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DOI: https://doi.org/10.34069/AI/2023.68.08.16
How to Cite:
Gudz, P., Gudz, M., Bezkhlibna, A., Zaytseva, V., & Brutman, A. (2023). Ensuring the competitiveness of the coastal region based
on the study of the impact of cluster analysis results on the development of tourism in the conditions of the regenerative
ecosystem. Amazonia Investiga, 12(68), 172-183. https://doi.org/10.34069/AI/2023.68.08.16
Ensuring the competitiveness of the coastal region based on the study of the
impact of cluster analysis results on the development of tourism in the
conditions of the regenerative ecosystem
Забезпечення конкурентоспроможності приморського регіону на основі дослідження
впливу результатів кластерного аналізу на розвиток туризму в умовах
відновлювальної екосистеми
Received: July 1, 2023 Accepted: August 28, 2023
Written by:
Gudz Petro1
https://orcid.org/0000-0001-7604-549X
Gudz Maryna2
https://orcid.org/0000-0002-1454-4987
Bezkhlibna Anastasiia3
https://orcid.org/0000-0003-1027-7452
Zaytseva Valentina4
https://orcid.org/0000-0003-1526-2292
Brutman Anna5
https://orcid.org/0000-0002-7774-5356
Abstract
The purpose of the study is to determine the
indicators’ influence of regenerative ecosystem
competitiveness cluster analysis on the
development of tourism in the coastal region.
The method of cluster analysis allowed analyzing
the Ukrainian region competitiveness in their
territories’ distinguishing with similar indicators in
social and economic development. This method
was applied in the assessment process of the
Ukrainian coastal region's competitiveness, which
has direct territorial access to the sea. This grouping
covered indicators from various industries, selected
basically on an understanding of the constituent
elements of competitiveness such as tourism,
production, infrastructure, demography and local
finances. It was considered on the basis of a
1
Doctor of Economic Sciences, Professor, Head of the Department of Economic Research, Kujawy and Pomorze University in
Bydgoszcz, Poland. WoS Researcher ID: AAE-8117-2020
2
Doctor of Economic Sciences, Professor, Faculty of International Tourism and Economics, Department of Economics and Customs,
National University “Zaporizhzhia Polytechnik” (Zaporizhzhia), Ukraine. WoS Researcher ID: AAE-3553-2020
3
Candidate of Economic Sciences, Assistant Professor, Department of Tourism, Hotel and Restaurant Business Faculty of
International Tourism and Economics, National University “Zaporizhzhia Polytechnik” (Zaporizhzhia), Ukraine. WoS Researcher
ID: ABH-7880-2020
4
Candidate of Pedagogical Sciences, Professor, Head of Department of Tourism, Hotel and Restaurant Business Faculty of
International Tourism and Economics, National University “Zaporizhzhia Polytechnik” (Zaporizhzhia), Ukraine. WoS Researcher
ID: GFY-6064-2022
5
Candidate of Economic Sciences, Assistant Professor, National University “Zaporizhzhia Polytechnik” (Zaporizhzhia), Ukraine,
Faculty of International Tourism and Economics, Head of the Department of Foreign Language Professional Studies, Ukraine.
WoS Researcher ID: AAF-8062-2020
Gudz, P., Gudz, M., Bezkhlibna, A., Zaytseva, V., Brutman, A. / Volume 12 - Issue 68: 172-183 / August, 2023
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complex grouping of economic (business
processes), social (human capital) and
administrative-management (administration)
indicators. This made it possible to reveal the
similarity of the social and economic development
of the coastal regions in dynamics, which have
direct access to the sea and opportunities to conduct
economic activities related to the maritime
economy.
The cluster analysis results formed an appropriate
informational and analytical justification for the
guideline selection for the regional strategic policy
of the regenerative ecosystems’ competitive
increasing as a basis for planning the actions of
local authority bodies.
Keywords: region, region competitiveness, coastal
regions, regenerative ecosystem, tourism
development.
Introduction
Decentralization process activation and current
challenges require regions to search for
advantages and disadvantages for competitive
increasing at the local, the national and global
levels. At the same time, it is important to
observe the principles of sustainable
development and preserve the balance of
regional ecosystems, which is a prerequisite for
balance at the national level. One of the most
essential structural units is the coastal regions,
which have significant advantages and create
prerequisites for the balance of ecosystems. This
affects the development of the tourism industry,
which determines the prospects for development
and forms the prerequisites for the intensive
development of the regional ecosystem. After all,
it is tourism that identifies the level of use of
regional potential and determines additional
opportunities for creating competitive
advantages.
Moreover, the regenerative ecosystem
competitiveness of the coastal regions is a
prerequisite for the development of tourism,
which in turn determines the future development
prospects of the region. Cluster analysis, which
is based on the selection and research of criteria
and indicators, allows identifying structural units
regarding the prospects of achieving a balanced
regional ecosystem. This affects competitiveness
and makes it possible to single out poles of
growth. One of which for the coastal region is the
tourism industry, which allows not only to
identify potential opportunities but also to
develop them in the future. This is due to the
combination and synergy of human, natural, and
other components of resource potential. Noted
above actualizes the need to analyze the criteria
and indicators of the cluster analysis of the
regenerative ecosystem competitiveness of the
coastal region. The purpose of the article is to
study the results of the cluster analysis of
indicators of the competitiveness of the region
for tourism under the condition of ecosystem
development of the coastal region
Theoretical Framework or Literature Review
The study of the competitiveness of the coastal
region allows for an empirical analysis of factors
that influence the development of tourism
business in the context of ecosystem
development (Zhu et al., 2014; Conner, 2009;
Filonich & Prachenko, 2007: Huang et al., 2017;
Romanko, 2015) Since tourism business changes
as a result of external factors, it may be a flexible
system that needs definitions and development
threats (Budeanu, 2016).
The choice of methodological approaches to the
evaluation of regional competitiveness factors
depends both on the purpose of the evaluation
and on the geographic, natural-climatic, and
economic features of the region's development
(Huggins et al., 2013; Nazarov, 2022). Coastal
regions, among others, distinguish features
related to the geographical location along the
coast of the sea, which affects the landscape,
ground structure, temperature regime and
weather in general, winds, vegetation, animal life
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(Conner, 2009). Taking into account these
features in the Use construction of the economic
complex of the region allows you to use the
specified features as competitive advantages that
hinder the development of energy, tourism and
recreation, sea transport (shipping), hotel and
restaurant business, extraction of minerals from
the sea shelf, aquaculture and fish production,
etc. economy of the coastal region allows you to
take into account changes in the development
system (biological, economic, social,
management system) in the overall development
strategy of the region (Dominati et al, 2010; Hu,
2014; Rodríguez-López et al., 2019).
The continuation of previous research is urgent
issues of the boundary expansion of strategic
planning and forecasting of the regions of
Ukraine. From the point of view of improving the
relevant management activities at the city level
(Pascal et al., 2023).
The issue of regional social, economic and
ecological development is one of the main tasks
of regional policy, especially in the context of the
decentralization reform. It is the subject of
research by several scientists. Scientists
(Burkynskyi et al., 2021) offer different
methodological approaches to assessing the
balance of the regional ecosystem, the most
common of which are sustainable models based
on the transition from an integral indicator to a
limited range of key indicators (results).
An important direction of research is conceptual
and methodological approaches to assessing the
regional economic development effectiveness
and cooperation based on positive European and
world experience, the introduction of relevant
mechanisms and tools into the practice of
managing the economic development of the
regions of Ukraine (Burkynskyi, 2021).
The scientific research analysis made it possible
to determine the shortcomings of existing
approaches (Goryachuk & Osypov, 2022). These
shortcomings include: integral indicators do not
have a social and economic essence, the use of a
significant number of indicators without
highlighting key ones, and double accounting of
indicators when calculating an integral indicator.
Lack of assessment of trends in key indicators of
development and assessments of development
indicators in the context of threshold values,
certain non-representative groups of indicators.
An important direction of research is the
identification of indicators of the development of
local territorial formations based on the
definition of certain strategic approaches
(Gonchenko et al., 2020), which are based on the
development of the local area such as physical
environment or business development, or human
resource development, or local public initiatives
in the context of the development of the tourist
business of the seaside region.
Fundamental studies are science-based works on
architecture and urban economic space planning,
which allows for the integration of landscape,
architectural construction, financial and
economic, marketing, and digital components as
a triangle. The triangle is power – business –
public (Gudz et al., 2020a)
It is worth mentioning that an important direction
of research is the formation of the foreign
economic potential of the region as a factor in the
competitive development of the territory. As well
as the assessment of the competitive
development of the region and the principles of
realizing the foreign economic potential of the
region (Gudz, 2020; Gudz et al., 2020b).
The use of cluster analysis to study the efficiency
of enterprise activity has become widely used
(Tkacheva, 2012). At the same time, it should be
noted that the issue of choosing and applying the
cluster method for assessing the competitiveness
of coastal regions remains unexplored.
The specified topic is the subject of own
research, which is the basis for continuing
scientific research in a certain direction
(Bezkhlibna, 2017; 2018; 2020; Koval et al.,
2018).
Methodology
The research methodology is based on the
application of cluster analysis, which allows us
to create a multidimensional statistical model for
grouping regions according to the similarity of
their socioeconomic parameters. The use of
analysis and synthesis, induction and deduction,
as well as graphical methods summarizes the
possibilities of grouping regions.
Within the framework of this Study, the above
cluster analysis stages were supplemented and
detailed. Based on them a cluster grouping
technique was adapted to the analysis of the
coastal areas’ competitiveness. It was developed
on the ecosystem approach. This technique
involves sequential execution of the main
procedures presented in Fig. 1. Each of the above
stages plays a significant role when using the
cluster method in the analysis of data on the
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social and economic competitiveness of the
Ukrainian coastal regions.
According to the described algorithm for the
grouping of the Ukrainian coastal regions, based
on the cluster analysis of competitiveness, the
main goal of the cluster grouping was determined
at Stage1.The purpose of the cluster analysis of
the competitiveness of the Ukrainian coastal
regions based on the ecosystem approach is to
conduct a further detailed study of individual
groups of coastal regions and improve their
development strategies as well as to identify the
relationship between the level of ecosystem
service development and rivalry.
Fig. 1. Algorithm of cluster analysis of the coastal regions’ competitiveness based on the ecosystem
approach.
(Developed by author)
The selection of variables in cluster analysis is
one of the most important stages in the research
process.
To carry out the analysis, statistical indicators of
the Primorie districts were taken as the source
data in accordance with the requests sent to the
regional statistical services and data from the
official websites of city, village and settlement
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councils. Primorie districts (including cities of
regional importance), for which the analysis was
carried out:
1. Zaporozhzhia region - Melitopol district
(including the city of Melitopol); Berdyansk
district (including the city of Berdyansk);
2. Mykolaiv region - Mykolaiv district
(including the city of Mykolaiv, the city of
Ochakiv);
3. Odesa region - Odesa district (incl. Odesa,
Yuzhne, Chernomorsk), Izmail district (incl.
Izmail), Bilhorod-Dnistrovsky district (incl.
Bilhorod-Dnistrovskyi);
4. Kherson region – Henicheskyi district,
Kakhovskyi district (incl. Kakhovka city,
Nova Kakhovka city), Skadovsk district
(incl. Gola Prystan city).
The criterion for selecting indicators (factors)
was group selection, the essence of which
consists in choosing a set of factors, for their
further grouping according to 5 socio-economic
subsystems and three blocks of elements of
competitiveness (table 1). This group of
subsystems provides a multidimensional picture
of the competitiveness of the socio-economic
situation of the coastal regions, taking into
account the ecosystem component of
development for 2019 and 2020. The analysis of
indicators by functional components will allow
for a more detailed investigation of problem
areas in management (organization) to solve and
eliminate identified problems and identify areas
with similar social and economic and ecosystem
development. Further justification of the need to
create coastal area clusters on an ecosystem
basis.
Table 1.
Selected factors for the cluster analysis of the coastal areas’ competitiveness of on an ecosystem basis.
Connection with
elements of
competitiveness
A group of factors
The strength of
the selected factor
Indexes
Unit
measurement
Business processes
Tourism
Recreational
services as a type
of ecosystem
services
The number of means of
accommodation
Unit
Number of tourists served
by tour operators and tour
agents
Wasp
The cost of tourist
vouchers sold by travel
agents. and law wasps
One thous.
Hrv.
Production
Use of electricity
Thous. kWh
Cargo handling of ports
million tons
Infrastructure
Opportunities for
the development
of the region and
increase in types
of ecosystem
services
The total area of
residential buildings put
into operation,
m²
Freight traffic of road
transport
Thous. tkm
Transportation of
passengers by road
transport
Thous.
wasps
Human capital
Demography
Population as an
active member of
the ecosystem
Number of live births
Wasp
The number of permanent
population,
Wasp
Number of dead,
Wasp
Administration
Local finance
Provision of
coastal areas
Territory budgets per
inhabitant
One thous.
hrv. for 1
person
(Design and Calculated by the author)
In cluster analysis, the poly ethical principle of
group formation is used – all characteristics
simultaneously participate in the grouping, that
is, they are taken into account at the same time
when assigning an object to one or another group.
At the same time, as a rule, clear boundaries for
each group are not indicated, and it is also not
known in advance how many groups it is
appropriate to distinguish in the studied
population (Tkacheva, 2012).
Since all algorithms used in cluster analysis
require the estimation of distances between
clusters or objects, it is necessary to set the
measurement scale. The selected indicators use
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different types of scales and units of
measurement that need to be standardized.
At the first stage, “natural” clusters are formed,
which can be substantiated for the next analysis.
Euclidean distance is defined as a measure of
closeness.
The measure of closeness defined by the
Euclidean distance is a geometric distance in n-
dimensional space and is calculated as follows:
󰇛 󰇜󰇛󰇜
 2
The most important result obtained as a result of
tree-like clustering is a hierarchical tree
(dendrogram) (Fig. 2-3). As soon as the
peculiarity begins, the areas that are “closer” to
each other are combined and form clusters. Each
diagram node represents a union of two or more
clusters. “Linkage distance” defines the distance
at which the corresponding clusters were joined.
All calculations were made using the Stat Soft
Statistics software.
Fig. 2. The result of the hierarchical classification of the competitiveness of coastal areas based on the
ecosystem approach in Euclidean distance, 2019.
(Design and Calculated by the author)
Describing the results of the hierarchical
classification of the competitiveness of coastal
regions based on the ecosystem approach in
Euclidean distance for 2019 (Fig. 4.3), we can
state that the coastal regions of Kherson
(Skadovsk and Genichesk) and Odesa
(Bilhorod-Dnistrovskyi, Izmail) regions are
grouped into clusters at the first stages in
Zaporizhzhia region, the unity of the indicators
of the coastal districts is not observed. In the next
step, Melitopol district joins Bilhorod-
Dnistrovskyi and Izmailskyi. Berdyanskyi
district is united with Kakhovskyi in a cluster at
the second stage. The single coastal district of t
Mykolaiv region is merging into a cluster with
Odesa district on the 6th stage.
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Fig. 3. The result of the hierarchical classification of the competitiveness of coastal areas based on the
ecosystem approach in Euclidean distance, 2020.
(Design and Calculated by the author)
In 2020 (Fig. 3), there was a change in the cluster
of Odesa and Mykolaiv districts. Odesa Primorie
District shows separation from the cluster.
Based on the variance analysis, calculations
shown in the table. 4.4-4.5, for further cluster
analysis using the k-means method, it is
recommended to use the statement about 4
natural clusters of coastal areas in 2019 and 5
natural clusters in 2020 (areas that are “closely in
touch” with each other are determined).
This hypothesis can be tested using the k-means
method, which consists in dividing the initial
data into clusters (according to their indicators)
and checking the significance of the differences
between the obtained groups. The k-means
method consists of the following: calculations
begin with a selected number of observations,
which are the centers of groups, after which the
object composition of clusters is changed in order
to minimize variability within clusters and
maximize variability between clusters. Each
subsequent observation refers to the group whose
degree of similarity with the center of gravity is
minimal. After changing the composition of the
cluster, a new center of gravity is calculated,
most often as a vector of averages for each
parameter. The algorithm continues until the
composition of the clusters stops changing.
When the classification results are obtained, it is
possible to calculate the average value of
indicators by every cluster in order to assess their
differences among themselves.
To determine the significance of the differences
between the obtained clusters, the method of
dispersion analysis was used for the years 2019-
2020, the results are shown in the table. 2-3.
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Table 2.
Results of dispersion analysis of the coastal area competitiveness based on the ecosystem approach, 2019
Variable
Between
SS
df
Within SS
df
F
Signif. P
The number of means of accommodation
6.412684
3
1.587316
5
6.7333
0.033086
Number of tourists served by tour operators and
tour agents
7.649793
3
0.350207
5
36.4061
0.000804
The cost of tourist vouchers sold by travel agents.
and law firm
7.956594
3
0.043406
5
305.5138
0.000004
Number of live births
5.649137
3
2.350863
5
4.0050
0.084700
The number of permanent population
5.962384
3
2.037616
5
4.8769
0.060294
Number of dead
6.769487
3
1.230513
5
9.1689
0.017831
The total area of residential buildings put into
operation
2.110977
3
5.889023
5
0.5974
0.643730
Freight traffic of road transport
6.907553
3
1.092447
5
10.5383
0.013334
Transportation of passengers by road transport
7.207160
3
0.792840
5
15.1505
0.006072
Use of electricity
2.633672
3
5.366328
5
0.8180
0.536980
Cargo handling of ports
5.904037
3
2.095963
5
4.6948
0.064494
Budget for 1 inhabitant
6.596971
3
1.403029
5
7.8366
0.024536
(Design and Calculated by the author)
When analyzing the results of variance analysis,
it is necessary to pay attention to the value of the
F-factor and the level of significance p (which
should not be higher than 0.05). Criteria that do
not satisfy this value are not significant for
cluster analysis. According to the results of the
dispersion analysis for 2019 (Table 4.4), it should
be noted that most of the criteria for the
competitiveness of coastal regions on an
ecosystem basis meet the necessary criteria. The
criteria for the number of live births, the size of
the permanent population, the total area of
residential buildings put into operation, the use of
electricity and port cargo handling turned out to
be unsatisfactory.
The results of the dispersion analysis for 2020
(Table 3) indicate that the factors of the number
of accommodation facilities, the total area of
residential buildings put into operation, the
transportation of passengers by road transport
and the budget for 1 pers. Residents are
insignificant.
Table 3.
Results of dispersion analysis of the competitiveness of coastal areas based on the ecosystem approach,
2020.
Variable
Between
SS
df
Within SS
Df
F
Signif. P
The number of means of accommodation
6.395218
4
1.604782
4
3,985
0.104575
Number of tourists served by tour operators and
tour agents
7.701143
4
0.298857
4
25,769
0.004082
The cost of tourist vouchers sold by travel
agents. and law firm
7.910597
4
0.089403
4
88,483
0.000372
Number of live births
7.958370
4
0.041630
4
191,168
0.000081
The number of permanent population
7.963274
4
0.036726
4
216,827
0.000063
Number of dead
7.924108
4
0.075892
4
104,413
0.000268
The total area of residential buildings put into
operation
6.872725
4
1.127275
4
6,097
0.053971
Freight traffic of road transport
7.981885
4
0.018115
4
440,634
0.000015
Transportation of passengers by road transport
6.712155
4
1.287845
4
5,212
0.069401
Use of electricity
7.558876
4
0.441124
4
17,135
0.008786
Cargo handling of ports
7.994830
4
0.005170
4
1546,285
0.000001
Budget for 1 person. resident
5.876263
4
2.123737
4
2,767
0.174002
Calculated by the author
The cluster analysis made it possible to combine
coastal regions similar in terms of
competitiveness criteria, taking into account the
fact that the criteria of significance changed, the
picture of the combination in 2019 and 2020 is
similar. Table 4 shows which of districts form
clusters based on the calculation results in 2019
and 2020.
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Table 4.
Selection of clusters using the k-means method
2019
2020
Storage
Distance
Storage
Distance
Cluster 1
Cluster 1
Berdyanskyi
0.321074
Berdyanskyi
0.320767
Kakhovskyi
0.321074
Kakhovskyi
0.320767
Cluster 2
Cluster 2
Henicheskyi
0.112770
Henicheskyi
0.271624
Skadovskyi
0.112770
Skadovskyi
0.271624
Cluster 3
Cluster 3
Melitopolskyi
0.262006
Melitopolskyi
0.347028
Izmailskyi
0.243035
Izmailskayi
0.276637
Belgorod-Dnistrovskyi
0.180436
Belgorod-Dnistrovskyi
0.213497
Cluster 4
Cluster 4
Odesa
0.902242
Odesa
0
Mykolaivskyi
0.902242
Cluster 5
Mykolaivskyi
0
Calculated by the author
It is vivid that the composition of clusters 1, 2,
and 3 does not change during 2019-2020,
changes occurred in cluster 4 – in 2020, Odesa
and Mykolaiv districts were separated. Each
group of districts was selected according to
clustering parameters for comparison with the
results of the multidimensional grouping of
districts according to selected indicators of
ecosystem-based competitiveness. The
generalization of the results made it possible to
state that economic, demographic, social, and
foreign economic, indicators of the development
of tourism, infrastructure, and production. These
indicators have a significant impact on the
grouping of coastal areas according to indicators
of competitiveness. Indicators of
competitiveness included satisfaction of the
needs of residents, improvement of their well-
being.
At the next stage of cluster analysis, we will
analyze the average values of the variable
clusters of the coastal regions in 2019 and 2020
(Fig. 4, 5).
Fig. 4. Graph of average values of coastal district clusters 2019.
Results and Discussion
According to the data of the analysis, it should be
noted that the first cluster (Berdyanskyi and
Kakhovskyi districts) is characterized by high
values of all indicators, compared to other
clusters, except for the territory budget indicator
per 1 inhabitant. It shows the effective
development of the components of the
competitiveness of the coastalregions, provided
that the funding is the lowest, compared to other
coastal regions.
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Clusters 2, 3, and 4 are characterized by close
average values of indicators that are lower than
the average level. This indicator includes the
number of accommodation facilities, the number
of tourists served by tour operators and tour
agents, and the cost of travel packages sold by
travel agents. It also includes the number of live
births, the number of permanent residents, and
the number of deaths. The total area of residential
buildings put into operation, traffic of road
transport, cargo handling of ports. The above-
mentioned average indicators of the clusters
testify to similar problems of regional
development, which require management efforts
of a corrective nature on the part of the regional
and national authorities. It is clearly shown that
Melitopol, Izmail, and Bilhorod-Dniestrovskyi
districts are characterized by average indicators
of clusters that are lower than the average level.
However, Genichesky and Skadovskyi districts
are characterized by high average indicators of
passenger transportation by on-land transport and
the territory's budget per 1 inhabitant. Electricity
consumption and territory budget per one
inhabitant is average for Odesa and Mykolaiv
districts.
Fig. 5. Graph of average values of clusters of seaside regions, 2020
According to the analysis of the average values
of the indicators of the clusters of the seaside
districts for the year 2020. It should be noted that
the first cluster has an overwhelmingly larger
number of indicators that are higher than the
average level. The first cluster has an
overwhelmingly larger number of indicators that
are higher than the average level (Berdyanskyi
and Kakhovskyi districts). The first cluster is
characterized by high values of all indicators,
compared to other clusters, in addition to
indicators of the total area of residential buildings
put into operation, electricity use, and the budget
of territories per 1 inhabitant. It shows the
effective development of the components of the
coastal region competitiveness and the
implementation of energy efficiency measures
under the condition of financing, which is the
lowest compared to other coastal regions.
Cluster 4, which the Odesa district belongs to,
has favorable starting conditions for the
development of the coastal region ecosystems. At
the same time it has the above-average
indicators: the number of accommodation
facilities; the number of tourists served by tour
operators and tour agents; the cost of travel
packages sold by travel agents; the number of
live births; the number of permanent population;
the number of dead. And it has the highest
average indicators of the total area of residential
buildings put into operation and the use of
electricity. The data of the study help to
distinguish the Odesa coastal district from others
into a separate cluster characterized by active
tourist activity and the construction of new
housing. It is recommended to pay attention to
increasing the energy efficiency of production
and advanced technologies regarding the use of
alternative energy sources.
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Low indicators of road freight traffic, road
passenger transportation, and the territory's
budget per inhabitant indicate the need to
improve road logistics and infrastructure.
Clusters 3, 4, and 5 are characterized by close
average values of indicators that are lower than
the average level. This is the number of
accommodation facilities; the number of tourists
served by tour operators and tour agents; the cost
of travel packages sold by travel agents; the
number of live births; the number of permanent
population; the number of dead; the total area of
residential buildings put into operation; traffic of
road transport; use of electricity; cargo
processing of ports. The above-mentioned
average indicators of the clusters testify to
similar problems of regional development. These
issues are related to the development of tourism,
increase in economic activity in the region,
adjustment of the demographic situation, and
development of transport infrastructure and
logistics (road and river transport).
Clearly shown that it is typical for Henicheskyi
and Skadovskyi districts that all the average
indicators of the clusters are lower than the
average level. However, Melitopol, Izmail, and
Bilhorod-Dniester districts are characterized by
above-average indicators of passenger
transportation by road transport and the
territory's budget per inhabitant. The indicators
are lower for Mykolaiv district. It is only for the
budget territory per 1 inhabitant.
Conclusions
The results of the conducted research made it
possible to determine the influence of cluster
analysis indicators of regenerative ecosystem
competitiveness on the development of tourism
in the coastal region. After all, tourism is one of
the poles of growth for the region and determines
the prospects for its development. After all,
revitalizing the development of the tourism
industry creates competitive advantages and
makes it possible to achieve a balanced
ecosystem. Thus, the selected criteria and
indicators for cluster analysis correlate with the
understanding of the components of the
competitiveness of the coastal region described.
The methodical approach to assessing the
competitiveness of the coastal region is
substantiated. The indicators form a certain basis
for the complex grouping of economic (business
processes), social (human capital), and
administrative-management (administration)
indicators. These indicators make it possible to
identify the similarity (in dynamics) of the socio-
economic development of the districts of the
coastal regions, which have direct access to the
sea and opportunities to conduct economic
activities related to the maritime economy.
The results of the research show that the method
of cluster analysis is carried out according to the
criteria of indicators of the competitiveness of
coastal regions on an ecosystem basis. This
method allows expanding the framework of ideas
about the state and possibilities of clustering of
coastal regions. In addition, the cluster approach
provides an appropriate informational and
analytical justification for the selection of
guidelines for the strategy of the regional policy
of increasing the competitiveness of regenerative
ecosystems. It can be used as a basis for planning
the actions of local self-government bodies.
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