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.
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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
ARTICLE INFO
Keywords:
Clustering techniques
Sustainable development
Tourism factors
Tourist clusters
Tourist routes
Tourism
ABSTRACT
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
proposed.
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
(2008),Andr
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
methodologies.
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
Heliyon
journal homepage: www.cell.com/heliyon
https://doi.org/10.1016/j.heliyon.2021.e06655
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-
nc-nd/4.0/).
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
Mn
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.
2.2. SECTUR
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
P
erez-V
azquez (2014),Camara and Morcate Labrada (2014) and Cort
es
(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
erez-V
azquez (2014),Castellanos
Factors
Accessibility
Human and Tourist
Capital
Cultural Factor
Natural Factor
Infraestructure
Touristic Facilities
Price and Quiality
Safety
Superestructure
Touris tic Activi ties
Accomodation
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.
Feeding
Accommodation
Recreation
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)
Restaurants
Transport Systems (massive transport)
Festivities ans Festivals
Road system (highways, roads, pedestrian
zones, etc)
Recreation Facilities
Supporting Services
Public (tourism organizations, support
institutions)
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
2
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
(2008),V
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
3
Sij ¼Pn
i¼1WijkGijk
Pn
i¼1Wijk
(1)
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~
n
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
4
V
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
on,
Floridablanca and Lebrija, these townships have several museums, such
as the Guane Archaeological Museum located in Floridablanca, "Mansi
on
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
5
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
6
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
aramo
This route focuses on landscape sightseeing tourism that begins with
Surat
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
aramo.
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
7
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.
Declarations
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.
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