{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:33:52Z","timestamp":1774542832946,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,9,4]],"date-time":"2018-09-04T00:00:00Z","timestamp":1536019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["OPP1134076"],"award-info":[{"award-number":["OPP1134076"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded population maps, little has been done to explore how remotely sensed, built-area datasets might be used to dasymetrically constrain these estimates. This study presents the effectiveness of three different high-resolution built area datasets for producing gridded population estimates through the dasymetric disaggregation of census counts in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. Modeling techniques include a binary dasymetric redistribution, a random forest with a dasymetric component, and a hybrid of the previous two. The relative merits of these approaches and the data are discussed with regards to studying human populations and related spatially explicit phenomena. Results showed that the accuracy of random forest and hybrid models was comparable in five of six countries.<\/jats:p>","DOI":"10.3390\/data3030033","type":"journal-article","created":{"date-parts":[[2018,9,5]],"date-time":"2018-09-05T03:08:55Z","timestamp":1536116935000},"page":"33","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Gridded Population Maps Informed by Different Built Settlement Products"],"prefix":"10.3390","volume":"3","author":[{"given":"Fennis J.","family":"Reed","sequence":"first","affiliation":[{"name":"Geography and Geosciences, University of Louisville, Louisville, KY 40292, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4898-1587","authenticated-orcid":false,"given":"Andrea E.","family":"Gaughan","sequence":"additional","affiliation":[{"name":"Geography and Geosciences, University of Louisville, Louisville, KY 40292, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9328-3753","authenticated-orcid":false,"given":"Forrest R.","family":"Stevens","sequence":"additional","affiliation":[{"name":"Geography and Geosciences, University of Louisville, Louisville, KY 40292, USA"}]},{"given":"Greg","family":"Yetman","sequence":"additional","affiliation":[{"name":"CIESIN, Columbia University, Palisades, NY 10964, USA"}]},{"given":"Alessandro","family":"Sorichetta","sequence":"additional","affiliation":[{"name":"WorldPop, Department Geography and Environment, University of Southampton, Southampton SO17 1B, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7270-941X","authenticated-orcid":false,"given":"Andrew J.","family":"Tatem","sequence":"additional","affiliation":[{"name":"WorldPop, Department Geography and Environment, University of Southampton, Southampton SO17 1B, UK"},{"name":"Flowminder Foundation, SE-11355 Stockholm, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,4]]},"reference":[{"key":"ref_1","unstructured":"(2018, April 23). UN World Population Prospects: The 2017 Revision. Available online: https:\/\/www.un.org\/development\/desa\/publications\/world-population-prospects-the-2017-revision.html."},{"key":"ref_2","unstructured":"(2018, April 23). UN World Urbanization Prospects: The 2014 Revision. Available online: https:\/\/esa.un.org\/unpd\/wup\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/1478-7954-9-4","article-title":"The effects of spatial population dataset choice on estimates of population at risk of disease","volume":"9","author":"Tatem","year":"2011","journal-title":"Popul. Health Metrics"},{"key":"ref_4","unstructured":"Hay, S.I., Graham, A.J., and Rogers, D.J. (2007). Determining Global Population Distribution: Methods, Applications and Data. Advances in Parasitology Global Mapping of Infectious Diseases: Methods, Examples and Emerging Applications, Academic Press."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Linard, C., Gilbert, M., Snow, R.W., Noor, A.M., and Tatem, A.J. (2012). Population Distribution, Settlement Patterns and Accessibility across Africa in 2010. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0031743"},{"key":"ref_7","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. Geog. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1177\/0956247807076960","article-title":"The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones","volume":"19","author":"Mcgranahan","year":"2007","journal-title":"Environ. Urban."},{"key":"ref_9","unstructured":"(2018, July 06). United Nations: Millennium Development Goals. Available online: http:\/\/www.un.org\/millenniumgoals\/."},{"key":"ref_10","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":"2014","journal-title":"Int. J. Dig. Earth"},{"key":"ref_11","first-page":"297","article-title":"Aerial Interpolation\u2014A Variant of the Traditional Spatial Problem","volume":"1","author":"Goodchild","year":"1980","journal-title":"Geo-Processing"},{"key":"ref_12","unstructured":"Balk, D., and Yetman, G. (2018, April 23). The Global Distribution of Population: Evaluating the Gains in Resolution Refinement. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.394.7599&rep=rep1&type=pdf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1476-072X-11-7","article-title":"Large-scale spatial population databases in infectious disease research","volume":"11","author":"Linard","year":"2012","journal-title":"Int. J. Health Geogr."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1559\/152304006779077309","article-title":"Intelligent Dasymetric Mapping and Its Application to Aerial Interpolation","volume":"33","author":"Mennis","year":"2006","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Tiecke, T.G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., Talip, K., Murray, S., Blankespoor, B., and Prydz, E.B. (2018, April 23). Mapping the world population one building at a time. Available online: https:\/\/arxiv.org\/abs\/1712.05839.","DOI":"10.1596\/33700"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1175\/1520-0450(1995)034<2577:SIOAAA>2.0.CO;2","article-title":"Smart Interpolation of Annually Averaged Air Temperature in the United States","volume":"34","author":"Willmott","year":"1995","journal-title":"J. Appl. Meteorol."},{"key":"ref_19","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_20","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_21","unstructured":"(2018, July 06). Gridded Population of the World (GPW), v4. Available online: http:\/\/sedac.ciesin.columbia.edu\/data\/collection\/gpw-v4."},{"key":"ref_22","unstructured":"(2017, November 08). GADM 2018 Database of Global Administrative Areas. Available online: http:\/\/www.gadm.org\/."},{"key":"ref_23","unstructured":"DLR, Earth Observation Center (2018, August 08). Global Urban Footprint. Available online: https:\/\/www.dlr.de\/eoc\/en\/desktopdefault.aspx\/tabid-5242\/8788_read-27139\/sortby-lastname\/."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2017.10.012","article-title":"Breaking new ground in mapping human settlements from space \u2013 The Global Urban Footprint","volume":"134","author":"Esch","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Carneiro, F.S.M., Halkia, S., Andreea, M., Kemper, T., Soille, P., and Syrris, V. (2016). Operating Procedure for the Production of the Global Human Settlement Layer from Landsat Data of the Epochs 1975, 1990, 2000, and 2014, Publications Office of the European Union.","DOI":"10.1109\/IGARSS.2016.7730897"},{"key":"ref_26","unstructured":"Facebook Connectivity Lab and Center for International Earth Science Information Network (2017, October 27). High Resolution Settlement Layer. Available online: https:\/\/ciesin.columbia.edu\/data\/hrsl\/."},{"key":"ref_27","unstructured":"DLR, Earth Observation Center (2017, October 27). Global Urban Footprint: Methodology. Available online: http:\/\/www.dlr.de\/eoc\/en\/desktopdefault.aspx\/tabid-9631\/16580_read-40465\/."},{"key":"ref_28","unstructured":"(2018, July 12). Global Human Settlement Layer. Available online: http:\/\/ghsl.jrc.ec.europa.eu\/."},{"key":"ref_29","unstructured":"Gros, A., and Tiecke, T. (2017, November 22). Connecting the World with Better Maps. Available online: https:\/\/code.facebook.com\/posts\/1676452492623525\/connecting-the-world-with-better-maps\/."},{"key":"ref_30","unstructured":"Three Global LC Maps for the 2000, 2005 and 2010 Epochs (2017, October 27). European Space Agency (ESA): Climate Change Initiative. Available online: https:\/\/www.esa-landcover-cci.org\/?q=node\/158."},{"key":"ref_31","first-page":"62","article-title":"Why VIIRS data are superior to DMSP for mapping nighttime lights","volume":"35","author":"Elvidge","year":"2013","journal-title":"Proc. Asia Pac. Adv. Netw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1002\/joc.1276","article-title":"Very high resolution interpolated climate surfaces for global land areas","volume":"25","author":"Hijmans","year":"2005","journal-title":"Int. J. Climatol."},{"key":"ref_33","unstructured":"Lehner, B., Verdin, K., and Jarvis, A. (2017, October 27). HydroSHEDS Technical Documentation. Available online: http:\/\/www.hydrosheds.org\/images\/inpages\/HydroSHEDS_TechDoc_v1_2.pdf."},{"key":"ref_34","unstructured":"(2017, November 08). Vector Map (VMap) Level 0. Available online: http:\/\/geoengine.nga.mil\/geospatial\/SW_TOOLS\/NIMAMUSE\/webinter\/rast_roam.html."},{"key":"ref_35","unstructured":"IUCN and UNEP (2017, October 27). The World Database on Protected Areas (WDPA). Available online: http:\/\/www.protectedplanet.net."},{"key":"ref_36","unstructured":"(2017, October 27). OpenStreetMap Base Data. Available online: http:\/\/www.openstreetmap.org\/."},{"key":"ref_37","unstructured":"Reed, F.J., Stevens, F.R., Gaughan, A.E., and Nieves, J. (2018, August 14). Effectiveness of Remotely Sensed Built Areas to Dasymetrically Constrain Gridded Population Estimates\u2014Script Samples. Available online: http:\/\/www.worldpop.org.uk\/data\/summary\/?doi=10.5258\/SOTON\/WP00643."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/02693799308901936","article-title":"GIS and spatial analytical problems","volume":"7","author":"Fotheringham","year":"1993","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_39","unstructured":"Liaw, A., and Wiener, M. (2017, November 02). Classification and Regression by Random Forest. R News. Available online: http:\/\/cran.r-project.org\/doc\/Rnews\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land cover detection","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","unstructured":"Reed, F., Gaughan, A., Stevens, F., Yetman, G., and Tatem, A. (2018). Effectiveness of Remotely Sensed Built Areas for Constraining and Modelling Gridded Population Estimates. Remote Sens., under review."},{"key":"ref_42","first-page":"1525","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)?","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci. Model. Dev. Discuss."},{"key":"ref_43","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. 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