{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:21:57Z","timestamp":1772796117337,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,12]],"date-time":"2020-06-12T00:00:00Z","timestamp":1591920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Humanities and Social Sciences Foundation of the Ministry of Education of China","award":["17YJCZH205"],"award-info":[{"award-number":["17YJCZH205"]}]},{"name":"the Environmental Monitoring Foundation of Jiangsu Province","award":["1903"],"award-info":[{"award-number":["1903"]}]},{"name":"Qing Lan Project of Jiangsu Province","award":["R2019Q03"],"award-info":[{"award-number":["R2019Q03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing data have been widely used in research on population spatialization. Previous studies have generally divided study areas into several sub-areas with similar features by artificial or clustering algorithms and then developed models for these sub-areas separately using statistical methods. These approaches have drawbacks due to their subjectivity and uncertainty. In this paper, we present a study of population spatialization in Beijing City, China based on multisource remote sensing data and town-level population census data. Six predictive algorithms were compared for estimating population using the spatial variables derived from The National Polar-Orbiting Partnership\/ Visible Infrared Imaging Radiometer Suite (NPP\/VIIRS) night-time light and other remote sensing data. Random forest achieved the highest accuracy and therefore was employed for population spatialization. Feature selection was performed to determine the optimal variable combinations for population modeling by random forest. Cross-validation results indicated that the developed model achieved a mean absolute error (MAE) of 2129.52 people\/km2 and a R2 of 0.63. The gridded population density in Beijing at a spatial resolution of 500 m produced by the random forest model was also adjusted to be consistent with the census population at the town scale. By comparison with Google Earth high-resolution images, the remotely-sensed population was qualitatively validated at the intra-town scale. Validation results indicated that remotely sensed results can effectively depict the spatial distribution of population within town-level districts. This study provides a valuable reference for urban planning, public health and disaster prevention in Beijing, and a reference for population mapping in other cities.<\/jats:p>","DOI":"10.3390\/rs12121910","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T05:56:27Z","timestamp":1592200587000},"page":"1910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Population Spatialization in Beijing City Based on Machine Learning and Multisource Remote Sensing Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Miao","family":"He","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4032-8759","authenticated-orcid":false,"given":"Yongming","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,12]]},"reference":[{"key":"ref_1","unstructured":"United Nations, Department of Economic and Social Affairs, Population Division (2019). World Urbanization Prospects: The 2018 Revision, United Nations."},{"key":"ref_2","first-page":"1","article-title":"Urbanization, economic growth and environmental pollution: Evidence from China","volume":"21","author":"Liang","year":"2019","journal-title":"Sustain. Comput. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Aghion, P., and Durlauf, S. (2005). Handbook of Economic Growth, Elsevier.","DOI":"10.1016\/S1574-0684(05)01206-2"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.habitatint.2017.04.002","article-title":"Urban environmental challenges in developing countries\u2014A stakeholder perspective","volume":"64","author":"Ameen","year":"2017","journal-title":"Habitat Int."},{"key":"ref_5","unstructured":"Palanivel, T. (2017). Rapid Urbanisation: Opportunities and Challenges to Improve the Well-Being of Societies, United Nation Development Programme."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mesev, V. (2003). Remotely-Sensed Cities, CRC Press.","DOI":"10.1201\/9781482264678"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Talukdar, K.K. (1998, January 3\u20135). Tele-Geoinformation Service for Sustainable Urban Management: A Satellite-based Observation Approach for the 21st Century. Proceedings of the International Symposium, Strasbourg, France.","DOI":"10.1007\/978-94-011-4812-2_19"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rashed, T., and J\u00fcrgens, C. (2010). Classification of Urban Areas: Inferring Land Use from the Interpretation of Land Cover. Remote Sensing of Urban and Suburban Areas, Springer.","DOI":"10.1007\/978-1-4020-4385-7"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.rse.2019.04.020","article-title":"Understanding an urbanizing planet: Strategic directions for remote sensing","volume":"228","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1080\/01431160512331316469","article-title":"Remote sensing of urban areas","volume":"26","author":"Maktav","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/S0065-308X(05)62004-0","article-title":"Determining global population distribution: Methods, applications and data","volume":"62","author":"Balk","year":"2006","journal-title":"Adv. Parasitol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1476-072X-9-45","article-title":"A high resolution spatial population database of Somalia for disease risk mapping","volume":"9","author":"Linard","year":"2010","journal-title":"Int. J. Health Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gaughan, A.E., Stevens, F.R., Linard, C., Jia, P., and Tatem, A.J. (2013). High resolution population distribution maps for Southeast Asia in 2010 and 2015. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0055882"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.compenvurbsys.2007.06.001","article-title":"Urban population distribution models and service accessibility estimation","volume":"32","author":"Langford","year":"2008","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5605","DOI":"10.1080\/01431161.2010.496800","article-title":"Assessing fine-spatial-resolution remote sensing for small-area population estimation","volume":"31","author":"Wang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1080\/19475705.2017.1345792","article-title":"Fine scale population density data and its application in risk assessment","volume":"8","author":"Calka","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_17","first-page":"490","article-title":"Urban Population Densities","volume":"114","author":"Clark","year":"1951","journal-title":"J. R. Stat. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"90","DOI":"10.2307\/622344","article-title":"Mapping Population Data from Zone Centroid Locations","volume":"14","author":"Martin","year":"1989","journal-title":"Trans. Inst. Br. Geogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1007\/s11111-010-0108-y","article-title":"A population density grid of the European Union","volume":"31","author":"Gallego","year":"2010","journal-title":"Popul. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"131","DOI":"10.2747\/1548-1603.45.2.131","article-title":"Population Estimation Using Geographically Weighted Regression","volume":"45","author":"Lo","year":"2008","journal-title":"GISci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.apgeog.2013.03.002","article-title":"Human population distribution modelling at regional level using very high resolution satellite imagery","volume":"41","author":"Lung","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0198-9715(97)01005-3","article-title":"Modeling population density with night-time satellite imagery and GIS","volume":"21","author":"Sutton","year":"1997","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.rse.2006.11.020","article-title":"Dasymetric modelling of small-area population distribution using land cover and light emissions data","volume":"108","author":"Briggs","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1080\/15481603.2015.1072400","article-title":"Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data","volume":"52","author":"Bagan","year":"2015","journal-title":"GISci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1080\/01431160500181861","article-title":"DMSP\/OLS night-time light imagery for urban population estimates in the Brazilian Amazon","volume":"27","author":"Amaral","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.1109\/JSTARS.2017.2703878","article-title":"Estimating Population Density Using DMSP-OLS Night-Time Imagery and Land Cover Data","volume":"10","author":"Sun","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2645","DOI":"10.1080\/01431160600981525","article-title":"The Nightsat mission concept","volume":"28","author":"Elvidge","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TGRS.2013.2247768","article-title":"Early on-orbit performance of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite","volume":"52","author":"Cao","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1002\/2013JD020475","article-title":"Suomi NPP VIIRS day-night band on-orbit performance","volume":"118","author":"Liao","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4937","DOI":"10.3390\/rs70404937","article-title":"A Test of the New VIIRS Lights Data Set: Population and Economic Output in Africa","volume":"7","author":"Chen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Stevens, F.R., Gaughan, A.E., Linard, C., and Tatem, A.J. (2015). Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0107042"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.ecolind.2018.11.013","article-title":"Fitting Chinese cities\u2019 population distributions using remote sensing satellite data","volume":"98","author":"Chen","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/17538947.2014.965761","article-title":"Exploring nationally and regionally defined models for large area population mapping","volume":"8","author":"Gaughan","year":"2015","journal-title":"Int. J. Digit. Earth"},{"key":"ref_34","first-page":"70","article-title":"Nighttime lights compositing using the VIIRS day-night band: Preliminary results","volume":"35","author":"Baugh","year":"2013","journal-title":"Proc. Asia Pac. Adv. Netw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2005.02.002","article-title":"Spatial analysis of global urban extent from DMSP-OLS night lights","volume":"96","author":"Small","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1016\/j.asr.2012.01.025","article-title":"Poverty assessment using DMSP\/OLS night-time light satellite imagery at a provincial scale in China","volume":"49","author":"Wen","year":"2012","journal-title":"Adv. Space Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.apenergy.2016.10.032","article-title":"Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data","volume":"184","author":"Shi","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, M., Cheng, W., Zhou, C., Li, M., Wang, N., and Liu, Q. (2017). GDP Spatialization and Economic Differences in South China Based on NPP-VIIRS Nighttime Light Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9070673"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2017.01.005","article-title":"Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics","volume":"192","author":"Bennett","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_40","first-page":"29","article-title":"Driving Force Analysis of Land Use Chang of Beijing Urban Areas in the Past 20 Years","volume":"36","author":"Xiao","year":"2013","journal-title":"Geomat. Spat. Inf. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.rse.2004.02.003","article-title":"Land surface temperature retrieval from Landsat TM5","volume":"90","author":"Sobrino","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Qin, Z., Li, W., Gao, M., and Zhang, H. (2006, January 3). Estimation of land surface emissivity for Landsat TM6 and its application to Lingxian region in North China. Proceedings of the Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI, Stockholm, Sweden.","DOI":"10.1117\/12.689310"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/TGRS.2008.2007125","article-title":"Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data","volume":"47","author":"Sobrino","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_45","first-page":"25","article-title":"Bayesian regularization of neural networks","volume":"458","author":"Burden","year":"2008","journal-title":"Methods Mol. Biol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Nguyen, D., and Widrow, B. (1990, January 17\u201321). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. Proceedings of the International Joint Conference of Neural Networks, San Diego, CA, USA.","DOI":"10.1109\/IJCNN.1990.137819"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/380995.380999","article-title":"Support vector machines: Hype or hallelujah?","volume":"2","author":"Bennett","year":"2000","journal-title":"SIGKDD Explor."},{"key":"ref_48","first-page":"1396","article-title":"A practical guide to support vector classification","volume":"101","author":"Hsu","year":"2008","journal-title":"BJU Int."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_52","first-page":"2769","article-title":"Measuring and Testing Dependence by Correlation of Distances","volume":"35","author":"Rizzo","year":"2008","journal-title":"Ann. Stat."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.asr.2019.09.035","article-title":"GDP spatialization in Ningbo City based on NPP\/VIIRS night-time light and auxiliary data using random forest regression","volume":"65","author":"Liang","year":"2020","journal-title":"Adv. Space Res."},{"key":"ref_54","first-page":"1220","article-title":"Mapping fine-scale population distributions at the building level by integrating multi-source geospatial big data","volume":"31","author":"Yao","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1016\/j.scitotenv.2018.12.276","article-title":"Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model","volume":"658","author":"Ye","year":"2019","journal-title":"Sci. Total Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/1910\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:38:16Z","timestamp":1760175496000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/1910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,12]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12121910"],"URL":"https:\/\/doi.org\/10.3390\/rs12121910","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,12]]}}}