we need to use an
expert criterion to speed up the interest site connection, meaning that
when we choose among feasible sites, we decide which ones to integrate
with the main route based on the expert criterion. In conclusion, clus-
tering techniques reduce the use of resources such as time, money and
expertise. Additionally, the application of this technique can be used in
future tourism studies in other places or sites where a tourism manager
can identify touristic activities, as well as natural, cultural and other
resources. Thus, this technique can be used to determine and design
adventurous, religious, gastronomic, and other exciting and attractive
tourism routes. Finally, this methodology allows government staff and
entrepreneurs to highlight and prioritize future investments, such as
transportation structure (e.g., new roads, bridges, cableways, among
others), which can improve tourism development.
Author contribution statement
Diana Carolina Rodriguez-Padilla: Performed the experiments;
Analyzed and interpreted the data; Wrote the paper.
Juan Benjamín Duarte-Duarte: Conceived and designed the experi-
ments; Contributed reagents, materials, analysis tools or data.
Leonardo Hern
an Talero-Sarmiento: Conceived and designed the ex-
periments; Performed the experiments; Contributed reagents, materials,
analysis tools or data; Wrote the paper.
Funding statement
This research did not receive any specic grant from funding agencies
in the public, commercial, or not-for-prot sectors.
Data availability statement
Data will be made available on request.
Declaration of interests statement
The authors declare no conict of interest.
Additional information
Supplementary content related to this article has been published
online at https://doi.org/10.1016/j.heliyon.2021.e06655.
Alam, A.S.A.F., Er, A.C., Begum, H., Alam, M.M., 2015. The factors of selecting Malaysia
as tourist destination. Mediterr. J. Soc. Sci. 6 (3), 491498.
Almeida, M.V., 2009. Matriz de Avaliaç~
ao do potential turístico de localidades receptoras.
Revista Turismo Em An
alise 20 (3), 541563.
es Cabello, S., Pascual Bellido, N., 2015. La construcci
on del turismo en nuevos
destinos: luces y sombras. El caso de La Rioja (Espa~
na). Revista de Ciencias Sociales y
Humanidades 24, 3049.
Aravena Paillalef, G., Espinosa Sepúlveda, A., Flores Ch
avez, J., 2013. Manual para el
desarrollo de circuitos de turismo de intereses especiales 1.
Auren Consultores, 2014. Recursos Turísticos, 30.
Blanco, M., 2008. Guía para la elaboraci
on del plan de desarrollo turístico de un
territorio, 45.
on, R.C., 1997. Planicacion del esp
acio turístico. 3ed. M
exico, Trillas. Retrieved
from. http://www.aptae.pe/archivos_up/0107-planicacion-del-espacio-turistico-r
Briedenhann, J., Wickens, E., 2004. Tourism routes as a tool for the economic
development of rural areas-vibrant hope or impossible dream? Tourism Manag. 25
(1), 7179.
Cadena, A., Christian, V., Ing, F., 2013. Nuevos atractivos turísticos tangibles en los
cantones: Tulc
an, Espejo y Mira para la creaci
on de circuitos turísticos.
Camara, Charles Jean, de los
Angeles MorcateLabrada, Flora, 2014. Arquitectura y
urbanismo. Arquit. Urban. 35 (1), 4867. Retrieved from. http://scielo.sld.cu/scielo
Camarena, D.A., 2016. Evaluaci
on de los recursos turísticos naturales del municipio de
San Pedro Lagunillas, Nayarit, M
exico, a partir de la metodología multicriterio,
pp. 4360.
Castellanos Menjura, Claudia Patricia, 2015. Evaluacion de los recursos turisticos con
vocacion ecoturistica y caracterizacion de la demanda turistica en las zonas de uso
publico de la Reserva Forestal Protectora del Cerro Quinini (Tibacuy-Cundinamarca).
Universidad Distrital. Retrieved from. http://repository.udistrital.edu.co/bitstrea
Cha, S., Uysal, M., 1995. Regional analysis of tourism resources for marketing purposes.
J. Hospit. Leisure Market. 2, 6174.
Conti, A., Charne, U., Moscoso, F.V., Comparato, G.J., Cassani, M.J., Avalís, V.S.,
Rucci, A.C., 2012. Evaluaci
on de atractivos para la identicaci
on de nuevos
productos turísticos. In: Caso de estudio: regi
on Capital de la provincia de Buenos
Aires, 18.
on de Santander, 2016. Plan Desarrollo Departamental. Gobernaci
on de
Defert, P., 1972. Essai de formulation dune typologie int
e des resources et activit
touristiques. In: M
ethodes de Recheches Touristiques et Leur Application Aux Pays et
egions En Voie de D
eveloppement, pp. 6475.
Deng, J., King, B., Bauer, T., 2002. Evaluating natural attractions for tourism. Ann.
Tourism Res. 29 (2), 422438.
Departamento Administrativo Nacional de estadística, 2018. Boletín T
ecnico Pobreza
Monetaria Santander Boletín T
ecnico. Departamento Administrativo Nacional de
estadística, pp. 113.
Eagles, P.F.J., Bowman, Margaret E., Chang-Hung Tao, Teresa, 2001. Guidelines for
Tourism in Parks and Protected Areas of East Asia.
Egül, A.Y.S
¸., Ik, T.U.S
¸., 2014. Tourist hotel location selection with analytic hierarchy
process. Market. Tourism Manag. Conf. 4758.
Estevao, C., Ferreira, J., 2009. The tourism clusters role in regional development:
presenting a competitiveness conceptual model. Tourism Destinat. Develop. Brand.
2003, 127139. Retrieved from. https://repositorio.ipcb.pt/bitstream/10400.11
Everitt, B.S., Landau, S., Leese, M., Stahl, D., 2011. Cluster Analysis. In: Quality and
Quantity, 14.
Garrido, M.A., S
anchez, J.A.L., Enriquez, A.F., 2015. Rutas turísticos-culturales e
itinerarios culturales como productos turísticos: reexiones sobre una metodología
para su dise~
no y evaluaci
on. An
alisis Espacial y Representaci
on Geogr
on y Aplicaci
on (October), 463471.
Gosal, A.S., Geijzendorffer, I.R., V
aclavík, T., Poulin, B., Ziv, G., 2019. Using social media
, machine learning and natural language processing to map multiple recreational
beneciaries. Ecosyst. Serv. 38 (June), 100958.
Gower, J.C., Legendre, P., 1986. Metric and Euclidean properties of dissimilarity
coefcients. J. Classif. 3 (1), 548.
Gunn, C.A., 2002. Tourism Planning: Basics, Concepts, Cases/Clare A. Gunn with Turgut
Var. Retrieved from. https://trove.nla.gov.au/work/10503424?q&sort¼holdingsþ
Guo, S., Jiang, Y., Long, W., 2020. Urban tourism competitiveness evaluation system and
its application: comparison and analysis of regression and classication methods.
Proc. Comput. Sci. 162 (Itqm 2019), 429437.
ICOMOS, 1999. Carta internacional sobre el turismo cultural. La Gesti
on del Turismo en
los sitios con Patrimonio, 6.
Infante S
anchez, E. del P., 2014. Elementos determinantes en Cundinamarca para el
desarrollo del turismo como actividad estrat
egica regional. Suma de Negocios 5 (10),
Instituto Valenciano de Tecnologías Turísticas, 2015. BIG DATA: retos y oportunidades
para el turismo. Instituto Valenciano de Tecnologías Turísticas, p. 84.
Jafari, J., 2005. El turismo como disciplina cientíca. Politic. Soc. 42 (1), 3956.
Jeambey, Z., 2016. Rutas Gastron
omicas y Desarrollo local: un ensayo de
on en Catalu~
na. Pasos: Revista de Turismo y Patrimonio Cultural,
ISSN-e 1695-7121, Vol. 14, No. 5, 2016, P
ags. 1187-1198, 14 (5), 11871198.
Karypis, M., Kumar, V., Steinbach, M., 2000. A comparison of document clustering
techniques. TextMining Workshop At.
Knezevic, R., 2008. Contents and assessment of basic tourism resources, 14 (1), 7994.
Kombol, T.P., 2000. Rural tourism on the Croatian islands - Sustainable development and
regenerative strategies, 102, 425431.
Laguna Marín- Yaseli, M., Nogu
es Bravo, D., 2003. La potencialidad turística del medio
natural en el lic de las Sierras Ibericas Riojanas mediante evaluaci
on multicriterio.
Zubia - Monogr. 13 (2001), 227240. Retrieved from. https://dialnet.unirioja.es/
Laitamaki, J., Tada, M., Liu, S., Setyady, N., Vatcharasoontorn, N., Zheng, F., 2016.
Tourism Industry, 14 (1), 729.
Landaluce Calvo, M. Isabel, 2011. An
alisis Factorial Tratamiento De Tablas Mixtas:
Nuevas equivalencias entre el ACP normado y el ACM, , 1st21. QUESTII O,
pp. 99108.
Li, J., Chu, C.-H., Wang, Y., Yan, W., 2002. An improved fuzzy c-means algorithm for
manufacturing cell formation. In: 2002 IEEE World Congress on Computational
Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE02.
Proceedings (Cat. No.02CH37291), 2, pp. 15051510.
J.B. Duarte-Duarte et al. Heliyon 7 (2021) e06655
Lillo, Adelaida, Ram
on Rodríguez, Ana, Sevilla-Jim
enez, Martín, 2006. Un marco de
alisis del capital humano en turismo. Papers de Turisme 39, 4559.
Lopez, Fernando, 2002. Libro Verde de la Accesibilidad en Espa~
na. Diagn
ostico y bases
para un plan integral de supresi
on de barreras. Madrid: Instituto de Migraciones y
Servicios Sociales.
Lucas, Robert C., 1968. Clawson and Knetsch, economics of outdoor recreation. Nat.
Resour. J. 8, 738743. Available at: http://digitalrepository.unm.edu/nrj/vol8/i
Maass, S.F., Luz, I., De, R., 2010. Evaluaci
on multicriterio de los recursos turísticos del
Parque Estatal Multicriteria evaluation: of tourism resources of the park sierra de.
Mart, M., Nicol, S., Melissa, H., Galv, O., 2013. Políticas públicas Y factores que
determinan La competitividad turística de Morelia. M
exico Y De Alcal
a De Henares 8
(2), 15981603.
Martínez, Mario Alberto, Osorio García, Maribel, Franco Maass, Sergio, Ramírez de la
O, Irma Luz, Nava Bernal, Gabino, 2010. Evaluaci
on Multicriterio de los recursos
turísticos del Parque Estatal. El periplo Sustentable 18, 133.
Meyer, D., 2004. Tourism Routes and Gateways: Key Issues for the Development of
Tourism Routes and Gateways and Their Potential for Pro-poor Tourism, pp. 131
Mikery Guti
errez, Mildred Joselyn, P
azquez, Arturo, 2014. M
etodos para el an
del potential turístico del territorio rural. Revista Mexicana de Ciencias Agrícolas (9),
17291740. https://doi.org/10.29312/remexca.v0i9.1060.
Ministerio de Cultura, 2010. Cartilla Política Cultura para el Pueblo Gitano. Ministerio de
Cultura, p. 24.
Ministerio de Cultura de Colombia, 2004. Raizales, isle~
nos descendientes de europeos y
africanos. In: Aw
a Kuaiker, Gente de La Monta~
na, (018000). Ministerio de Cult
ura de Colombia, pp. 111. Retrieved from. http://www.mincultura.gov.co/areas/
Minitab 18, 2018. Dendrograma. Retrieved from. https://support.minitab.com/es-mx/m
MINCIT, 2003. Seguridad Turística: Reto competitivo de Colombia. Ministerio de
comercio de Colombia.
Mishra, P.G.M.S., 2009. Asia Pacic Journal of Marketing and Logistics. Retrieved from.
Morteza, Z., Reza, F.M., Seddiq, M.M., Sharareh, P., Jamal, G., 2016. Selection of the
optimal tourism site using the ANP and fuzzy TOPSIS in the framework of Integrated
Coastal Zone Management: a case of Qeshm Island. Ocean Coast Manag. 130,
Navarro, D., 2015. Recursos turísticos y atractivos turísticos: conceptualizaci
on y valoraci
on. Cuad. Tur. (35), 335.
Nilashi, M., Ahani, A., Dalvi, M., Yadegaridehkordi, E., Samad, S., Ibrahim, O., Akbari, E.,
2019. Preference learning for eco-friendly hotels recommendation: a multi-criteria
collaborative ltering approach. J. Clean. Prod. 215, 767783.
OEA, 1987. Touristics Resources. CICATUR-OEA.
Open Africa, 2002. Information Relating to the African Dream Project. Open Africa, Cape
Padín Fabeiro, C., Pardellas de Blas, X.X., 2004. Una propuesta de turismo sostenible para
el municipio de Caldas de Reis (Pontevedra). Cuad. Tur. 13 (13), 107126. Retrieved
from. http://dialnet.unirioja.es/servlet/articulo?codigo¼1033354&info¼resumen
Pitchayadejanant, K., Nakpathom, P., 2018. Data mining approach for arranging and
clustering the agro-tourism activities in orchard. Kasetsart J. Soc. Sci. 39 (3),
Podani, J., 2012. Extending Gowers General Coefcient of Similarity to Ordinal
Characters, 48 (2), 331340.
Porter, M.E., 2000. Location, competition, and economic development: local clusters in A
global economy. Econ. Dev. Q. 14 (1), 1521.
Quesada Castro, Renato, 2006. Elementos Del Turismo. EUNED, pp. 1316.
atz, T., Puczk
o, L., 1998. Rural Tpurism and Sustainable Development in Hungary.
Rural Tourism Management: Sustainable OptionsInternational Conference, Conference
Proceedings. SAC, Auchincruive, Ayr, pp. 450464.
Real Academia Espa~
nola (RAE). (Spanish Royal Academy), 2005. Spanish dictionary.
Twenty-second edition. Available at: https://dle.rae.es/infraestructura?m¼form
(consulted in octubre, 2018).
Reyez, O., S
anchez, C., 2005. Metodología para determinar el potential de los recursos
turísticos naturales en el Estado de Oaxaca, M
exico. Cuad. Tur. 1 (16), 153173.
Rocha, H., 2004. Entrepreneurship and development: the role of clusters. Small Bus. Econ.
23, 363400.
Rubio, M.L., Duval, V.S., Pezzola, A., 2016. An
alisis de Localizaci
on de Emprendimientos
Turísticos en El Sector Norte del Partido de Villarino (Argentina). InterEspaço:
Revista de Geograa e Interdisciplinaridade 2 (5), 935.
SECTUR, 1975. ¿Que Hacemos? Retrieved from. https://www.gob.mx/sectur/es/#192.
Smith, S.L.J., 1987. Regional tourism analysis of tourism resources. Tourism 14, 254273.
Smith, S., 2013. Identication of funtional tourism in North America. Am. Pharmaceut.
Rev. 16 (5), 1320.
Solís, M.J., Errazuriz, M.J., Caradeuc, C., Seabra, G., 2013. Evaluaci
on multicriterio de la
potencialidad turística de un territorio. caso de estudio parque nacional pan de
azúcar, regi
on de Atacama. Chile 112.
Triantaphyllou, E., 2000. Multi-Criteria Decision Making Methods. (March), pp. 521.
UNWTO, 2008. In: Responding to Global Challenges Climate Change and Tourism Responding
to Global Challenges (Copyright; World Tourism Organization and United Nations
Environment. World Tourism Organization and the United Nations Environment
Programme, Madrid, Spain.
UNWTO, 2013. Sustainable Tourism for Development Guidebook. Enhancing Capacities
for Sustainable Tourism for Development in Developing Countries, 228.
Vanegas, Juan Gabriel, 2017. Evaluaci
on Multicriterio e inventario de atractivos
turísticos. Espacios 38, 16.
Wilks, D.S., 2011. Cluster Analysis. Int. Geophys. 100, 603616.
Zimmer, P., Grassman, S., 1996. Evaluar el potential turístico de un territorio. In:
Seminario Leader.
J.B. Duarte-Duarte et al. Heliyon 7 (2021) e06655
... Duarte-Duarte et al. [2] proposed that the similarity between tourist attractions can be identified by clustering technology. He considered that the eight factors of natural, cultural, tourist plant, infrastructure, superstructure, accessibility, human and tourist capital, and security were the key indices affecting the line selection of tourist lines, and evaluated the tourist routes by clustering technology evaluation method. ...
Full-text available
Tourist-dedicated train is the product of the combination of railway transportation and tourism, and its tour line selection includes the selection of nodes and lines. Based on the principle of decision-making, taking technical factors, tourism factors, regional economic factors, and passenger flow factors as criteria, this paper analyzes the decision-making indices affecting the tour line and establishes the decision-making index system. Subsequently, the prospect theory considering the bounded rationality and psychological factors of decision makers is combined with vague set fuzzy decision theory, and a comprehensive decision method based on vague set and prospect theory is proposed. Finally, the feasibility of the proposed decision method is verified by an example. The research shows that the established decision-making index system is representative, and the proposed decision-making method is scientific and effective for the decision making of tourist lines of tourist-dedicated train. The decision-making results can be used as a reference for the formulation of tour lines and line plans of tourist-dedicated train.
... S. Kim et al., 2021). In the past five years, the main research methods use neural network predictive model (Li, 2020) deep learning (Taecharungroj & Mathayomchan, 2019), gravity analysis (Zhang et al., 2022), cluster analysis (Duarte-Duarte et al., 2021), spatial measurement (Kim et al., 2022), social network analysis (Bustamante et al., 2019;Chung et al., 2020;Gan et al., 2021;Li & Weng, 2016) and so on. Besides, using the spatial distribution of tourist attractions, scholars also conducted the study of travel route recommendation (Castillo-Vizuete et al., 2021) and recommendation system construction (Abbasi-Moud et al., 2021). ...
Full-text available
Chengdu-Chongqing economic circle (hereinafter referred to as “Chengyu Region”) is a key construction region of China's major development strategy. The development of regional tourism plays an important role in the optimization of regional economy and industrial structure. In this paper, ArcGIS 10.5 was used as the main analysis tool to analyze the temporal and spatial distribution of A-level tourist attractions in Chengyu Region, and makes factor analysis and interactive analysis on the factors affecting the distribution of tourist attractions by geographic detector model. The results show that: 1. The distribution of tourism attractions in Chengyu Region is mainly concentrated type, and the distribution of the natural landscape, cultural landscape, rural pastoral and modern entertainment in Chengyu Region are all concentrated type. 2. In terms of the distribution characteristics of kernel density, there is a significant deviation between the spatial distribution of A-level tourist attractions and tourism income in Chengyu Region. Through the analysis of the dynamic development of A-level tourist attractions in 2010, 2015 and 2021, tourism in Chengyu Region show a good situation of "driven by two cities and blooming in many places." Among them, Chengdu and Chongqing have obvious advantages, and Yibin has become an important city second only to Chengdu and Chongqing. 3. In terms of spatial correlation, the spatial distribution of A-level tourist attractions in Chengyu Region has a significant spatial autocorrelation. The local spatial autocorrelation of A-level tourist attractions includes H–H (high-high), H–L (high-low) and L–H (low–high) clustering types. 4. In terms of influencing factors, traffic location, water system, topography and social and economic development level are the important factors affecting the spatial distribution of regional A-level tourist attractions. Among them, the level of social and economic development has the greatest impact on regional tourism. Finally, based on relevant theories, this paper puts forward countermeasures and suggestions for regional coordinated development, so as to guide the sustainable development and management innovation of regional tourism industry.
... International best practices in touristic clustering of the territory are presented in the papers of such authors as Juan B.Duarte-Duarte Leonardo, H.Talero-Sarmiento, Diana C.Rodríguez-Padilla, 2021 [1]. Social, economic and spatial aspects of clustering are given in the papers of Marta Dereka, Edyta Woźniakb, Sylwia Kulczyka, 2019 and used in the course of conceptual developement of this article [2]. ...
Full-text available
The article is concerned with basic concepts related to development of an ecological-excursion cluster in the Rostov region. The socio-economic conditions of its formation are described. The research base on which the research related to the study of touristic clusters is found is reviewed. It also gives grounds for the possibility of functioning of the ecological and recreational cluster, defines its functions and importance for the development of inbound and domestic tourism, the economy and the social sphere of the region. The zoning of the territory of the Rostov region according to the prevailing types of ecological and recreational clusters is proposed and the specifics of their activities and the development of types of tourism are indicated.
Hosting mega sports events is a key driver for sustainable development, particularly through fostering tourism marketing and planning. Although the 2022 FIFA World Cup has received considerable attention, as the first global event hosted in the Middle East and Arab world, studies that highlight and investigate the potential benefits of hosting such an event are rare. This study assessed the geographical accessibility to archaeological sites, monuments, and museums across Qatar, providing clear guidelines on how to represent the national cultural and historical heritage to global audiences during the 2022 mega event, to maximise socioeconomic revenue. Spatial data were assembled within GIS platforms to assess accessibility utilising geospatial techniques, such as the average nearest neighbour, near analysis, spatial autocorrelation (Moran ‘I Index), and cost distance to estimate travel times. The analysis indicated that the archaeological sites are spatially clustered, predominantly concentrated in Doha and Al-Rayan municipalities, where historical and cultural landmarks are located. However, most castles, forts, and archaeological sites located outside the capital zone are less accessible, and at a long distance from hotels and residential areas. As the hosting stadiums are located along the northeast coast and within the most populous zone, the museums, towers, and cultural landscapes are easily accessible within a short travel time (less than 10 minutes). As a host community, planners and policymakers in Qatar may benefit from this research, as a spatial guideline to promote sustainable development of tourism, through facilitating accessibility to monuments and museums, as well as enriching the representation of the national tangible heritage to a global audience.
Recently, it has been proven that the emotional aspect directly influences the learning process, so that, based on data mining techniques, this behavior has been sought to be characterized. This has made clustering techniques become one of the most used techniques for this purpose. However, studies where emotional data obtained from a person’s brain activity are used, are rare. For this reason, the present study aims to implement and compare advanced clustering techniques based on emotional metrics obtained through Brain-Computer Interfaces, captured in an AR-Sandbox, which fulfills the role of a learning environment. The evaluation of these techniques is carried out using internal criteria such as silhouette coefficient, Composed Density Between and within, Calinski-Harabasz and other statistical measures. When carrying out this study, it was obtained as a result that, the Density-Based Spatial Clustering of Application with Noise and Density-Based Hierarchical Spatial Clustering of Noisy Applications algorithms as the Density-based clustering methods, presented a better level of well-separation, cohesion and compaction, in comparison to the rest of the techniques implemented.
Nowadays, one of the growing and remarkable branches of tourism is ecotourism. Ecotourism has a great impact on the development of a region from different perspectives. Hence, by improving and developing sustainable ecotourism in a region, it will be possible to develop and improve its economic, social, and environmental situation. The Lafour region is one of the potential hubs for ecotourism activities in Iran. Despite the high potential of this region for ecotourism activities, it has not yet achieved its suitable position in this domain. Hence, the main goal of this study is to develop sustainable goals for ecotourism in the region. To meet the target, first, the Strengths, Weaknesses, Opportunities, and Threats (SWOT factors) are identified through interviews with tourists, local residents, and the experts, filed observation, and considering similar works in the literature review. Then, some practical and useful strategies are determined based on SWOT analysis and consulting with the experts. The important point of the study is proposing an applicable heuristic clustering method based on SWOT analysis to classify the strategies into different clusters. To prioritize the strategies in each cluster, the commonly used Multi-Criteria Decision Making (MCDM) method called the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is applied. This paper concludes and reveals that the three strategies including ST6 “Encouraging investors and entrepreneurs to establish ecotourism centers in the region”, ST5 “Establishing and improving the hygiene units and the medical clinics in the region”, and ST3 “Improving and diversifying tourism services and products to attract tourists and increase their satisfaction” have the greatest impact on accomplishing sustainable development goals in the region.
Full-text available
El artículo que se ofrece muestra cómo, a partir de la realización de una evaluación de recursos, se puede concebir la viabilidad de impulsar el turismo en un área natural protegida. El Parque Estatal Sierra de Nanchititla en el Estado de México (PESN), México, es una de las áreas naturales que, en mayor medida, ha logrado mantener su ecosistema en el centro del país, en virtud de su accidentada orografía y su relativo alejamiento de las áreas de concentración urbana. El inventario realizado tiene su valor justamente en la investigación de campo, que implicó recorrer un amplio territorio, clasificar y discriminar qué recursos son potencialmente aprovechables para el turismo alternativo. La jerarquización que se obtiene al aplicar la metodología de evaluación multicriterio permite identificar cuáles son los recursos sobre los que hay que trabajar los productos turísticos que generarían alternativas económicas sustentables.
Full-text available
Under the background of economic transformation, tourism is one of the most dynamic and promising tertiary industries. How to improve the competitiveness of tourism has become a new idea for industrial upgrading in various regions. This paper builds an evaluation system consisting of five major aspects, based on the data of 75 tourist destinations; then, uses cluster analysis to classify cities, and uses logistic regression, SVM and random forest methods to predict the tourism competitiveness of sample cities and compare the advantages and disadvantages of the two methods - classification and regression. From the results of the empirical test, the results of the classification method are generally better than the results of the regression, and in the classification method, the results of the SVM are better than the results of the random forest. In this case, the SVM model gives full play to its ability to solve the problem of nonlinear classification.
Full-text available
La integración del turismo a las actividades productivas rurales representa una estrategia para mejorar las condiciones de vida de los pobladores. Se han planteado estudios del potencial del territorio rural para integrar actividades turísticas desde diferentes perspectivas. El objetivo de este ensayo fue analizar los métodos de investigación utilizados para determinar el potencial turístico del territorio rural y discutir los alcances y limitantes de estos métodos. Se analizó la literatura internacional en métodos y enfoques de investigación utilizados para abordar la capacidad del territorio rural para actividades turísticas en sus diferentes modalidades. Los métodos se agruparon por dimensión (social, económica, ambiental) para determinar el potencial turístico y por paradigma de investigación. Se encontró diversidad en métodos, desde diferentes paradigmas de investigación y diferentes disciplinas con objetivos similares. Además de inconsistencias entre el objetivo de sustentabilidad en el desarrollo rural; donde se estudia una dimensión de la realidad yseatribuyemayorrelevanciaalosrecursosbiogeográficos sin considerar las dimensiones sociales o económicas. Se requiere la integración de diferentes dimensiones (social, ambiental, económica, política e institucional) que definan las características propias de cada territorio, bajo un enfoque de sistemas complejos considerando la interacción entre sus componentes y al contexto.
Conference Paper
Full-text available
El trabajo que se presenta es parte de un proyecto de investigación sobre las posibilidades de diversificación de la oferta turística en base al patrimonio cultural y natural. El área tomada como caso de estudio, la región Capital de la provincia de Buenos Aires (partidos de La Plata, Berisso y Ensenada), cuenta con un rico patrimonio que incluye todas las categorías patrimoniales reconocidas en la actualidad. Algunos componentes de ese patrimonio están incluidos en la oferta turística en tanto otros no han sido considerados hasta el momento, por lo que constituyen un capital ocioso pasible de ser activado. La hipótesis principal del proyecto de investigación consiste en que es posible diversificar la oferta turística a partir de la identificación y puesta en valor de bienes pertenecientes al patrimonio cultural y natural, contribuyendo de este modo al desarrollo integral de las comunidades locales. Resulta necesario, no obstante, proceder a cumplir los requisitos para que tales atractivos puedan ser considerados productos turísticos. En tal marco, el trabajo que se presenta se orienta a cubrir dos ejes temáticos. El primero, de corte teórico, pretende contribuir a la definición y delimitación conceptual de las categorías de atractivo y producto turístico. Si bien en diversas oportunidades estos conceptos son utilizados como sinónimos, en realidad comprenden e implican un conjunto de características y particularidades que los diferencian. En vista a que el primer eje temático tenga una utilidad operativa y fáctica, se complementa dicha contribución teórica a través de la elaboración de un instrumento metodológico capaz de identificar el grado de cumplimiento de las variables que hacen y conforman al producto turístico. En este sentido, el segundo aporte es de tipo metodológico y consiste básicamente en aplicar un esquema en que se tienen en cuenta las variables inherentes a un producto turístico (superestructura, infraestructura, equipamiento turístico o planta y distribución/comercialización) y el grado de cumplimiento de las mismas para determinar efectivamente si se trata de un producto potencial, emergente o consolidado. El esquema propuesto ha sido aplicado a algunos atractivos de la región, lo que ha permitido elaborar algunas conclusiones tanto de tipo teóricas como operativas.
Full-text available
The paper attains to finding the association among interested activities in orchard that stimulate the tourists to travel. The knowledge obtained in this study is applying data mining techniques to create the association rule in order to find out the pattern of activities for orchard tourism. The evoked set of activities is the most frequent set which travelers would like to do when they visit orchard. The tool of analyzing the association rules is Rapid Miner 7.3. The result shows that the highest recommended activity is reaping and tasting the fruit. Another activities, which also are recommended to be arranged more into orchard, consist of walking, shopping, and feeding animal in the orchard.
The article looks at the attractive factors of basic tourism resources and the structure of their attractions. The general term ‘resource’ refers to both natural and anthropogenic resources, while the content of this concept refers to elements used in creating a tourism product. Basic tourism resources are the most important factors of tourism processes, with a vital attribute of direct and indirect tourism resources being their substitutability. Natural (biotropic) resources are considered the guiding factors of a tourism offering, and they command great attention when planning tourism development and designing a tourism product. In addition to the basic types of natural tourism resources (geomorphologic, climate, hydrographic, bio-geographical, protected natural heritage), there are also many sub-types. Anthropogenic (atropic) tourism resources are human creations, the features of which attract tourists. They impact on how the cultural needs of tourists are met. In a tourism product, they generally take the form of cultural goods and ethno-social, artistic and ambient resources. Today, potential tourism resources are the focus of research, together with existing tourism resources, the contents and importance of which change and grow over time.
Information and numbers on the use and appreciation of nature are valuable information for protected area (PA) managers. A promising direction is the utilisation of social media, such as the photo-sharing website Flickr. Here we demonstrate a novel approach, borrowing techniques from machine learning (image analysis), natural language processing (Latent Semantic Analysis (LSA)) and self-organising maps (SOM), to collect and interpret >20,000 photos from the Camargue region in Southern France. From the perspective of Cultural Ecosystem Services (CES), we assessed the relationship between the use of the Camargue delta and the presence of natural elements by consulting local managers. Clustering algorithms applied to results of the LSA data revealed six distinct user groups, which included those interested in nature, ornithology, religious pilgrimage, general tourists and aviation enthusiasts. For each group, we produced high-resolution spatial and seasonal maps, which matched known recreational attractions and annual festivals in the Camargue. The accuracy of the group identification, and the spatial and temporal patterns of photo activity, in the Camargue delta were evaluated by local managers of the Camargue regional park. This study demonstrates how PA managers can harness social-media to monitor recreation and improve their management decision making.
The crucial role of customers’ positive experience and their subsequent word-of-mouth have been highlighted by both scholars and practitioners for all industry sectors. In response to an increasing concern of environmental sustainability and sensitivity of consumers for deteriorating environment, eco-friendly (green) products and services gained tremendous attention. TripAdvisor is increasingly known as one of the most popular e-tourism platforms. Understanding and predicting the traveler’ preferences by advanced big data analytics technology is an important task that the recommendation engine of this platform does. In this paper, we aim to develop a new soft computing method with the aid of machine learning techniques in order to find the best matching eco-friendly hotels based on the several quality factors in TripAdvisor. We develop the method using dimensionality reduction and prediction machine learning techniques to improve the scalability of prediction from the large number of users’ ratings. The proposed soft computing method is evaluated on a large dataset discovered from the TripAdvisor platform. The results show that the combination of dimensionality reduction and prediction machine learning techniques is robust in processing the large number of the ratings provided by users on the features of eco-friendly hotels and predicting travelers’ choice preferences of eco-friendly hotels in TripAdvisor.