Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method
Fine-scale population spatial distribution plays an important role in urban microcosmic research, influencing infrastructure placement, emergency evacuation management, business decisions, and urban planning. In the past, nighttime light (NTL) data were generally used to map the spatial distribution...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10021321/ |
| _version_ | 1811170540282445824 |
|---|---|
| author | Wei Guo Jinyu Zhang Xuesheng Zhao Yongxing Li Jinke Liu Wenbin Sun Deqin Fan |
| author_facet | Wei Guo Jinyu Zhang Xuesheng Zhao Yongxing Li Jinke Liu Wenbin Sun Deqin Fan |
| author_sort | Wei Guo |
| collection | DOAJ |
| description | Fine-scale population spatial distribution plays an important role in urban microcosmic research, influencing infrastructure placement, emergency evacuation management, business decisions, and urban planning. In the past, nighttime light (NTL) data were generally used to map the spatial distribution of the population at a large scale because of their low spatial resolution. The new generation of Luojia1-01 NTL data can be used for fine-scale social and economic analysis with its high spatial resolution and quantitative range. However, due to the geometry and background noise of the data themselves, the accuracy of the original NTL data is still low. Points-of-interest (POI) also can be used to map the population spatialization, but the indicative relationship between the POI and population is not clear, especially in rural and urban areas with different landscape structures. To solve the above-mentioned problems, this study proposes an improved nighttime light (INTL) index to better use the Luojia1-01 NTL data. Meanwhile, a zonal classification model based on INTL and impervious surface area is proposed to distinguish urban and rural areas. Compared with previous research and existing datasets, our result had the highest accuracy (<italic>R</italic>² = 0.86). This study explains that the INTL index is applicable to population spatialization research with the emergence of high-resolution and multispectral NTL satellite data. Moreover, the zonal classification model in this research can significantly improve the accuracy of population spatialization in rural areas. This study provides a possible way to use NTL and POI data in other social and economic spatialization research. |
| first_indexed | 2024-04-10T16:59:21Z |
| format | Article |
| id | doaj.art-2ece73580c86451ba186c877993c028b |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-10T16:59:21Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-2ece73580c86451ba186c877993c028b2023-02-07T00:00:08ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01161589160010.1109/JSTARS.2023.323818810021321Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification MethodWei Guo0https://orcid.org/0000-0002-3615-7128Jinyu Zhang1https://orcid.org/0000-0002-6192-4720Xuesheng Zhao2Yongxing Li3Jinke Liu4Wenbin Sun5Deqin Fan6School of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaSchool of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaSchool of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaSchool of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaSchool of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaSchool of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaSchool of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, ChinaFine-scale population spatial distribution plays an important role in urban microcosmic research, influencing infrastructure placement, emergency evacuation management, business decisions, and urban planning. In the past, nighttime light (NTL) data were generally used to map the spatial distribution of the population at a large scale because of their low spatial resolution. The new generation of Luojia1-01 NTL data can be used for fine-scale social and economic analysis with its high spatial resolution and quantitative range. However, due to the geometry and background noise of the data themselves, the accuracy of the original NTL data is still low. Points-of-interest (POI) also can be used to map the population spatialization, but the indicative relationship between the POI and population is not clear, especially in rural and urban areas with different landscape structures. To solve the above-mentioned problems, this study proposes an improved nighttime light (INTL) index to better use the Luojia1-01 NTL data. Meanwhile, a zonal classification model based on INTL and impervious surface area is proposed to distinguish urban and rural areas. Compared with previous research and existing datasets, our result had the highest accuracy (<italic>R</italic>² = 0.86). This study explains that the INTL index is applicable to population spatialization research with the emergence of high-resolution and multispectral NTL satellite data. Moreover, the zonal classification model in this research can significantly improve the accuracy of population spatialization in rural areas. This study provides a possible way to use NTL and POI data in other social and economic spatialization research.https://ieeexplore.ieee.org/document/10021321/Geographically weighted regression (GWR)improved nighttime light (INTL) indexnighttime light (NTL)points-of-interest (POI)population spatialization |
| spellingShingle | Wei Guo Jinyu Zhang Xuesheng Zhao Yongxing Li Jinke Liu Wenbin Sun Deqin Fan Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Geographically weighted regression (GWR) improved nighttime light (INTL) index nighttime light (NTL) points-of-interest (POI) population spatialization |
| title | Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method |
| title_full | Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method |
| title_fullStr | Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method |
| title_full_unstemmed | Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method |
| title_short | Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method |
| title_sort | combining luojia1 01 nighttime light and points of interest data for fine mapping of population spatialization based on the zonal classification method |
| topic | Geographically weighted regression (GWR) improved nighttime light (INTL) index nighttime light (NTL) points-of-interest (POI) population spatialization |
| url | https://ieeexplore.ieee.org/document/10021321/ |
| work_keys_str_mv | AT weiguo combiningluojia101nighttimelightandpointsofinterestdataforfinemappingofpopulationspatializationbasedonthezonalclassificationmethod AT jinyuzhang combiningluojia101nighttimelightandpointsofinterestdataforfinemappingofpopulationspatializationbasedonthezonalclassificationmethod AT xueshengzhao combiningluojia101nighttimelightandpointsofinterestdataforfinemappingofpopulationspatializationbasedonthezonalclassificationmethod AT yongxingli combiningluojia101nighttimelightandpointsofinterestdataforfinemappingofpopulationspatializationbasedonthezonalclassificationmethod AT jinkeliu combiningluojia101nighttimelightandpointsofinterestdataforfinemappingofpopulationspatializationbasedonthezonalclassificationmethod AT wenbinsun combiningluojia101nighttimelightandpointsofinterestdataforfinemappingofpopulationspatializationbasedonthezonalclassificationmethod AT deqinfan combiningluojia101nighttimelightandpointsofinterestdataforfinemappingofpopulationspatializationbasedonthezonalclassificationmethod |