Spatial organization management: modeling the functioning of eco-clusters in the context of globalization
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Date
2022
Authors
Ovcharenko, I.
Khodakivska, O.
Sukhomlyn, L.
Shevchenko, O.
Lemeshenko, I.
Martynov, A.
Zos-Kior, M. V.
Hnatenko, I.
Michkivskyy, S.
Biliavska, L. H.
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Abstract
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Abstract
The issue of spatial organization of eco-clusters has always been under the close attention of scientists. Greening
of production, resource conservation, economical use of natural resources, increasing the greenhouse effect and
industrial emissions are forcing market stakeholders to plan cluster associations aimed at minimizing the negative
impact of human activities on the environment. At the same time, this issue is extremely complex and needs careful
study. In particular, the concept of formation and location of eco-clusters in the context of globalization should be
based on the institutional environment, legislative field, labor market and other territorial conditions where the
eco-cluster is planned to be located. It is important to form a cluster core, which will be the administrative center
of eco-cluster management. In this regard, the purpose of the article is to model the activity of eco-clusters based
on a neural network approach to the management of their spatial organization.
In this research, on the basis of training a neural network using regional indicators of institutional support and
development of the labor market, the solution to the problem of the spatial organization of eco-clusters on the
territory of Ukraine is described. The authors used the tools of artificial intelligence to model the spatial location
and organization of eco-clusters. They proceeded from the premise that in each territory there is a certain set of
labor, institutional, production, technological, managerial and information resources, the successful use of which
will allow to effectively modeling cluster associations, and propose to recombine eco-clusters using the method
of neural modeling. The input data for modeling eco-clusters is the use of 3,102 units of indicators by the neural
network, which characterize the institutional and resource provision of a particular region of Ukraine. Given the
wide range of input digital data, their various definitions (absolute or relative) neural network makes it possible
Journal of Hygienic Engineering and Design
to automatically summarize and organize them. After completing the training of the neural network, analysis
of errors and deviations, we obtain spatial graphical images of the optimal location of eco-clusters in Ukraine.
The proposed neural network approach makes it possible to optimize the process of economic and statistical
modeling of a significant array of data characterizing the main parameters of the environment in which ecological
cluster associations operate.
The trained neural network allowed obtaining a map of the optimal location of eco-clusters, taking into account the
available in a particular area of institutional, informational, innovative, technological and other types of resources.
Based on the theory of synergetic systems, such a spatial arrangement of the eco-cluster will allow in the best ways
to use available resources for their accumulation and multiplication in the cluster. The proposed neural network
approach makes it possible to optimize the process of economic and statistical modeling of a significant array
of data characterizing the main parameters of the environment in which ecological cluster associations operate.
The method of spatial modeling of eco-clusters proposed by the authors using the tools of artificial intelligence
allows determining the best administrative centers of cluster development that can strengthen the territorial socioeconomic
development based on innovation and economical production. The step-by-step process of eco-cluster
modeling presented by the authors will allow all stakeholders interested in the market to use artificial networks
in the process of planning progressive spatial development. In addition, the proposed method of modeling ecoclusters,
neural network activation and training do not require significant financial, technical and labor resources,
which is a positive phenomenon in the context of ensuring resource-saving development of any area.
Keywords
Spatial organization management, modeling, eco-clusters, context of globalization