{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T13:38:28Z","timestamp":1761917908280,"version":"build-2065373602"},"reference-count":68,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T00:00:00Z","timestamp":1591142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41830648 and 41771453"],"award-info":[{"award-number":["41830648 and 41771453"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chongqing R&amp;D Project of the high technology and major industries","award":["[2017] 1231"],"award-info":[{"award-number":["[2017] 1231"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Gridded population results at a fine resolution are important for optimizing the allocation of resources and researching population migration. For example, the data are crucial for epidemic control and natural disaster relief. In this study, the random forest model was applied to multisource data to estimate the population distribution in impervious areas at a 30 m spatial resolution in Chongqing, Southwest China. The community population data from the Chinese government were used to validate the estimation accuracy. Compared with the other regression techniques, the random forest regression method produced more accurate results (R2 = 0.7469, RMSE = 2785.04 and p &lt; 0.01). The points of interest (POIs) data played a more important role in the population estimation than the nighttime light images and natural topographical data, particularly in urban settings. Our results support the wide application of our method in mapping densely populated cities in China and other countries with similar characteristics.<\/jats:p>","DOI":"10.3390\/ijgi9060369","type":"journal-article","created":{"date-parts":[[2020,6,4]],"date-time":"2020-06-04T04:36:09Z","timestamp":1591245369000},"page":"369","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7752-9426","authenticated-orcid":false,"given":"Yun","family":"Zhou","sequence":"first","affiliation":[{"name":"Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Karst Ecosystem, Ministry of Education, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, Southwest University, Chongqing 400715, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3783-8363","authenticated-orcid":false,"given":"Mingguo","family":"Ma","sequence":"additional","affiliation":[{"name":"Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Karst Ecosystem, Ministry of Education, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, Southwest University, Chongqing 400715, China"}]},{"given":"Kaifang","family":"Shi","sequence":"additional","affiliation":[{"name":"Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Karst Ecosystem, Ministry of Education, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, Southwest University, Chongqing 400715, China"}]},{"given":"Zhenyu","family":"Peng","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Big Data Application in Spatial Planning, Chongqing Planning &amp; Design Institute, Chongqing 401120, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,3]]},"reference":[{"key":"ref_1","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. 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