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DOI: https://doi.org/10.34069/AI/2024.76.04.1
How to Cite:
Arenas-Resendiz, T., Patiño-Ortiz, J., Martínez-Cruz, M.A., Dorantes-Benavidez, H., & Chávez-Pichardo, M. (2024). Network
science to correlate COVID-19 and tourism indicators in Mexico. Amazonia Investiga, 13(76), 9-30.
https://doi.org/10.34069/AI/2024.76.04.1
Network science to correlate COVID-19 and tourism indicators in
Mexico
Ciencia de redes para correlacionar COVID-19 e indicadores del turismo en México
Received: February 20, 2024 Accepted: April 27, 2024
Written by:
Tanya Arenas-Resendiz1
https://orcid.org/0000-0003-0385-0791
Julián Patiño-Ortiz2
https://orcid.org/0000-0001-8106-9293
Miguel Angel Martínez-Cruz3
https://orcid.org/0000-0002-4431-9262
Humberto Dorantes-Benavidez4
https://orcid.org/0000-0003-1490-1873
Mauricio Chávez-Pichardo5
https://orcid.org/0000-0002-3378-0440
Abstract
In this paper we analyze tourism as complex
system susceptible to external perturbations, like
COVID-19 public health emergency. The
research objective is to confirm pertinence of
using transdisciplinary tools such as complexity
approach and network analysis to understand and
represent tourism occupancy dynamic. We used
network science methodology to introduce an
analysis that integrates two Mexican tourism
industry indicators: Tourist Destinations
occupancy rates and Hospitality-Gastronomy
jobs; correlated with COVID-19 in Mexico
pandemic indicator: Confirmed cases. The
analysis results are based on centrality measures
used to describe organizational patterns in
tourism dynamic, besides we identified some
1
Doctora en Ingeniería de Sistemas, Investigadora en Instituto Politécnico Nacional IPN, Universidad Rosario Castellanos URC y
Centro de Ciencias de la Complejidad C3-UNAM, CDMX, México. WoS Researcher ID: ADO-1382-2022
2
Doctor en Ciencias en Ingeniería Mecánica y Doctor en Ciencias en Administración, Profesor Investigador en el Instituto Politécnico
Nacional ESIME, Zacatenco, CDMX, México. WoS Researcher ID: HMV-3376-2023
3
Doctor en Ingeniería de Sistemas, Profesor Investigador en el Instituto Politécnico Nacional ESIME, Zacatenco, CDMX, México.
WoS Researcher ID: ADX-7792-2022
4
Doctor en Ingeniería de Sistemas, Profesor Investigador en Tecnológico Nacional de México TecNM-Tecnológico de Estudios
Superiores del Oriente del Estado de México TESOEM Estado de México. WoS Researcher ID: KFQ-2120-2024
5
Doctor en Ingeniería de Sistemas, Profesor Investigador en el Tecnológico de Estudios Superiores del Oriente del Estado de México
TESOEM, Estado de México. WoS Researcher ID: KFS-9130-2024
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generic properties of tourism occupancy
distribution.
Keywords: Correlation, Data analysis, Network
analysis, Systems engineering, Tourism.
Introduction
We applied network science field published in
Science, 1999 and Nature, 2000 by Barabási;
pertinent to analyse tourism (Scott et al., 2007);
(Baggio, 2017); (Provenzano et al., 2018);
(Arenas et al., 2019). Considering no published
study has constructed yet complex networks that
correlate:
• Mexican tourist destinations occupancy
rates.
• Mexican Hospitality and Gastronomy Jobs.
• and Mexican COVID-19 statistics.
We justify network science application to
analyse COVID-19 impacts on Mexican tourism,
based on definition of complex systems as those
with many interrelated compounds with
difficulty to derive their collective behavior from
an isolated knowledge of their components
(Barabási, 2016).
Another complex network approach published on
Nature by Albert et al. (2000) provides us with
reasons to justify our subject belongs to complex
systems order. To contribute understanding of
tourism as complex system, susceptible to
perturbations like COVID-19, having direct
implications for destinations occupancy rates and
jobs, enabling understanding from interacting
components perspective; pertinent to consider
significant tourism contribution with 8.7% of
Mexico’s GDP (Gobieno de Mexico, 2019) and
according to National Survey of Occupation and
Employment (ENOE from its Spanish initials)
first trimester 2019 employed population in
tourism sector reached 4 million 246 thousand
direct jobs, meaning 8.7% of total employment
nationwide ratifying tourism industry importance
in mexican economy (Gobieno de Mexico,
2019).
In each section of the article, the reader will find:
• In Literature Review, the main trends and
gaps in existing literatura
• In Methodology the description of networks
following Power-Law mathematical
formalism, the software used, analysis of
each network, limitations of the method and
technique used
• Results and Discussion about metrics for
tourist destinations occupancy distributions;
hospitality and gastronomy jobs; COVID-19
confirmed cases and implications.
• Conclusions describe research contribution,
limitations and future directions.
Literature Review
We contribute with tourism data analytics using
network science, emphasizing correlations
among different databases. Proposing
interdisciplinary approach for tourism studies.
Network science, according to Barabási (2016) is
possible because fast data sharing methods and
cheap digital storage that made viable creation of
network maps to describe behaviour of complex
systems consisting of multiple interacting
components. Since the size of most networks of
practical interest have huge amount of data
behind them; we consider tourism indicators can
be mapped as a network.
Table 1.
Literature
Application
Authors
Social networks
, 2016)Barabási(
Web search advertising in Google, Facebook, Twitter,
Linkedin, Cisco, Apple, Akamai
, 2016)Barabási(
Health
(International Human Genome Sequencing
Consortium, 2001) al., 2001) (Venter et (Hopkins, 2008) Loscalzo, 2011) &(Gulbahce, Barabási,
Biology
Barabási, 2004) &(Oltvai
Medicine
Loscalzo, 2011) &(Gulbahce, Barabási,
Arenas-Resendiz, T., Patiño-Ortiz, J., Martínez-Cruz, M.A., Dorantes-Benavidez, H., Chávez-Pichardo, M. /
Volume 13 - Issue 76: 9-30 / April, 2024
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(Do Valle et al., 2021)
military -Terrorism
(Wilson, 2010) Ronfeldt, 2001) &(Arquilla
Epidemics
2009) ,et al. (Balcan Geisel, 2004) &(Hufnagel, Brockmann, Barabasi, 2009) &(Wang, Gonzalez, Hidalgo,
Neuroscience
al., 2014) te h(O Kötter, 2005) &(Sporns, Tononi,
Management
, 2008)et al. (Wu
Tourism
Baggio, 2007) &(Scott, Cooper (Baggio, 2017) Baggio, 2018) &(Provenzano, Hawelka al., 2019) t(Arenas e
Personal elaboration.
In summary, the main trends existing in network
science literature relate to health, biology,
medicine, epidemics and neuroscience; which
give the context that physical systems are more
frequently analyzed with network science,
followed by technological applications; gaps
remain on social complexity considering new
drivers like employment, economic indicators,
type of destinations that represent future research
opportunities to broad current examples of
network science applied to tourism that remain
on state of the art, analyzing research lines,
topics, authors, countries, to get the main trends
in academic tourism research; instead we see
pertinence and huge potential on analyzing with
network science tourist routes, travel patterns,
market segments, occupancy indicators, and
elements that could provide inferences and be
more illustrative of tourist consumption
behaviour, travel decisions and consumer
markets preferences that nowadays remains
limited yet on network science applications.
Methodology
We applied network analysis based on graph
theory (Barabási, 2002; Barabási & Albert, 1999;
Watts, 2004; Watts & Strogatz, 1998); our results
are representative to scale free networks theory
that defines networks whose degree distribution
follows a power law that persists in different
network sizes (Barabási, 2016). Another
theoretical argument congruent with our results
is that in networks with power law degree
distribution most nodes have only a few links,
these numerous small nodes are held together by
a few highly connected hubs (Barabási, 2016). In
that way, the identification of those hubs in our
results show the important role some states and
tourist destinations have: driving strong
sustained travel demand; their contribution to
Hospitality and Gastronomy jobs and COVID19
confirmed cases ranges (COVID.GOB, 2020).
Description of research methodology used begins
creating architecture of the networks we want to
analyse, then identify their organizing principles
and express mathematical formalism behind
them to contribute understanding of tourism as
complex system.
Our networks model P(k)=ck^(-γ) follows
empirical nature, focusing on data, function and
utility; describing system’s properties and
behavior; like power law distribution (Barabási,
2016) revealing key information based on
quantitative characterization; deepen in our case
into occupancy rates, jobs and COVID-19
confirmed cases distributions on Mexican
destinations (COVID.GOB, 2020), towards
characterization of pandemic impacts on
Mexican tourism industry dynamic.
Our networks distributions are represented by
󰇛󰇜 for where:
▪ is an appropriate normalization factor.
▪ is the exponent of connections
distribution.
▪ is the minimum grade of any given node.
▪ the cut degree depending on the network
size.
To prove usefulness of the used method, in Table
2 we compare two main networks models;
emphasizing power-law pertinence for our study
given its advantages.
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Table 2.
Comparison Power-Law vs Poisson
Model
Advantages
Disadvantages
Power-
Law
1. Clusters or hubs. Reveal key elements
to understand the complex system
behavior. That lead to identify: * Occupancy levels that replicate the most
between destinations * Which destinations concentrate bulk of
tourism, i.e., drive strong sustained travel
demand * States classification according to
Hospitality and Gastronomy employment
ranges * States by COVID-19 confirmed cases
ranges * States by employment and COVID19
confirmed cases ranges 2. Topology with numerous small degree
nodes coexisting with highly connected
nodes. That in our study ratify cluster
presence. 3. Size of each node proportional to its
degree. Lead to identify robust tourist
destinations that constantly ensure relevant
tourist consumption for Mexico and
COVID-19 decreasing occupancy effects
in tourist destinations. 4. Many nodes with only few links. Lead
to identify states and tourist destinations
with low level ranges of: * Hospitality Gastronomy employment * COVID-19 confirmed cases 5. Few clusters with more links. Lead to
identify states and tourist destinations with
high level ranges of: * Hospitality and Gastronomy
employment * COVID-19 confirmed cases 6. Lack scale characteristic, congruent
with most networks representative of socio
economic complex systems 7. Number of links a node can have is not
restricted, consistent with most real socio
economic networks in which elements
have multiple interactions.
1. Fragility in network topology when
removing clusters. For our study purposes, this
“disadvantage” works in our favor as it
confirms our results regarding the important
role some tourist destinations have, that we
might consider to better understand Mexican
tourism industry dynamic.
Poisson or
Random
networks
1. Robust network topology to random
elimination of nodes. Contrary to our
study purposes of identifying most
important states and tourist destinations in
terms of occupancy, hospitality and
Gastronomy employment as well as
COVID-19 confirmed cases.
1. Most nodes have same number of links
which is not consistent with our created
networks 2. Restricted to a characteristic scale, which is
not consistent with most real socio economical
networks. 3. Hubs absence, meaning not having highly
connected nodes denying roles importance
between states and tourist destinations. 4. Limits the number of links a node can have.
Contrary to our findings in tourist networks.
Personal elaboration.
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Since network science emphasizes correlations
and interactions among different databases, to
describe behaviour of tourism as complex
system, we elaborate network maps about:
1. Mexican tourism industry indicators:
1.1 Destinations occupancy rates
1.2 Hospitality and Gastronomy jobs
2. COVID-19 in Mexico indicator:
2.1 Confirmed cases
We used Netdraw Ucinet software to elaborate
network maps (Borgatti et al., 2002) having links
between nodes to indicate existent interactions.
UCINET for Windows is a software package for
the analysis of network data. It was developed by
Lin Freeman, Martin Everett and Steve Borgatti
on 2002. It comes with the NetDraw network
visualization tool, that we used to create and
analyze our networks.
1. Networks about mexican tourism industry
indicators:
1.1. Destinations occupancy rates. First
interaction is about tourist destinations
and their occupancy rate registered on
certain date. Figure 1.
Figure 1. Tourist destinations and its occupancy rate.
Personal elaboration.
Second type: occupancy rates clusters on certain dates, and tourist destinations belonging to those clusters.
Figure 2.
Figure 2. Tourist destinations by occupancy rates clusters.
Personal elaboration.
For indicators: Hospitality and Gastronomy jobs
as well as COVID-19 Confirmed cases, given
INEGI (2020) and COVID.GOB (2020) data
sources are displayed by state; Figure 3 specifies
correspondence between states and tourist
destinations.
Figure 3. Tourist destinations by state.
Personal elaboration.
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1.2. Hospitality and Gastronomy jobs. Figure 4 about states and their employment range to April 21st
2020.
Figure 4. States and their employment range.
Personal elaboration.
2. For COVID-19 indicator:
2.1 Confirmed cases. Figure 5. States by confirmed cases ranges 11th June 2020.
Figure 5. Confirmed cases range by state.
Personal elaboration.
For integral perspective Figure 6 integrates both jobs and confirmed cases ranges, with their corresponding
states.
Figure 6. States by jobs and confirmed cases ranges.
Personal elaboration.
Limitations of the method and technique
Relies on accurate data to build the networks, real
data may be incomplete, uncertain, or non
available; another challenge is to choose
indicators or drivers that enable accurate
analysis; specifically time consuming and
demanding to prepare relational data to interpret
causality.
Results and Discussion
Since the contribution of network maps is to
describe the detailed behaviour of a system
consisting of various interacting components.
The findings for each indicator are as follows.
Figure 7 offers structure of analysis findings.
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Figure 7. Results.
Personal elaboration.
Results:
1. On networks about mexican tourism
industry indicators:
1.1. Destinations and occupancy rates.
Bipartite networks (Figure 1) which
first set of nodes are 70 mexican tourist
destinations; and second set occupancy
rates each destination had from January-
May 2020, 2019 and 2018 (DATATUR,
2020, 2019, 2018).
Considering in Mexico flight suspensions, self-
isolation and quarantine began entirely on april
2020, first graphs for this research illustrate
January-March accumulated rates in 2018, 2019
and 2020 (Figures 8-10) to evidence how
mexican tourist destinations registered
occupancy rates under normal consumption
conditions without COVID-19.
After running Analytic Technologies Harvard
software on 2-Mode Centrality (Borgatti et al.,
2002) results found Highest Degree Centrality
for occupancy rates between 51-60.9 on January
toMarch 2018 (Figure 8).
That degree centrality identified in Figure 8 with
blue circles is important because it shows from
January-March 2018, under normal consumption
conditions, occupancy rates that replicate the
most between destinations are 51-60.9 per cent
concentrating bulk of tourism on 20 destinations:
Los Mochis, Sin; Salamanca, Gto; Veracruz
Boca del Rio, Ver; Acapulco, Gro; Manzanillo,
Col; La Paz, B.C.S; Durango, Dgo; San Juan del
Río, Qro; Irapuato, Gto; Oaxaca, Oax;
Hermosillo, Son; Ciudad Juarez, Chih;
Campeche, Camp; Mazatlan, Sin; Loreto, B.C.S;
Zona Corredor Los Cabos; León, Gto; San
Miguel de Allende, Gto; Tijuana, B.C and
Mexicali, B.C. that registered referred
occupancy.
Another observation is that maximum occupancy
rate for same period was 86-90.9 registered by
two destinations: Playacar, Q. Roo and Puerto
Vallarta, Jal. (Figure 8).
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Figure 8. Degree centrality for accumulated occupancy January-March 2018.
Personal elaboration.
On 2-Mode Centrality analysis (Borgatti et al.,
2002) January-March 2019 we found Highest
Degree Centrality for occupancy rates between
46-50 .9 (Figure 9).
Deegree centrality circled in blue, Figure 9 on
January-March 2019 under normal consumption
conditions, 12 destinations replicate occupancy
rates between 46-50.9 concentrated bulk of
tourism in: Villahermosa, Tab; San Juan del Rio,
Qro; Campeche, Camp; La Paz, B.C.S; Culiacan,
Sin; Durango, Dgo; Acapulco, Gro; Guadalajara,
Jal; Chihuahua, Chih; Los Mochis, Sin; Oaxaca,
Oax and Loreto, B.S.C. And having maximum
occupancy for the same period 86-90.9 registered
by Nuevo Vallarta, Nay (Figure 9).
Figure 9. Degree centrality for accumulated occupancy January-March 2019.
Personal elaboration.
On 2-Mode Centrality analysis (Borgatti et al.,
2002) January-March 2020 COVID decreasing
occupancy effects in mexican destinations
became visible, given flight suspensions and
measures including self-isolation were applied in
Mexico’s travel market sources like United
States and European countries; thus Highest
Degree Centrality for occupancy rates was 36-
45.9 (Figure 10) on 22 destinations: Taxco, Gro;
Puerto Escondido Oax; San Juan del Río, Qro;
Chihuahua, Chih; Toluca, Mex; Loreto, B.C.S;
Xalapa, Ver; Piedras Negras, Coah; León Gto;
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Manzanillo, Col; Zacatecas, Zac; Durango, Dgo;
Tijuana, B.C; San Luis Potosi, S.L.P; Queretaro,
Qro; Aguascalientes, Ags; Veracruz Boca del
Rio, Ver; Villahermosa, Tab; Guadalajara, Jal;
Culiacan, Sin; Oaxaca, Oax; and Campeche,
Camp.
COVID-19 decreasing occupancy effects is
confirmed again in Nuevo Vallarta, Nay; that in
same period of previous year registered
maximum occupancy 86-90.9 (Figure 9)
decreasing 10 percent by January-March 2020
with maximum occupancy 76-80.9 (Figure 10).
Figure 10. Degree centrality for accumulated occupancy January-March 2020.
Personal elaboration.
Deepening analysis January-March accumulated
rates 2018, 2019 and 2020 (Figures 8-10) a fourth
network map (Figure 11) was built from second
type of interaction represented in Figure 2
focusing on destinations that concentrated bulk
of tourism in three periods:
▪ 20 destinations January-March 2018
▪ 12 destinations January-March 2019
▪ 22 destinations January-March 2020
Finding that destinations represented with red
circle nodes, but mostly: Loreto, B.C.S;
Campeche, Camp; San Juan del Río, Qro;
Oaxaca, Oax and Durango, Dgo. represented
with brown circle nodes are robust destinations
that constantly ensure relevant tourist
consumption for Mexico (Figure 11).
Figure 11. Destinations that concentrated bulk of tourism January-March 2018, 2019, 2020.
Personal elaboration.
Jan- Jan-
from from
Jan- from
Jan- from
Jan- from
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April and May analysis was done separately to
get information about COVID-19 decreasing
occupancy effects; considering in Mexico during
those months in 2020 measures including flight
suspensions, self-isolation and quarantine
prevailed throughout whole country territory.
Figure 12 corresponds April 2018 analysis for
previous occupancy rates registered by
destinations under normal consumption
conditions without COVID-19. Finding Highest
Degree Centrality for occupancy rates 46-55.9;
concentrated greater amount of tourism flow on
20 destinations: Chihuahua, Chih; Acapulco,
Gro; Xalapa, Ver; La Paz, B.C.S; Campeche,
Camp; Celaya, Gto; San Juan del Rio, Qro;
Colima, Col; Oaxaca, Oax; Culiacan, Sin;
Tijuana B.C; Guadalajara, Jal; Cozumel, Q. Roo;
Manzanillo, Col; Zona Corredor Los Cabos;
Salamanca, Gto; Los Mochis, Sin; Durango,
Dgo; Toluca, Mex and Irapuato, Gto. And
maximum occupancy rate 91-95.9 registered on
Akumal, Q.Roo.
Figure 12. Degree centrality April 2018.
Personal elaboration.
Likewise, Figure 13 corresponds to April 2019
analysis for previous occupancy rates registered
by destinations under normal consumption
conditions without COVID-19. Having found
Highest Degree Centrality for occupancy rates
41-45.9 and 61-65.9; i.e having concentrated
greater amount of tourism flow on 21
destinations: Mexicali, B.C; Zacatecas, Zac; San
Jose del Cabo; Queretaro, Qro; Ciudad de
Mexico; Merida, Yuc; Loreto, B.C.S; Durango,
Dgo; Bahias de Huatulco, Oax; Aguascalientes,
Ags; Isla Mujeres, Q.Roo; Culiacan, Sin; Tecate,
B.C; Comitan de Dominguez, Chis; Tuxtla
Gutierrez, Chis; Puerto Escondido, Oax; Piedras
Negras, Coah; Taxco, Gro; San Cristobal de las
casas, Chis; Guanajuato, Gto and San Miguel de
Allende Gto. And maximum occupancy rate 86-
90.9 registered in Playacar, Q.Roo
.
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Figure 13. Degree centrality April 2019.
Personal elaboration.
2-Mode Centrality analysis (Borgatti et al., 2002)
for April 2020 COVID-19 decreasing occupancy
effects in destinations is clearly visible, having
Highest Degree Centrality for occupancy rates 0-
5.9 in most destinations; an unprecedented
situation with highest occupancy rate registered
by Mazatlan, Sin. 16-20.9 (Figure 14).
Figure 14. Degree centrality on April 2020.
Personal elaboration.
May analysis was done separately to get
information about COVID-19 decreasing
occupancy effects in destinations.
Figure 15 corresponds to May 2018 analysis
refering previous occupancy rates registered
under normal consumption conditions without
COVID-19. Having found Highest Degree
Centrality for occupancy rates 41-45.9 and 51-
55.9; i.e concentrated greater amount of tourism
flow on 19 destinations: Zona Corredor Los
Cabos; Manzanillo, Col; Villahermosa, Tab;
Salamanca, Gto; San Juan del Río, Qro;
Acapulco, Gro; La Paz, B.C.S; Celaya, Gto;
Irapuato, Gto; Leon, Gto; Pachuca, Hgo;
Mazatlan, Sin; Guadalajara, Jal; Isla Mujeres,
Q.Roo; Cozumel, Q. Roo; Bahias de Huatulco,
Oax; Veracruz Boca del Rio, Ver; Culiacan, Sin
and Zacatecas, Zac. With the maximum
occupancy rate from 86 to 90.9 reported by 2 Q.
Roo destinations: Playacar and Akumal.
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Figure 15. Degree centrality on May 2018.
Personal elaboration.
Figure 16 corresponds to May 2019 analysis
refering previous occupancy rates under normal
consumption conditions without COVID-19.
Having found Highest Degree Centrality for
occupancy rates 51-55.9 and 61-65.9;
concentrating greater amount of tourism flow on
19 destinations: Piedras Negras, Coah;
Manzanillo, Col; Los Mochis, Sin; Villahermosa,
Tab; Zacatecas, Zac; Loreto, B.C.S; Guadalajara,
Jal; San Jose del Cabo; Pachuca, Hgo; Culiacan,
Sin; Tijuana, B.C; San Lis Potosi, S.L.P; Zona
Corredor Los Cabos; Durango, Dgo;
Aguascalientes, Ags; Queretaro, Qro; Mazatlan,
Sin; Puebla, Pue and Mexicali, BC. With the
maximum occupancy rate 81-85.9 reported again
by 2 Q. Roo destinations in the same month of
previous year: Playacar and Akumal.
Figure 16. Degree centrality on May 2019.
Personal elaboration.
For May 2020 COVID-19 pandemic decreasing
occupancy effects were exacerbated, nullifying
tourism activity in most destinations and
registering Highest Degree Centrality for
occupancy rates 0-5.9 in 7 destinations: Toluca,
Mex; Oaxaca, Oax; San Juan de los Lagos, Jal;
Bahias de Huatulco, Oax; Guanajuato, Gto;
Puerto Escondido, Oax and Valle de Bravo, Mex.
With maximum occupancy 11-16 on Celaya, Gto
and Ciudad Juarez, Chih (Figure 17).
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Figure 17. Degree centrality on May 2020.
Personal elaboration.
The most important metric for our research
purpose is Highest Degree Centrality for
occupancy rates in figures 8-17, given we looked
for destinations that concentrated bulk of tourism
and maximum occupancy levels registered. We
generated those data using Analytic
Technologies Harvard software on 2-Mode
Centrality (Borgatti et al., 2002). The analysis
generated other 4 metrics that support our metric
of interest, which is degree centrality (Table 3).
Table 3.
Metrics for tourist destinations that concentrated bulk of tourism january-march 2018, 2019 and 2020
Cluster
Occupancy range
Degree
2-Local
Eigenvector
Closeness
Betweeness
jan-mar 2020
36-40.9
0.157142863
0.024693878
0.281663418
0.838709652
0.088587321
jan-mar 2020
41-45.9
0.157142863
0.024693878
0.486292988
0.787878811
0.063163474
jan-mar 2019
46-50.9
0.171428576
0.029387757
0.648508668
0.939759016
0.079402491
jan-mar 2018
51-55.9
0.142857149
0.020408165
0.463044554
0.772277236
0.049962241
jan-mar 2018
56-60.9
0.142857149
0.020408165
0.221835926
0.838709652
0.08558818
Personal elaboration using Ucinet (Borgatti et al., 2002).
Degree, consists of the sums of ties values,
meaning most common occupancy level
registered by destinations across all periods
analysed is 46-50.9%. Complementary metrics:
2-Local represents our mode network as bipartite
graph with balanced incoming and outgoing
links. Eigenvector, calculates eigenvector of the
largest positive eigenvalue as measure of
centrality, ratifying robustness. Closeness is a
metric that gives the overall network closeness
centralization and is useful to measure distance
by sums of the lengths of all the paths or all the
trails; a metric that can be thought as an index of
the expected time-until-arrival for things flowing
through the network via optimal paths.
Betweennes is a measure of information control.
Highest values in all metrics support our finding
that destinations represented with brown circle
node linked to jan-mar 2019 cluster, are robust
destinations that constantly ensure tourist
consumption for Mexico; represented in tourism
behavioral dynamic (figure 11).
Although it is necessary to carry out more in-
depth analysis integrating other indicators to
quantify correlations degree; as well as verifiable
effective incentives application; both are beyond
this research paper scope. However, we have
identified some essential characteristics and
destinations that concentrate bulk of tourism that
might be considered when focusing marketing
intelligence initiatives and public-private
partnerships.
Having concluded analysis for the first Mexican
tourism industry indicator: Destinations
occupancy rates (Figures 8-17); before
continuing with the rest indicators Hospitality
and Gastronomy jobs and COVID-19 Confirmed
cases, given INEGI and COVID.GOB primary
data sources are displayed by state; Figure 18
-
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was built following type of interaction in Figure
3. To specify correspondence between states of
the republic and tourist destinations; even though
in Figures 8-17, names of the corresponding
states were abbreviated after name of tourist
destination.
Figure 18. Correspondence between states of the republic and tourist destinations.
Personal elaboration.
Analysis for states of the republic reveals highest degree for Puebla, supporting is the state with most
tourist destinations (Table 4).
Table 4.
Correspondence degree between states and tourist destinations
State
Degree
Aguascalientes
0.009346
Baja California
0.046729
Baja California Sur
0.037383
Campeche
0.009346
Coahuila
0.009346
Colima
0.018692
Chiapas
0.056075
Chihuahua
0.037383
CdMx
0.009346
Durango
0.009346
Guanajuato
0.056075
Guerrero
0.028037
Hidalgo
0.046729
Jalisco
0.046729
Mexico
0.037383
Michoacan
0.009346
Morelos
0.028037
Nayarit
0.009346
Nuevo Leon
0.037383
Oaxaca
0.037383
Puebla
0.140187
Queretaro
0.028037
Quintana Roo
0.056075
San Luis Potosi
0.009346
Sinaloa
0.065421
Tabasco
0.009346
Tlaxcala
0.009346
Veracruz
0.028037
Yucatán
0.046729
Zacatecas
0.028037
Personal elaboration using Ucinet (Borgatti et al., 2002).
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Identify States of the Republic with more tourist
destinations, is useful to propose focalized restart
of tourism after COVID-19.
The next mexican tourism industry indicator for
this research analysis is:
Hospitality and Gastronomy jobs. Bipartite:
states-employment ranges network was built to
connect data following type of interaction in
Figure 4.
After running Analytic Technologies Harvard
software 2-Mode Centrality (Borgatti et al.,
2002) for Hospitality and Gastronomy jobs in
Mexican territory by April 21st 2020, five
employment ranges were identified from Scarce
to Maximum; finding considerable number of
states and therefore tourist destinations classify
on Few employment range 51755-96577 in
contrast Maximum range 883776 hospitality and
gastronomy jobs reported by Baja California
state (Figure 19) with its 5 tourist destinations:
Tecate, San Felipe, Mexicali, Tijuana and Playas
de Rosarito.
Our Network analysis allows sizing and
graphically represent number of Hospitality and
Gastronomy jobs affected in Mexico by COVID-
19.
Figure 19. States by Hospitality and gastronomy employment ranges.
Personal elaboration.
Our finding about most states classifying on Few
employment range is supported by software metrics with .40 degree, .16 on 2-mode local
linkage and 1 eigenvector robustness.
Table 5.
Metrics for states by hospitality and gastronomy employment ranges
Employment
Degree
2-Local
Eigenvector
Maximun 883776
0.033333335
0.001111111
0
Abundant 265312 to 321024
0.06666667
0.004444445
0
Moderate 108212 to 186573
0.266666681
0.071111113
8.27E-08
Few 51755 to 96577
0.400000006
0.160000026
1
Scarce 22040 to 44000
0.200000003
0.040000003
6.76E-16
Personal elaboration using Ucinet (Borgatti et al., 2002).
Having concluded mexican tourism industry
indicators; the last set analyzed is COVID-19
indicator:
Confirmed cases. Bipartite: states-confirmed
cases ranges network was built to connect data
following type of interaction in Figure 5.
After running software 2-Mode Centrality
(Borgatti et al., 2002) for COVID-19 confirmed
cases in Mexico by 11th June 2020, four ranges
were identified from Scarce to Maximum;
finding that considerable number of states and
therefore tourist destinations classify on Low
level range of confirmed cases 1320-2959
compared to Maximum range 21631-34077
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COVID-19 confirmed cases reported by Mexico
state and Ciudad de Mexico (Figure 20) and their
corresponding 5 tourist destinations: El Oro,
Toluca, Ixtapan de la Sal, Valle de Bravo and
CDMX. Useful information for responsible
tourism restart having identified the most and the
least infected destinations, crucial to restoring
trust and confidence in the sector focalizing
promotional campaigns and tourism product
development initiatives.
Figure 20. States by COVID-19 confirmed cases ranges.
Personal elaboration.
Our finding about most states classifying Low
level confirmed cases is supported by software
metrics with .43 degree and .18 on 2-mode local
linkage. Consistent with early stages of pandemic
in Mexico.
Table 6.
Metrics for states by COVID-19 confirmed cases ranges
Range
Degree
2-Local
Maximum level of confirmed cases 21631 to 34077
0.06666667
0.004444445
Moderate level of confirmed cases 3018 to 6620
0.333333343
0.111111097
Low level of confirmed cases 1320 to 2959
0.433333337
0.187777787
Scarce confirmed cases 252 to 918
0.166666672
0.02777778
Personal elaboration using Ucinet (Borgatti et al., 2002).
Deepen into findings for Mexico’s Hospitality
and Gastronomy jobs and COVID-19 Confirmed
cases (Figures 19 and 20); network map on
Figure 21 was built to have integral perspective
considering type of interaction in Figure 6
regarding both jobs and confirmed cases ranges,
with their corresponding states.
Findings confirm COVID deep negative impacts
particularly for Mexico and Ciudad de Mexico
CDMX, having maximum level of confirmed
cases and abundant hospitality and gastronomy
jobs affected. However, outcomes for
overcoming pandemic are observed on
Campeche that corresponds to moderate
hospitality and gastronomy jobs and scarce
COVID confirmed cases; also Nuevo Leon and
Oaxaca with moderate employment and low
confirmed cases; likewise Baja California
reporting moderate confirmed cases and
maximum hospitality and gastronomy jobs;
furthermore most states and destinations report
low level COVID confirmed cases (Figure 21).
Besides occupancy pattern in Oaxaca and
Campeche evidencing their role as generators of
sustained tourism flow (Figure 11). In fact, on
June 22nd Campeche obtained the Safe Travel
Stamp from the World Travel and Tourism
Council (WTTC) because of sanitary protocols
standardization in hotels, restaurants, tour
operators and other tourism service providers;
hence the first POSTCOVID Corridor in Latin
America was inaugurated in Mexico integrated
by Campeche, Yucatan and Quintana Roo
destinations (Mexico desconocido, s.f).
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Figure 21. States by employment and COVID19 confirmed cases ranges.
Personal elaboration.
Most states according to software analysis
classified Low level of confirmed cases and Few
hospitality and gastronomy jobs with degree
metrics .43 and .40; as well as .18 and .16 on 2-
mode local linkage. Probing correlation between
confirmed cases and employment rate; verifiable
on lowest degree and 2-Local metrics for
Maximum hospitality and gastronomy jobs as
well as confirmed cases; finding there are more
cases per capita in densely populated areas.
Even though it is out of scope of this paper, some
insights for most affected areas providing
emergency economic assistance through
monetary measures like credit lines at reduced
rate or exemption/reduction of social security
contributions, wage subsidies or special support
mechanisms for hospitality and gastronomy jobs
might be helpful.
Table 7.
Metrics for States by employment and COVID19 confirmed cases ranges
Range
Degree
2-Local
Maximum level of confirmed cases 21631 to 34077
0.06666667
0.004444445
Moderate level of confirmed cases 3018 to 6620
0.333333343
0.111111097
Low level of confirmed cases 1320 to 2959
0.433333337
0.187777787
Scarce confirmed cases 252 to 918
0.166666672
0.02777778
Maximum hospitality and gastronomy jobs 883776
0.033333335
0.001111111
Abundant hospitality and gastronomy jobs 265312 to 321024
0.06666667
0.004444445
Moderate hospitality and gastronomy jobs 108212 to 186573
0.266666681
0.071111113
Few hospitality and gastronomy jobs 51755 to 96577
0.400000006
0.160000026
Scarse hospitality and gastronomy jobs 22040 to 44000
0.200000003
0.040000003
Personal elaboration using Ucinet (Borgatti et al., 2002).
To complement our study we support our
findings with quantitative characterization of our
networks, given networks distributions reveal
information towards better understanding of
tourism mobility dynamic during analyzed
periods.
We analyzed occupancy levels on different
months and years. Finding that under normal
conditions without COVID-19 visited
destinations that concentrate bulk of tourism
behave according to power law.
( > ) = 1 ― ( ≤ ) = 󰇡
󰇢 (1)
Conserving same statistical distribution
regardless year or month of the information
(Figure 22).
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Figure 22. occupancy levels distributions in Mexico.
Personal elaboration.
Regardless each destination conditions, we found
statistical similarity of visitors distribution
among different periods. Which supports two
generic properties seen in social networks: alien
to single characteristic scale and high clustering
degree. Implying small destinations are
organized hierarchically into larger groups,
maintaining free-scale topology, following
power law distribution.
() =  ― 0 ≤ ≤ (2)
Our interpretation of scale free and scale
invariance generic properties found is that
tourism occupancy is preserved regardless period
or destination type; another finding is that
distributions confirm tourism occupancy is not
random. And we consider it is one of the firsts
steps to understand underlying dynamic of
tourism as complex system.
On Figure 23 we analyzed correlation between
confirmed cases and reduction in tourism,
finding by may 2020 moderate level of
confirmed cases prevailed among destinations
with reduction in tourism occupancy from 1-5.9
level. Identifying that Puebla state occupancy,
was the most affected.
Figure 23. COVID-19 confirmed cases and occupancy reduction.
Personal elaboration.
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Highest degree measures found by software: .42 for moderate confirmed cases; .23 for 1-5.9 occupancy
and .14 for Puebla (Table 8).
Table 8.
Degree measures COVID-19 confirmed cases and occupancy reduction
Degree
Aguascalientes
0.009345794
Baja California
0.046728972
Baja California Sur
0.037383176
Campeche
0.009345794
Coahuila
0.009345794
Colima
0.018691588
Chiapas
0.056074765
Chihuahua
0.037383176
CdMx
0.009345794
Durango
0.009345794
Guanajuato
0.056074765
Guerrero
0.028037382
Hidalgo
0.046728972
Jalisco
0.046728972
Mexico
0.037383176
Michoacan
0.009345794
Morelos
0.028037382
Nayarit
0.009345794
Nuevo León
0.037383176
Oaxaca
0.037383176
Puebla
0.140186921
Queretaro
0.028037382
Quintana Roo
0.056074765
San Luis Potosi
0.009345794
Sinaloa
0.065420561
Tabasco
0.00945794
Tlaxcala
0.009345794
Veracruz
0.028037382
Yucatán
0.046728972
Zacatecas
0.028037382
.9 on may 2020
0.037383176
1 to 5.9 on may 2020
0.028037382
6 to 10.9 on may 2020
0.018691588
11 to 16 on may 2020
0.009345794
0
9 on april 2020
0.196261689
1 to 5.9 on april 2020
0.233644858
6 to 10.9 on april 2020
0.130841121
11 to 15.9 on april 2020
0.037383176
16 to 20.9 on april 2020
0.009345794
Low confirmed cases
0.392523378
Moderate confirmed cases
0.429906547
Scarse confirmed cases
0.102803737
Maximum confirmed cases
0.046728972
Personal elaboration using Ucinet (Borgatti et al., 2002).
If it follows from this example to give continuity
to complexity approach for tourism, in further
research we recommend to develop models that
integrate more indicators that allow quantify
correlations degree between variables. By now,
our power law occupancy distribution networks
model contributes finding and representing
generic properties of tourism occupancy dynamic
as pertinent alternative to understand tourism
from transdisciplinary perspective.
Implications
From this research, academics can model other
tourist networks as we have confirmed some
essential characteristics of tourism dynamic can
be projected.
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Conclusions
This is an attempt to confirm network science
pertinence to analyse tourism dynamics, useful to
provide quantitative characterization for our
understanding of tourism organization principles
and some underlying patterns behind this
activity.
Our research contributes:
• With verifiable application of network
science to tourism análisis
• Representing tourism dynamic in terms of
centrality measures
• Expressing mathematical formalism behind
tourism organizing principles
• Identifying generic properties of tourism
occupancy distribution
• Modeling tourism and pandemic indicator in
tourist destinations
• Approaching tourism as complex system
with interacting elements and susceptible to
external perturbations
• As example of future tourism data analytics
using network science
• With detailed descriptions of tourism
complex Dynamic
• Modeling tourist occupancy in Mexico
under normal consumption conditions
• Modeling tourist occupancy in Mexico
affected by COVID-19
• Identifying destinations that concentrate
bulk of tourism and maximum occupancy
rates registered, useful to consider when
focusing marketing intelligence initiatives
and public-private partnerships.
• Identify Mexican states with more tourist
destinations, useful to propose focalized
tourism restart after COVID-19
• As alternative to correlate indicators that
capture tourism dynamic complexity
• Confirming mexican tourism destinations
occupancy have same trend regardless
month/year
Perhaps our findings don’t have capacity to
influence tourism decision makers, still our
metrics results add value to project some
characteristics of tourism dynamic, and are
congruent with reality having strong correlation
between confirmed cases and employment rate in
densely populated areas; confirming correlation
between confirmed cases and reduction in
tourism.
This paper shows network science pertinence in
tourism; and usage of transdisciplinary tools.
Discussion on the limitations of the study
Scope in terms of considered indicators remains
limited yet, in tourism network analysis real
demanding phase is to acces and then gather
representative elements of the tourism
complexity, and foresee connections between
those elements; being careful on the data
classification; unveil constantly changing and
evolving dynamics over seasons and
destinations; such as the possible lack of
generalization of the results in this research case
to other countries or contexts linked to other
sociodemographic characteristics. It is also
important to have in consideration several
different patterns that might arise from the
network analysis given tourism inherent
complexity according to market segments and
tourist offer; in that way depending on the type
and intention of the network designed making
sense out of the relational data analyzed for
enhanced predictability of tourist indicators as
well as their practical significance and
visualization as a complex system.
Future directions for research
In future works we can do similar analysis in
other countries for degree distribution and
organization principles comparison purposes; to
be able to make generalizations. And to consider
the analysis of other variables like marketing
strategies, consumer segments, tourist
preferences, currency flows, flights availability,
classification of natural, cultural tourist
attractions, destinations internet access,
sustainability indicators, demographic impacts
derived from tourism activity and also
perturbations or elements that affect and limit
tourist activity like insecurity, emitted warnings
for certain destinations, visa restrictions, adverse
political environment or considerable cultural
differences between visitors and host
communities. Still network science to analyse
tourism susceptible to external perturbations like
COVID-19 in this paper; is pertinent to reveal
some tourism dynamic basic properties,
providing evidence to develop understanding of
tourism complexity.
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