{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T11:48:08Z","timestamp":1772970488678,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T00:00:00Z","timestamp":1571961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDA 20030302"],"award-info":[{"award-number":["XDA 20030302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Many methods have been used to generate gridded population maps by downscaling demographic data. As one of these methods, the accuracy of the dasymetric model depends heavily on the covariates. Point-of-interest (POI) data, as important covariates, have been widely used for population estimation. However, POIs are often used indiscriminately in existing studies. A few studies further used selected categories of POIs identified based only on the nonspatial quantitative relationship between the POIs and population. In this paper, the spatial association between the POIs and population distribution was considered to identify the POIs with a strong spatial correlation with the population distribution, i.e., population-sensitive POIs. The ability of population-sensitive POIs to improve the fine-grained population mapping accuracy was explored by comparing the results of random forest dasymetric models driven by population-sensitive POIs, all POIs, and no POIs, along with the same sets of multisource remote sensing and social sensing data. The results showed that the model driven by population-sensitive POI had the highest accuracy. Population-sensitive POIs were also more effective in improving the population mapping accuracy than were POIs selected based only on their quantitative relationship with the population. The model built using population-sensitive POIs also performed better than the two popular gridded population datasets WorldPop and LandScan. The model we proposed in this study can be used to generate accurate spatial population distribution information and contributes to achieving more reliable analyses of population-related social problems.<\/jats:p>","DOI":"10.3390\/rs11212502","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T11:05:18Z","timestamp":1572001518000},"page":"2502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Improving the Accuracy of Fine-Grained Population Mapping Using Population-Sensitive POIs"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1368-8898","authenticated-orcid":false,"given":"Yuncong","family":"Zhao","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Qiangzi","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China"}]},{"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0166-4450","authenticated-orcid":false,"given":"Xin","family":"Du","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,25]]},"reference":[{"key":"ref_1","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_2","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_3","first-page":"1220","article-title":"Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data","volume":"31","author":"Yao","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_4","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_5","first-page":"849","article-title":"LandScan: A global population database for estimating populations at risk","volume":"66","author":"Dobson","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s12665-011-1079-8","article-title":"Social vulnerability assessment of natural hazards on county-scale using high spatial resolution satellite imagery: A case study in the Luogang district of Guangzhou, South China","volume":"65","author":"Zeng","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1111\/1540-6237.8402002","article-title":"Social vulnerability to environmental hazards","volume":"84","author":"Cutter","year":"2003","journal-title":"Soc. Sci. Q."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.healthplace.2015.09.009","article-title":"Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network","volume":"36","author":"Jia","year":"2015","journal-title":"Health Place"},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1007\/s11113-007-9050-9","article-title":"Areal interpolation of population counts using pre-classified land cover data","volume":"26","author":"Reibel","year":"2007","journal-title":"Popul. Res. Policy Rev."},{"key":"ref_14","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_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"150045","DOI":"10.1038\/sdata.2015.45","article-title":"High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020","volume":"2","author":"Sorichetta","year":"2015","journal-title":"Sci. Data"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.1073\/pnas.1715305115","article-title":"Spatially disaggregated population estimates in the absence of national population and housing census data","volume":"115","author":"Wardrop","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_20","unstructured":"Balk, D., and Yetman, G. (2004). The Global Distribution of Population: Evaluating the Gains in Resolution Refinement, Center for International Earth Science Information Network (CIESIN), Columbia University."},{"key":"ref_21","unstructured":"Tobler, W., Uwe, D., Jone, G., and Kelly, M. (1995). The Global Demography Project (95-6), National Center for Geographic Information and Analysis Department of Geography."},{"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":"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_24","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1111\/j.1749-8198.2009.00220.x","article-title":"Dasymetric mapping for estimating population in small areas","volume":"3","author":"Mennis","year":"2009","journal-title":"Geogr. Compass"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1111\/1467-9671.00022","article-title":"Singly-and doubly-constrained methods of areal interpolation for vector-based GIS","volume":"3","author":"Mrozinski","year":"1999","journal-title":"Trans. GIS"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1080\/00045608.2013.843439","article-title":"Dasymetric modeling and uncertainty","volume":"104","author":"Nagle","year":"2014","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.rse.2012.11.022","article-title":"Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data","volume":"130","author":"Azar","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_29","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_30","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_31","doi-asserted-by":"crossref","unstructured":"Li, K., Chen, Y., and Li, Y. (2018). The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data. Remote Sens., 10.","DOI":"10.3390\/rs10101650"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1016\/j.scitotenv.2018.06.244","article-title":"Dasymetric mapping of urban population in China based on radiance corrected DMSP-OLS nighttime light and land cover data","volume":"643","author":"Li","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1128","DOI":"10.3390\/s90201128","article-title":"An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies","volume":"9","author":"Yang","year":"2009","journal-title":"Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, X., Ye, T., Zhao, N., Chen, Q., Yue, W., Qi, J., Zeng, B., and Jia, P. (2019). Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data. Remote Sens., 11.","DOI":"10.3390\/rs11050574"},{"key":"ref_35","first-page":"173","article-title":"Community scale livability evaluation integrating remote sensing, surface observation and geospatial big data","volume":"80","author":"Zhang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"276","DOI":"10.3390\/ijgi2020276","article-title":"A photogrammetric approach for assessing positional accuracy of OpenStreetMap\u00a9 roads","volume":"2","author":"Agouris","year":"2013","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Touya, G., Antoniou, V., Olteanu-Raimond, A., and Damme, M. (2017). Assessing crowdsourced POI quality: Combining methods based on reference data, history, and spatial relations. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6030080"},{"key":"ref_38","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","volume":"21","author":"Gao","year":"2017","journal-title":"Trans. GIS"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.compenvurbsys.2014.12.001","article-title":"Mining point-of-interest data from social networks for urban land use classification and disaggregation","volume":"53","author":"Jiang","year":"2015","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_41","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_42","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_43","doi-asserted-by":"crossref","unstructured":"Wang, Y., Gu, Y., Dou, M., and Qiao, M. (2018). Using spatial semantics and interactions to identify urban functional regions. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040130"},{"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","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Q., Huang, H., Wu, W., Du, X., and Wang, H. (2017). The combined use of remote sensing and social sensing data in fine-grained urban land use mapping: A case study in Beijing, China. Remote Sens., 9.","DOI":"10.3390\/rs9090865"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1080\/00330124.2010.547792","article-title":"A point-based intelligent approach to areal interpolation","volume":"63","author":"Zhang","year":"2011","journal-title":"Prof. Geogr."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1080\/01431160010007015","article-title":"Census from Heaven: An estimate of the global human population using night-time satellite imagery","volume":"22","author":"Sutton","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"23","DOI":"10.5194\/isprs-annals-IV-4-W2-23-2017","article-title":"Applying Thiessen Polygon Catchment Areas and Gridded Population Weights to Estimate Conflict-Driven Population Changes in South Sudan","volume":"4","author":"Jordan","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_50","unstructured":"Zvoleff, A., Rosa, M., and Ahumada, J. (2014, January 15\u201319). Monitoring Population and Land Use Change in Tropical Forest Protected Areas. Proceedings of the AGU Fall Meeting Abstracts, San Francisco, CA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.scitotenv.2008.03.011","article-title":"Use of local Moran\u2019s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland","volume":"398","author":"Zhang","year":"2008","journal-title":"Sci. Total Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.scitotenv.2013.11.046","article-title":"The identification of \u2018hotspots\u2019 of heavy metal pollution in soil\u2013rice systems at a regional scale in eastern China","volume":"472","author":"Li","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Koperski, K., and Han, J. (1995). Discovery of Spatial Association Rules in Geographic Information Databases, in International Symposium on Spatial Databases, Springer.","DOI":"10.1007\/3-540-60159-7_4"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/335191.335372","article-title":"Mining Frequent Patterns without Candidate Generation","volume":"29","author":"Han","year":"2000","journal-title":"ACM Sigmod Rec."},{"key":"ref_55","first-page":"1","article-title":"Arules-A computational environment for mining association rules and frequent item sets","volume":"14","author":"Hornik","year":"2005","journal-title":"J. Stat. Softw."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_58","unstructured":"Tibshirani, R. (1996). Bias, Variance and Prediction Error for Classification Rules, Citeseer, Department of Preventive Medicine and Biostatistics and Department of Statistics, University of Toronto."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007519102914","article-title":"An efficient method to estimate bagging\u2019s generalization error","volume":"35","author":"Wolpert","year":"1999","journal-title":"Mach. Learn."},{"key":"ref_60","unstructured":"R Core Team (2013). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_61","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_62","first-page":"1","article-title":"A grounding-based ontology of data quality measures","volume":"2018","author":"Mocnik","year":"2018","journal-title":"J. Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2502\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:29:29Z","timestamp":1760189369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2502"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,25]]},"references-count":62,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11212502"],"URL":"https:\/\/doi.org\/10.3390\/rs11212502","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,25]]}}}