Abstract
A subtractive clustering-based methodology is introduced in this paper, that is applicable to solve spatial resource allocation problems that often arise in waste management. To satisfy spatially distributed demands, resources have to be allocated so that they would have the same spatial distribution. They are often characterized by limited capacity and a catchment area that should also be considered. We propose a flexible subtractive clustering-based methodology that addresses these challenges. The influence of resources and demands on their neighborhood is described by tunable basis functions. Resources are handled as clusters whose centers should be located. The demand arising at a spatial point and its neighborhood is summarized by a potential value that defines the ability to form a new cluster. The main contribution of this paper is a modified subtractive clustering method involving road network-based geodesic distances that is applicable to solve urban and large-scale spatial resource allocation problems as well. A case study about the optimization of textile waste containers in Hungary is introduced to verify the above-mentioned issues. The results show that the proposed method can be used effectively to obtain a resource supply system that is highly consistent with the spatially distributed needs.
Data availability
The datasets analyzed during the current study and the Python implementation of the proposed algorithm made by Éva Kenyeres (Veszprém, Hungary) are available at https://github.com/kenyevica/Subtractive-clustering_Textile-waste-containers (accessed on 5 March 2026).
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Funding
Open access funding provided by University of Pannonia. This research has been supported in part by the National Research, Development and Innovation Office through the project no. 2022-1.1.1-KK-2022-00002, titled “Establishment of a waste management competence center at the University of Pannonia”; and in part by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund through the Monitoring Complex Systems by Goal-Oriented Clustering Algorithms project under Grant OTKA 143482.
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É. K.: Conceptualization of this study, Methodology, Software, Writing; A. K.: Conceptualization of this study, Supervision of the work, Review; J. A.: Conceptualization of this study, Supervision of the work, Review. All authors reviewed the manuscript.
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Kenyeres, É., Kummer, A. & Abonyi, J. Subtractive clustering for spatial resource allocation problems in waste management. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45718-4
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DOI: https://doi.org/10.1038/s41598-026-45718-4
