Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area
The spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9874787/ |
| _version_ | 1818056148259962880 |
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| author | Yaping Chen Akot Deng |
| author_facet | Yaping Chen Akot Deng |
| author_sort | Yaping Chen |
| collection | DOAJ |
| description | The spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light (NTL) data to identify urban and rural areas, which is likely to have an impact on the identification results due to the large brightness difference of lights. Therefore, based on NTL data and combine with data level fusion algorithm, this study separately fuses point of interest (POI) data that representing the quantity distribution of urban infrastructure and Baidu migration big (BM)data that representing the change relationship of regional population mobility to identify urban and rural areas by using deep learning method. The results show that the highest accuracy of urban-rural spatial identification with single NTL data is 84.32% and kappa is 0.6952, while the highest accuracy identified by data fusion is 95.02% and kappa is 0.8259. It can be seen that the differences caused by light brightness are effectively corrected after data fusion, which greatly improves the accuracy of urban and rural spatial identification. By comparing the results of NTL data modified by different big data, this study analyzes and identifies the accuracy of urban and rural area by using deep learning method, which not only enriches the study of data fusion in urban area, but also provides a basis for analyzing regional urban-rural relations and urban-rural development. Therefore, this study is believed to have important practical value for the coordinated development of urban and rural areas. |
| first_indexed | 2024-12-10T12:24:14Z |
| format | Article |
| id | doaj.art-6f700ac66d6f44aca605a4a358dccb89 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T12:24:14Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6f700ac66d6f44aca605a4a358dccb892022-12-22T01:48:59ZengIEEEIEEE Access2169-35362022-01-0110935139352410.1109/ACCESS.2022.32034339874787Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural AreaYaping Chen0Akot Deng1https://orcid.org/0000-0002-9090-4614School of CML Engineering Architecture, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang, ChinaArchitecture and Planning, Sudan University of Science & Technology, Khartoum, SudanThe spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light (NTL) data to identify urban and rural areas, which is likely to have an impact on the identification results due to the large brightness difference of lights. Therefore, based on NTL data and combine with data level fusion algorithm, this study separately fuses point of interest (POI) data that representing the quantity distribution of urban infrastructure and Baidu migration big (BM)data that representing the change relationship of regional population mobility to identify urban and rural areas by using deep learning method. The results show that the highest accuracy of urban-rural spatial identification with single NTL data is 84.32% and kappa is 0.6952, while the highest accuracy identified by data fusion is 95.02% and kappa is 0.8259. It can be seen that the differences caused by light brightness are effectively corrected after data fusion, which greatly improves the accuracy of urban and rural spatial identification. By comparing the results of NTL data modified by different big data, this study analyzes and identifies the accuracy of urban and rural area by using deep learning method, which not only enriches the study of data fusion in urban area, but also provides a basis for analyzing regional urban-rural relations and urban-rural development. Therefore, this study is believed to have important practical value for the coordinated development of urban and rural areas.https://ieeexplore.ieee.org/document/9874787/Urban-rural differencePOInight lightBM~dataZhengzhou |
| spellingShingle | Yaping Chen Akot Deng Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area IEEE Access Urban-rural difference POI night light BM~data Zhengzhou |
| title | Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area |
| title_full | Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area |
| title_fullStr | Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area |
| title_full_unstemmed | Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area |
| title_short | Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area |
| title_sort | using poi data and baidu migration big data to modify nighttime light data to identify urban and rural area |
| topic | Urban-rural difference POI night light BM~data Zhengzhou |
| url | https://ieeexplore.ieee.org/document/9874787/ |
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