I. P. Molina Alarcón, L. Tonon-Ordóñez, J. L. Zambrano-Martinez, and M. Orellana,

“Data Visualization Model for Multi-party Analysis and Strategic Decision-Making in International Trade”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.

Data Visualization Model

for Multi-party Analysis

and Strategic Decision-

Making in International

Trade

ARTICLE HISTORY

Received 28 August 2024

Accepted 22 October 2024

Inés Paola Molina Alarcón

Computer Science Research and Development Laboratory (LIDI)

Universidad del Azuay

Cuenca, Ecuador

paola.molina1612@es.uazuay.edu.ec

ORCID: 0009-0003-0093-2860

Luis Tonon-Ordóñez

Computer Science Research and Development Laboratory (LIDI)

Universidad del Azuay

Cuenca, Ecuador

ltonon@uazuay.edu.ec

ORCID: 0000-0003-2360-9911

Jorge Luis Zambrano-Martinez

Computer Science Research and Development Laboratory (LIDI)

Universidad del Azuay

Cuenca, Ecuador

jorge.zambrano@uazuay.edu.ec

ORCID: 0000-0002-5339-7860

Marcos Orellana

Computer Science Research and Development Laboratory (LIDI)

Universidad del Azuay

Cuenca, Ecuador

marore@uazuay.edu.ec

ORCID: 0000-0002-3671-9362

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14448095

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

Data Visualization Model for Multi-party Analysis

and Strategic Decision-Making in International

Trade

Inés Paola Molina Alarcón

Computer Science Research &

Development Laboratory (LIDI)

Universidad del Azuay

Cuenca, Ecuador

paola.molina1612@es.uazuay.edu.ec

ORCID: 0009-0003-0093-2860

Luis Tonon-Ordóñez

Computer Science Research &

Development Laboratory (LIDI)

Universidad del Azuay

Cuenca, Ecuador

ltonon@uazuay.edu.ec

ORCID: 0000-0003-2360-9911

Marcos Orellana

Computer Science Research &

Development Laboratory (LIDI)

Universidad del Azuay

Cuenca, Ecuador

marore@uazuay.edu.ec

ORCID: 0000-0002-3671-9362

Jorge Luis Zambrano-Martinez

Computer Science Research &

Development Laboratory (LIDI)

Universidad del Azuay

Cuenca, Ecuador

jorge.zambrano@uazuay.edu.ec

ORCID: 0000-0002-5339-7860

Abstract—This paper presents a detailed analysis of Ecuador’s

non-oil exports over ten years. The study was performed using the

SPEM methodology and data-cleaning processes. The results

highlight a notable coherence in analyzing the most relevant export

items and the main trading partners, providing essential information

for strategic decision-making. Furthermore, recommendations

related to the technical conditions necessary to achieve precise and

accurate communication through data visualization were

considered, and adequate answers to the questions generated in the

business knowledge stage contributed to the users’ knowledge.

Furthermore, the study suggests incorporating import data to

enhance the analysis and provide a foundation for future research in

this area.

Keywords: data integrity; multi-party analysis, international

trade, strategic decision-making, tableau

I. INTRODUCTION

In the current era, analyzing large volumes of data is

crucial for making informed decisions in various sectors,

including foreign trade. Data visualization emerges as a

powerful tool for converting complex information into

understandable graphical representations, allowing patterns,

trends, and areas for improvement to be identified.

Sosa [1] highlights the usefulness of data visualization in

understanding the evolution of the export sector in countries

such as Ecuador, where the economy depends mainly on

exporting primary products. This tool allows us to explore

changes in export patterns over time, identify the main product

categories and destination countries, and evaluate the

effectiveness of export policies and strategies.

Traditional analyses of the export sector often focus on a

single product or category, limiting the possibility of

comprehensive comparisons and evaluations. In conjunction

with data mining techniques such as those proposed by Kirk

[2], data visualization allows us to overcome these limitations.

The present work proposes a visualization model that uses

data visualization and data mining to provide diverse users

with convincing, clear, and attractive results. This model is

based on cleaning, consolidating, summarizing, and

presenting large amounts of Ecuadorian export data using data

mining techniques and visualization tools to improve

decision-making in the export sector.

This approach is evident in several publications, such as

Araque & Arguello [3], Tercero et al. [4], and Tonon Ordóñez

et al. [5] who have analyzed cocoa, copper, and banana,

respectively. However, this unitary approach uses extensive

data visualization tools and data mining and cleaning

techniques. The potential benefit of this research is that it

simplifies the use of data, aiding end users and businesses to

make more informed decisions through enhanced data

visualization. With these tools, it is possible to interact with

them and extract valuable information without the need to

restart from the data preparation phase for each product or

item [6], [7]. This can save significant time and effort and

allow the user to perform benchmarking and evaluations more

efficiently and effectively.

The work methodology is represented by Systems Process

Engineering Metamodel 2.0 (SPEM 2.0). The process was

performed in four steps: data preprocessing, labeling and

inclusion of valuable fields for the analysis, application of the

visualization model, and finally, the validation of the model.

The items analyses are denominated by the Harmonized

System, as recommended in the United Nations Department

of Economic and Social Affairs [8] report, since comparability

of information between countries is allowed.

This article is structured as follows: Section II contains the

related works that influenced this research. Section III

describes the methodology and theoretical framework used in

this research. Then, Section IV presents the results that were

obtained in the study. Likewise, Section V presents the

discussions regarding similar works and our contribution, and

finally, Section VI presents the conclusions of this research.

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14448095

I. P. Molina Alarcón, L. Tonon-Ordóñez, J. L. Zambrano-Martinez, and M. Orellana,

“Data Visualization Model for Multi-party Analysis and Strategic Decision-Making in International Trade”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.

II. R

ELATED WORK

Throughout history, many people have contributed to the

development of data visualization. Thus, some of the pioneers

in data analysis are presented, such as William Playfair, who

was the first to use graphs to represent economic data,

contributing to the popularization of bar, line, and sector

graphs [9]. In the 1850s, Florence Nightingale (1820-1910)

shared her professional vision as a nurse using data

visualization techniques to show the importance of sanitary

conditions in the care of soldiers during the Crimean War [10].

At the end of the 19th century, Karl Pearson (1857-1936)

introduced the concept of correlation coefficient, which

measures the relationship between two variables. Pearson also

pioneered statistical techniques in genetics and created the

chi-square test [11]. Ronald Fisher (1890-1962) is the father

of modern statistics. In the 1920s, Fisher developed statistical

techniques for data analysis in genetics and biology and was

one of the first to use the maximum likelihood method to

estimate parameters [12]. John Tukey (1915-2000) was one of

the first to use data visualization for exploratory analysis,

creating techniques such as the box-and-whisker plot and the

matrix scatterplot [13]. Another pioneer was Jaque Bertin

(1918-2010), who, using computers, produced the publication

Semiologie Graphique, which showed the link between data

and its visual function, the science of graphical representation

of data, and the basis for visual data analysis [12].

Concerning the visualization of foreign trade data, there

are interpretations from several decades ago, focusing on the

graphical representation of economic data through graphs and

maps, such as “The Atlas of United States Exports,” published

by the United States Department of Commerce. A wide range

of data on United States exports was presented as maps,

graphs, and tables with that Atlas. The data were presented by

country and sector, which allows the identification of foreign

trade trends and patterns. Another graphical representation is

“Comtrade Data Visualization”, an international trade

database managed by the United Nations. This data

visualization tool allows users to explore global trade by

country and product. The visualization uses interactive charts

and maps to show trade trends and exchange patterns [8].

Finally, TradeMap is a data visualization tool developed by

the European Commission that allows users to explore

international trade between the European Union and other

countries. The tool uses interactive graphs, charts, and maps

to show trading trends and patterns [14]. These findings

underscore the importance of data visualization as a

fundamental component in understanding trade data.

Over the past years, we have witnessed a transformative

leap in data visualization technology. This leap has allowed

the development of more sophisticated and practical data

analysis and interpretation tools, expanding new horizons for

researchers and professionals [15]. Data visualization tools are

not just about visual appeal but practicality and functionality.

They allow exploration in different dimensions and the

visualization of complex patterns and relationships that would

be difficult to detect otherwise. Several studies, such as that

by Skender & Manevska [16], note in their review that various

tools exist for visualization and visual data analysis. New

techniques and approaches, such as K-means cluster, Gravity

Equation, and 3D data visualization, have practical

applications in analyzing trade and economic data [17]. These

new options have allowed greater flexibility and creativity in

data presentation and analysis [18] and led to valuable insights

and recommendations.

Regarding data visualization research, there is a plethora

of scientific literature available in the field, including

theoretical, applied, and evaluation studies of visualization

tools. Durán, J. & Zaclicever, D. [18] used the grouping of

countries by region with trade agreements: Andean

Community of Nations (CAN), Caribbean Community

(CARICOM), Southern Common Market (MERCOSUR),

Central American Common Market (CACM) and analyzed

the amounts of regional exports. In the study by Tercero et al.

[4], copper trade flows between countries were examined,

generating representations on maps and direction arrows,

where the thickness of the lines was used to identify the

transaction amounts and the grouping of countries by region,

which quickly detected the movement between origin and

destination.

In the work of Morrison et al. [14], the health of a

country’s economy was measured based on the number of

exported items; they analyzed the complexity of the products

produced and described complex economic systems with

networks connected by points whose central location denotes

importance. The non-linear iterative evaluation of complexity

was described as a specialized process and very susceptible to

the variation in the number of games studied and data

cleaning. Finally, the recommendation does not analyze tariff

items at a four-digit level but at six for greater detail. Straka et

al. [19] obtained an algorithm that allows for the measurement

of the similarities between export products and the level of

technological advance of the countries through the bipartite

representation of the International Trade network, using the

calculation of the entropy and projective models. They

exported information from 1995 to 2010, identifying country

communities with color keys on a world map and analyzing

specific sections of years to validate their estimates. Dong et

al. [20] studied global wheat consumption, verifying the

influence of climate patterns in each region studied. The

information came from the United Nations database, and they

segmented the countries as protagonists and peripherals

concerning world trade. They used bar graphs, choropleth

maps, and line graphs. They recommended studying the other

variables not considered, such as oil price, water availability,

and socio-political conditions.

Dar et al. [21] made visualizations and an estimate of the

commercial profile of the South Korean economy based on the

growth that its products have in the world market to serve as a

guide in the policies and business decisions made by the

government. They found a positive evolution in their trade

relationship with other Asian countries. They recommended

viewing the country’s trade profile for a greater understanding

of trade, as it can provide detailed and easily accessible

information, which allows precise estimates to be made. In the

context of analyzing bilateral trade data involving more than

two hundred countries, together with China, Ye et al. [23]

proposed a geospatial analysis methodology known as the

Digital Trade Feature Map (DTFM). This methodology offers

a broad and detailed vision for analyzing the characteristics of

specific products and their relationships with other products.

The authors used the import and export values in Cartesian

coordinates to implement this approach. Then, they calculated

the differences between these values and plotted them as a line

on a Cartesian plane. Subsequently, they evaluated the volume

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14448095

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

of commercial exchange and its range-size distribution based

on the criterion of transaction amount versus frequency. In this

way, they identified rare or high-trade products located at the

top of the distribution and low-trade or repetitive products at

the bottom. The authors concluded that the DTFM method is

a valuable tool for analyzing trade trends, especially when

information on imports and exports is available. This

approach is a fundamental preliminary step before the

statistical data analysis, providing a solid and meaningful

perspective. Qaiser et al. [22] measured South Korea’s

economic complexity index, using time series to identify

patterns and make trade estimates. They considered that the

visualization and forecasting of imports, exports, Gross

Domestic Product (GDP), and GDP per capita benefit

international trade. The analysis, visualization, and

forecasting of global trade must be determined in an

increasingly detailed and precise manner. They managed to

identify the main trading partners and make estimates of the

country’s imports and exports, establishing a positive

correlation with GDP and GDP per capita. In other studies,

such as that of Kim et al. [23], considering several

fundamental aspects when designing data visualizations is

essential. This includes knowing the type of user, the specific

objectives of the visualization, and the size of the device on

which the information is presented so that the appropriate

tools are used to display the content without distortion, avoid

loss of meaning, and optimize the user's interaction

experience. For this purpose, they validated 378 pairs of

visualizations from various sources and data. They defined 76

characterizations that align with the specific objectives sought

with visualization to satisfy the users’ needs and expectations.

According to Wu et al. [26], the world economy is

becoming more interconnected, and there is a growing need to

understand industry trends better. For this reason, Wu et al.

introduced VIEA, a web-based system with various views and

interactive features. VIEA lets us know the critical economic

trends, where those issues are produced, trade patterns, and the

economic comparison between industries. Medina López et al.

[17] studied about Ecuadorian exports to create a knowledge

base for trade specialists. To achieve this, a data visualization

tool examines data from 2008 to 2018. The well-established

CRoss Industry Standard Process for Data Mining (CRISP-

DM) method for data mining projects was used as a

foundation. This method involves five steps adapted to

develop the mining and visualization model. The result is an

interactive tool that allows users to explore key trade variables

for Ecuador. This user-friendly interface makes it easier to

analyze exports and ultimately supports informed decision-

making in foreign trade.

According to Ren, D. [24], the Tableau Desktop software

was recognized as configurable in the form of shelves due to

its referent types of representations that obey the creation of

different internal rules of the program and allow the

combination of several attributes on the same screen. It has

multiple representations for the measurements and dimensions

of the data. It was also indicated that the flexibility for creating

graphs is limited by the control at a high level of granularity

of the data and the need for more options to combine different

screens.

III. M

ATERIALS AND METHODS

In the methodological process, the SPEM 2.0 approach

methodology effectively represented each stage of the data

analysis process. The methodology was divided into four

consecutive steps, as shown in Fig. 1.

Fig. 1. The methodology used in the research

Thus, according to Fig. 1, the execution responsibilities

are established in different methodology stages: the data

analyst is responsible for the execution from steps 1 to 3, and

the analysis specialist is accountable for the last step. The

dataset of this study is obtained from the Central Bank of

Ecuador, corresponding to the annualized export records

performed between 2008 and 2018.

This data added the name of the receiving country, the

International Organization for Standardization (ISO) Code,

and values exported as Free on Board (FOB) and Metric Tons

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14448095

I. P. Molina Alarcón, L. Tonon-Ordóñez, J. L. Zambrano-Martinez, and M. Orellana,

“Data Visualization Model for Multi-party Analysis and Strategic Decision-Making in International Trade”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.

(MT). Computer Science Research & Development

Laboratory (LIDI) researchers preprocessed the data set

provided at the University of Azuay. These researchers

included columns with information on Population GDP, GDP

per capita [25], distance to Ecuador from other countries, and

force of attraction [17], [26].

The columns of the dataset have the following

characteristics.

 Number of variables: 40

 Data set type: static

 Categorical: 19

 Numerical: 21

 Number of observations: 318,629

 Missing cells: 860,207

 Dataset size: 39.1 MB

A. Data preprocessing

In this initial phase, cleaning and transformation tasks

were performed on the original data set in Comma Separated

Values (CSV) format to ensure quality and consistency. This

included standardizing formats and correcting missing data.

The process started with identifying the data types present in

the initial set, including specific data types. A detailed

description of each type can be found in Table I.

TABLE I. DATASET DESCRIPTION

Field name Description

Type of

variables

Exp_region Container name Categorical

Exp_Cou_iso

Export country code (3-digit ISO

format)

Categorical

Cou_Name_e

Export destination country name

(Spanish)

Categorical

Exp_Year Export year Real

Pbi_Value GDP value of export year Real

Pop_Value Population in export year Real

Pbi_Percap_

Value

GDP per capita value in export year Real

Pbi_Ecu_Val

Ecuador’s GDP value in export year Real

Exp_Subpnan NANDINA subheading of export Integer

Exp_Descnan NANDINA description of export Categorical

Exp_Ton Quantity of tons exported Real

Exp_Fob FOB value in millions of dollars Real

Par_code Current heading code Integer

Par_code_Na

Heading code according to

NANDINA codification

Integer

Par_code_Ec

Local heading code (Ecuador) Integer

Par_Desc

Local heading description

(Ecuador)

Categorical

Par_Desc_Co

mplete

Complete heading description up to

the las level

Categorical

Par_Uf Tariff heading unit measure Categorical

Par_Section

Section code where heading

belongs

Integer

Field name Description

Type of

variables

Par_Section_

Desc

Description of section where

heading belongs

Categorical

Par_Cod_Lvl

Tariff heading level 1 code Integer

Par_Desc_L1 Tariff heading level 1 description Categorical

Par_Cod_Lvl

Tariff heading level 2 code Integer

Par_Desc_L2 Tariff heading level 2 description Categorical

Par_Cod_Lvl

Tariff heading level 3 code Integer

Par_Desc_L3 Tariff heading level 3 description Categorical

Par_Level

Level of detail to which heading

belongs

Integer

This step was performed with the Tableau Prep Builder

tool; the changes applied sought to adjust to the requirements

of the data analyst and the program that was subsequently used

in the visualization stage [27]. The pre-processing stages were

i) renaming headers and cleaning fields, ii) changing the type

of fields, iii) cleaning and trimming fields, and iv) filling

empty fields.

B. Re-labeling and inclusion of new fields and data

At this stage, Tableau Desktop was used to load and

prepare the data for visualization. Previous adjustments were

made, including the geographic coordinates of the four

countries mentioned in Table II. This table details countries

whose location was not automatically recognized by the

software and was manually populated with relevant

geographic information.

TABLE II. RE-LABELING AND FIELD INCLUSION

Country

Initial

Value

Final Value

Bermudas Islands N/A Lat. 32.33 Long. -64.75

United States

Minor Outlying Islands

N/A Lat. 5.875 Long. -162.057

United State Virgin

Islands

N/A Lat. 18.33 Long. -64.8963

Trans-boundary waters

N/A

Data from this location is

filtered because its physical

location cannot be

determined.

It is essential to clarify that we applied a filter to exclude

data corresponding to the location of international waters or

Transboundary waters during the initial data load. This

decision was based on the challenge of pinpointing a fixed

geographical point for its representation on the map.

Additionally, crucial information such as population, GDP,

and other valuable data were unavailable for this location

visualization.

Table III details the calculation formulas used to create

additional fields, such as the Average Price, MT, and the

Number of Items. These formulas allowed us to answer

critical questions through descriptive statistical analysis, such

as: How much does each country sell? What products are

sold? With which country are the most transactions

performed?

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14448095

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

Additionally, given that the analysis focused on Ecuador’s

nonoil exports, answers were sought to the following

questions: How much does each country buy? What products

does it buy? With which country are most transactions

performed? These questions are aligned with the

recommendations of the World Trade Organization in its

publication “A Practical Guide to Trade Policy Analysis".

TABLE III. FORMULAS CALCULATION

Field name

Tableau Formulas

Average Price MT [Millones de USD/FOB]/[TM]

Exported tariff

headings

COUNTD([Par Codigo Nan])

Annual change SUM([TM]) / TOTAL(SUM([TM]))

MT Variation

ZN(SUM([TM])) -

LOOKUP(ZN(SUM([TM])), -1)) /

ABS(LOOKUP(ZN(SUM([TM])), -1))

USD/FOB

Variation

ZN(SUM([Millones de USD/FOB])) -

LOOKUP(ZN(SUM([Millones de

USD/FOB])), -1)) / ABS(LOOKUP(ZN(SUM(

[Millones de USD/FOB])), -1)

C. Application of the visualization model

In this stage, the visualization model was designed to

represent the data in an understandable and compelling format

based on Gestalt visualization principles, establishing an

appropriate pipeline (route) [28]. Various visualization

techniques, such as graphs, charts, and interactive dashboards,

were used to communicate the patterns and trends identified

in the data [29]. Thus, the available data were considered as

viewable.

As we can observe, Table IV details the fields and

elements represented in the visualization, detailing the

purpose pursued by each view.

1) Interactions

The interactions added to the visualization aim to facilitate

the users’ journey through the designed environment. Table V

specifies the interactions included in the representation and

their usefulness for the user, considering the visualization

principles.

D. Model validation

The expert on the subject verified the results shown by the

applied model provided, and adequate answers to the

questions generated in the business knowledge stage

contributed to the users’ knowledge. For that, Munzner [10]

considers four levels of validation:

 Mastery of the situation

 Use of correct terminology

 Review understanding of the problem to be

solved

 Try answering basic questions

The subsections detailed below are the types of validation

necessary to apply during testing a visualization model,

preferably during user interviews so that it contributes to the

area of knowledge.

1) Data abstraction

In this context, the need to evaluate the listed approaches

is emphasized, which helps to improve clarity and

effectiveness in data management in analysis and

presentation.

 Compare with existing reports.

 Have an alternative scenario.

 Evaluate other visualizations.

 Check simplicity.

TABLE IV. APPLICATION OF THE VISUALIZATION MODEL I

Field name Data Type Applied Visualization Represented Element Objective

Continent/Country

Geographical

(GIS)

Map Surface Spatial Positioning

Millons od USB/FOB Numeric

Circle size

Bar size

Rectangle area

Marks Comparison

MT Numeric

Bar size

Rectangle area

Marks Comparison

Year Date Line Marks Order

Continent

Geographical

(GIS)

Map

Region area

Surface

Region

Spatial Positioning

Section Numeric Circle size

Marks

Comparison

Section name Text Data size Structured list

Order

Group

International Subheading

codes

Numeric Textual representation in 6 digits Structured list Comparison

Chapter description Text Data table Structured list

Order

Group

Regional Subheading

codes

Numeric Textual representation in 8 digits Structured list Comparison

Regional Subheading

description

Text Data table Structured list Order Group

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14448095

I. P. Molina Alarcón, L. Tonon-Ordóñez, J. L. Zambrano-Martinez, and M. Orellana,

“Data Visualization Model for Multi-party Analysis and Strategic Decision-Making in International Trade”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.

TABLE V. APPLICATION OF THE MODEL VIEW II PATTERN

Sheet name

Applied Visualization

Applied Interaction

Function

Overview

Annual evolution of exports by

continent

Continent filter Filtering of other views

Export by section Click on named circular area Filtering of other views

World map

Slider to select year for display,

reproducible for the entire period

Display by country of exported values in

Dollars

Top trading partners

Click on rectangular area Filtering of other views

By tariff items

Price comparison by continent and

section

Click on Section, Discount Tariff

item N1, Item description N2

Filtering of other views on the sheet

Sliding bar to select the name of the

section to display

Filtering of other views

Top 10 trading partners by item, sub-

item N1 and N2

Click on horizontal bar Filtering of other views

Annual change

Year-over-Year Evolution of Main

Trading Partners

Sliding chart to select the year for

comparison

Filtering from other views

Click on horizontal bar, Country

code

Filtering from other views

Volume of traded tariff lines Click on the continent name Filter from own view

Exports by section Click on the section name

Greater detail Level 1 and Level 2,

Section Code, Value in sales dollars in

pre-filtered period.

The subsections detailed below are the types of validation

necessary to apply during testing a visualization model,

preferably during user interviews so that it contributes to the

area of knowledge.

2) Visual coding, functional interactions

These three elements combine to improve the user

experience in exploring and understanding data through a

visual interface.

 Validate manipulability

 Interaction validity

 Validity response to actions

3) Algorithmics Implementation

It ensures that operations and algorithms are efficient and

fast in providing results in real time or with acceptable

response times. They ensured that the software complies with

usage licenses and that visualizations were available for

seamless integration into the workflow. This is essential to

maintain effectiveness in implementing data processing and

visualization systems. Thus, these aspects are fundamental to

guarantee efficient and legal operation in algorithm and data

management.

 Validate response speed (Software or own

code).

 Validate access to licenses and views.

IV. R

ESULTS

Following the research objectives, the results of the data

analysis process are presented. The development was

performed with Tableau software, which provides significant

details in visual analysis.

A. Prepared dataset to be analyzed with tools display

Each variable was analyzed in case of missing data to

prepare the dataset and reach a correct analysis. Depending on

the data type, different methods were used to clean the data to

reduce the noise and inconsistencies found in the dataset. The

number of records prepared for the analysis in Tableau was

318,629.

B. Model visualization of the export evolution

Based on the data prepared for Tableau software, it

includes the graphs corresponding to the inter-annual

variation of export amounts of Ecuador in millions of dollars

and tons between 2008 and 2018 in non-oil items. As we can

observe in Fig. 2, the most representative years in positive

variation from the point of view of the tons exported were

2011 and 2014, having their counterpart in the years 2010 and

2012 in terms of millions of dollars/FOB. The years with the

most significant positive variation were 2010, 2011, and 2014.

However, the years that represented negative percentages

were 2009, 2015, and 2016.

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14448095

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

Fig. 2. Annual Variation of Exported Amounts (2008-2018)

Table VI contains information on the percentage of annual

participation of each continent in total non-oil exports

between 2008 and 2018. America has had the highest average

percent participation throughout the years. Years show a

decreasing trend from 50.52 % to 42.72%.

Over the same period, Europe has shown a significant

level of participation, albeit with a decreasing trend. This is

particularly noticeable as it transitions from 44.46% in 2008

to 38.08% in 2018. In other words, it has been a region of

accelerated growth since 2012, increasing its share from 8.10

% to 18.90 % of the total amount exported in millions of

dollars/FOB for 2018.

TABLE VI. ANNUAL EVOLUTION OF NOPN-OIL EXPORTS IN SHARE

PERCENTAGE BY CONTINENT IN MILLIONS USD/FOB

Year/

Continent

Africa America Asia

Australia

Oceania

Europe

2008 0.11 50.52 4.62 0.29 44.46

2009 0.10 49.18 4.17 0.35 46.20

2010 0.12 49.23 5.27 0.43 44.95

2011 0.27 49.89 6.97 0.36 42.51

2012 0.40 52.05 8.10 0.34 39.11

2013 0.30 50.37 9.20 0.40 39.74

2014 0.25 52.75

10.3

0.37 36.34

2015 0.63 49.67

13.6

0.48 35.61

2016 0.30 47.78

13.2

0.52 38.15

2017 0.15 45.65

13.3

0.54 40.31

2018 0.12 42.42

18.9

0.48 38.08



0.25% 49.05%

9.81

0.39% 40.50%

We can see in Fig. 3 that from 2008 to 2018, in exports to

the five continents, America appears as the largest trading

partner in terms of USD/FOB; in second place is Europe. Then

there is Asia, while polygons with a smaller area represent the

trade exchange with Africa and Oceania.

Suppose the same analysis is performed in terms of tons.

In that case, Europe is the commercial destination with the

most significant participation, followed by America and Asia,

which shows growth, based on the graph drawn as areas in this

historical record. The cumulative development of exports to

the different continents, particularly in America, Europe, and

Asia, is a promising sign for the global market. Commercial

transactions with Africa and Oceania, on the other hand, are

negligible.

Fig. 3. Evaluation of Non-Oil Exports by Continent in USD/FOB and MT

America has a 49.21% participation in purchases in

dollars, Europe occupies the second place with 35.23%, while

with the reference Tons, Europe occupies the first place with

46.06 %, and America participates with 37.19 %. It can be

understood that the countries located within these continents,

including Ecuador, made the most outstanding exports in the

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14448095

I. P. Molina Alarcón, L. Tonon-Ordóñez, J. L. Zambrano-Martinez, and M. Orellana,

“Data Visualization Model for Multi-party Analysis and Strategic Decision-Making in International Trade”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.

period analyzed. Another critical analysis focused on the main

trading partners, where the accumulated purchase amount of

millions of USD/FOB and tons is analyzed between 2008 and

2018. The analysis highlights the participation in the

purchases of the ten main trading partners of Ecuador, which

in the Americas are the United States, Colombia, and

Venezuela, while in Europe, Russia, Italy, Germany, the

Netherlands, and Spain emerged, and finally in Asia, China,

and Vietnam. As can be observed in Fig. 4, the geographical

location of Ecuador’s main trading partners is shown, where

the size of the circle is the amount in millions of USD/FOB of

exports made in 2018.

Fig. 4. Location and Identification of Main Commercial Partners in 2018

Fig. 5 indicates the percentage of participation in

Ecuador’s exports from 2008 and 2018 of the ten main trading

partners, finding that the United States has a decrease both in

millions of dollars since it changes from 31.23% to 29.38%

and in tons, which ranges from 26.36% in 2008 to 22.87% in

2018. This decrease in percentage participation contrasts with

what happens with countries on the Asian continent, where

China and Vietnam have grown significantly. The first

country goes from 0,87 % to 11.04 % and the second from

0.25 % to 13.99 % in millions of dollars; with this in the last

year analyzed, they are already part of Ecuador’s ten main

commercial partners. Concerning Europe, the percentage

variation in participation in total exports could be more

notable, except for Italy and Spain, which shows a downward

trend.

Fig. 5. Comparison of Importance in Total Exported 2008 vs 2018

The most exported products were detailed by ordering

from the highest to the smallest sum of the accumulated

purchase in terms of millions USD/FOB from 2008 to 2018,

without applying country or item filters.

The most crucial section within exports is not Ecuadorian

oil companies, but rather those products composed of the plant

kingdom, followed by products from the food industries such

as alcoholic liquids, vinegar, tobacco substitutes, beverages,

tobacco, and tobacco manufacturers. In contrast, the smaller

products are weapons, ammunition, and their parts and

accessories.

Using a TreeMap in Fig. 6, the sections can be identified

as most representative of the exports made by Ecuador. The

amounts in millions of Dollars and tons are described in the

same view, and a significant color code has been created for

the rectangles, which can be associated with the data they

represent.

Fig. 6. TreeMap by Exported Section

These are those tariff headings identified with a 6-digit

coding, which come from the sections evidenced in the

previous section and whose accumulated export amount in

terms of Millions of USD/FOB between the years studied,

placing them as the 15 most representative. In Fig. 7, we can

note that code 080390 (“Edible fruits and nuts; citrus peels,

melons or watermelons”) comes to first place in the list,

constituting 22% of the total, followed by 030617 (“Fish and

crustaceans, mollusks, and other aquatic invertebrates”) with

16% by weight, referring to the same amount.

The abstraction of the information is facilitated by using a

bar graph in Fig. 7, which also provides a legend that includes

color coding by years (recent blue, previous red). The

existence of items that have not been traded in recent years

can be noticed. Also, the total export amount is represented by

the height of the bar.

Fig. 7. Top 15 most essential tariff headings and contribution per

accumulated year Millions of USD/FOB 2008-2018

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14448095

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

With the previously analyzed data, the need to develop a

model of visualization of export data emerges, allowing the

study of non-oil items marketed by Ecuador between 2008 and

2018. Figs. 8, 9, and 10 are included, where the analysis of

one continent at a time is proposed to test the capacity of the

model to provide the required information in consolidated

views. Fig. 8 consolidates in quadrant I: the variation in

importance in exports in Millions of USD/FOB and TM

between 2008 and 2018 for the countries that belong to the

American continent and are part of the top 10 partners. In

quadrant II, the evolution of values exported throughout the

period is evident in Ecuador’s commercial sectors. Quadrant

III shows the geographical location of each of the countries to

which exports have been made, characterizing with the size of

the circles the amount of their transactions in terms of Millions

of USD/FOB. Finally, in quadrant IV, a tree graph identifies

the sections and the number of exports in that year in Millions

of USD/FOB and Tons.

Fig. 8. Overview – America Case

Fig. 9 shows that when the name of the Vegetable

Kingdom Products section is used as a filter and the continent

filter, such as America, is kept active, the 6-digit codes of the

most relevant items in quadrant I are displayed. Exports: The

color legend allows us to identify the years they were

exported. In quadrant II, the list of countries identified as the

largest trading partners of that continent is shown; in quadrant

III, the total amount exported both in Millions of USD/FOB

and MT is provided, and finally, in quadrant IV, the variation

in exports of the included items is illustrated and validated

year after year.

Fig. 9. Section-Level Match Level 3 Identification – America Case

Fig. 10 contains more significant details about the items

corresponding to the Products of the Vegetable Kingdom

section. 2015 to 2018 have been selected as a filter for this

example. In quadrant I, we find a double-axis graph with an

area diagram for the amount in Millions of USD/FOB and a

line graph as the second axis, with the value of MT exported

in the filtered period. Quadrants II and III contain the list of

items that integrate the most representative item of exports in

the section with a calculation of the average price per item,

and in quadrant IV, a table with the number of items that were

exported to each country on the chosen continent.

Fig. 10. Level 4 Tariff Items (8-digit tariff items) Cumulative Exports for the Year 2015 in Millions USD/FOB

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

10.5281/zenodo.14448095

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

Through the visualizations shown above, it can be

observed that the selection of appropriate graphic elements

provides the user with a better assimilation of information.

Also, it reduces the time and effort required to obtain

conclusions. It gives the user a comprehensive vision of the

topic by going from a macro level to reaching specific details,

with fewer steps than conventional systems or programs,

highlighting the prior data treatment with mining techniques.

Choosing appropriate graphical representations can become a

competitive advantage when making decisions—actions or

decisions with data disposal.

V. D

ISCUSSION

The research findings are aligned with the existing

literature on main exports between 2016 and 2018 in Ecuador,

as per the United Nations United Nations Department of

Economic and Social Affairs [8] 4-digit tariff classification

and main partner continents, offering a fresh perspective on

the subject.

Through the results of this study, we approve the analysis

of Durán, J. & Zaclicever, D. [18] on the commercial

relationship with Colombia in textile materials and their

manufacturers, further validating their findings.

Similarly, the publication of Casanova et al. [30] on the

tariff items exported to China between 2008 and 2014 further

supports our observations. Likewise, the most frequent

destinations for Ecuadorian products mentioned in [31] and

[5] concur with those determined by analyzing the export data

from this research.

Hence, this study focuses solely on the values of

Ecuadorian exports without calculating trade balance

balances. Additionally, the analysis is limited to Ecuador and

its trading partners and does not include the items in Section

V of Ecuador’s National Tariff corresponding to Mineral

Products.

Therefore, the results of this research agree with the

existing literature on Ecuador’s main exports, such as the

commercial relationship with Colombia in textile materials

and the frequent destinations of Ecuadorian products. Data

preparation, exploratory analysis, and visualization were

performed using good practices recommended in the

literature. The study is limited to Ecuadorian exports and does

not include an analysis between countries or the Mineral

Products categories.

VI. C

ONCLUSIONS

Records of Ecuador’s export transactions constitute a

competitive advantage for users who must make decisions.

Therefore, a comprehensive review of the literature and

available data sources on export data visualization was

conducted. Additionally, academic studies and industry

reports were identified that highlight the usefulness of this tool

for exploring and understanding data.

The information provided by LIDI and the Central Bank

of Ecuador was divided into a central "facts" table and

peripheral "dimension" tables (star model) to improve

efficiency and facilitate accurate queries. Thus, dashboards

with graphs, tables, maps, and other visualizations were

prepared to validate the results. These were socialized with

interested parties, who evaluated the ability of the model to

respond accurately and adjust to reality. The results were

compared with existing studies, and satisfactory answers were

obtained.

Due to the need for more data visualization in specific

areas such as population, GDP, and distance between

countries due to its low relevance, it can be included in future

research, such as expanding the scope of the analysis and

considering other relevant variables.

CKNOWLEDGMENT

This work was partially supported by the vice rectorate of

Research at Universidad del Azuay for their financial and

academic support and the entire staff in the Computer Science

Research & Development Laboratory (LIDI).

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LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

AUTHORS

Inés Paola Molina Alarcón. She graduated with a Master’s Degree

in Information Systems with a major in Business Intelligence from

the University of Azuay (UDA) in 2022. He obtained his Business

Administration degree from the University of Azuay (UDA) in

2020. She is pursuing a Master’s Degree in User Experience at the

University of the Americas (UDLA). Her research interests include

data visualization, information accessibility, usability, user experience,

and extensive data analysis.

Luis Tonon-Ordóñez. Economist from the University of Azuay.

Higher Diploma in Finance, Securities Market, and Trust Business

from the University of Azuay. Diploma of Advanced Studies (Law)

from the Pablo de Olavide University in Spain. Higher Diploma in

International Negotiation from the University of Azuay. Master in

Business Administration from the University of Azuay. Coordinator of

the School of Economics - Faculty of Administrative Sciences at the

University of Azuay.

Inés Paola Molina Alarcón

Luis Tonon-Ordóñez

I. P. Molina Alarcón, L. Tonon-Ordóñez, J. L. Zambrano-Martinez, and M. Orellana,

“Data Visualization Model for Multi-party Analysis and Strategic Decision-Making in International Trade”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 1, January 2025

AUTHORS

Jorge Luis Zambrano-Martinez. Ph.D. in Computer Science received in

Department of Networking Research Group (GRC) at the Universitat

Politècnica de València (UPV) from Spain in 2019, included an awarded

international doctoral and an awarded Cum Laude. He graduated

in Master’s Degree in Information and Communication Technology

Security at Universitat Oberta de Catalunya in 2018. He graduated in

Master’s Degree in Computer Engineering at Universitat Politècnica

de València (UPV) in 2015. He graduated in Systems Engineering

at Polytechnic University Salesian (Ecuador) in 2011. His research

interests include Vehicular Networks, Smart Cities and IoT, Network

Security, ITS, and Computer Vision.

Marcos Orellana. Systems Engineer from the University of Azuay.

Master in Information Systems Management and Business Intelligence

from the University of the Armed Forces (ESPE) and Master in

University Teaching from the University of Azuay. PhD candidate in

Computer Science at the National University of La Plata, Argentina.

Research Professor in Data Science and Artiﬁcial Intelligence at the

University of Azuay. Head and Director of the Computer Science

Research and Development Laboratory (LIDI).

Jorge Luis Zambrano-Martinez

Marcos Orellana

I. P. Molina Alarcón, L. Tonon-Ordóñez, J. L. Zambrano-Martinez, and M. Orellana,

“Data Visualization Model for Multi-party Analysis and Strategic Decision-Making in International Trade”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 1, 2025.