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Research Article

Scale effects-aware bottom-up population estimation using weakly supervised learning

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Article: 2341788 | Received 10 Nov 2023, Accepted 29 Feb 2024, Published online: 16 Apr 2024

References

  • Bai, Zhongqiang, Juanle Wang, Mingming Wang, Mengxu Gao, and Jiulin Sun. 2018. “Accuracy Assessment of Multi-Source Gridded Population Distribution Datasets in China.” Sustainability 10 (5): 1363, Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/su10051363.
  • Baker, Jack, David Swanson, and Jeff Tayman. 2023. “Boosted Regression Trees for Small-Area Population Forecasting.” Population Research and Policy Review 42 (4): 51. https://doi.org/10.1007/s11113-023-09795-x.
  • Bondarenko, Maksym, David Kerr, Alessandro Sorichetta, and Andrew Tatem. 2020. “Census/Projection-Disaggregated Gridded Population Datasets, Adjusted to Match the Corresponding UNPD 2020 Estimates, for 183 Countries in 2020 Using Built-Settlement Growth Model (BSGM) Outputs.” University of Southampton. https://eprints.soton.ac.uk/444005/.
  • Boo, Gianluca, Edith Darin, Douglas R. Leasure, Claire A. Dooley, Heather R. Chamberlain, Attila N. Lázár, Kevin Tschirhart, et al. 2022. “High-Resolution Population Estimation Using Household Survey Data and Building Footprints.” Nature Communications 13 (1): 1330, Nature Publishing Group. https://doi.org/10.1038/s41467-022-29094-x.
  • Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
  • Bright, Eddie A., Phil R. Coleman, and Jerome E. Dobson. 2000. “LandScan: A Global Population Database for Estimating Populations at Risk.” Photogrammetric Engineering and Remote Sensing 66: 849–858.
  • Cáceres, Aimy, Paulo Santos, Feliciano Tchalo, Michael Mills, and Martim Melo. 2013. “Human Use of Natural Resources and the Conservation of the Afromontane Forest in Mount Moco, Angola.” Journal of Sustainable Development in Africa 15 (January): 91–101.
  • Chen, Hongxing, Bin Wu, Bailang Yu, Zuoqi Chen, Qiusheng Wu, Ting Lian, Congxiao Wang, Qiaoxuan Li, and Jianping Wu. 2021. “A New Method for Building-Level Population Estimation by Integrating LiDAR, Nighttime Light, and POI Data.” Journal of Remote Sensing 2021 (May), Science Partner Journal. https://doi.org/10.34133/2021/9803796.
  • Cheng, Yang, Siyao Gao, Shuai Li, Yuchao Zhang, and Mark Rosenberg. 2019. “Understanding the Spatial Disparities and Vulnerability of Population Aging in China.” Asia & the Pacific Policy Studies 6 (1): 73–89. https://doi.org/10.1002/app5.267.
  • Cheng, Zhifeng, Jianghao Wang, and Yong Ge. 2022. “Mapping Monthly Population Distribution and Variation at 1-Km Resolution across China.” International Journal of Geographical Information Science 36 (6): 1166–1184. Taylor & Francis: https://doi.org/10.1080/13658816.2020.1854767.
  • Chi, Guangqing, and Paul R. Voss. 2011. “Small-Area Population Forecasting: Borrowing Strength across Space and Time.” Population, Space and Place 17 (5): 505–520. https://doi.org/10.1002/psp.617.
  • Chi, Guangqing, and Donghui Wang. 2017. “Small-Area Population Forecasting: A Geographically Weighted Regression Approach.” In The Frontiers of Applied Demography, edited by David A. Swanson, 449–471. Applied Demography Series. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-43329-5_21.
  • Chi, Guangqing, Xuan Zhou, and Paul R. Voss. 2011. “Small-Area Population Forecasting in an Urban Setting: A Spatial Regression Approach.” Journal of Population Research 28 (2): 185–201. https://doi.org/10.1007/s12546-011-9053-6.
  • Freire, Sergio, Kytt MacManus, Martino Pesaresi, Erin Doxsey-Whitfield, and Jane Mills. 2016. “Development of New Open and Free Multi-Temporal Global Population Grids at 250 m Resolution.” Population 250.
  • Georganos, Stefanos, Sebastian Hafner, Monika Kuffer, Catherine Linard, and Yifang Ban. 2022. “A Census from Heaven: Unraveling the Potential of Deep Learning and Earth Observation for Intra-Urban Population Mapping in Data Scarce Environments.” International Journal of Applied Earth Observation and Geoinformation 114 (November): 103013. https://doi.org/10.1016/j.jag.2022.103013.
  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. Adaptive Computation and Machine Learning Series. London, England: MIT Press.
  • Grippa, Taïs, Catherine Linard, Moritz Lennert, Stefanos Georganos, Nicholus Mboga, Sabine Vanhuysse, Assane Gadiaga, and Eléonore Wolff. 2019. “Improving Urban Population Distribution Models with Very-High Resolution Satellite Information.” Data 4 (1): 13. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/data4010013.
  • Hauer, Mathew E. 2019. “Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway.” Scientific Data 6 (1): 190005. Nature Publishing Group. https://doi.org/10.1038/sdata.2019.5.
  • Hu, Qiushi, Rui Li, Huayi Wu, and Zhaohui Liu. 2022. “Construction of a Refined Population Analysis Unit Based on Urban Forms and Population Aggregation Patterns.” International Journal of Digital Earth 15 (1): 79–107. Taylor & Francis. https://doi.org/10.1080/17538947.2021.2013963.
  • Ioffe, Sergey, and Christian Szegedy. 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.” arXiv. https://doi.org/10.48550/arXiv.1502.03167.
  • Jacobs, Nathan, Adam Kraft, Muhammad Usman Rafique, and Ranti Dev Sharma. 2018. “A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery.” In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 33–42. SIGSPATIAL ‘18. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3274895.3274934.
  • Jain, Deepty, and Geetam Tiwari. 2017. “Population Disaggregation to Capture Short Trips – Vishakhapatnam, India.” Computers, Environment and Urban Systems 62 (March): 7–18. https://doi.org/10.1016/j.compenvurbsys.2016.10.003.
  • Leasure, Douglas R., Warren C. Jochem, Eric M. Weber, Vincent Seaman, and Andrew J. Tatem. 2020. “National Population Mapping from Sparse Survey Data: A Hierarchical Bayesian Modeling Framework to Account for Uncertainty.” Proceedings of the National Academy of Sciences 117 (39): 24173–24179. https://doi.org/10.1073/pnas.1913050117.
  • Leyk, Stefan, Andrea E. Gaughan, Susana B. Adamo, Alex De Sherbinin, Deborah Balk, Sergio Freire, Amy Rose, et al. 2019. “The Spatial Allocation of Population: A Review of Large-Scale Gridded Population Data Products and Their Fitness for Use.” Earth System Science Data 11 (3): 1385–1409. https://doi.org/10.5194/essd-11-1385-2019.
  • Mei, Yuao, Zhipeng Gui, Jinghang Wu, Dehua Peng, Rui Li, Huayi Wu, and Zhengyang Wei. 2022. “Population Spatialization with Pixel-Level Attribute Grading by Considering Scale Mismatch Issue in Regression Modeling.” Geo-Spatial Information Science 25 (3): 365–382. Taylor & Francis. https://doi.org/10.1080/10095020.2021.2021785.
  • Metzger, Nando, John E. Vargas-Muñoz, Rodrigo C. Daudt, Benjamin Kellenberger, Thao Ton-That Whelan, Ferda Ofli, Muhammad Imran, Konrad Schindler, and Devis Tuia. 2022. “Fine-Grained Population Mapping from Coarse Census Counts and Open Geodata.” Scientific Reports 12 (1): 20085. Nature Publishing Group. https://doi.org/10.1038/s41598-022-24495-w.
  • Miller, Harvey, and Shih-Lung Shaw. 2001. “Geographic Information Systems for Transportation (GIS -T): Principles and Applications,” January.
  • Neal, Isaac, Sohan Seth, Gary Watmough, and Mamadou S. Diallo. 2022. “Census-Independent Population Estimation Using Representation Learning.” Scientific Reports 12 (1): 5185. Nature Publishing Group. https://doi.org/10.1038/s41598-022-08935-1.
  • Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. 2019. “PyTorch: An Imperative Style, High-Performance Deep Learning Library.” In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, edited by Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, and Roman Garnett, 8024–35. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf.
  • Shang, Shuoshuo, Shihong Du, Shouji Du, and Shoujie Zhu. 2021. “Estimating Building-Scale Population Using Multi-Source Spatial Data.” Cities 111 (April): 103002. https://doi.org/10.1016/j.cities.2020.103002.
  • Stevens, Forrest R., Andrea E. Gaughan, Catherine Linard, and Andrew J. Tatem. 2015. “Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data.” Edited by Luís A. Nunes Amaral. PLoS One 10 (2): e0107042. https://doi.org/10.1371/journal.pone.0107042.
  • Swanwick, Rachel H., Quentin D. Read, Steven M. Guinn, Matthew A. Williamson, Kelly L. Hondula, and Andrew J. Elmore. 2022. “Dasymetric Population Mapping Based on US Census Data and 30-m Gridded Estimates of Impervious Surface.” Scientific Data 9 (1): 523. https://doi.org/10.1038/s41597-022-01603-z.
  • Tu, Wenna, Zhang Liu, Yunyan Du, Jiawei Yi, Fuyuan Liang, Nan Wang, Jiale Qian, Sheng Huang, and Huimeng Wang. 2022. “An Ensemble Method to Generate High-Resolution Gridded Population Data for China from Digital Footprint and Ancillary Geospatial Data.” International Journal of Applied Earth Observation and Geoinformation 107 (March): 102709. https://doi.org/10.1016/j.jag.2022.102709.
  • Wang, Shunli, Rui Li, Jie Jiang, and Yao Meng. 2021. “Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan.” Future Internet 13 (10): 251. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/fi13100251.
  • Warszawski, L., K. Frieler, V. Huber, F. Piontek, O. Serdeczny, X. Zhang, Q. Tang, et al. 2017. “Center for International Earth Science Information Network—CIESIN—Columbia University.(2016). Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades. NY: NASA Socioeconomic Data and Applications Center (SEDAC).” Atlas of Environmental Risks Facing China Under Climate Change 228.
  • Weber, Eric M., Vincent Y. Seaman, Robert N. Stewart, Tomas J. Bird, Andrew J. Tatem, Jacob J. McKee, Budhendra L. Bhaduri, Jessica J. Moehl, and Andrew E. Reith. 2018. “Census-Independent Population Mapping in Northern Nigeria.” Remote Sensing of Environment 204 (January): 786–798. https://doi.org/10.1016/j.rse.2017.09.024.
  • Weerasinghe, Oshadhi, and Saman Bandara. 2023. “Modified Traffic Analysis Zones Approach for the Estimation of Passenger Flow Distribution in Urban Areas.” Journal of Urban Planning and Development 149 (1): 04022045. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000881.
  • Wilson, Tom, Irina Grossman, Monica Alexander, Phil Rees, and Jeromey Temple. 2022. “Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs.” Population Research and Policy Review 41 (3): 865–898. https://doi.org/10.1007/s11113-021-09671-6.
  • Yang, Mengmeng, Jinsong Ma, Peihong Jia, Yingxia Pu, and Gang Chen. 2011. “The Use of Spatial Autocorrelation to Analyze Changes in Spatial Distribution Patterns of Population Density in Jiangsu Province, China.” 2011 19th International Conference on Geoinformatics, 1–6. https://doi.org/10.1109/GeoInformatics.2011.5980909.
  • Yao, Yao, Xiaoping Liu, Xia Li, Jinbao Zhang, Zhaotang Liang, Ke Mai, and Yatao Zhang. 2017. “Mapping Fine-Scale Population Distributions at the Building Level by Integrating Multisource Geospatial Big Data.” International Journal of Geographical Information Science, February, 1–25. https://doi.org/10.1080/13658816.2017.1290252.
  • Ye, Tingting, Naizhuo Zhao, Xuchao Yang, Zutao Ouyang, Xiaoping Liu, Qian Chen, Kejia Hu, et al. 2019. “Improved Population Mapping for China Using Remotely Sensed and Points-of-Interest Data within a Random Forests Model.” Science of The Total Environment 658 (March): 936–946. https://doi.org/10.1016/j.scitotenv.2018.12.276.
  • Yin, Xu, Peng Li, Zhiming Feng, Yanzhao Yang, Zhen You, and Chiwei Xiao. 2021. “Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA).” ISPRS International Journal of Geo-Information 10 (10): 681. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/ijgi10100681.
  • Yuan, Nicholas Jing, Yu Zheng, and Xing Xie. 2012. “Segmentation of Urban Areas Using Road Networks.” Microsoft, Albuquerque, NM, USA, Tech. Rep. MSR-TR-2012-65. https://www.semanticscholar.org/paper/Segmentation-of-Urban-Areas-Using-Road-Networks-Yuan-Zheng/c512a4b6e80613abb88276810ed26e4b5b407c63.
  • Zhang, Boen, Gang Xu, Limin Jiao, Jiafeng Liu, Ting Dong, Zehui Li, Xiaoping Liu, and Yaolin Liu. 2019. “The Scale Effects of the Spatial Autocorrelation Measurement: Aggregation Level and Spatial Resolution.” International Journal of Geographical Information Science 33 (5): 945–966. Taylor & Francis: https://doi.org/10.1080/13658816.2018.1564316.
  • Zhao, F. X., F. W. Zhao, and H. Sun. 2019. “A Coevolution Model of Population Distribution and Road Networks.” Physica A: Statistical Mechanics and Its Applications 536 (December): 120860. https://doi.org/10.1016/j.physa.2019.04.096.
  • Zheng, Hao, Zhanlei Yang, Wenju Liu, Jizhong Liang, and Yanpeng Li. 2015. “Improving Deep Neural Networks Using Softplus Units.” 2015 International Joint Conference on Neural Networks (IJCNN), 1–4. https://doi.org/10.1109/IJCNN.2015.7280459.
  • Zhou, Zhi-Hua. 2018. “A Brief Introduction to Weakly Supervised Learning.” National Science Review 5 (1): 44–53. https://doi.org/10.1093/nsr/nwx106.
  • Zong, Zefang, Jie Feng, Kechun Liu, Hongzhi Shi, and Yong Li. 2019. “DeepDPM: Dynamic Population Mapping via Deep Neural Network.” In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, 1294–1301. AAAI’19/IAAI’19/EAAI’19. Honolulu, Hawaii, USA: AAAI Press. https://doi.org/10.1609/aaai.v33i01.33011294.