Global urban and rural settlement dataset from 2000 to 2020
Abstract Accurate mapping of global urban and rural settlements is crucial for understanding their distinct expansion patterns and ecological impacts. However, existing global datasets focus mainly on urban settlements and ignore the delineation of rural settlements. Therefore, this study proposed a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04195-y |
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| _version_ | 1832571998080860160 |
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| author | Zhitao Liu Sheng Huang Chuanglin Fang Luotong Guan Menghang Liu |
| author_facet | Zhitao Liu Sheng Huang Chuanglin Fang Luotong Guan Menghang Liu |
| author_sort | Zhitao Liu |
| collection | DOAJ |
| description | Abstract Accurate mapping of global urban and rural settlements is crucial for understanding their distinct expansion patterns and ecological impacts. However, existing global datasets focus mainly on urban settlements and ignore the delineation of rural settlements. Therefore, this study proposed a framework for delineating between urban and rural settlements based on dynamic thresholds defined by area and light brightness and constructed the first global 100-meter resolution urban and rural settlements dataset (GURS) spanning from 2000 to 2020, integrating GHS-BUILT-S R2023A, NPP-VIIRS-like nighttime light, and OpenStreetMap data. An accuracy assessment of 44,474 independent samples showed that GURS achieved an overall accuracy of 91.22% with a kappa coefficient of 0.85, outperforming nine multi-scale reference datasets in delineating global urban and rural settlements. GURS offers deep insights into the dynamics of global settlements, facilitating urban-rural comparative studies on socio-economic characteristics, environmental impacts, and governance modes, thereby enhancing the sustainable management of settlements. |
| format | Article |
| id | doaj-art-4ee356ad0ad34fe48bc73c8d92e20e10 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-4ee356ad0ad34fe48bc73c8d92e20e102025-02-02T12:07:57ZengNature PortfolioScientific Data2052-44632024-12-0111111310.1038/s41597-024-04195-yGlobal urban and rural settlement dataset from 2000 to 2020Zhitao Liu0Sheng Huang1Chuanglin Fang2Luotong Guan3Menghang Liu4Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesSchool of Resources and Environment, University of Chinese Academy of SciencesKey Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesKey Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesKey Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesAbstract Accurate mapping of global urban and rural settlements is crucial for understanding their distinct expansion patterns and ecological impacts. However, existing global datasets focus mainly on urban settlements and ignore the delineation of rural settlements. Therefore, this study proposed a framework for delineating between urban and rural settlements based on dynamic thresholds defined by area and light brightness and constructed the first global 100-meter resolution urban and rural settlements dataset (GURS) spanning from 2000 to 2020, integrating GHS-BUILT-S R2023A, NPP-VIIRS-like nighttime light, and OpenStreetMap data. An accuracy assessment of 44,474 independent samples showed that GURS achieved an overall accuracy of 91.22% with a kappa coefficient of 0.85, outperforming nine multi-scale reference datasets in delineating global urban and rural settlements. GURS offers deep insights into the dynamics of global settlements, facilitating urban-rural comparative studies on socio-economic characteristics, environmental impacts, and governance modes, thereby enhancing the sustainable management of settlements.https://doi.org/10.1038/s41597-024-04195-y |
| spellingShingle | Zhitao Liu Sheng Huang Chuanglin Fang Luotong Guan Menghang Liu Global urban and rural settlement dataset from 2000 to 2020 Scientific Data |
| title | Global urban and rural settlement dataset from 2000 to 2020 |
| title_full | Global urban and rural settlement dataset from 2000 to 2020 |
| title_fullStr | Global urban and rural settlement dataset from 2000 to 2020 |
| title_full_unstemmed | Global urban and rural settlement dataset from 2000 to 2020 |
| title_short | Global urban and rural settlement dataset from 2000 to 2020 |
| title_sort | global urban and rural settlement dataset from 2000 to 2020 |
| url | https://doi.org/10.1038/s41597-024-04195-y |
| work_keys_str_mv | AT zhitaoliu globalurbanandruralsettlementdatasetfrom2000to2020 AT shenghuang globalurbanandruralsettlementdatasetfrom2000to2020 AT chuanglinfang globalurbanandruralsettlementdatasetfrom2000to2020 AT luotongguan globalurbanandruralsettlementdatasetfrom2000to2020 AT menghangliu globalurbanandruralsettlementdatasetfrom2000to2020 |