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Clasificación de rutas turísticas= por medio de deep learning


Classification of tourist routes through deep learning

 

1

 

Héctor Fernando Gómez Alvarado                        https://orcid.org/0000-0002-7310-4260

Universidad Técn= ica de Ambato, Carrera de Psicopedagogía, Tungurahua, = hf.gomez@uta.edu= .ec

2

María José Mayorga Ases                                     ht= tps://orcid.org/0000-0003-1897-739X

Universidad Técnica de Ambato, Carrera de Psicopedagogía, Ambato, Tungurahua, Ecuador.

mariajmayorga@uta.edu.ec

 

3

Leticia Abigail Mayorga Ases                               https://orcid.org/0000-0003-0586-2390 Universidad Técnica de Ambat= o, Facultad Ingeniería Civil y Mecánica. Ingeniería Mecánica,

la.mayorga@uta.edu.ec

 

4

Silvia Elena Malo Martínez                                  https://orcid.org/0000-0002-9110-3915

Universidad Técnica de Particular de Loja, Departamento de Filosof= ía, Artes y Humanidades, Tungurahua

semalo@utpl.edu.ec

 

 

 

 

 

Artículo de Investigación Científica y Tecnológi= ca

Enviado: 24/12/2021

Revisado: 29/12/2021

Aceptado: 03/01/2022

Publicado:08/03/2023

   DOI: https://d= oi.org/10.33262/concienciadigital.v6i1.4.2038

 

 

 

 

Cítese:

 

 

Gómez Alvarado, H. F., Mayorga Ases, M. J., Mayorga Ases, L. A., & Malo Martínez, S. E. (202= 3). Clasificación de rutas turísticas por medio de deep learning. ConcienciaDigital, 6(1.4), 880-897. https://d= oi.org/10.33262/concienciadigital.v6i1.4.2038

 

 

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Palabras claves:

aprendizaje profundo, rutas turístic= as, clústeres, kmeans, clima

 

Resumen

Introducción. Actualmente el deep learning o aprendizaje profundo tiene aplicaciones de todo tipo, el turismo no es la excepción, la minería de d= atos ha permitido optimizar procesos dentro de la industria del turismo como la demanda turística, conocer la preferencia de rutas turísticas de las pers= onas permite optimizar recursos y plantear mejoras dentro de este sector.  Objetivo. Determinar si se pueden clasificar rutas turísticas por medio de herramie= ntas del deep learning o aprendizaje profundo. Metodología. El diseño de investigación fue cualitativo, se empleó técnicas como la entrevista, para ello se plantean dos hipótesis, la primera tiene que ver con la relación entre el tipo = de clima del destino turístico y l= a preferencia de los turistas, la segunda hipótesis es la verificación de la <= /span>conformación de clusters turísticos en ba= se a la preferencia de las personas. Como herramientas de verificación se empleó la comprobación directa y el prog= rama Weka con la opción de clusters SimpleKMeans que permite <= span style=3D'letter-spacing:-.3pt'>la identificación de las preferencias de los tu= ristas en base a la minería de datos de 31 personas. Resultados. Los resultados indican que la mayor cantidad de personas entrevistadas prefieren desti= nos turísticos en climas calurosos, sin= embargo, este no fue un parámetro determinante en la conformació= n de clusters. Conclusión.= En el estudio se determinó que sí se puede formar clusters de clasificac= ión de rutas turísticas en base a las preferencias de las personas.

 

 

Keywords<= /span>: deep le= arning, tourist routes, clusters, kmeans, weather

 

AbstractIntroduction. Currently = deep learning or deep learning has applications of all kinds, tourism is not t= he exception, data mining has allowed optimizing processes within the tourism industry such as tourist demand, knowing the preference of people's touri= st routes allows optimizing resources and propose improvements within this s= ector. Target. Determine if tourist routes can be classified by means of deep learning or deep learning tools= . Methodology. The research design was qualitative, techniques such as the interview were us= ed, for these two hypotheses are proposed, the first has to do with the relationship between the type of climate of the tourist destination and t= he preference of tourists, the second hypothesis is the verification of the conformation of tourist clusters based on the preference of the people. As verification tools, direct verification and the Weka program were used wi= th the SimpleKMeans clusters option that allows the identification of touris= ts' preferences based on the data mining of 31 people. Results. The results indicate that the largest number of peop= le interviewed prefer tourist destinations in hot climates, however, this was not a determining parameter in the formation of clusters. Conclusion. The study determine= d that it is possible to form clusters for the classification of tourist routes based on people's preferences.

 

 

 

Introducción

Aprendizaje profundo

El aprendizaje profundo o deep learning se basa en el que la máquina pueda aprender y resolver problemas de forma intuitiva, imitando al cerebro h= umano (Law et al., 2019), es decir, se requiere un aprendizaje automático, diseñar, analizar algoritmos y extraer patrones de los datos para que la máquina pueda hacer predicciones. Esta tecnología= que aún es nueva, modela datos a gran escala empleando estructuras com= plejas con diferentes arquitecturas; un ejemplo de esto, es el uso de redes neuronales para = el procesamiento de la información (Hao et al., 2016).

En el aprendizaje profundo se emplean redes neuronales que operan entre capas, esto permite alcanzar un nivel profundo q= ue puede ser utilizado para traba= jos como reconocimiento de voz, re= conocimiento de imágenes y predicción de eve= ntos futuros en base a la informació= n histórica (LeCun et al., 2015). El= nivel de error en estas tareas gira alrededor del 30% sin embargo, este nivel continúa mejorando día a día con los avances tecnológicos (Hao et al., 2016).

Dentro de las redes neuronales profundas existen múltiples niveles de abstracción que se compone de<= span style=3D'letter-spacing:-.5pt'> un aprendizaje de características básicas en las capas iniciales,= mientras que, los conceptos de nivel superior se ubican en las capas más profundas. Los avances más recientes permiten = el aprendizaje automático de caracterí= sticas con diferentes jerarquías empleando datos etiquetados y no etiquetados mediante capac= idades mejoradas de procesamien= to gráfico (Ahmad et al., 2019). En la figura 1 se muestra un esquema de la interacción de los datos de entrada entre capas inferiores y superiores hasta definir los datos de salida, sin embargo, esta interacción no es únicamente de correspondencia, más bien, los datos de entrada pueden subdividirse en vari= as capas y relacionarse con otras.

 

 

Figura 1

Ejemplo de interacción entre múltiples capas<= o:p>


Nota: Los datos de entrada interactúan con las demás cap= as de datos hasta obtener información de salida. Fuente: <= span style=3D'font-size:10.0pt;mso-ansi-language:ES-EC'>Ma et al. (2018)=

Tipos de redes en el aprendizaje profundo

Los diferentes tipos= de arquitecturas de redes neuronales permiten resol= ver problemas de una manera jerárquic= a con enfoques distintos.<= /span>

·      =    Redes de aprendizaje profundo supervisado

Redes empleadas para aprender= a diferenciar elementos de datos de diversas clases, la tarea que desempeña la red tiene que ver con la asigna= ción de etiquetas, clasificación y regresión de patrones. Las arquitecturas útiles en estas tareas son: redes neuronales profundas o Deep Neural Networks (DNN, po= r sus siglas en inglés), red neuronal convolucional o Convolutional Neural Net= work (CNN, por sus siglas en inglés) y red neuronal recurrente o Recurrent Neural Network (RNN, por sus sig= las en inglés) (Ahmad et al., 2019).

Las DNN forman jerarquías en cada capa, esto permite que las redes aprendan patrones desde los niveles más básicos, hasta características muy abstractas<= span style=3D'letter-spacing:-.5pt'> en cuanto a los datos de entrada. El flujo de la información se complementa entre sí, puesto que los datos de salida de una capa se convierten en datos de entrada de la capa subsiguiente hasta llegar a las capas profundas. En el caso de texto, imágenes y videos, se emplean las e= structuras CNN en donde la información atraviesa tres capas, capas convolucionales en donde se aprenden características importantes y se emp= lean filtros, capas de agrupación en donde se reduce el tamaño de los m= apas característicos, cuando trabajan= conjuntamente estas dos capas se puede extraer rasgos locales de la entrada y combinarlas para obtener datos global= es; finalmente, se encuentran las capas de conexión (Ahmad et al., 2019).

Cuando se desea detectar patrones de datos en series de tiempo se emplea la arquitectura RNN que mantiene recurrentes conexiones de red en intervalos de tiempo secuenciales, estas redes se = aplican para fuentes de datos de voz, = videos y texto; actualmente existe una forma más avanzada de RNN que mantiene memo= ria a corto plazo, se conoce como = larga red de memoria a corto plazo o Long Short-Term Memory (LS= TM, por sus siglas en inglés) <= !--[if supportFields]><= /span> ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"xXeM53xi","properties":{"= formattedCitation":"(Ahmad et\\uc0\\u160{}al., 2019)","plainCitation":"(Ahmad et&n= bsp;al., 2019)","noteIndex":0},"citationItems":[{"id&q= uot;:148,"uris":["http://zotero.org/users/local/RyWKqe1k/ite= ms/9GE3HKXK"],"uri":["http://zotero.org/users/local/RyW= Kqe1k/items/9GE3HKXK"],"itemData":{"id":148,"= type":"chapter","abstract":"This chapter introduces the various methods existing beneath the umbrella of deep learning paradigm, their intricate details, and their applications in vario= us fields. Deep learning has substantially improved the predictive capacity of computing devices, due to the availability of big data, with the help of superior learning algorithms. It has made it possible as well as practical = to integrate machine learning with sophisticated applications including image recognition, object detection, self-driving cars, drug discovery, and disea= se detection. The superior and reliable performance of deep learning methods h= as attracted the attention of researchers working in every field of science to utilize their strengths in order to solve problems. In addition to that, the knowledge reuse in deep learning is an interesting aspect of this technology which will also be discussed.","collection-title":"Spri= ngerBriefs in Computer Science","container-title":"Deep Learning: Convergence to Big Data Analytics","event-place":"Singapore","ISBN&qu= ot;:"9789811334597","language":"en","not= e":"DOI: 10.1007/978-981-13-3459-7_3","page":"31-42","= publisher":"Springer","publisher-place":"Sing= apore","source":"Springer Link","title":"Deep Learning Methods and Applications","URL":"https://doi.org/10.1007/978-981-13= -3459-7_3","author":[{"family":"Ahmad",&= quot;given":"Jamil"},{"family":"Farman",= "given":"Haleem"},{"family":"Jan",&= quot;given":"Zahoor"}],"editor":[{"family&quo= t;:"Khan","given":"Murad"},{"family"= ;:"Jan","given":"Bilal"},{"family":= "Farman","given":"Haleem"}],"accessed&qu= ot;:{"date-parts":[["2021",12,10]]},"issued":= {"date-parts":[["2019"]]}}}],"schema":"h= ttps://github.com/citation-style-language/schema/raw/master/csl-citation.js= on"} (Ahmad et al., 2019).

·&nb= sp;        Redes de aprendizaje profundo no supervisado

En este tipo de redes, el aprendizaje profundo no es supervisado, es decir, que la información como etiquetas y clasificaciones, no se encuent= ra dentro de este proceso de aprendizaje; por el contrario, en esta metodología se emplea un codificador automático. El codificador obtiene una repr= esentación compacta que contiene los datos de entrada que, al ser relativamente corta, permite la reconstrucción de los datos originales. Durante este proceso surge el ap= rendizaje de características de los datos de forma no supervisada, para este proceso se usa una arquitectura simple que consta de modelos generativos probabilísticos con múltiples capas (Ahmad et al., 2019).

Se pueden combinar los dos ti= pos de redes de aprendizaje profundo cuando existe un gran caudal de datos que= no pueden ser etiquetados, sin em= bargo, con el uso de redes de aprendizaje no supervisado se puede obtener los parámetros iniciales, a través de este primer proceso, se puede emplear redes de aprendizaje supervisado para la clasificación de l= a información.

Aplicaciones del aprendizaje profundo<= /span>

Con el aprendizaje profundo se pueden ejecutar diferentes tipos de actividades, para el caso de los seres humanos, son sencillas de procesar, sin embargo, para una máquina son más complejas. Eventualmente se ha empleado esta tecnología en el reconocimient= o y clasificación de imágenes, reconocimiento facial, reconocimiento automático= de voz, reconocimiento de características de imágenes en videos, procesamiento= de lenguaje (generación y traducción de textos) (Hao et al., 2016).

Los avances recientes del emp= leo del aprendizaje profundo en la visión por computadora brindan aplicaciones = en todos los campos; por ejemplo, sistemas de reconocimiento en vehículos autónomos, diagnóstico y detección de enfermedades, procesamiento, producci= ón y mejoramiento de imágenes, etc. Por otro lado, en el ámbito de la búsqueda de información, esta tecnología brinda la posib= ilidad de navegar en la red para encon= trar datos relacionados con los requerimientos de los usuarios que pueden efectu= arse a través de imágenes, audio, v= ideo y texto; = el mecanismo de operación de la búsqueda de información se basa en la extracción de características de la entrada de la información mediante algoritmos de transformación (Ahmad et al., 2019).

Figura = 2


Arquitectura CNN

Nota: La imagen se presenta co= mo datos de entrada de donde se detectan parámetros como bordes, formas y obje= tos para conectarlos entre capas y obtener los datos finales. Fuente: Ma et al. (2018)

En la figura 2 se muestra el = empleo de la arquitectura CNN en imágenes, de la imagen de entrada se puede aplicar capas en forma jerárquica desde la información más simple como bordes o esquinas de las imágenes hasta información más compleja capa por capa (Ma et al., 2018).

En el procesamiento del lengu= aje, el aprendizaje profundo puede comprender expresiones lingüísticas complejas= que son de uso cotidiano, los recursos de donde se interpreta este lenguaje provienen de datos textuales como formularios, análisis de sentimientos y emociones, opiniones, textos, traducción de idiomas, búsquedas web, correos electrónicos, informes y reconocimiento de documentos (Ahmad et al., 2019).

Aprendizaje profundo en el turismo

El aprendizaje profundo es una tecnología versátil, hoy en día su aplicación está introduciéndose en todo = tipo de áreas del conocimiento; el turismo, no es la excepción. Las últimas innovaciones dentro de esta área tienen que ver con la simplificación de va= rias actividades que el turista lleva a cabo, como es el caso de la organización, planificación, selección de destinos turísticos, alojamiento, transacciones económicas, transporte, etc. Sin embargo, el empleo del aprendizaje profundo dentro del turismo es reciente, y no ha sido explorado en su totalidad.

Los investigadores <= /span> ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"Cut6sLw9","properties":{"= formattedCitation":"(Law et\\uc0\\u160{}al., 2019)","plainCitation":"(Law et&nbs= p;al., 2019)","noteIndex":0},"citationItems":[{"id&q= uot;:132,"uris":["http://zotero.org/users/local/RyWKqe1k/ite= ms/FX5VIPLA"],"uri":["http://zotero.org/users/local/RyW= Kqe1k/items/FX5VIPLA"],"itemData":{"id":132,"= type":"article-journal","abstract":"Tradition= al tourism demand forecasting models may face challenges when massive amounts = of search intensity indices are adopted as tourism demand indicators. Using a = deep learning approach, this research studied the framework in forecasting month= ly Macau tourist arrival volumes. The empirical results demonstrated that the = deep learning approach significantly outperforms support vector regression and artificial neural network models. Moreover, the construction and identifica= tion of highly relevant features from the proposed deep network architecture pro= vide practitioners with a means of understanding the relationships between vario= us tourist demand forecasting factors and tourist arrival volumes. This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field","container-title":"Annals of Tourism Research","DOI":"10.1016/j.annals.2019.01.014",&qu= ot;ISSN":"0160-7383","journalAbbreviation":"A= nnals of Tourism Research","language":"en","page":"4= 10-423","source":"ScienceDirect","title"= :"Tourism demand forecasting: A deep learning approach","title-short":= "Tourism demand forecasting","URL":"https://www.sciencedirect.com/scien= ce/article/pii/S0160738319300143","volume":"75",&q= uot;author":[{"family":"Law","given":&qu= ot;Rob"},{"family":"Li","given":"Ga= ng"},{"family":"Fong","given":"Davis Ka Chio"},{"family":"Han","given":"= Xin"}],"accessed":{"date-parts":[["2021"= ,12,8]]},"issued":{"date-parts":[["2019",3,1]= ]}}}],"schema":"https://github.com/citation-style-language/s= chema/raw/master/csl-citation.json"} Law et al. (2019), emplearon el aprendizaje profundo para = el pronóstico del volumen mensual de turistas en Macao, la arquitectura que emplearon tiene el soporte necesario para la construcción e identificación = de características y factores de previsión relevantes en esta tarea. La importancia de la predicción de la demanda turística no surge de la casuali= dad, puesto que el turismo también forma parte de las principales actividades económicas en muchos países, conocer el flujo de turistas en ciertas épocas= del año y en eventos específicos, puede definir con un menor riesgo de pérdidas económicas el número de entradas, alimentos, disponibilidad en alojamientos, presupuesto de promociones turísticas y todo tipo de estrategias que involu= cran un alto costo y esfuerzo de recursos humanos.

Las plataformas de búsqueda permiten obtener datos como la = planificación de los viajes, selección de atracciones turísticas, planificación de transporte, entre otros, sin e= mbargo, se debe tener en cuenta el buscador que tiene mayor cantidad de usuarios en función de la región de estudio. Otro dato muy importante para determinar un modelo = de demanda turística corresponde al volumen de turistas mensuales. De este análisis se obtienen palabras clave que, a través del análisis de tendencias se obtienen resultados diarios y mensuales de los intereses de l= os turistas, este ejercicio se pu= ede hacer en diferentes idiomas, con lo cual posibilita obtener información de= otras regiones (Law et al., 2019; Labzioui, 2020). Para el tipo de redes de aprendizaje pr= ofundo que se requiere emplear en este caso LSTM, se emplea una demanda histórica de turismo en series de tiem= po, los factores multivalentes del = análisis son los datos previamente correlacionados de las palabras clave (Labzioui, 2020). El = mecanismo LSTM identifica automáticamente un conjunto de características influyentes = en cada paso de tiempo en el caso= de Macao, algunas de estas características corresponden al transporte, clima y gastronomía; sin embargo, dife= rentes mercados pueden presentar otro tipo de conjuntos de características que son usuales entre los turistas que = acuden en estas zonas (Law et al., 2019).

La visualización de imágenes = de las reseñas turísticas también puede = ser relevante en la elección del turista. Ma et al. (2018), proponen el empleo de las fotos subidas por los usuarios en <= span style=3D'letter-spacing:-.1pt'>las reseñas para hacer un análisis de= su efecto en la utilidad de sus comentarios; mediante la visión por computadora se procesó la información de hoteles que tienen detalles de su servicio en línea junto con los comentarios y reseñas con fotografía= s; además de la información proven= iente de redes sociales. Con los avan= ces tecnológicos, las fotografías proporcionadas por los usuarios cumplen un papel fundamenta= l en la evaluación de productos, este tipo de retroalimentación causa un mayor impacto en la percepción de los consumidores y en la toma de decisio= nes.

Las variables de entrada empleadas en el análisis de Ma et al. (2018), fueron de reseñas de texto, fotos<= span style=3D'letter-spacing:-.25pt'> y la combinación de texto y fotografías, clase de hotel, respuestas de los usuarios (la información fue útil) = y la calificación de la reseña. Para el procesamiento del contenido textual emplearon procesamiento del lenguaje cotidiano o N= atural Language Processing (NPL, por sus siglas en inglés), en el procesamiento de las imágenes aplicaron directamente CNN con la finalidad de extraer característic= as de la imagen y se combinaron las metodologías en un módulo LSTM= para elaborar funciones de secuencia. Los resultados reflejaron que la combinación de reseñas textuales c= on fotografías fue más eficiente y arrojaron un mayor rendimiento.

El aprendizaje profundo también permite el reconocimiento masivo de datos de imagen y contenido visual, por ese motivo este recurso es útil para la determinación de la preferencia y comportamiento de los turistas en sus destinos turísticos.

Zhang et al. (2019), plantearon este tipo de análisis en el caso de las fotos de Beijing subidas en redes sociales. La visión por computadora permite obtener datos acerca de los destinos turísticos y la experiencia del viaje. Previo a esto, se hizo = un análisis de correlación ente las cualidades afectivas de los lugares y los adjetivos que describen las imágenes, gran cantidad de esta información también puede obtenerse de las plataformas de redes sociales (Jeong et al., 2020).

Para el análisis por visión por computador emplearon arquitectura de aprendizaje profundo ResNet que mejora el desempeño de las redes convolucionales y ejecuta tareas de reconocimiento de escenas y clasificación de imágenes, en este caso, = los datos de entrada son las fotografías de los turistas= . Los resultados del análisis sugieren que l= os lugares escénicos tradicionales son los de mayor preferencia de los turistas, específicamente los edificios tradicionales. La información es de relevancia puesto que un estudio preliminar de las preferencias = puede ser una pauta para la promoción de marketing (Zhang et al., 2019).

Dentro del ámbito de la planificación de rutas, viajes compartidos, transporte y monitoreo del tráf= ico, el uso del aprendizaje profundo = también posibilita la estimación de tiempos de movilización. En estos análisis de tiempos, se emplean redes convolucionales con= datos geográficos para la captura de correlaciones espaciales incluyendo aspectos como las maniobras de conducción; por otro lado, se emplean redes neuronales recurrentes LSTM para dependencias temporales de características obtenidas de mapas y factores externos como las condiciones climáticas, hábitos del conductor, día de la semana (Wang et al., 2018). En el reconocimiento de recursos turíst= icos se han desarrollado diferentes avanc= es tecnológicos empleando dispositivos móviles, por ejemplo, el desarrollo de aplicaciones con guías turísticas interactivas e= mpleando reconocimiento de imágenes y texto (M. Wang, 2020). En establecimientos turísticos como hoteles y restaurantes se emplean aplicaciones para la gestión y organización de reservas, ocupación e ingresos, la recopilación de estos datos permite h= acer investigaciones de demanda y tendencias turísticas. Otros recursos importan= tes para el turismo es la predicción de precios en los viaje= s, chatbots en donde se brinda at= ención al cliente durante todo el día (Renteria,  2021). <= o:p>

Rutas turísticas

Para la elaboración de rutas turísticas se deben tener en cuenta diferentes aspectos, por ejemplo, la modalidad de turismo, las actividades que se pueden llevar a cabo en los diferentes puntos, los lugares que se visitan, gastronomía y atractivos turísticos (Vinces et al., 2021). <= span style=3D'letter-spacing:-.15pt'>Sin embargo, el éxito de un destino turístico está relacionado con la buena estructuración de los productos turísticos y la elaboración de un itinerario adecuado. Una ruta turística posee una agrupación = de características que la hacen vi= able como, por ejemplo, una adecuada infraestructura, tipo de rutas, duración, costo y recursos complementarios<= span style=3D'letter-spacing:-.8pt'> que requieren tener en cuenta los tu= ristas para llevar a cabo las actividades de la ruta y el análisis de la cadena de valor del producto o servicio turíst= ico (Rodríguez & Abeal, 2016).

Clasificación de rutas turísticas

Un factor muy importante para la clasificación de rutas turísticas son los atractivos turísticos, el fl= ujo de los turistas se presenta a través de la región emisora de donde parten los tur= istas, la ruta de destino y la región receptora o destino, dentro de = los diferentes destinos existen= dos variables de gran importancia, son l= os alojamientos y los atractivos turísticos, co= mo se presenta en la figura 3. La importancia de este factor radica en que es lo que motiva a las personas a movilizarse hacia la región de destino y dentro de esta variable existe un sinnúmero de tipologías que definen la intención turística (Enseñat, 2020).

Figura = 3


Sistema turístico – espacial

                           Nota: Esquema de los componentes de las rutas turísticas. Fuente: Enseñat (2020)

Dentro de los destinos turíst= icos existe una gran variación de atractivos turísticos que están dentro de una misma zona, los destinos pueden definirse como los países, ciudades o puntos territoriales en donde se agrupan otros sitios de interés para los turistas o visitantes, mientras que los atractivos turísticos son zonas naturales o sitios implementados por el ser humano que están abiertos al público para diferentes fines. Debido a que los atractivos turístico= s la clave en la elección de la ruta turística se debe conocer el niv= el de interés que tienen en las perso= nas, con este fin, los atributos qu= e influyen en este parámetro están lig= ados al tipo de sitio turístico (Enseñat, 2020).

Los destinos turís= ticos cuentan con una cultura propia, operadores turísticos y una comunidad que en conjunto brindan una experiencia satisfactoria y atractiva a quienes visitan las zonas. Otros aspectos que hacen atractivos a los destinos turísticos pueden ser la natur= aleza, el hospedaje y la imagen que en general se tiene del de= stino, y toda esta información es la que se puede recopilar a través de medios sociales, páginas web, blogs y= el big data; uno de los principales canales de flujo de información son las redes sociales en donde se almacenan datos de tipo UGC o contenidos generados por el usuario como, por ejemplo: sentimientos, imágenes, videos y= la interacción que se produce en estas plataformas. Analizar los sentimientos,<= span style=3D'letter-spacing:-.15pt'> opiniones y expresiones de las personas permite evaluar cada sitio y conocer el nivel de impacto que tienen entre los turista= s (Molinar et al., 2017).

En el presente análisis se verificará la influencia del clima en la selección de las preferencias del destino turístico de las pe= rsonas, así como también la factibilida= d de creación de clusters empleando = la tecnología del aprendizaje profundo con herramientas como Simple KMeans de Weka= .

Metodología

Para el estudio se entrevistó a una población de 31 estudiantes de la carrera de turismo de la facultad de ciencias humanas y de la educación, quienes eligieron el destino turístico de su preferencia colocando 1 en la casilla cuando existe preferencia por el destino turístico y 0 cuando no existe preferencia. Se plantearon diversos destinos turísticos que cuentan con diferentes condiciones climáticas.

Para poder procesar<= span style=3D'letter-spacing:.05pt'> los datos, se conformaron tablas en función de las preferencias= turísticas y del clima que caracteri= za a cada destino turístico, para ello se = normalizaron los datos entre 0 y 1.

En la etapa de verificación de conformación de clusters en función de las preferencias de<= span style=3D'letter-spacing:-1.6pt'> los turistas se empleó la her= ramienta Weka desarrollada por la Univer= sidad de Waikato, que emplea diferentes opciones de procesamiento de minería de datos, para la formación de = clusters se seleccionó Simple Kmeans= para la verificación de las distancias entre los centroides de los clusters= o el valor de k.

En base a los resultados de W= eka se verificó si existen características similares entre los clusters y el clima que rige en los destinos turísticos para conocer si existe una influe= ncia entre ellos.

Resultados

Se entrevistó a 31 estudiante= s, en la tabla 1 se muestran los resultados de sus preferencias turísticas de los destinos o sitios turísticos del Ecuador.

Tabla 1

Resultados de preferencias turísticas – estud= iantes facultad de ciencias humanas y de la educación

No Estudiante

Papallacta

Cotacachi

Mindo

Santo Domingo

los Tsáchilas<= /span>

Esmeraldas

Cayambe

Volcán

Chimborazo<= /span>

Montañita

Baños

Salinas

Salinas <= /span>de

Guaranda

Volcán

Tungurahua<= /span>

Tisaleo

Pinllo

Tena=

Quito

1<= /span>

0

1

0<= /span>

1<= /span>

1

1

1<= /span>

1<= /span>

1

1

1<= /span>

1<= /span>

1

1<= /span>

1<= /span>

0

2<= /span>

1

1

1<= /span>

1

1

1

1

1

1

1

1

1

1

1

1<= /span>

1

3<= /span>

1

0

0<= /span>

0

0

1

1

1

1

0

0

0

0

0

1<= /span>

1

4<= /span>

1

1

1<= /span>

1

0

1

1

1

1

1

1

1

1

1

1<= /span>

1

5<= /span>

1

1

1<= /span>

1

1

1

1

1

1

1

0

1

0

1

0<= /span>

1

6<= /span>

0

1

0<= /span>

1

1

1

1

1

1

1

1

1

1

1

0<= /span>

1

7<= /span>

0

0

0<= /span>

0

1

1

1

1

1

1

0

1

0

0

1<= /span>

1

8<= /span>

0

0

0<= /span>

1

1

0

0

1

1

1

0

1

1

1

1<= /span>

1

9<= /span>

1

1

1<= /span>

1

1

1

1

1

1

1

1

0

0

0

1<= /span>

1

10=

1

1

1<= /span>

1

1

1

1

1

1

1

1

1

1

1

1<= /span>

1

11=

1

0

1<= /span>

1<= /span>

1

1

1<= /span>

1<= /span>

1

1

1<= /span>

1<= /span>

1

1<= /span>

1<= /span>

1

12=

1

1

1<= /span>

0

1

1

1

0

1

1

0

1

0

0

1<= /span>

0

13=

0

1

1<= /span>

1

0

 

0

1

1

0

1

0

0

1

0<= /span>

1

14=

1

0

0<= /span>

0

1

1

1

0

1

0

0

1

0

1

0<= /span>

1

15=

1

0

1<= /span>

1

1

0

0

0

1

1

0

0

0

1

1<= /span>

1

16=

1

1

1<= /span>

1

1

0

0

0

1

1

0

1

0

1

1<= /span>

0

17=

0

1

0<= /span>

1

1

1

1

0

0

0

1

0

0

0

1<= /span>

0

18=

1

0

1<= /span>

1

1

1

1

1

1

1

1

0

0

0

0<= /span>

1

19=

1

1

1<= /span>

0<= /span>

1

0

1<= /span>

1<= /span>

1

1

1<= /span>

1<= /span>

0

0<= /span>

1<= /span>

1

20

1

1

1

1

1

0

1

0

1

1

0

1

0

1

1

1

21

1

1

1

0

0

0

0

1

1

1

1

0

0

0

1

 

22

1

1

1

0

1

0

1

0

1

1

0

1

1

1

0

1

23

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

24

1

1

0

1

1

1

1

1

1

1

0

1

1

1

1

0

 

 

Tabla 1

Resultados de preferencias turísticas – Estudiantes Facultad de Ciencias Humanas y de la Educación (continuación)

No Estudiante

Papallacta

Cotacachi

Mindo

Santo Domingo

de los Tsáchilas

Esmeraldas

Cayambe

Volcán

Chimborazo

Montañita

Baños

Salinas

Salinas= de

Guaranda

Volcán

Tungurahua

Tisaleo

Pinllo

Tena

Quito

25

1

0

1

0

1

1

1

1

1

1

1

1

0

0

1

1

26

1

1

1

1

1

0

0

0

1

1

1

0

0

1

1

0

27

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

28

0

0

1

1

1

1

0

1

0

1

0

1

1

1

1

1

29

1

0

1

1

0

0

0

1

1

1

1

1

1

1

1

 

30

0

0

1

1

1

0

1

0

1

0

1

0

1

0

1

0

31

1

1

1

1

1

0

1

1

0

1

0

1

1

1

0

1

Totales

23

20

23

23

26

19

23

22

28

26

18

22

15

21

24

22

Fuente: Estudio= de preferencias turísticas estudiantes Universidad Técnica de Ambato

 

De la tabla 1 se puede resaltar que tres destinos turísticos obtuvieron mayor frecuencia en cuanto a las preferencias de las personas, en este caso, 28 personas prefieren Baños (Baños de Agua Santa) y, 26 personas prefieren Esmeraldas y Salinas.

En la tabla 2 se muestran los= datos del tipo de clima que es característico en los 16 destinos turísticos planteados para el estudio.

 

Tabla 2

Clima característico de los destinos turístic= os

 =

Destino Turístico

Clima característico del sitio

Frío

Templado

Caluroso

TOTAL

Papallacta

1

0

0

1

Esmeraldas

0

0

1

1

 

 

 

 

Tabla 2

Clima característico de los destinos turístic= os (continuación)

 =

Destino Turístico

Clima característico del sitio

Frío

Templado

Caluroso

TOTAL

Cayambe

0

1

0

1

Santo Domingo de los Tsá= chilas

0

0

1

1

Quito<= /p>

1

0

0

1

Mindo<= /p>

1

0

0

1

Cotacachi

1

0

0

1

Tisaleo

1

0

0

1

Pinllo=

1

0

0

1

Tena

0

1

0

1

Volcán de Chimborazo

1

0

0

1

Montañita

0

0

1

1

Baños de Agua Santa=

0

1

1

2

Salinas de Guaranda=

0

1

0

1

Salinas

0

0

1

1

Volcán Tungurahua

1

0

0

1

TOTAL<= /p>

8

4

5

17

Fuente: Estudio de preferencias turísticas estudiantes Universidad Técnica de Ambato

 

Con la información obtenida en la tabla 1= se realizó una comprobación directa para identificación de las preferencias, de los resultados con mayor frecuencia se hizo una verificación con respecto al clima, según el tipo de clima característico del sitio, como se presenta en= la tabla 2.

Tabla 3

Resultados de las preferencias con relación al clima

Preferencia de destino turístico<= span style=3D'font-size:10.0pt;line-height:115%;font-family:"Times New Roman",= serif; mso-ansi-language:ES-EC;mso-bidi-font-weight:bold'>

Cl= ima

Esmeraldas

Caluroso

Baños

Templado / Caluroso

Salinas

Caluroso

Fuente: Estudio de preferencias turísticas estudiantes Universidad Técnica de Ambato

 

El resultado de la verificaci= ón directa se muestra en la tabla 3 en donde se indica que los sitios que tien= en mayor preferencia en la población de 31 estudiantes corresponden a climas calurosos.

Para la elaboración de los clusters empleando la herramienta Weka se utilizó la misma base de datos presentada = en la tabla 1, en esta tabla se resumen las preferencias de los destinos turísticos con el valor de 1.

En la figura 4 se muestra el = preprocesamiento de la información con los 16 atributos que corresponden a los diferentes si= tios turísticos del Ecuador, de los cuales se hizo un análisis de preferencias en base a la población de 31 estudiantes, en este caso no se aplicó ningún fil= tro y se procesó la información directamente en la opción cluster del programa.

Figura = 4

Preprocesamiento de los destinos turísticos en Weka

Fuente: Estudio de preferencias turísticas estudiantes Universidad Técnica de Ambato, datos procesados en W= eka

&n= bsp;

En <= /span>la figura 5 se muestran los resultados de k valor aplicando la herramienta Simple KMeans para la conformación de clusters. Como resultado se obtuvo 2 clusters, uno con 23 datos y otro con 8, en total se analizaron los 36 datos por atributo que se ingresaron de la tabla 1.

 

Figura = 5

Formación de cluster emple= ando Simple KMeans en Weka

=

Fuente: Estudio de preferencias turísticas estudiantes Universidad Técnica de Ambato

Nota: datos procesados en Weka.

 

En el cluster 0 que co= ntiene 23 datos de afinidad se visual= iza que los destinos turísticos que= tienen un valor de k mayor son Papallacta, Mindo, Baños, Salinas y Quito. Por otro l= ado, en el cluster 1 se observa que los destinos turísticos con un valor de k mayor son Santo Domingo de los Tsáchilas, Esmeraldas y = Tena. Con estos valores obtenidos de KMeans se puede determinar los destinos turísticos done existe menor variabilidad entre los datos, de igual manera, en el modelo general se aplica un valor de R cuadrado de 84,84% lo cual in= dica que existe un buen ajuste en el = modelo propuesto, para la obtención de= estos resultados, el programa ejecuta = 3 iteraciones.

Conclusiones<= o:p>

·      =    Luego del análisis de preferencias de los turistas se puede determinar que sí eligen su destino turístico en base <= span style=3D'letter-spacing:.1pt'>al clima, los datos analizados en la t= abla 3 concuerdan con el tipo de clima = que se obtienen en el cluster 1, en donde se evidencia que las personas prefieren climas calurosos o templados.

·      =    En la formación de los clusters 0 y 1 se puede determinar que no coincide el tipo de clima entre los dos grupos en funci= ón del valor de k mayor puesto que en el cluster 0 existen destinos turísticos de clim= a frío, caluroso y templado, mientras que en el cluster 1 no se verifica la preferencia del clima= frío.

 

Referencias bibliográficas

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ISSN: 2600-5859

Vol. 6 No. 1.4, pp. 880 – 897, marzo 2023
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