{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T23:32:11Z","timestamp":1781307131984,"version":"3.54.1"},"reference-count":60,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering","award":["20210211"],"award-info":[{"award-number":["20210211"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. Fine-scale population spatialization is essential for urbanization and disaster prevention. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. In this paper, 30 features are extracted using easily accessible multisource geographic data. Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. The results show that 91% of the buildings in Lin\u2019an District have absolute error values of less than six compared with the actual population data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R2 values are 0.44 and 0.18. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. Future work will improve the model accuracy in densely populated areas.<\/jats:p>","DOI":"10.3390\/rs14081811","type":"journal-article","created":{"date-parts":[[2022,4,9]],"date-time":"2022-04-09T05:13:08Z","timestamp":1649481188000},"page":"1811","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Population Spatialization Model at the Building Scale Using Random Forest"],"prefix":"10.3390","volume":"14","author":[{"given":"Mengqi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Rd., Wuhan 310029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinglin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Rd., Wuhan 310029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bozhao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Rd., Wuhan 310029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1403-4394","authenticated-orcid":false,"given":"Zhongliang","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Rd., Wuhan 310029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengjun","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Rd., Wuhan 310029, China"},{"name":"Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.compenvurbsys.2005.01.006","article-title":"A Cokriging Method for Estimating Population Density in Urban Areas","volume":"29","author":"Wu","year":"2005","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1111\/gean.12012","article-title":"An Evaluation of Small Area Population Estimation Techniques Using Open Access Ancillary Data: Small Area Population Estimation Techniques","volume":"45","author":"Langford","year":"2013","journal-title":"Geogr. Anal."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15888","DOI":"10.1073\/pnas.1408439111","article-title":"Dynamic Population Mapping Using Mobile Phone Data","volume":"111","author":"Deville","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1080\/13658816.2014.909045","article-title":"Fine-Resolution Population Mapping Using OpenStreetMap Points-of-Interest","volume":"28","author":"Bakillah","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s10708-007-9105-9","article-title":"LandScan USA: A High-Resolution Geospatial and Temporal Modeling Approach for Population Distribution and Dynamics","volume":"69","author":"Bhaduri","year":"2007","journal-title":"GeoJournal"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3553","DOI":"10.1080\/01431160600617202","article-title":"Residential Population Estimation Using a Remote Sensing Derived Impervious Surface Approach","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.apgeog.2014.02.009","article-title":"A Fine-Scale Spatial Population Distribution on the High-Resolution Gridded Population Surface and Application in Alachua County, Florida","volume":"50","author":"Jia","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1080\/13658810701349078","article-title":"A Spatio-temporal Population Model to Support Risk Assessment and Damage Analysis for Decision-making","volume":"21","author":"Ahola","year":"2007","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s11069-012-0389-9","article-title":"Multi-Level Geospatial Modeling of Human Exposure Patterns and Vulnerability Indicators","volume":"68","author":"Aubrecht","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1111\/j.1365-3156.2005.01487.x","article-title":"The Accuracy of Human Population Maps for Public Health Application","volume":"10","author":"Hay","year":"2005","journal-title":"Trop. Med. Int. Health"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"272","DOI":"10.2747\/1538-7216.46.4.272","article-title":"China\u2019s Urban Population Statistics: A Critical Evaluation","volume":"46","author":"Zhou","year":"2005","journal-title":"Eurasian Geogr. Econ."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/1747423X.2017.1303546","article-title":"Improving Land Use Inference by Factorizing Mobile Phone Call Activity Matrix","volume":"12","author":"Mao","year":"2017","journal-title":"J. Land Use Sci."},{"key":"ref_15","first-page":"841","article-title":"Building Population Mapping with Aerial Imagery and GIS Data","volume":"13","author":"Ural","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","unstructured":"Deichmann, U. (1996). A Review of Spatial Population Database Design and Modeling, National Center for Geographic Information and Analysis. Technical Report 96-3."},{"key":"ref_17","unstructured":"Jones, H.R. (1990). Population Geography, Guilford Press. [2nd ed.]."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1080\/01621459.1979.10481647","article-title":"Smooth Pycnophylactic Interpolation for Geographical Regions","volume":"74","author":"Tobler","year":"1979","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_19","unstructured":"Langford, M., Maguire, D., and Unwin, D. (2014). The areal interpolation problem: Estimating population using remote sensing in a GIS framework. Handling Geographical Information: Methodology and Potential Applications, Longman Pub Group."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1559\/152304006779077309","article-title":"Intelligent Dasymetric Mapping and Its Application to Areal Interpolation","volume":"33","author":"Mennis","year":"2006","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1559\/1523040041649407","article-title":"Dasymetric Estimation of Population Density and Areal Interpolation of Census Data","volume":"31","author":"Holt","year":"2004","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1559\/152304001782173727","article-title":"Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation","volume":"28","author":"Eicher","year":"2001","journal-title":"Cartogr. Geogr. Inf. Sci."},{"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":"31","DOI":"10.1111\/0033-0124.10042","article-title":"Generating Surface Models of Population Using Dasymetric Mapping","volume":"55","author":"Mennis","year":"2003","journal-title":"Prof. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4807","DOI":"10.1016\/j.scitotenv.2010.06.032","article-title":"Multi-Layer Multi-Class Dasymetric Mapping to Estimate Population Distribution","volume":"408","author":"Su","year":"2010","journal-title":"Sci. Total Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.compenvurbsys.2005.07.005","article-title":"Rapid Facilitation of Dasymetric-Based Population Interpolation by Means of Raster Pixel Maps","volume":"31","author":"Langford","year":"2007","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1002\/(SICI)1099-1220(199709)3:3<203::AID-IJPG68>3.0.CO;2-C","article-title":"World Population in a Grid of Spherical Quadrilaterals","volume":"3","author":"Tobler","year":"1997","journal-title":"Int. J. Popul. Geogr."},{"key":"ref_28","unstructured":"CIESIN, and WRI (2000). Gridded Population of the World (GPW), Version 2. Center for International Earth Science Information Network (CIESIN) Columbia University, International Food Policy Research Institute (IFPRI) and World Resources Institute (WRI), CIESIN, Columbia University."},{"key":"ref_29","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":"Volume 62","author":"Balk","year":"2006","journal-title":"Advances in Parasitology"},{"key":"ref_30","unstructured":"CIESIN, and CIAT (2005). Global Rural-Urban Mapping Project (GRUMP), Alpha Version. Center for International Earth Science Information Network (CIESIN), Columbia University, International Food Policy Research Institute (IFPRI) and World Resources Institute (WRI), Socioeconomic Data and Applications Center (SEDAC), Columbia University."},{"key":"ref_31","first-page":"849","article-title":"LandScan: A Global Population Database for Estimating Populations at Risk","volume":"66","author":"Bright","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"S142","DOI":"10.1016\/S0140-6736(13)61396-3","article-title":"Quantifying the Effects of Using Detailed Spatial Demographic Data on Health Metrics: A Systematic Analysis for the AfriPop, AsiaPop, and AmeriPop Projects","volume":"381","author":"Tatem","year":"2013","journal-title":"Lancet"},{"key":"ref_33","unstructured":"European Commission, Joint Research Centre (JRC) (2021, December 01). GHS-POP R2015A\u2014GHS Population Grid, Derived from GPW4, Multitemporal (1975, 1990, 2000, 2015)\u2014OBSOLETE RELEASE, Available online: http:\/\/data.europa.eu\/89h\/jrc-ghsl-ghs_pop_gpw4_globe_r2015a."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2018.03.007","article-title":"Mapping Population Density in China between 1990 and 2010 Using Remote Sensing","volume":"210","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1080\/13658816.2016.1244608","article-title":"Sensing Spatial Distribution of Urban Land Use by Integrating Points-of-Interest and Google Word2Vec Model","volume":"31","author":"Yao","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5635","DOI":"10.1080\/01431161.2010.496799","article-title":"Spatial Refinement of Census Population Distribution Using Remotely Sensed Estimates of Impervious Surfaces in Haiti","volume":"31","author":"Azar","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","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."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1080\/13658816.2017.1324976","article-title":"Classifying Urban Land Use by Integrating Remote Sensing and Social Media Data","volume":"31","author":"Liu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1080\/00045608.2015.1018773","article-title":"Social Sensing: A New Approach to Understanding Our Socioeconomic Environments","volume":"105","author":"Liu","year":"2015","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2017.06.039","article-title":"Using Multi-Source Geospatial Big Data to Identify the Structure of Polycentric Cities","volume":"202","author":"Cai","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.landusepol.2017.08.008","article-title":"Biophysical and Socioeconomic Determinants of Tea Expansion: Apportioning Their Relative Importance for Sustainable Land Use Policy","volume":"68","author":"Zhang","year":"2017","journal-title":"Land Use Policy"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"126968","DOI":"10.1016\/j.ufug.2020.126968","article-title":"Do Landscape Amenities Impact Private Housing Rental Prices? A Hierarchical Hedonic Modeling Approach Based on Semantic and Sentimental Analysis of Online Housing Advertisements across Five Chinese Megacities","volume":"58","author":"Su","year":"2021","journal-title":"Urban For. Urban Green."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"102309","DOI":"10.1016\/j.habitatint.2020.102309","article-title":"Unraveling the Impact of TOD on Housing Rental Prices and Implications on Spatial Planning: A Comparative Analysis of Five Chinese Megacities","volume":"107","author":"Su","year":"2021","journal-title":"Habitat Int."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s12518-010-0028-7","article-title":"Development of Track Log and Point of Interest Management System Using Free and Open Source Software","volume":"2","author":"Yoshida","year":"2010","journal-title":"Appl. Geomat."},{"key":"ref_45","first-page":"71","article-title":"POI Pulse: A Multi-Granular, Semantic Signature\u2013Based Information Observatory for the Interactive Visualization of Big Geosocial Data","volume":"50","author":"McKenzie","year":"2015","journal-title":"Cartogr. Int. J. Geogr. Inf. Geovis."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1111\/tgis.12289","article-title":"Extracting Urban Functional Regions from Points of Interest and Human Activities on Location-Based Social Networks: GAO et Al","volume":"21","author":"Gao","year":"2017","journal-title":"Trans. GIS"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hu, T., Yang, J., Li, X., and Gong, P. (2016). Mapping Urban Land Use by Using Landsat Images and Open Social Data. Remote Sens., 8.","DOI":"10.3390\/rs8020151"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1111\/j.1467-9671.2009.01171.x","article-title":"A GIS Approach to Estimation of Building Population for Micro-Spatial Analysis","volume":"13","author":"Lwin","year":"2009","journal-title":"Trans. GIS"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1002\/widm.8","article-title":"Classification and Regression Trees","volume":"1","author":"Loh","year":"2011","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_50","first-page":"251","article-title":"Random Forest: A Review","volume":"7","author":"Goel","year":"2017","journal-title":"Int. J. Adv. Res. Comput. Sci. Softw."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1080\/21642583.2014.956265","article-title":"Random Forests: From Early Developments to Recent Advancements","volume":"2","author":"Fawagreh","year":"2014","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random Forests for Classification in Ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"160005","DOI":"10.1038\/sdata.2016.5","article-title":"Spatiotemporal Patterns of Population in Mainland China, 1990 to 2010","volume":"3","author":"Gaughan","year":"2016","journal-title":"Sci. Data"},{"key":"ref_54","first-page":"230","article-title":"Comparative Analysis of Serial Decision Tree Classification Algorithms","volume":"3","author":"Anyanwu","year":"2009","journal-title":"Int. J. Comput. Sci. Secur."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3178582","article-title":"A Survey of Random Forest Based Methods for Intrusion Detection Systems","volume":"51","author":"Resende","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_56","unstructured":"(2021, December 26). Scikit-Learn 1.0. Available online: Https:\/\/Github.Com\/Scikit-Learn\/Scikit-Learn."},{"key":"ref_57","unstructured":"Liu, Y. (2005). Mathematical Model of Multiple Linear Regression. J. Shenyang Inst. Eng., 128\u2013129."},{"key":"ref_58","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 Methodol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., and Wu, J. (2019). Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh. Remote Sens., 11.","DOI":"10.3390\/rs11040375"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature Selection with the Boruta Package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1811\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:50:46Z","timestamp":1760136646000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1811"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,8]]},"references-count":60,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14081811"],"URL":"https:\/\/doi.org\/10.3390\/rs14081811","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,8]]}}}