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=
.
Esta o=
bra
está bajo una licencia internacional Creative
Commons Atribución-=
NoComercial
4.0
Enviado: 2023/07/14 Aceptado: 2023=
/08/15
Publicado: 2023/10/15
Resumen El comerc=
io exterior
desempeña un papel fundamental en la economía de un país, involucrando una gran cantidad
de transacciones y datos. Este estudio se enfoca en analizar, comprender y demostrar las exportaci=
ones
de Ecuador con el objetivo de generar conocimiento para los expertos en este
campo. Para lograrlo, se desarrolló un modelo de visualizació=
n de
datos que abarca el período comprendido entre 2008 y 2018. El estudio
fue basado en la metodología CRISP-DM, ampliamente utilizada en
proyectos de minería de datos. Esta metodología se adapta a través de cinco fases iterativas para el desarrollo del modelo de minería y visualización.
Mediante la implementación de esta metodología, se
logró generar un modelo visual que proporciona una herramient=
a efectiva
para manipular las principales variables relacionadas c=
on el comercio
exterior. El resultado
de este estudio fue la creación de un modelo visual adaptado a una herramienta que permi=
te
explorar y analizar las exportaciones de Ecuador. Este modelo proporciona una interfaz intuitiva
para manipular y examinar las variables clave,
lo que puede facilitar la toma de decisiones informadas en el
ámbito del comercio exterior.
Palabras clave:
Clusterizaci&oac=
ute;n , Exportaciones, Minería de datos, Modelo
de gravedad, Modelo visual, Técnicas de
visualización.
<=
/o:p>
Abstract Foreign trade is fundamental to <=
span
class=3DSpellE>the economy as it involves many
transactions and data from=
a country. This study analyses exports from Ecuador to generate knowledge for experts in this field . A data
visualization model =
covering 2008 and 2018 was developed to achieve
this . The study was based on
the CRISP-DM methodology, widely used in data mining projects . This methodology is adapted through five iter=
ative phases to develop
the mining and visualization model . Through the implementation
of this methodology ,
it was possible
to generate a visua=
l model th=
at provides an effective tool to manipulate the main variables related to
foreign trade . The result was the creation
of a graphical model adapted into
a tool to explore a=
nd analyse Ecuador's exports . This model
provides an intuiti=
ve
interface to manipulate and
examine the main va=
riables,
which can facilitate informed decision-making =
in foreign trade .
Keywords: Clustering , Data mining ,
Exports , Gravity model , Visual model , Visualization techniques .
Introducción A nivel mundial, con el pasar de l=
os
años, los países generan grandes cantidades de datos gracias
a los avances tecnol&o=
acute;gicos, permitiendo generar datos de manera más sencilla y rápida, al igual que su almacenamiento. Es=
ta
información, se genera debido a las múltiples transacciones
derivadas de todas las funciones que tienen a su cargo los estados, donde el
comercio exterior representa una gran parte
de estas actividades. Las exportacio=
nes representan una de las actividades
económicas más importantes de un país, la cual consist=
e en
el envío de productos a través de fronteras. Estas transaccio=
nes
de comercio exterior producen grandes cantidades de datos que son almacenad=
os
generando conocimiento informativo, no obstante, es necesario transf=
ormarlo en conocimiento productivo, que genere valor y aporte a la toma de decisiones económicas del país. El Ecuador, según el programa informático Solución Comercial
Integrada Mundial (WITS), exportó 2,252 productos a 156
países en el último año (World Integrated Trade Solution , 2023). Toda la información de estas
transacciones se encuentra recopilada,=
clasificada y almacenada en el Banco Central del Ecuador (BCE) desde el año 1990 que se encuentran disponibles p=
ara su
consulta en el portal web del BCE.
Sin embargo, los mismos no dispone=
n de
medios analíticos visuales que simplifiquen y faciliten su entendimi=
ento
al momento del análisis. Esto genera que los usuarios consulten enormes
archivos con tablas complejas, sin obtener un análisis visual que disminuya el trabajo de procesamiento de los datos. De igual manera, el presente estudio
utiliza el modelo de gravedad que asume los flujos comerc=
iales
entre dos países al ser directamente proporcionales a ciertas variab=
les
de las economías e inversamente proporcionales a la distancia
(Vásquez Bernal y Tonon Ordóñez, 2021). No obstante, es necesario analizar
todos estos datos
y transformarlos en conocimientos que puedan ser entendidos por los expertos
como personas no inmersas en este
campo de estudio, y al mismo tiempo compartirlas para generar más va=
lor (Cairo, 2011). Por tanto, es necesario crear
visualizaciones de datos
que expongan y ayuden al análisis de manera
clara y eficaz mediante gráficos, utilizando técnicas que
combinen la estética y funcionalidad (Po et al., 2020) sobre las
exportaciones ecuatorianas.
Por lo tanto, en el presente estud=
io se
desarrolló un modelo de visualización de datos de comercio
exterior que permita analizar, comprender y demostrar mediante gráfi=
cas
las exportaciones del Ecuador
entre los años 2010 y 2018 a través del tratamiento<=
span
style=3D'letter-spacing:-.5pt'> y procesamiento
de los datos almacenados en el BCE para generar nuevo conocimiento, permiti=
endo
que las personas puedan analizar
y comprender ágilmente las exportacion=
es del Ecuador con sus socios
&nbs=
p;
comerciales a través de esta herramienta. Asim=
ismo,
se empleó el método CRoss Industry Standard Process <=
span
class=3DSpellE>for Data<=
span
style=3D'letter-spacing:-.6pt'> Mining (CRISP-DM) que consta de cinco fases iterativas esenciales, desde la comprensi&oacut=
e;n
del negocio hasta la creación del modelo visual como resultado final=
.
Este
artículo est&aac=
ute; estructurado de la siguiente manera: La Sección 2 presenta los trabajos
relacionados con métodos similares, la Sección 3 explica la metodología utilizada para realizar e=
ste estudio,
la Sección 4 expone los resultados que se han obtenido al aplicar la metodología,
y la Sección 5 presenta las conclusiones obtenidas, conjuntamente con
los trabajos.
Trabajos Relacionados El comercio exterior, especialment=
e las
exportaciones, está experimentando un crecimiento cada vez mayor en
Ecuador y a nivel mundial. Esto genera diariamente un gran volumen de
transacciones y datos que se almacenan sin generar información para =
la
toma de decisiones. Actualment=
e, los investigadores se enfrentan al desafío de representar visualmente los resultados del
análisis de datos, ya que poseen amplios conocimientos en
estadística y programación, pero en su mayoría carecen=
de
conceptos adecuados en diseño y visualización (Sustaita Guerr=
ero,
2018).
La investigación de Boar et<=
span
style=3D'letter-spacing:-.75pt'> al. (2017) realiza
un análisis profundo
del comercio exterior de importaciones y exporta=
ciones
utilizando datos públicos y redes complejas, llegando a identificar<=
span
style=3D'letter-spacing:-.75pt'> la relación entre
las empresas distribuidoras globales y las fluctuaciones del mercado
de valores. Por el contrario, Jiang et al. (201=
8)
estudian los cambios en la dependencia del comercio exterior y la estructura
comercial de la provincia de Yunnan en China mediante el análisis de=
los
datos de comercio exterior extraídos del Banco Mundial, demostrando =
que
el comercio exterior aumenta con el pasar del tiempo, aunque a un ritmo más lento en comparación
con el crecimiento del Producto Interno Bruto (PIB) y el volumen tot=
al
del comercio exterior de esta provincia.
Devyatkin et al. (2018) plantean el objetivo de revelar combinaciones de
productos básicos y países asociados que<=
span
style=3D'letter-spacing:-.75pt'> experimentarían un crecimiento en el valor
de las exportaciones, a través de datos abiertos
del comercio internacional y la producción de las Naciones
Unidas, el Fondo Monetario Internacional, =
bases de datos meteorológicas y modelos de regresión cuantil basados en redes neuronales=
.
Velichko et al. (2018) investigan sobre el comercio interno de Rusia, cre=
ando
un modelo de flujo de redes
múltiples utiliz=
ando la plataforma en la nube denominada aplicaciones inteligentes, control y
plataforma como servicio (IACPaaS ) para visuali=
zar el
análisis. Estos investigadores realizan simulaciones del tráf=
ico
comercial interregional en el Lejano Oriente de Rusia, aprovechando todos l=
os
datos disponibles de comercio y generando visualizaciones para exponer los =
resultados
de su investigación.
Asi=
mismo, existen estudios que vinculan el comercio exterior con técnic=
as de minería de
datos, como el análisis realizado por Jia et al. (2019) =
para predecir el destino de las exportaciones de
petróleo crudo de América Latina utilizando técnicas de
aprendizaje automático. Estos i=
nvestigadores utilizan un conjunto de datos del envío de petróleo crudo procesados con técnicas
de minería de datos, como árboles de decisión,
bosques aleatorios y árboles potenciados, y entrenan un modelo que
permita predecir los destinos de exportación del petróleo cru=
do.
&nbs=
p;
La<=
/span> representación visual de datos <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>es fundamental para la toma de decisiones en el ámbito del comercio
exterior, como lo evidencian Jiang et al. (2021)
que usan varias
gráficas y técnicas de visualización para comprender el comercio exterior a nivel mundial, identificando tendencias
y comportamientos comerciales significativos.
Materiales y Métodos El presente estudio está
fundamentado en el método del modelo de la minería de datos y la obtención de un producto
final integral (IBM, 2021).
Por lo general, los investigadores=
que
aplican este método avanzan a través de las diferentes fases =
del
proyecto según sea necesario para mejorar la calidad de los datos y =
el
modelo, de acuerdo con los objetivos del negocio (Salt=
z ,
2021). CRISP-DM se basó en la metodología de descubrimient=
o de conocimiento en bases de datos (KDD) (Timarán Pereira
et al., 2015) y se tomó como referencia=
para establecer la presente investigación, cuyas fases son:
i) Comprender el negocio, ii)
Comprender los datos, iii ) Procesar la
información, iv ) Aplicar minería de datos (cluste=
rizar ),
y v) Desarrollar el modelo visual. En la Figura 1 se presenta la
metodología implementada en la investigación.
Figura 1 Metodología utilizada en la investigación
Comprender el negocio En la primera etapa de esta
investigación se realizó la comprensión del negocio, la
misma que tuvo a varios
involucrados como son los expertos
en el área de=
economía, estudios internacionales y el analista de datos, así =
como
la lectura de artículos científicos sobre el funcionamiento de exportaciones, partidas,
entre otros temas fundamentales. Los expertos
en economía manifestaron el apoyo que representa el poseer modelos
visuales para detectar
&nbs=
p;
comportamientos, cambios y tendencias dentro del come=
rcio
exterior en el Ecuador. Por lo tanto, se identificó la necesidad de
desarrollar gráficos con técnicas de visualización de
datos que permitan una mejor comprensión de la información del comercio exterior,
y que permitan la r&a=
acute;pida interpretación e identificación de patrones de comercio en todo el mundo. Para lograr
un análisis completo fue fundamental el uso de gráficos
geoespaciales y espaciotemporales a un=
a escala mundial, caracterizando así el espacio geográfico de las exportaciones desde Ecuador
hacia el mundo.
Comprender los datos Una vez definidos los requerimient=
os de
los expertos y la comprensión del negocio en el cual se
desarrolló el proyecto para conformar el modelo visual, fue necesario
comprender la informació=
n que
se encuentra en las bases de datos públicas del BCE. Toda la
información del BCE fue descargada desde el sitio oficial y procesada hacia archivos de formato xlsx. Estos
archivos fueron clasificados por continentes e internamente cada país
hace referencia a un archivo xlsx, que a su vez internamente está
dividido por años. Todos los datos descargados fueron clasificados en la estructura representada en<=
span
style=3D'letter-spacing:-.6pt'> la Tabla 1, la cual hace referencia al año del registro de exportación, =
la
cláusula de libre a bordo (FOB) en millones de dólares, la
cantidad de la exportación en toneladas y el número de partid=
a.
Tabla 1 Estructura=
de la información descargada desde el BCE
NOMBRE DEL CAMPO
DESCRIPCIÓN =
Año
Año del regist=
ro de exportación.
FOB
Contiene sobre
las partidas arancelarias.
=
Toneladas
Contiene las subsecciones de las partidas arancelarias. <=
o:p>
=
Partida
Contiene el PIB.
Los datos recolectados fueron sobr=
e las
exportaciones de Ecuador desde 1990 hasta 2022
para su análisis. Sin embargo, debido
a las circunstancias excepcionales causadas
por la pandemia de COVID-19, los datos comerciales presentaban
anomalías evidentes, debido a fluctuaciones de valores extremadamente anormales caus=
adas por la pandemia. Por lo tanto,
se optó por analizar los datos más recientes y libres =
de
irregularidades causadas por situaciones excepcionales, abarcando el periodo
comprendido entre 2008 y 2018.
Procesar la información En esta fase se realizó la
preparación de los datos para el modelo, garantizando su integridad
y validez. Esta etapa representó entre el 50% y el 80% del esfuerzo total del proyecto.
En el presente estudio, se tomaron en cuenta los archivos que
contenían toda la información necesaria sobre las exportacion=
es
de Ecuador al mundo en el periodo de 2008 a 2018. Luego, estos archivos
fueron importados a una base de datos relacional, donde se organizó la información
y se estructuró en tablas que permitieron realizar operaciones para depurar los datos. En la Tabla 2 se presentan los nombres y descripciones de las tablas
que se han utilizado<=
span
style=3D'letter-spacing:-.75pt'> en la base de datos.
Tabla 2 Tabla de la base de datos de exportaciones.
NOMBRE <=
/span>DE
LA TABLA
DESCRIPCIÓN =
=
COM_CONTINENT
Contiene información de los
contenientes.
NOMBRE DE
LA TABLA
DESCRIPCIÓN =
COM_COUNTRY
Contiene información de los
países.
COM_ECU_COU_DIST
Contiene la distancia de Ecuador a los países.
COMEX_EXPORT_BCE
Contiene las
exportaciones de Ecuador al mundo. =
COMEX_PARTIDA
Contiene sobre
las partidas arancelarias.
COMEX_PARTIDA_SUB
Contiene las subsecciones de las partidas arancelarias. <=
o:p>
COMEX_PBI
Contiene el PBI
de los países.
Dado el gran volumen de
información resultante y la gran cantidad de atributos en el conjunt=
o de datos, fue necesario seleccionar aquellos atrib=
utos que brindaran más información al modelo visual y que cumplan con los
objetivos del estudio.
Asi=
mismo, se llevó a cabo <=
span
lang=3DES style=3D'letter-spacing:-.2pt'>un proceso de reconocimiento y limpieza de los datos <=
span
lang=3DES style=3D'letter-spacing:-.2pt'>mediante t&=
eacute;cnicas de preprocesamiento, con el fin de reducir
el ruido y abordar posibles
inconsistencias que pudieran existir en la información. Se
identificaron datos nulos y aquellos que estaban fuera del rango esperado, =
con
el objetivo de minimizar los errores que podrían surgir en la siguie=
nte etapa.
Finalmente, el conjunto
de datos obtenido
con todas las variables seleccionadas está representado en la Tabla
3, las cuales fueron<=
span
style=3D'letter-spacing:-.05pt'> las bases para la construcción del modelo de clustering<=
/span> y
el modelo visual.
Tabla 3 Variables seleccionadas de <=
span
style=3D'letter-spacing:.05pt'> exportaciones
VARIABLES
DESCRIPCIÓN =
=
EXP_COU_ISO3
Contiene el có=
digo
ISO3 del nombre del país.
=
EXP_YEAR
Contiene el añ=
o del
registro de exportación.
=
PIB_VALUE
Contiene el valor del PIB del
país destino
=
DISTFROMECU_KM
Contiene las distancias desde
Ecuador.
=
EXP_TON
Contiene el valor de las toneladas.
=
EXP_DESCNAN
Contienen la
descripción de la partida.
=
EXP_FOB
Contiene el valor de =
FOB.
=
PAR_CODIGO
Contiene el có=
digo
de la partida arancelaria.
=
PAR_CODIGO_ECU
Contiene el có=
digo
de la partida en Ecuador.
=
PAR_NIVEL
Contiene el nivel de =
la
partida arancelaria.
=
PIB_ECU_VALUE
Contiene el valor del=
PIB
de Ecuador. =
Clusterizar Una vez que se definió el conjunto de datos, se procedió a aplicar las técnicas de minería
de
datos, ya que permiten descubrir información oculta que los datos pueden contener. Para ello, es necesario conocer cu&aac=
ute;l es el flujo
comercial de un país que puede ser medido por diferentes
modelos, siendo el más importante el modelo de gravedad. Este modelo
determina el nivel de relación =
comercial existente
entre dos países, ya que se basa en la ley de gravitación universal de Isaac Newton que permite establecer una
relación empírica entre el tamaño económico de =
un
país y el volumen de sus importaciones y exportaciones. En la
ecuación 1 se representa el modelo de gravedad de Krugman et al. (20=
01).
T =
i j A ×
Yi × Yj
i j
(1)
Donde
A
es una constante, Yi es
el PIB del paí=
s i , Yj representa el PIB del país j y
Di j es la distancia entre los dos países.
Además, se utilizó la
técnica de agrupamiento denominada K-means,
que segmenta y agrupa la información del conjunto de datos en función de ciertos atributos similares, identificando
patrones relevantes para su análisis del comercio exterior (<=
span
class=3DSpellE>Oyelade et al., 2019). Esta técnica fue aplicada
al dataset detallado en la Tabla
3, seleccionando tres variables principales que son tonela=
das,
FOB y PIB, y una variable más obtenida de la Ecuación 1 model=
o de
gravedad.
En este estudio, utilizó
información correspondiente a 11 años de transacciones
comerciales a diferentes niveles de partidas arancelarias, lo que
dificultó la exploración de patrones. Por lo tanto, se realizó una segmentación por año y por nivel de las partidas arancelarias
existentes, filtrando los datos para lograr un análisis
más detallado y poder clasificarlos. Sin embargo, el conjunto de dat=
os
disponible no contiene suficientes registros para realizar el agrupamiento =
de
las partidas de nivel uno y ocho, por lo que se omitieron en esta etapa de =
agrupamiento.
&nbs=
p;
Figura 2 Proceso implementado en la investigación
Para aplicar la minería de =
datos
a la información recopilada, se utilizó la herramienta RapidMiner debido a su versatilidad, eficiencia, amplia
documentación y una comunidad en línea que brinda
soporte. Como se observa en la Figura
2, el proceso comien=
za agregando el conjunto de=
datos
resultante de la etapa anterior al software. Luego, se generó un
atributo único que identifica =
cada registro, el cual se compone de la combinación del cód=
igo del país, el código de la
partida arancelaria y el año del registro,<=
span
style=3D'letter-spacing:-.75pt'> por ejemplo
“RUS20080603”. A continuación, se realizaron las repeticione=
s del proceso descrito
en los correspondientes años, para cubrir los 11
años de datos.
En este proceso,
se filtró por cada año de iteraci&oac=
ute;n y se agregó otro proceso de repetición para los nivel=
es de
las partidas arancelarias como se puede observar en la Figura 3.
&nbs=
p;
Figura 3 Proceso de repetición para los años
De la misma manera que pasó en la iteración por años,=
dentro del proceso
de repetición par=
a los
niveles de las partidas arance=
larias
se realizó un filtrado excluyendo los niveles dos y ocho debido a la escasez de registros correspondiente=
s a
dichos niveles.
&nbs=
p;
Para solucionar esta situación, se
proporcionó un subproceso para bifurcaciones condicionales en función de las condiciones especificadas pa=
ra las partidas arancelarias que no sean dist=
intas
de uno y ocho, como se muestra en la Figura 4.
Figura 4 Proceso iterativo de los niveles
de partida arancelaria
Seguidamente, como se presenta en la Figura 2, se
combinaron las tablas resultantes de los modelos, generando una única
tabla consolidada con la información analizada. Y, para terminar, se almacenó en un archivo
el resultado del análisis que fue utilizado
para la creación del modelo visual.<=
/span>
Para la última parte de esta fase en la
metodología propuesta, se realizó el proceso de clusterización . Por lo que se realizó una duplicidad de los datos de entrada
para posteriormente uni=
rse con
los resultados del clustering . Asimismo, se seleccionaron los atributos que fueron utiliza=
dos
para el proceso de agrupamiento, pero fue necesario normalizar los valores =
de
los atributos seleccionados, ajustando el rango de los valores, igualando la
desviación estándar y el promedio de los atributos para reali=
zar
el análisis de agrupamiento. <=
/span>Y por último se realizó=
el agrupamiento de datos seleccionados y normalizados<=
span
style=3D'letter-spacing:-.15pt'> mediante
el algoritmo k-means ,=
como se observa en la Figura 5.
Figura 5 Proceso de clusterizaci&oa=
cute;n de los datos
Desarrollar el modelo visual En la quinta
etapa de esta metodología, se procedió a construir el modelo visual,
el cual se desarroll&oa=
cute;
teniendo en cuenta los requisitos establecidos y definidos por los expertos=
en
la fase inicial. La necesidad de visualizar directamente los cambios en el comercio
exterior resultó
crucial, por lo que los gráficos principales que conformaron el mode=
lo
visual deben tener un enfoque geoespac=
ial y
espaciotemporal. Estos gráficos estaban acompañados de
representaciones visuales de las variaciones, permitiendo la
observación de los cambios en el comportamiento de las exportaciones=
a
lo largo de los años. Para ello, se utilizó una paleta de col=
ores
y formas apropiadas que facilitan la detección de cambios a simple
vista.
Para esta fase de la metodología se utiliz&o=
acute;
el software Tableau 2022.2 con la funció=
n de geocodificaci&o=
acute;n , que genera coordenadas geográficas que pueden ser aplicadas
a los mapas gratuitos de OpenStreetMap
(OSM). Estas coordenadas geográficas fueron utilizadas para representar los países en el conjunto de datos después de la etapa de agrupamiento. Se empleó una paleta de colores en gradiente, del rojo al dorado, para resaltar y facilitar la comprensión de los
cambios en los valores efectuados en las visualizaciones. Se utilizaron<=
span
style=3D'letter-spacing:-.75pt'> mapas
coropléticos junto con la paleta
de colores y las visualizaciones geoespaciales, proporcionando una representación
visual detallada y en colores para el receptor.
&nbs=
p;
En<=
/span> cuanto a la disposición de las visualizaciones en el modelo visual, se creó <=
/span>un prototipo de=
interfaz de usuario en forma de maqueta, que incluye las dimensiones y la distribución precisa
de las visualizaciones que estaban presentes en los
paneles de resumen (dashboards ). Estos paneles
presentan el mensaje a transmitir a través de las visualizaciones
expuestas.
Figura 6 Tablero de maquetas
I
En este contexto,
tal como se muestra en la Figura 6, de acuerdo con los requerimientos de los expertos, se
incorporó un gráfico geoespacial como elemento central del mo=
delo
visual para facilitar la comprensión de los datos de manera efectiva. En la parte inferior, se encuentra un gráfico de barras o columnas que permite a los expertos
visualizar las partidas
arancelarias más
frecuentes en el comercio exterior
de Ecuador. Además, se incluyó=
; un gráfico espaciotemporal que facilita el
análisis de las partidas a lo largo del tiempo, proporcionando una
visión clara de las
variaciones entre las toneladas y el año de exportación. Es
importante destacar que, en este caso, las visualizaciones del mapa y las barras permiten filtrar
la información dentro
de la misma
visualización.
Figura 7 Tablero de maquetas
II
Como se observa
en la Figura 7, posee un gráfico geoespacial qu=
e representa gran parte de<=
span
style=3D'letter-spacing:-.15pt'> la visualización debido
al mensaje que se desea
comunicar al receptor. Luego, se ubicó una visualización tipo escala para la
representación de los cambios de valores de un determinado atributo =
en
contraste con la visualización geoespacial. Finalmente, se
visualizó un gráfico de espacio temporal que representó
las variaciones existentes del atributo seleccionado. De este modo, el
único filtro en este tablero fue la visualización geoespacial=
.
Figura 8 Tablero de maquetas
III
Y,<=
/span> por último, se incluyó una visualizaci&=
oacute;n tipo <=
span
lang=3DES style=3D'letter-spacing:-.2pt'>radar, que en conjunto con la visualización geoespacial
conformó la parte principal de este tablero, como se observa en la
Figura 8. En la parte inferior,=
existen
dos filtros de selección única para la depuración de la información y una escala para intervalos
de valores. Adicionalmente, existen dos visualizaciones del mismo tamaño
que representan un gráfico de barras de valores promedio
filtrados y otra de la paleta de colores
del panel, que ayuda a la clasificación.
Resultados y Discusión Los primeros resultados obtenidos =
en
esta investigación se derivan de la etapa de cl=
usterización ,
donde se generaron un total de 66 modelos de clusterin=
g
que fueron utilizados en la creación del modelo visual. Cada uno de
estos modelos cuenta con una tabla interna que resume la informaci&o=
acute;n más relevante de cada clúster. Así, en la Tabla
4, se muestran los cinco clústeres obtenidos pa=
ra el
año 2010 y el segundo nivel de partida arancelaria, junto con el
número de elementos y los centros correspondientes a cada
clúster.
Tabla 4 Clústeres
para el año 2010<=
span
style=3D'letter-spacing:-.05pt'> con partida arancelaria nivel 2
CLÚSTERES
NÚMEROS DE ELEMENTOS
CENTRO DEL CLÚSTER
1
168
=
0.908
2
2
=
0.011
3
12
=
0.011
4
7
=
0.038
5
6
=
0.032
El modelo visual constituye la par=
te
central de los resultados de esta investigación, facilitando el análisis de las exportaciones ecuatorianas. =
Para lo cual, en la Figura
9, se presenta el panel de control que representa las exportacion=
es
de crudo más significativas de Ecuador hacia el resto del mundo. Este panel permite
al usuario final filtrar los datos mediante
un mapa mundial interactivo, que puede ser visualizado a través de coropletas . Además, con este gráfico espaciotemporal mues=
tra
las variaciones en tiempo real de las exportaciones de crudo a lo largo de los años. Cabe destacar<=
span
style=3D'letter-spacing:-.45pt'> que se utilizó una escala de colores para resaltar los países que importan una mayor cantidad
de crudo, con tonalidades más intensas de rojo,
mientras que los tonos más amarillos se a=
signan
a los países con menor importancia. Esto permitió tener una
visualización clara de los principales importadores y los cambios notables
que se produjeron a lo =
largo
del tiempo.
Figura 9 Modelo visual de los países importadores d=
el petróleo
En el segundo panel de control, el
usuario final puede analizar las variables más relevantes del comercio exterior
de Ecuador. En este panel,
se combinaron características como las exportaciones por pa&iacut=
e;ses,
productos y las variaciones existentes para lograr un análisis
exhaustivo mediante el modelo visual de manera interactiva con el mapa mund=
ial,
como se presenta en la Figura 10. Por consiguiente, el modelo visual
proporciona la descripción de las partidas arancelarias más
utilizadas por el país seleccionado a través de un grá=
fico
de barras ordenadas descendentemente. Por último, se puede seleccionar una partida arancelaria específica
y visualizar las variaciones en las toneladas exportadas a lo largo =
del
tiempo mediante una representación espacio-temporal.
Por último, en el tercer pa=
nel se
tiene al algoritmo de minería de datos, denominado "Clustering ”, que a través del conjunto de
visualizaciones permite realizar un análisis integral de las
exportaciones. De esta manera, se examinó de forma indirecta el FOB,
PIB, toneladas y la fuerza del
comercio internacional por medio del modelo de gravedad de cada país.
Por lo tanto, se seleccionó una partida arancelaria y un año específico para el análisis; acto seguido, el
&nbs=
p;
panel pr=
esentó los clústeres existentes y los valores
de cada atributo
mediante la visualización de un gráfico de radar. Además, se brindó=
; la opción de seleccionar un clúster en particular para filtrar las visualizac=
iones del mapa y el gráfico de barras, proporcionando información sobre los promedios de atributos
correspondientes, como se muestra en la Figura 11.
Figura 10 Modelo visual de las exportaciones, productos y variaciones
La evaluación de la fuerza =
del
comercio internacional requiere en primera instancia la aplicación del modelo de gravedad, utilizando variables adicio=
nales como el riesgo nacional, la inseguridad y las tasas de educación, entre otras. Estos factores mejoran la precisión de la fuerza
del comercio y ofrecen
un enfoque adicional para realizar un análisis meticuloso del mercado de
exportación. Aunque se utiliza
una de las técnicas de minería de datos más relevantes, el análisis
de las exportaciones pu=
ede ampliarse
mediante el uso de otras técnicas bien establecidas como los
árboles de decisión y la predicción, lo que permitiría obtener conclusi=
ones diferentes según los objetivos de los expertos.
Esta investigación
demostró claramente que existe un consenso en el uso de datos de comercio exterior extraídos de fuentes
públicas, coincidiendo con varios autores como =
Devyatkin
et al. (2018), Jiang et al. (2021) y Velichko <=
span
style=3D'letter-spacing:-.2pt'>et al. (2018), quienes resaltan
la importancia y utilidad
del análisis <=
span
style=3D'letter-spacing:-.4pt'> de los datos generados por las transacciones comerciales, aportando valor a los datos y contribuyendo a la toma de decisiones. Además, se determinó que muchos autores
no utilizan principalme=
nte
técnicas de visualización en combinación con
técnicas de minería de datos, lo que permite un anális=
is
integral de la información pública disponible de cada pa&iacu=
te;s
en relación con el comercio
exterior. En este sentido, se destaca el trabajo de Hui (2018),
quien realiza un análisis profundo de los datos comerciales para la aplicación de estrategias<=
span
style=3D'letter-spacing:-.2pt'> de marketing, pero no presenta los resultados a
través de visualizaciones de datos, sino a través de
resúmenes en forma de tablas. Varios autores, como Boar
et al. (2017) y M. Jiang et al. (2018) respalda=
n la
importancia de la visualización de datos para comunicar los resultad=
os
de manera efectiva y facilitar su comprensión y difusi&oacut=
e;n. Aunque
en el estudio actual=
se seleccionó un amplio rango
&nbs=
p;
de años para el análisis de las exporta=
ciones
ecuatorianas, ampliar el período de tiempo daría lugar a resultados diferentes, mejorando a&uacu=
te;n más el nivel de detalle del modelo visual al incluir datos excepcionales relacionados con la pandemia. Sería beneficioso utilizar un enfoque similar
al desarrollado en este trabajo para obtener resultados diversos y
enriquecer el análisis.
Figura 11 Modelo visual de algoritmo de miner&iacut=
e;a de datos
en las exportaciones
Conclusiones En este <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>estudio se emplearon técnicas de minería de datos =
y visualización para desarrollar =
una serie =
de modelos visuales que representan<=
/span> las exportaciones de Ecuador al mundo =
entre =
2008 y 2018. La construcción de estos modelos
se basa en un diagrama
de metamodelo de ingeniería
de procesos de software (SPEM), el cual guía los pasos involucrados =
en
la comprensión del negocio, la comprensión de los datos, el
procesamiento de la información, la agrupación y la
creación del modelo visual.
Una primera etapa del estudio consi=
stió
en clasificar los datos disponibles utilizando un enfoque de agrupaci&oacut=
e;n,
mientras que la etapa del modelo visual involucró la creación=
de
prototipos para el diseño de la visualización y la
aplicación de procedimientos probados. Se utilizó una variedad de enfoques de visualización de datos en la creación del modelo visual,
lo cual permite a los usuarios comprender y analizar las
representaciones de manera eficiente y rápida. Para segmentar el mercado global y explorar
en mayor profundidad los datos, se utilizó
la técnica de clusterización de la minería de datos, sobre
una base de datos creada
manualmente a partir de datos públicos obtenidos del<=
span
style=3D'letter-spacing:-.65pt'> BCE. Por lo tanto, el modelo visual
desarrollado en este estudio es una herramienta eficiente que<=
span
style=3D'letter-spacing:-.75pt'> facilita
la toma de decisiones en el ámbito económico y
comercial de las exportacione=
s ecuatorianas. Lo que permite
evaluar gráficame=
nte continentes, países, precios,
toneladas, partidas arancelarias y FOB a través de los años de las exportaciones.
Como un trabajo futuro, se destaca =
la
importancia y utilidad del modelo de gravedad para analizar la fuerza del
comercio con otras naciones, utilizando factores adicionales como los índices de educaci&oac=
ute;n, el riesgo nacional
y la inseguridad. E=
stos factores
podrían mejorar<=
span
style=3D'letter-spacing:-.15pt'> la precisión en la medición de la fuerza
del comercio y proporcionar otra perspectiva en el estudio
&nbs=
p;
detallado del mercado de exportaci&oac=
ute;n. Además, =
se puede <=
span
lang=3DES style=3D'letter-spacing:-.4pt'> ampliar el anális=
is de las exportaciones=
aplicando técnicas conocidas como los árboles de
decisión y la previsión con la finalidad de estimar proyeccio=
nes
de resultados posibles desde diferentes puntos de vista acordes a los objet=
ivos
de los expertos.
Reconocimientos Los autores desean agradecer al
Vicerrectorado de Investigaciones de la Universidad del Azuay por el apoyo financiero y académico, así
como a todo el personal de la escuela de Ingeniería de Ciencias de la
Computación, y el Laboratorio de Investigación y Desarrollo e=
n Informática
(LIDI).
Referencias Boar , R., Iovanovici , A., y Ciocarlie , H. (2017).
Complex networks analysis of international <=
/span>import-export <=
/span>trade . 2017 I=
EEE 14th
International Scientific C=
onference
on Informatics ,=
31-34. https://doi.org/10.1109/ INFORMATICS.=
2017.8327217
Cairo, A.
(2011). El arte de lo funcional:
infografía y visualización de información . Alamut .
Devyatkin Suvorov , R., Tikhomiro=
v ,
I., y Otmakhova , Y. (2018). Neural Networks for Food Export
Gain Forecasting . 2018 International Conference
on Intelligent Systems (IS) , 312-317. https://doi.org/10.1109/ IS.2018.8710561
Hui, =
Z. (2018).
Analysis of Inter=
national Marketing Strategy Based on
Intelligent
Mining Algorithm for Big<=
span
style=3D'letter-spacing:-.45pt'> Data. 2018 11th International Conference on Intelligent Computation Technology and Automation (ICICTA) ,
261-264. https://doi.org/10.1109/ICICTA.2018.00065 <=
/a>
Jiang , H.,=
Shen,
J., Chou, Q., Dong, Z., y Cheng, S. (2021). Visual Ana=
lytics
for the Internation=
al Trade . 2021 5=
th
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n
Vision , Image and <=
span
class=3DSpellE>Signal Processing (ICVISP), 296-301. https://doi. org /10.1109/ICVISP54630.2021.00059
Jiang ,
M., Niu , L., Zhang, Y., Wang, Z., y Ren, X. (20=
18). A
Big-Data-Analysis -Based Research Study on the Changes Both in the Dependence on Foreign =
Trade and=
in the Trade Structure (2000-2015) of the Yunnan Province=
of China. 2018 International Conference on Intelligent Transportation<=
/span>, Big Data y Smart City (ICITBS) , 235-239. <=
span
lang=3DES>https://doi.org/10.1109/ICITBS.20=
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Krugman, P., Obstfeld,
M., y Melitz , M. J. (2001).
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span
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nStreetMap . (s. f.). OSM . =
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I., Oladipupo, O., Emebo , O.=
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O.
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00-1
Po, <=
/span>L., Bikakis ,<=
span
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Papastefanatos , <=
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Velichko , A., Gribova , V., y Fedorishchev , <=
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