Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.2.1. Population Products
2.2.2. Nighttime Light Data
2.2.3. Land Use Data
2.3. Multi-Class Weighted Dasymetric Mapping
2.4. Evaluation Indicators
3. Results
3.1. Result of the Disaggregation
3.2. Accuracy Evaluation
4. Conclusions and Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Nations, U. Transforming Our World: The 2030 Agenda for Sustainable Development; United Nations: New York, NY, USA, 2015. [Google Scholar]
- Jochem, W.C.; Sims, K.; Bright, E.A.; Urban, M.L.; Rose, A.N.; Coleman, P.R.; Bhaduri, B.L. Estimating traveler populations at airport and cruise terminals for population distribution and dynamics. Nat. Hazards 2013, 68, 1325–1342. [Google Scholar] [CrossRef]
- UNGGIM. Geospatial Industry Advancing Sustainable Development Goals; Geospatial Media and Communications: Noida, India, 2021. [Google Scholar]
- Makinde, O.A.; Sule, A.; Ayankogbe, O.; Boone, D. Distribution of health facilities in Nigeria: Implications and options for Universal Health Coverage. Int. J. Health Plan. Manag. 2018, 33, E1179–E1192. [Google Scholar] [CrossRef] [PubMed]
- Kuupiel, D.; Adu, K.M.; Apiribu, F.; Bawontuo, V.; Adogboba, D.A.; Ali, K.T.; Mashamba-Thompson, T.P. Geographic accessibility to public health facilities providing tuberculosis testing services at point-of-care in the upper east region, Ghana. BMC Public Health 2019, 19, 718. [Google Scholar] [CrossRef] [Green Version]
- Aubrecht, C.; Özceylan, D.; Steinnocher, K.; Freire, S. Multi-level geospatial modeling of human exposure patterns and vulnerability indicators. Nat. Hazards 2013, 68, 147–163. [Google Scholar] [CrossRef]
- Li, S.; Schlebusch, C.; Jakobsson, M. Genetic variation reveals large-scale population expansion and migration during the expansion of Bantu-speaking peoples. Proc. R. Soc. B-Biol. Sci. 2014, 281, 20141448. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cui, Y.; Li, S.J.; Wu, W.; Huang, H.; Liu, M. The application of residential distribution monitoring based on GF-1 images. In Proceedings of the 9th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR)—Multispectral Image Acquisition, Processing, and Analysis, Enshi, China, 31 October–1 November 2015. [Google Scholar]
- Freire, S.; Aubrecht, C.; Wegscheider, S. Advancing tsunami risk assessment by improving spatio-temporal population exposure and evacuation modeling. Nat. Hazards 2013, 68, 1311–1324. [Google Scholar] [CrossRef]
- Yang, X.C.; Ye, T.T.; Zhao, N.Z.; Chen, Q.; Yue, W.Z.; Qi, J.G.; Zeng, B.; Jia, P. Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data. Remote Sens. 2019, 11, 574. [Google Scholar] [CrossRef] [Green Version]
- Chen, J.D.; Fan, W.; Li, K.; Liu, X.; Song, M.L. Fitting Chinese cities’ population distributions using remote sensing satellite data. Ecol. Indic. 2019, 98, 327–333. [Google Scholar] [CrossRef]
- Ma, T. Multi-Level Relationships between Satellite-Derived Nighttime Lighting Signals and Social Media-Derived Human Population Dynamics. Remote Sens. 2018, 10, 1128. [Google Scholar] [CrossRef] [Green Version]
- Bai, Z.Q.; Wang, J.L. Generation of High Resolution Population Distribution Map in 2000 and 2010: A Case Study in the Loess Plateau, China. In Proceedings of the 23rd International Conference on Geoinformatics (Geoinformatics), Wuhan, China, 19–21 June 2015. [Google Scholar]
- Bakillah, M.; Liang, S.; Mobasheri, A.; Arsanjani, J.J.; Zipf, A. Fine-resolution population mapping using OpenStreetMap points-of-interest. Int. J. Geogr. Inf. Sci. 2014, 28, 1940–1963. [Google Scholar] [CrossRef]
- Palacios-Lopez, D.; Bachofer, F.; Esch, T.; Heldens, W.; Hirner, A.; Marconcini, M.; Sorichetta, A.; Zeidler, J.; Kuenzer, C.; Dech, S.; et al. New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products. Sustainability 2019, 11, 6056. [Google Scholar] [CrossRef] [Green Version]
- Leyk, S.; Gaughan, A.E.; Adamo, S.B.; de Sherbinin, A.; Balk, D.; Freire, S.; Rose, A.; Stevens, F.R.; Blankespoor, B.; Frye, C.; et al. The spatial allocation of population: A review of large-scale gridded population data products and their fitness for use. Earth Syst. Sci. Data 2019, 11, 1385–1409. [Google Scholar] [CrossRef] [Green Version]
- Li, K.N.; Chen, Y.H.; Li, Y. The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data. Remote Sens. 2018, 10, 1650. [Google Scholar] [CrossRef] [Green Version]
- Li, S.; Zhao, C.W. Study on Population Spatialization of Henan Province Based on Land Use and DMSP/OLS Data. J. Nat. Sci. Hunan Norm. Univ. 2019, 42, 9–15. [Google Scholar]
- Sun, W.C.; Zhang, X.; Wang, N.; Cen, Y. Estimating Population Density Using DMSP-OLS Night-Time Imagery and Land Cover Data. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2017, 10, 2674–2684. [Google Scholar] [CrossRef]
- Li, X.M.; Zhou, W.Q. Dasymetric mapping of urban population in China based on radiance corrected DMSP-OLS nighttime light and land cover data. Sci. Total Environ. 2018, 643, 1248–1256. [Google Scholar] [CrossRef]
- Lloyd, C.T.; Chamberlain, H.; Kerr, D.; Yetman, G.; Pistolesi, L.; Stevens, F.R.; Gaughan, A.E.; Nieves, J.J.; Hornby, G.; MacManus, K. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data 2019, 3, 108–139. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Stevens, F.R.; Gaughan, A.E.; Linard, C.; Tatem, A.J. Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 2015, 10, 22. [Google Scholar] [CrossRef] [Green Version]
- Wardrop, N.A.; Jochem, W.C.; Bird, T.J.; Chamberlain, H.R.; Clarke, D.; Kerr, D.; Bengtsson, L.; Juran, S.; Seaman, V.; Tatem, A.J. Spatially disaggregated population estimates in the absence of national population and housing census data. Proc. Natl. Acad. Sci. USA 2018, 115, 3529–3537. [Google Scholar] [CrossRef] [Green Version]
- Qiu, Y.; Zhao, X.S.; Fan, D.Q.; Li, S.N.; Zhao, Y.J. Disaggregating population data for assessing progress of SDGs: Methods and applications. Int. J. Digit. Earth 2022, 15, 2–29. [Google Scholar] [CrossRef]
- Su, M.D.; Lin, M.C.; Hsieh, H.I.; Tsai, B.W.; Lin, C.H. Multi-layer multi-class dasymetric mapping to estimate population distribution. Sci. Total Environ. 2010, 408, 4807–4816. [Google Scholar] [CrossRef] [PubMed]
- Eicher, C.L.; Brewer, C.A. Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation. Am. Cartogr. 2001, 28, 125–138. [Google Scholar] [CrossRef]
- Gervasoni, L.; Fenet, S.; Perrier, R.; Sturm, P. Convolutional neural networks for disaggregated population mapping using open data. In Proceedings of the 5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), Turin, Italy, 1–4 October 2018; pp. 594–603. [Google Scholar]
- Monteiro, J.; Martins, B.; Murrieta-Flores, P.; Pires, J.M. Spatial Disaggregation of Historical Census Data Leveraging Multiple Sources of Ancillary Information. Isprs Int. J. Geo-Inf. 2019, 8, 327. [Google Scholar] [CrossRef] [Green Version]
- Mennis, J. Generating Surface Models of Population Using Dasymetric Mapping. Prof. Geogr. 2003, 55, 31–42. [Google Scholar] [CrossRef]
- Gong, P.; Chen, B.; Li, X.C.; Liu, H.; Wang, J.; Bai, Y.Q.; Chen, J.M.; Chen, X.; Fang, L.; Feng, S.L.; et al. Mapping essential urban land use categories in China (EULUC-China): Preliminary results for 2018. Sci. Bull. 2020, 65, 182–187. [Google Scholar] [CrossRef] [Green Version]
- Gong, P.; Liu, H.; Zhang, M.N.; Li, C.C.; Wang, J.; Huang, H.B.; Clinton, N.; Ji, L.Y.; Li, W.Y.; Bai, Y.Q.; et al. Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Sci. Bull. 2019, 64, 370–373. [Google Scholar] [CrossRef] [Green Version]
- Semenov-Tian-Shansky, B. Russia: Territory and Population: A Perspective on the 1926 Census. Geogr. Rev. 1928, 18, 616–640. [Google Scholar] [CrossRef]
- Gallego, F.J.; Batista, F.; Rocha, C.; Mubareka, S. Disaggregating population density of the European Union with CORINE land cover. Int. J. Geogr. Inf. Sci. 2011, 25, 2051–2069. [Google Scholar] [CrossRef]
- Tan, M.; Liu, K.; Liu, L.; Zhu, Y.; Wang, D. Spatialization of population in the Pearl River Delta in 30 m grids using random forest model. Prog. Geogr. 2017, 36, 1304–1312. [Google Scholar]
- Wu, B.; Yang, C.S.; Wu, Q.S.; Wang, C.X.; Wu, J.P.; Yu, B.L. A building volume adjusted nighttime light index for characterizing the relationship between urban population and nighttime light intensity. Comput. Environ. Urban Syst. 2023, 99, 10. [Google Scholar] [CrossRef]
- Zhao, Y.C.; Li, Q.Z.; Zhang, Y.; Du, X. Improving the Accuracy of Fine-Grained Population Mapping Using Population-Sensitive POIs. Remote Sens. 2019, 11, 2502. [Google Scholar] [CrossRef] [Green Version]
- Yu, B.L.; Lian, T.; Huang, Y.X.; Yao, S.J.; Ye, X.Y.; Chen, Z.Q.; Yang, C.S.; Wu, J.P. Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement. Int. J. Geogr. Inf. Sci. 2019, 33, 687–706. [Google Scholar] [CrossRef]
Data Type | Data | Time | Spatial Resolution | Data Format |
---|---|---|---|---|
Population data | WorldPop | 2018 | 100 m | Raster |
NTL data | SDGSAT-1 (Pan band) | 13 April 2022 13:54:56 (UTC) | 10 m | Raster |
23 April 2022 14:05:47 (UTC) | ||||
Land use data | E-China | 2018 | \ | Vector |
FROM-GCL10 | 2017 | 10 m | Raster | |
OSM | 2018 | \ | Vector |
Type | Index | Specifications |
---|---|---|
Orbit | Type | sun-synchronous orbit |
Altitude | 505 km | |
Inclination | 97.50 | |
Glimmer Imager | Swath Width | 300 km |
Bands of Glimmer Imager | P: 450~900 nm B: 430~520 nm G: 520~615 nm R: 615~690 nm | |
Spatial Resolution of Glimmer Imager | P: 10 m, RGB: 40 m |
Land Use Type | Number of Grid | Population | Population Density | Distribution Coefficient |
---|---|---|---|---|
0 Roads | 16,627 | 167,355 | 10.07 | 0.0249 |
1 Cropland | 606,419 | 1,435,796 | 2.37 | 0.0058 |
2 Forest | 2,664,744 | 1,741,610 | 0.65 | 0.0016 |
3 Grassland | 111,893 | 163,295 | 1.46 | 0.0036 |
4 Shrubland | 52,894 | 80,696 | 1.53 | 0.0038 |
5 Wetland | 189 | 479 | 2.53 | 0.0063/0 |
6 Water | 10,793 | 50,069 | 4.64 | 0.0115/0 |
8 Impervious | 46,162 | 216,364 | 4.69 | 0.0116 |
9 Barren | 290 | 568 | 1.96 | 0.0048 |
101 Residential | 11,559 | 519,723 | 44.96 | 0.1110 |
201 Business office | 39 | 2523 | 64.70 | 0.1598 |
202 Commercial service | 1903 | 91,158 | 47.90 | 0.1183 |
301 Industrial | 15,493 | 364,577 | 23.53 | 0.0581 |
402 Transportation stations | 21 | 485 | 23.08 | 0.0570 |
403 Airport facilities | 675 | 19,704 | 29.19 | 0.0721 |
501 Administrative | 155 | 3806 | 24.56 | 0.0606 |
502 Educational | 812 | 19,397 | 23.89 | 0.0590 |
503 Medical | 81 | 5624 | 69.43 | 0.1714 |
504 Sport and cultural | 44 | 554 | 12.60 | 0.0311 |
505 Park and greenspace | 278 | 3125 | 11.24 | 0.0278 |
Districts or Counties | WorldPop 100 m Data | Disaggregation Result | Error (%) |
---|---|---|---|
Xiufeng District | 251,802 | 252,785 | 0.39 |
Diecai District | 84,472 | 85,467 | 1.18 |
Xiangshan District | 238,192 | 242,596 | 1.85 |
Qixing District | 248,196 | 249,327 | 0.46 |
Yanshan District | 79,175 | 81,022 | 2.33 |
Lingui District | 507,945 | 509,689 | 0.34 |
Yangshuo County | 279,971 | 281,560 | 0.57 |
Lingchuan County | 412,393 | 413,141 | 0.18 |
Quanzhou County | 651,855 | 656,391 | 0.70 |
Xing’an County | 338,658 | 341,345 | 0.79 |
Yongfu County | 240,579 | 241,112 | 0.22 |
Guanyang County | 241,877 | 244,137 | 0.93 |
Longsheng Various Nationalities Autonomous County | 158,806 | 159,934 | 0.71 |
Ziyuan County | 151,768 | 152,718 | 0.63 |
Single County | 377,957 | 378,577 | 0.16 |
Gongcheng Yao Autonomous County | 258,842 | 261,244 | 0.93 |
Lipu City | 364,608 | 366,977 | 0.65 |
Total | 4,887,096 | 4,918,023 | 0.63 |
Data | Area 1 | Area 2 | Area 3 | Area 4 | ||||
---|---|---|---|---|---|---|---|---|
IE | SF | IE | SF | IE | SF | IE | SF | |
WorldPop (1 km) | 0.49 | 1.21 | 1.58 | 2.01 | 2.03 | 1.32 | 1.83 | 0.71 |
WorldPop (100 m) | 5.5 | 5.43 | 5.18 | 2.13 | 5.37 | 2.2 | 5.71 | 4.02 |
Disaggregation result 1 | 6.38 | 16.24 | 6.1 | 11.41 | 5.9 | 8.14 | 6.38 | 15.53 |
Disaggregation result 2 | 6.69 | 19.28 | 6.65 | 16.12 | 6.54 | 11.52 | 6.52 | 18.3 |
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Liu, C.; Chen, Y.; Wei, Y.; Chen, F. Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example. Remote Sens. 2023, 15, 2926. https://doi.org/10.3390/rs15112926
Liu C, Chen Y, Wei Y, Chen F. Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example. Remote Sensing. 2023; 15(11):2926. https://doi.org/10.3390/rs15112926
Chicago/Turabian StyleLiu, Can, Yu Chen, Yongming Wei, and Fang Chen. 2023. "Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example" Remote Sensing 15, no. 11: 2926. https://doi.org/10.3390/rs15112926