Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data
The differences in the definition of urban areas lead to our contrasting or inconsistent understanding of global urban development and their corresponding socioeconomic and environmental impacts. The existing urban areas were widely identified by the boundaries of built-environment or social-connect...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2022.2161199 |
| _version_ | 1797679059195396096 |
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| author | Kaifang Shi Yizhen Wu Shirao Liu Zuoqi Chen Chang Huang Yuanzheng Cui |
| author_facet | Kaifang Shi Yizhen Wu Shirao Liu Zuoqi Chen Chang Huang Yuanzheng Cui |
| author_sort | Kaifang Shi |
| collection | DOAJ |
| description | The differences in the definition of urban areas lead to our contrasting or inconsistent understanding of global urban development and their corresponding socioeconomic and environmental impacts. The existing urban areas were widely identified by the boundaries of built-environment or social-connections, rather than urban entities that are essentially the spatial extents of human activity agglomerations. Thus, this study has attempted to map and evaluate global urban entities (2000–2020) from a perspective of an updated urban concept of urban entities based on the consistent remotely sensed nighttime light data. First, a K-means algorithm was developed to cluster urban and non-urban pixels automatically in consideration of global region division. Then, a post-processing was conducted to enhance the temporal and logical consistency of urban entities during the study period. Rationality assessment indicates that urban entities derived from remotely sensed nighttime light data more effectively reflect the spatial agglomeration extents of human activities than those of physical urban areas. Global urban entities increased from 157,733 km2 in 2000 to 470,632 km2 in 2020 accompanied by a differentiated urban expansion at global, continental, and national levels. Our study provides long-time series and fine-resolution datasets (500 m) and new research avenues for spatiotemporal analysis of global urban entity expansion with the improvement of the understanding of urbanization and the emergence of effective urban mapping theories and approaches. |
| first_indexed | 2024-03-11T23:08:54Z |
| format | Article |
| id | doaj.art-e7e8071f416b40709be50172d78a350d |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:54Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-e7e8071f416b40709be50172d78a350d2023-09-21T12:43:09ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262023-12-0160110.1080/15481603.2022.21611992161199Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light dataKaifang Shi0Yizhen Wu1Shirao Liu2Zuoqi Chen3Chang Huang4Yuanzheng Cui5Anhui Normal UniversitySouthwest UniversitySouthwest UniversityFuzhou UniversityNorthwest UniversityHohai UniversityThe differences in the definition of urban areas lead to our contrasting or inconsistent understanding of global urban development and their corresponding socioeconomic and environmental impacts. The existing urban areas were widely identified by the boundaries of built-environment or social-connections, rather than urban entities that are essentially the spatial extents of human activity agglomerations. Thus, this study has attempted to map and evaluate global urban entities (2000–2020) from a perspective of an updated urban concept of urban entities based on the consistent remotely sensed nighttime light data. First, a K-means algorithm was developed to cluster urban and non-urban pixels automatically in consideration of global region division. Then, a post-processing was conducted to enhance the temporal and logical consistency of urban entities during the study period. Rationality assessment indicates that urban entities derived from remotely sensed nighttime light data more effectively reflect the spatial agglomeration extents of human activities than those of physical urban areas. Global urban entities increased from 157,733 km2 in 2000 to 470,632 km2 in 2020 accompanied by a differentiated urban expansion at global, continental, and national levels. Our study provides long-time series and fine-resolution datasets (500 m) and new research avenues for spatiotemporal analysis of global urban entity expansion with the improvement of the understanding of urbanization and the emergence of effective urban mapping theories and approaches.http://dx.doi.org/10.1080/15481603.2022.2161199nighttime light datasnpp-viirs-likeurban entitieshuman activity agglomeration extent |
| spellingShingle | Kaifang Shi Yizhen Wu Shirao Liu Zuoqi Chen Chang Huang Yuanzheng Cui Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data GIScience & Remote Sensing nighttime light data snpp-viirs-like urban entities human activity agglomeration extent |
| title | Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data |
| title_full | Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data |
| title_fullStr | Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data |
| title_full_unstemmed | Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data |
| title_short | Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data |
| title_sort | mapping and evaluating global urban entities 2000 2020 a novel perspective to delineate urban entities based on consistent nighttime light data |
| topic | nighttime light data snpp-viirs-like urban entities human activity agglomeration extent |
| url | http://dx.doi.org/10.1080/15481603.2022.2161199 |
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