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A new method for identifying industrial clustering using the standard deviational ellipse
Industrial agglomeration has attracted extensive attention from economists and geographers, yet it is still a challenge to identify the multi-agglomeration spatial structure and degree of industrial agglomeration in continuous space—there is still a lack of a more targeted industrial clustering meth...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9834335/ https://www.ncbi.nlm.nih.gov/pubmed/36631559 http://dx.doi.org/10.1038/s41598-023-27655-8 |
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author | Zhao, Ziwei Zhao, Zuoquan Zhang, Pei |
author_facet | Zhao, Ziwei Zhao, Zuoquan Zhang, Pei |
author_sort | Zhao, Ziwei |
collection | PubMed |
description | Industrial agglomeration has attracted extensive attention from economists and geographers, yet it is still a challenge to identify the multi-agglomeration spatial structure and degree of industrial agglomeration in continuous space—there is still a lack of a more targeted industrial clustering method. The clustering method and the standard deviational ellipse (simply, ellipse) model have advantages in identifying the spatial structure and representing spatial information respectively. On this basis, we propose an ellipse-based approach to identifying industrial clusters. Our ellipse-based approach rests upon group nearest neighbor using the group-based nearest neighbor (GNN) ordering and spatial compactness matrix, where a number of point sequences with varying lengths, generated under the GNN ordering, are characterized by an ellipse and the elliptical parameters of these point sequences formulate the values and structure of the compactness matrix. Clustering is reformulated to identify ellipses with a specified parameter among a number of potential candidate ellipses, with significant changes (especially in the area) used as the cutoff criterion for determining the clusters’ border point. Our approach is illustrated in the location pattern of firms in Shanghai City, China in comparison with four well-known clustering methods. With the combination of elliptical parameters and spatial compactness, our approach may bring a new analytical ground for future industrial clustering research. |
format | Online Article Text |
id | pubmed-9834335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98343352023-01-13 A new method for identifying industrial clustering using the standard deviational ellipse Zhao, Ziwei Zhao, Zuoquan Zhang, Pei Sci Rep Article Industrial agglomeration has attracted extensive attention from economists and geographers, yet it is still a challenge to identify the multi-agglomeration spatial structure and degree of industrial agglomeration in continuous space—there is still a lack of a more targeted industrial clustering method. The clustering method and the standard deviational ellipse (simply, ellipse) model have advantages in identifying the spatial structure and representing spatial information respectively. On this basis, we propose an ellipse-based approach to identifying industrial clusters. Our ellipse-based approach rests upon group nearest neighbor using the group-based nearest neighbor (GNN) ordering and spatial compactness matrix, where a number of point sequences with varying lengths, generated under the GNN ordering, are characterized by an ellipse and the elliptical parameters of these point sequences formulate the values and structure of the compactness matrix. Clustering is reformulated to identify ellipses with a specified parameter among a number of potential candidate ellipses, with significant changes (especially in the area) used as the cutoff criterion for determining the clusters’ border point. Our approach is illustrated in the location pattern of firms in Shanghai City, China in comparison with four well-known clustering methods. With the combination of elliptical parameters and spatial compactness, our approach may bring a new analytical ground for future industrial clustering research. Nature Publishing Group UK 2023-01-11 /pmc/articles/PMC9834335/ /pubmed/36631559 http://dx.doi.org/10.1038/s41598-023-27655-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhao, Ziwei Zhao, Zuoquan Zhang, Pei A new method for identifying industrial clustering using the standard deviational ellipse |
title | A new method for identifying industrial clustering using the standard deviational ellipse |
title_full | A new method for identifying industrial clustering using the standard deviational ellipse |
title_fullStr | A new method for identifying industrial clustering using the standard deviational ellipse |
title_full_unstemmed | A new method for identifying industrial clustering using the standard deviational ellipse |
title_short | A new method for identifying industrial clustering using the standard deviational ellipse |
title_sort | new method for identifying industrial clustering using the standard deviational ellipse |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9834335/ https://www.ncbi.nlm.nih.gov/pubmed/36631559 http://dx.doi.org/10.1038/s41598-023-27655-8 |
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