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Estimating urban spatial structure based on remote sensing data

Understanding the spatial structure of a city is essential for formulating a spatial strategy for that city. In this study, we propose a method for analyzing the functional spatial structure of cities based on satellite remote sensing data. In this method, we first assume that urban functions consis...

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Autores principales: Kii, Masanobu, Tamaki, Tetsuya, Suzuki, Tatsuya, Nonomura, Atsuko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232531/
https://www.ncbi.nlm.nih.gov/pubmed/37258561
http://dx.doi.org/10.1038/s41598-023-36082-8
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author Kii, Masanobu
Tamaki, Tetsuya
Suzuki, Tatsuya
Nonomura, Atsuko
author_facet Kii, Masanobu
Tamaki, Tetsuya
Suzuki, Tatsuya
Nonomura, Atsuko
author_sort Kii, Masanobu
collection PubMed
description Understanding the spatial structure of a city is essential for formulating a spatial strategy for that city. In this study, we propose a method for analyzing the functional spatial structure of cities based on satellite remote sensing data. In this method, we first assume that urban functions consist of residential and central functions, and that these functions are measured by trip attraction by purpose. Next, we develop a model to explain trip attraction using remote sensing data, and estimate trip attraction on a grid basis. Using the estimated trip attraction, we created a contour tree to identify the spatial extent of the city and the hierarchical structure of the central functions of the city. As a result of applying this method to the Tokyo metropolitan area, we found that (1) our method reproduced 84% of urban areas and 94% of non-urban areas defined by the government, (2) our method extracted 848 urban centers, and their size distribution followed a Pareto distribution, and (3) the top-ranking urban centers were consistent with the districts defined in the master plans for the metropolitan area. Based on the results, we discussed the applicability of our method to urban structure analysis.
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spelling pubmed-102325312023-06-02 Estimating urban spatial structure based on remote sensing data Kii, Masanobu Tamaki, Tetsuya Suzuki, Tatsuya Nonomura, Atsuko Sci Rep Article Understanding the spatial structure of a city is essential for formulating a spatial strategy for that city. In this study, we propose a method for analyzing the functional spatial structure of cities based on satellite remote sensing data. In this method, we first assume that urban functions consist of residential and central functions, and that these functions are measured by trip attraction by purpose. Next, we develop a model to explain trip attraction using remote sensing data, and estimate trip attraction on a grid basis. Using the estimated trip attraction, we created a contour tree to identify the spatial extent of the city and the hierarchical structure of the central functions of the city. As a result of applying this method to the Tokyo metropolitan area, we found that (1) our method reproduced 84% of urban areas and 94% of non-urban areas defined by the government, (2) our method extracted 848 urban centers, and their size distribution followed a Pareto distribution, and (3) the top-ranking urban centers were consistent with the districts defined in the master plans for the metropolitan area. Based on the results, we discussed the applicability of our method to urban structure analysis. Nature Publishing Group UK 2023-05-31 /pmc/articles/PMC10232531/ /pubmed/37258561 http://dx.doi.org/10.1038/s41598-023-36082-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Kii, Masanobu
Tamaki, Tetsuya
Suzuki, Tatsuya
Nonomura, Atsuko
Estimating urban spatial structure based on remote sensing data
title Estimating urban spatial structure based on remote sensing data
title_full Estimating urban spatial structure based on remote sensing data
title_fullStr Estimating urban spatial structure based on remote sensing data
title_full_unstemmed Estimating urban spatial structure based on remote sensing data
title_short Estimating urban spatial structure based on remote sensing data
title_sort estimating urban spatial structure based on remote sensing data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232531/
https://www.ncbi.nlm.nih.gov/pubmed/37258561
http://dx.doi.org/10.1038/s41598-023-36082-8
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