Disaggregating population data for assessing progress of SDGs: methods and applications

References

• Ahmedov, A., and I. Dudova. 2014. “Bulgarian Population Grid 2011 – Geostatistics by Mixed Methods.” Technical report, WP1B.3 Dataset.
   Google Scholar
• Ahola, T., K. Virrantaus, J. M. Krisp, and G. J. Hunter. 2007. “A Spatio-Temporal Population Model to Support Risk Assessment and Damage Analysis for Decision-Making.” International Journal of Geographical Information Science 21 (8): 935–953. doi:10.1080/13658810701349078.
   Web of Science ®Google Scholar
• Allen, C., M. Smith, M. Rabiee, and H. Dahmm. 2021. “A Review of Scientific Advancements in Datasets Derived from Big Data for Monitoring the Sustainable Development Goals.” Sustainability Science 16 (5): 1701–1716. doi: 10.1007/s11625-021-00982-3.
   Web of Science ®Google Scholar
• Andres, R. J., T. A. Boden, and D. M. Higdon. 2016. “Gridded Uncertainty in Fossil Fuel Carbon Dioxide Emission Maps, a CDIAC Example.” Atmospheric Chemistry and Physics 16 (23): 14979–14995. doi:10.5194/acp-16-14979-2016.
   Web of Science ®Google Scholar
• Andrew, N. L., P. Bright, L. Rua, S. J. Teoh, and M. Vickers. 2019. “Coastal Proximity of Populations in 22 Pacific Island Countries and Territories.” PLoS ONE 14 (9): e0223249. doi:10.1371/journal.pone.0223249.
   PubMed Web of Science ®Google Scholar
• Archila Bustos, M. F., O. Hall, and M. Andersson. 2015. “Nighttime Lights and Population Changes in Europe 1992-2012.” Ambio 44 (7): 653–665. doi:10.1007/s13280-015-0646-8.
   PubMed Web of Science ®Google Scholar
• Aubrecht, C., J. Ungar, and S. Freire. 2011. “Exploring the Potential of Volunteered Geographic Information for Modeling Spatio-Temporal Characteristics of Urban Population: A Case Study for Lisbon Metro Using Foursquare Check-in Data.” Proceedings of the 7th International Conference on Virtual Cities and Territories, Lisbon, Portugal, October 11–13.
   Google Scholar
• Azar, D., R. Engstrom, J. Graesser, and J. Comenetz. 2013. “Generation of Fine-Scale Population Layers Using Multi-Resolution Satellite Imagery and Geospatial Data.” Remote Sensing of Environment 130: 219–232. doi:10.1016/j.rse.2012.11.022.
   Web of Science ®Google Scholar
• Balk, D. L., U. Deichmann, G. Yetman, F. Pozzi, S. I. Hay, and A. Nelson. 2006. “Determining Global Population Distribution: Methods, Applications and Data.” Advances in Parasitology 62: 119–156. doi:10.1016/S0065-308X(05)62004-0.
   PubMed Web of Science ®Google Scholar
• Balk, D., F. Pozzi, G. Yetman, U. Deichmann, and A. Nelson. 2005. “The Distribution of People and the Dimension of Place: Methodologies to Improve the Global Estimation of Urban Extents.” International Society for Photogrammetry and Remote Sensing, Proceedings of the Urban Remote Sensing Conference, Tempe, AZ, March.
   Google Scholar
• Bhaduri, B., E. Bright, P. Coleman, and M. L. Urban. 2007. “LandScan USA: A High-Resolution Geospatial and Temporal Modeling Approach for Population Distribution and Dynamics.” GeoJournal 69 (1-2): 103–117. doi:10.1007/s10708-007-9105-9.
   Google Scholar
• Bojinski, S., M. Verstraete, T. C. Peterson, C. Richter, A. Simmons, and M. Zemp. 2014. “The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy.” Bulletin of the American Meteorological Society 95 (9): 1431–1443. doi:10.1175/BAMS-D-13-00047.1.
   Web of Science ®Google Scholar
• Bombelli, A., J. Masó, I. Serral, S. Jules-Plag, H. P. Plag, and I. Mcallum. 2015. “D2.2: EVs Current Status in Different Communities and Way to Move Forward.” Accessed March 1, 2021. http://ddd.uab.cat/record/146882.
   Google Scholar
• Bordogna, G., P. Carrara, L. Criscuolo, M. Pepe, and A. Rampini. 2014. “On Predicting and Improving the Quality of Volunteer Geographic Information Projects.” International Journal of Digital Earth 9 (2): 134–155. doi:10.1080/17538947.2014.976774.
   Web of Science ®Google Scholar
• Briggs, D. J., J. Gulliver, D. Fecht, and D. M. Vienneau. 2007. “Dasymetric Modelling of Small-Area Population Distribution Using Land Cover and Light Emissions Data.” Remote Sensing of Environment 108 (4): 451–466. doi:10.1016/j.rse.2006.11.020.
   Web of Science ®Google Scholar
• Burke, M., A. Driscoll, D. Lobell, and S. Ermon. 2021. “Using Satellite Imagery to Understand and Promote Sustainable Development.” Science 371 (6535): e8628. doi:10.1126/science.abe8628.
   Web of Science ®Google Scholar
• Calka, B., and E. Bielecka. 2019. “Reliability Analysis of LandScan Gridded Population Data. The Case Study of Poland.” ISPRS International Journal of Geo-Information 8 (5): 222. doi:10.3390/ijgi8050222.
   Web of Science ®Google Scholar
• Calka, B., J. N. Da Costa, and E. Bielecka. 2017. “Fine Scale Population Density Data and its Application in Risk Assessment.” Geomatics, Natural Hazards and Risk 8 (2): 1440–1455. doi:10.1080/19475705.2017.1345792.
   Web of Science ®Google Scholar
• Chen, J., and Z. L. Li. 2018. “Chinese Pilot Project Tracks Progress Towards SDGs.” Nature 563 (7730): 184. doi:10.1038/d41586-018-07309-w.
   PubMedGoogle Scholar
• Chen, J., T. Pei, S. L. Shaw, F. Lu, M. X. Li, S. F. Cheng, X. L. Liu, and H. C. Zhang. 2018. “Fine-grained Prediction of Urban Population Using Mobile Phone Location Data.” International Journal of Geographical Information Science 32 (9): 1770–1786. doi:10.1080/13658816.2018.1460753.
   Web of Science ®Google Scholar
• Chen, J., S. Peng, X. S. Zhao, Y. J. Ge, and Z. L. Li. 2019. “Measuring Regional Progress Towards SDGs by Combining Geospatial and Statistical Information.” Acta Geodaetica et Cartographica Sinica 48 (4): 473–479. doi:10.11947/j.AGCS.2019.20180563.
   Google Scholar
• Choi, J., M. Hwang, G. Kim, J. Seong, and J. Ahn. 2016. “Supporting the Measurement of the United Nations’ Sustainable Development Goal 11 Through the Use of National Urban Information Systems and Open Geospatial Technologies: A Case Study of South Korea.” Open Geospatial Data, Software and Standards 1: 4. doi:10.1186/s40965-016-0005-0.
   Google Scholar
• Cohen, J. E., and C. Small. 1998. “Hypsographic Demography: The Distribution of Human Population by Altitude.” Proceedings of the National Academy of Sciences 95 (24): 14009–14014. doi:10.1073/pnas.95.24.14009.
   PubMed Web of Science ®Google Scholar
• Corbane, C., T. Kemper, S. Freire, C. Louvrier, and M. Pesaresi. 2016. “Monitoring the Syrian Humanitarian Crisis with the JRC’s Global Human Settlement Layer and Night-Time Satellite Data.” EUR 27933. Luxembourg (Luxembourg): Publications Office of the European Union; 2016. JRC101733. doi:10.2788/297909.
   Google Scholar
• Corbane, C., M. Pesaresi, P. Politis, V. Syrris, A. J. Florczyk, P. Soille, L. Maffenini, et al. 2017. “Big Earth Data Analytics on Sentinel-1 and Landsat Imagery in Support to Global Human Settlements Mapping.” Big Earth Data 1 (1-2): 118–144. doi:10.1080/20964471.2017.1397899.
   Google Scholar
• Deichmann, U. 1996a. “A Review of Spatial Population Database Design and Modeling.” Technical Report 96-3. Santa Barbara, California, USA: National Center for Geographic Information and Analysis (NCGIA).
   Google Scholar
• Deichmann, U. 1996b. “Asia Population Database Documentation.” Accessed February 12, 2021. http://na.unep.net/siouxfalls/globalpop/asia/.
   Google Scholar
• Dobson, J. E., E. A. Bright, P. R. Coleman, R. C. Durfee, and B. A. Worley. 2000. “LandScan: A Global Population Database for Estimating Populations at Risk.” Photogrammetric Engineering & Remote Sensing 66 (7): 849–857.
   Web of Science ®Google Scholar
• Doll, C. N. H., and S. Pachauri. 2010. “Estimating Rural Populations Without Access to Electricity in Developing Countries Through Night-Time Light Satellite Imagery.” Energy Policy 38 (10): 5661–5670. doi:10.1016/j.enpol.2010.05.014.
   Web of Science ®Google Scholar
• Dong, P. L., S. Ramesh, and A. Nepali. 2010. “Evaluation of Small-Area Population Estimation Using LiDAR, Landsat TM and Parcel Data.” International Journal of Remote Sensing 31 (21): 5571–5586. doi:10.1080/01431161.2010.496804.
   Web of Science ®Google Scholar
• Dong, N., X. H. Yang, H. Y. Cai, and F. J. Xu. 2017. “Research on Grid Size Suitability of Gridded Population Distribution in Urban Area: A Case Study in Urban Area of Xuanzhou District, China.” PLoS ONE 12 (1): e0170830. doi:10.1371/journal.pone.0170830.
   PubMed Web of Science ®Google Scholar
• Doxsey-Whitfield, E., K. MacManus, S. B. Adamo, L. Pistolesi, J. Squires, O. Borkovska, and S. R. Baptista. 2015. “Taking Advantage of the Improved Availability of Census Data: A First Look at the Gridded Population of the World, Version 4.” Papers in Applied Geography 1 (3): 226–234. doi:10.1080/23754931.2015.1014272.
   Google Scholar
• Ehrlich, D., T. Kemper, M. Pesaresi, and C. Corbane. 2018. “Built-up Area and Population Density: Two Essential Societal Variables to Address Climate Hazard Impact.” Environmental Science & Policy 90: 73–82. doi:10.1016/j.envsci.2018.10.001.
   PubMed Web of Science ®Google Scholar
• Eicher, C. L., and C. A. Brewer. 2001. “Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation.” Cartography and Geographic Information Science 28 (2): 125–138. doi:10.1559/152304001782173727.
   Google Scholar
• Elvidge, C. D., K. E. Baugh, E. A. Kihn, H. W. Kroehl, E. R. Davis, and C. W. Davis. 1997. “Relation Between Satellite Observed Visible-Near Infrared Emissions, Population, Economic Activity and Electric Power Consumption.” International Journal of Remote Sensing 18 (6): 1373–1379. doi:10.1080/014311697218485.
   Web of Science ®Google Scholar
• Elvidge, C. D., M. L. Imhoff, K. E. Baugh, V. R. Hobson, I. Nelson, J. Safran, J. B. Dietz, and B. T. Tuttle. 2001. “Night-time Lights of the World: 1994-1995.” ISPRS Journal of Photogrammetry and Remote Sensing 56 (2): 81–99. doi:10.1016/S0924-2716(01)00040-5.
   Web of Science ®Google Scholar
• Esch, T., M. Marconcini, A. Felbier, A. Roth, W. Heldens, M. Huber, M. Schwinger, H. Taubenböck, A. Müller, and S. Dech. 2013. “Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks from Global Data of the TanDEM-X Mission.” IEEE Geoscience and Remote Sensing Letters 10 (6): 1617–1621. doi:10.1109/LGRS.2013.2272953.
   Web of Science ®Google Scholar
• Fisher, P. F., and M. Langford. 1995. “Modelling the Errors in Areal Interpolation between Zonal Systems by Monte Carlo Simulation.” Environment and Planning A: Economy and Space 27: 211–224. doi:10.1068/a270211.
   Web of Science ®Google Scholar
• Fisher, P. E., and M. Langford. 1996. “Modeling Sensitivity to Accuracy in Classified Imagery: A Study of Areal Interpolation by Dasymetric Mapping.” The Professional Geographer 48 (3): 299–309. doi:10.1111/j.0033-0124.1996.00299.x.
   Web of Science ®Google Scholar
• Fotheringham, A. S., M. Charlton, and C. Brunsdon. 1996. “The Geography of Parameter Space: An Investigation of Spatial Non-Stationarity.” International Journal of Geographical Information Systems 10 (5): 605–627. doi:10.1080/02693799608902100.
   Web of Science ®Google Scholar
• Freire, S., A. J. Florczyk, M. Pesaresi, and R. Sliuzas. 2019. “An Improved Global Analysis of Population Distribution in Proximity to Active Volcanoes, 1975-2015.” ISPRS International Journal of Geo-Information 8 (8): 341. doi:10.3390/ijgi8080341.
   Web of Science ®Google Scholar
• Freire, S., K. MacManus, M. Pesaresi, E. Doxsey-Whitfield, and J. Mills. 2016. “Development of New Open and Free Multi-Temporal Global Population Grids at 250 m Resolution.” Presented at the 19th AGILE Conference on Geographic Information Science, Helsinki, Finland, June 14–17.
   Google Scholar
• Freire, S., M. Schiavina, A. J. Florczyk, K. MacManus, M. Pesaresi, C. Corbane, O. Borkovska, et al. 2020. “Enhanced Data and Methods for Improving Open and Free Global Population Grids: Putting ‘Leaving No One Behind’ Into Practice.” International Journal of Digital Earth 13 (1): 61–77. doi:10.1080/17538947.2018.1548656.
   Web of Science ®Google Scholar
• Frye, C., E. Nordstrand, D. J. Wright, C. Terborgh, and J. Foust. 2018. “Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement.” Data Science Journal 17 (20): 1–12. doi:10.5334/dsj-2018-020.
   Google Scholar
• Gallego, F. J. 2010. “A Population Density Grid of the European Union.” Population and Environment 31 (6): 460–473. doi:10.1007/s11111-010-0108-y.
   Web of Science ®Google Scholar
• Gaughan, A. E., T. Oda, A. Sorichetta, F. R. Stevens, M. Bondarenko, R. Bun, L. Krauser, G. Yetman, and S. V. Nghiem. 2019. “Evaluating Nighttime Lights and Population Distribution as Proxies for Mapping Anthropogenic CO2 Emission in Vietnam, Cambodia and Laos.” Environmental Research Communications 1 (9): 091006. doi:10.1088/2515-7620/ab3d91.
   Google Scholar
• Ge, Y., Y. Jin, A. Stein, Y. H. Chen, J. H. Wang, J. F. Wang, Q. M. Cheng, H. X. Bai, M. X. Liu, and P. M. Atkinson. 2019. “Principles and Methods of Scaling Geospatial Earth Science Data.” Earth-Science Reviews 197: 102897. doi:10.1016/j.earscirev.2019.102897.
   Web of Science ®Google Scholar
• Gervasoni, L., S. Fenet, R. Perrier, and P. Sturm. 2018. “Convolutional Neural Networks for Disaggregated Population Mapping Using Open Data.” IEEE 5th International Conference on Data Science and Advanced Analytics, October, 594–603.
   Google Scholar
• Goodchild, M. F., L. Anselin, and U. Deichmann. 1993. “A Framework for the Areal Interpolation of Socioeconomic Data.” Environment and Planning A: Economy and Space 25 (3): 383–397. doi:10.1068/a250383.
   Web of Science ®Google Scholar
• Goodchild, M. F., and N. S. Lam. 1980. “Areal Interpolation: A Variant of the Traditional Spatial Problem.” Geo-Processing 1: 297–312.
   Google Scholar
• Gregory, I. N. 2002. “The Accuracy of Areal Interpolation Techniques: Standardising 19th and 20th Century Census Data to Allow Long-Term Comparisons.” Computers, Environment and Urban Systems 26 (4): 293–314. doi:10.1016/S0198-9715(01)00013-8.
   Google Scholar
• Grippa, T., S. Georganos, S. Vanhuysse, M. Lennert, N. Mboga, and É Wolff. 2019. “Mapping Slums and Model Population Density Using Earth Observation Data and Open Source Solutions.” 2019 Joint Urban Remote Sensing Event (JURSE), Vannes, France, May 22–24. IEEE.
   Google Scholar
• Hallisey, E., E. Tai, A. Berens, G. Wilt, L. Peipins, B. Lewis, S. Graham, B. Flanagan, and N. B. Lunsford. 2017. “Transforming Geographic Scale: A Comparison of Combined Population and Areal Weighting to Other Interpolation Methods.” International Journal of Health Geographics 16 (1): 29. doi:10.1186/s12942-017-0102-z.
   PubMedGoogle Scholar
• Hay, S. I., A. M. Noor, A. Nelson, and A. J. Tatem. 2005. “The Accuracy of Human Population Maps for Public Health Application.” Tropical Medicine and International Health 10 (10): 1073–1086. doi:10.1111/j.1365-3156.2005.01487.x.
   PubMed Web of Science ®Google Scholar
• Holt, J. B., C. P. Lo, and T. W. Hodler. 2004. “Dasymetric Estimation of Population Density and Areal Interpolation of Census Data.” Cartography and Geographic Information Science 31 (2): 103–121. doi:10.1559/1523040041649407.
   Google Scholar
• Huang, X., C. Z. Wang, Z. L. Li, and H. Ning. 2021a. “A 100 m Population Grid in the CONUS by Disaggregating Census Data with Open-Source Microsoft Building Footprints.” Big Earth Data 5 (1): 112–133. doi:10.1080/20964471.2020.1776200.
   Google Scholar
• Huang, X., D. Zhu, F. Zhang, T. Liu, X. Li, and L. Zou. 2021b. “Sensing Population Distribution from Satellite Imagery via Deep Learning: Model Selection, Neighboring Effects, and Systematic Biases.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 5137–5151. doi:10.1109/JSTARS.2021.3076630.
   Web of Science ®Google Scholar
• Hyman, G., G. Lema, A. Nelson, and U. Deichmann. 2004. Latin America and Caribbean Population Database Documentation. Mexico: International Center for Tropical Agriculture.
   Google Scholar
• Ii, S., and F. Xiao. 2010. “A Global Shallow Water Model Using High Order Multi-Moment Constrained Finite Volume Method and Icosahedral Grid.” Journal of Computational Physics 229 (5): 1774–1796. doi:10.1016/j.jcp.2009.11.008.
   Web of Science ®Google Scholar
• James, W. H. M., N. Tejedor-Garavito, S. E. Hanspal, A. Campbell-Sutton, G. M. Hornby, C. Pezzulo, K. Nilsen, et al. 2018. “Gridded Birth and Pregnancy Datasets for Africa, Latin America and the Caribbean.” Scientific Data 5 (1): 180090. doi:10.1038/sdata.2018.90.
   PubMedGoogle Scholar
• Juran, S., P. N. Broer, S. J. Klug, R. C. Snow, E. A. Okiro, P. O. Ouma, R. W. Snow, A. J. Tatem, J. G. Meara, and V. A. Alegana. 2018. “Geospatial Mapping of Access to Timely Essential Surgery in sub-Saharan Africa.” BMJ Global Health 3 (4): e000875. doi:10.1136/bmjgh-2018-000875.
   PubMed Web of Science ®Google Scholar
• Kanter, D. R., M. H. Schwoob, W. E. Baethgen, J. E. Bervejillo, M. Carriquiry, A. Dobermann, B. Ferraro, et al. 2016. “Translating the Sustainable Development Goals Into Action: A Participatory Backcasting Approach for Developing National Agricultural Transformation Pathways.” Global Food Security 10: 71–79. doi:10.1016/j.gfs.2016.08.002.
   Google Scholar
• Karssenberg, D., and K. D. Jong. 2005. “Dynamic Environmental Modelling in GIS: 1. Modelling in Three Spatial Dimensions.” International Journal of Geographical Information Science 19 (5): 559–579. doi:10.1080/13658810500032362.
   Web of Science ®Google Scholar
• Kugler, T. A., K. Grace, D. J. Wrathall, A. de Sherbinin, D. V. Riper, C. Aubrecht, D. Comer, et al. 2019. “People and Pixels 20 Years Later: the Current Data Landscape and Research Trends Blending Population and Environmental Data.” Population and Environment 41 (2): 209–234. doi:10.1007/s11111-019-00326-5.
   Web of Science ®Google Scholar
• Kumar, P., H. Sajjad, P. K. Joshi, C. D. Elvidge, S. Rehman, B. S. Chaudhary, B. R. Tripathy, J. Singh, and G. Pipal. 2019. “Modeling the Luminous Intensity of Beijing, China Using DMSP-OLS Night-Time Lights Series Data for Estimating Population Density.” Physics and Chemistry of the Earth, Parts A/B/C 109: 31–39. doi:10.1016/j.pce.2018.06.002.
   Web of Science ®Google Scholar
• Laaribi, A., and L. Peters. 2019. GIS and the 2020 Census: Modernizing Official Statistics. Redlands, CA: Esri Press.
   Google Scholar
• Lam, N. S. 1983. “Spatial Interpolation Methods: A Review.” The American Cartographer 10 (2): 129–150. doi:10.1559/152304083783914958.
   Web of Science ®Google Scholar
• Lam, N. S., and D. A. Quattrochi. 1992. “On the Issues of Scale, Resolution, and Fractal Analysis in the Mapping Sciences*.” The Professional Geographer 44 (1): 88–98. doi:10.1111/j.0033-0124.1992.00088.x.
   Web of Science ®Google Scholar
• Langford, M. 2006. “Obtaining Population Estimates in Non-Census Reporting Zones: An Evaluation of the 3-Class Dasymetric Method.” Computers, Environment and Urban Systems 30 (2): 161–180. doi:10.1016/j.compenvurbsys.2004.07.001.
   Web of Science ®Google Scholar
• Langford, M. 2007. “Rapid Facilitation of Dasymetric-Based Population Interpolation by Means of Raster Pixel Maps.” Computers, Environment and Urban Systems 31 (1): 19–32. doi:10.1016/j.compenvurbsys.2005.07.005.
   Web of Science ®Google Scholar
• Langford, M. 2013. “An Evaluation of Small Area Population Estimation Techniques Using Open Access Ancillary Data.” Geographical Analysis 45 (3): 324–344. doi:10.1111/gean.12012.
   Web of Science ®Google Scholar
• Langford, M., D. J. Maguire, and D. J. Unwin. 1991. “The Areal Interpolation Problem: Estimating Population Using Remote Sensing in a GIS Framework.” In Handling Geographical Information: Methodology and Potential Applications, edited by I. Masser, and M. Blakemore, 55–77. London: Longman.
   Google Scholar
• Langford, M., and D. J. Unwin. 1994. “Generating and Mapping Population Density Surfaces Within a Geographical Information System.” The Cartographic Journal 31 (1): 21–26. doi:10.1179/000870494787073718.
   PubMed Web of Science ®Google Scholar
• Lawford, R. 2014. The GEOSS Water Strategy: From Observations to Decisions. Japan Aerospace Exploration Agency.
   Google Scholar
• Le Blanc, D., and R. Perez. 2008. “The Relationship between Rainfall and Human Density and its Implications for Future Water Stress in Sub-Saharan Africa.” Ecological Economics 66 (2-3): 319–336. doi:10.1016/j.ecolecon.2007.09.009.
   Web of Science ®Google Scholar
• Levin, N., C. C. M. Kyba, Q. L. Zhang, A. S. de Miguel, M. O. Román, X. Li, B. A. Portnov, et al. 2020. “Remote Sensing of Night Lights: A Review and an Outlook for the Future.” Remote Sensing of Environment 237: 111443. doi:10.1016/j.rse.2019.111443.
   Web of Science ®Google Scholar
• Leyk, S., A. E. Gaughan, S. B. Adamo, A. de Sherbinin, D. Balk, S. Freire, A. Rose, et al. 2019. “The Spatial Allocation of Population: A Review of Large-Scale Gridded Population Data Products and Their Fitness for Use.” Earth System Science Data 11 (3): 1385–1409. doi:10.5194/essd-11-1385-2019.
   Web of Science ®Google Scholar
• Li, Z. L., X. Y. Gong, J. Chen, J. Mills, S. N. Li, Z. Xu, P. Ti, and H. Wu. 2020. “Functional Requirements of Systems for Visualization of Sustainable Development Goal (SDG) Indicators.” Journal of Geovisualization and Spatial Analysis 4: 5. doi:10.1007/s41651-019-0046-x.
   Google Scholar
• Lin, J., and R. G. Cromley. 2015. “Evaluating Geo-located Twitter Data as a Control Layer for Areal Interpolation of Population.” Applied Geography 58: 41–47. doi:10.1016/j.apgeog.2015.01.006.
   Web of Science ®Google Scholar
• Linard, C., M. Gilbert, and A. J. Tatem. 2011. “Assessing the Use of Global Land Cover Data for Guiding Large Area Population Distribution Modelling.” GeoJournal 76 (5): 525–538. doi:10.1007/s10708-010-9364-8.
   PubMedGoogle Scholar
• Linard, C., and A. J. Tatem. 2012. “Large-scale Spatial Population Databases in Infectious Disease Research.” International Journal of Health Geographics 11: 7. doi:10.1186/1476-072X-11-7.
   PubMed Web of Science ®Google Scholar
• Lindstrom, E., J. Gunn, A. Fischer, A. McCurdy, L. K. Glover, K. Alverson, B. Berx, et al. 2012. “A Framework for Ocean Observing.” By the Task Team for an Integrated Framework for Sustained Ocean Observing. UNESCO 2012, IOC/INF-1284. doi:10.5270/OceanObs09-FOO.
   Google Scholar
• Liverman, D. M. 2018. “Geographic Perspectives on Development Goals: Constructive Engagements and Critical Perspectives on the MDGs and the SDGs.” Dialogues in Human Geography 8 (2): 168–185. doi:10.1177/2043820618780787.
   Web of Science ®Google Scholar
• Lloyd, C. T., H. Chamberlain, D. Kerr, G. Yetman, L. Pistolesi, F. R. Stevens, A. E. Gaughan, et al. 2019. “Global Spatio-Temporally Harmonised Datasets for Producing High-Resolution Gridded Population Distribution Datasets.” Big Earth Data 3 (2): 108–139. doi:10.1080/20964471.2019.1625151.
   PubMedGoogle Scholar
• Lloyd, C. T., A. Sorichetta, and A. J. Tatem. 2017. “High Resolution Global Gridded Data for Use in Population Studies.” Scientific Data 4 (1): 170001. doi:10.1038/sdata.2017.1.
   PubMed Web of Science ®Google Scholar
• Lu, Y. L., N. Nakicenovic, M. Visbeck, and A. S. Stevance. 2015. “Policy: Five Priorities for the UN Sustainable Development Goals.” Nature 520 (7548): 432–433. doi:10.1038/520432a.
   PubMed Web of Science ®Google Scholar
• Luck, G. W. 2007. “The Relationships between Net Primary Productivity, Human Population Density and Species Conservation.” Journal of Biogeography 34 (2): 201–212. doi:10.1111/j.1365-2699.2006.01575.x.
   Web of Science ®Google Scholar
• Lwin, K., and Y. Murayama. 2009. “A GIS Approach to Estimation of Building Population for Micro-Spatial Analysis.” Transactions in GIS 13 (4): 401–414. doi:10.1111/j.1467-9671.2009.01171.x.
   Web of Science ®Google Scholar
• Ma, Y. J., W. Xu, X. J. Zhao, and Y. Li. 2017. “Modeling the Hourly Distribution of Population at a High Spatiotemporal Resolution Using Subway Smart Card Data: A Case Study in the Central Area of Beijing.” ISPRS International Journal of Geo-Information 6 (5): 128. doi:10.3390/ijgi6050128.
   Web of Science ®Google Scholar
• McCleary, G. F., Jr. 1969. “The Dasymetric Method in the Thematic Cartography.” PhD diss., University of Wisconsin, Madison.
   Google Scholar
• Mennis, J. 2003. “Generating Surface Models of Population Using Dasymetric Mapping.” The Professional Geographer 55 (1): 31–42. doi:10.1111/0033-0124.10042.
   Web of Science ®Google Scholar
• Mennis, J. 2009. “Dasymetric Mapping for Estimating Population in Small Areas.” Geography Compass 3 (2): 727–745. doi:10.1111/j.1749-8198.2009.00220.x.
   Google Scholar
• Mennis, J., and T. Hultgren. 2006. “Intelligent Dasymetric Mapping and Its Application to Areal Interpolation.” Cartography and Geographic Information Science 33 (3): 179–194. doi:10.1559/152304006779077309.
   Google Scholar
• Meyer, W. B., and B. L. Turner. 1992. “Human Population Growth and Global Land-Use/Cover Change.” Annual Review of Ecology and Systematics 23: 39–61. doi:10.1146/annurev.es.23.110192.000351.
   Google Scholar
• Michanowicz, D. R., S. R. Williams, J. J. Buonocore, S. T. Rowland, K. E. Konschnik, S. A. Goho, and A. S. Bernstein. 2019. “Population Allocation at the Housing Unit Level: Estimates Around Underground Natural Gas Storage Wells in PA, OH, NY, WV, MI, and CA.” Environmental Health 18 (1): 58. doi:10.1186/s12940-019-0497-z.
   PubMedGoogle Scholar
• Monteiro, J., B. Martins, P. Murrieta-Flores, and J. M. Pires. 2019. “Spatial Disaggregation of Historical Census Data Leveraging Multiple Sources of Ancillary Information.” ISPRS International Journal of Geo-Information 8 (8): 327. doi:10.3390/ijgi8080327.
   Web of Science ®Google Scholar
• Monteiro, J., B. Martins, and J. M. Pires. 2018. “A Hybrid Approach for the Spatial Disaggregation of Socio-Economic Indicators.” International Journal of Data Science and Analytics 5 (2-3): 189–211. doi:10.1007/s41060-017-0080-z.
   Google Scholar
• Murdock, A. P., A. J. P. Harfoot, D. Martin, S. Cockings, and C. Hill. 2015. “OpenPopGrid: An Open Gridded Population Dataset for England and Wales.” GeoData, University of Southampton.
   Google Scholar
• Nagle, N. N., B. P. Buttenfield, S. Leyk, and S. Spielman. 2014. “Dasymetric Modeling and Uncertainty.” Annals of the Association of American Geographers 104 (1): 80–95. doi:10.1080/00045608.2013.843439.
   PubMed Web of Science ®Google Scholar
• National Bureau of Statistics. 2020. “Seventh National Census.” Accessed February 20, 2021. http://www.stats.gov.cn/ztjc/zdtjgz/zgrkpc/dqcrkpc/index.html.
   Google Scholar
• Nelson, A. 2004. “African Population Database Documentation.” Accessed March 21, 2021. http://na.unep.net/siouxfalls/globalpop/africa/Africa_index.html.
   Google Scholar
• Nicholls, R. J., R. S. J. Tol, and A. T. Vafeidis. 2008. “Global Estimates of the Impact of a Collapse of the West Antarctic Ice Sheet: An Application of FUND.” Climatic Change 91 (1-2): 171–191. doi:10.1007/s10584-008-9424-y.
   Web of Science ®Google Scholar
• Nieves, J. J., F. R. Stevens, A. E. Gaughan, C. Linard, A. Sorichetta, G. Hornby, N. N. Patel, and A. J. Tatem. 2017. “Examining the Correlates and Drivers of Human Population Distributions Across Low- and Middle-Income Countries.” Journal of The Royal Society Interface 14 (137): 20170401. doi:10.1098/rsif.2017.0401.
   PubMed Web of Science ®Google Scholar
• Palacios-Lopez, D., F. Bachofer, T. Esch, W. Heldens, A. Hirner, M. Marconcini, A. Sorichetta, et al. 2019. “New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products.” Sustainability 11 (21): 6056. doi:10.3390/su11216056.
   Web of Science ®Google Scholar
• Palacios-Lopez, D., F. Bachofer, T. Esch, M. Marconcini, K. MacManus, A. Sorichetta, J. Zeidler, S. Dech, A. J. Tatem, and P. Reinartz. 2021. “High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent.” Remote Sensing 13 (6): 1142. doi:10.3390/rs13061142.
   Web of Science ®Google Scholar
• Pereira, H. M., S. Ferrier, M. Walters, G. N. Geller, R. H. G. Jongman, R. J. Scholes, M. W. Bruford, et al. 2013. “Essential Biodiversity Variables.” Science 339 (6117): 277–278. doi:10.1126/science.1229931.
   PubMed Web of Science ®Google Scholar
• Petrov, A. 2012. “One Hundred Years of Dasymetric Mapping: Back to the Origin.” The Cartographic Journal 49 (3): 256–264. doi:10.1179/1743277412Y.0000000001.
   Web of Science ®Google Scholar
• Plag, H. P., and S. A. Jules-Plag. 2020. “A Goal-Based Approach to the Identification of Essential Transformation Variables in Support of the Implementation of the 2030 Agenda for Sustainable Development.” International Journal of Digital Earth 13 (2): 166–187. doi:10.1080/17538947.2018.1561761.
   Web of Science ®Google Scholar
• Qiu, Y., X. S. Zhao, D. Q. Fan, and S. N. Li. 2019. “Geospatial Disaggregation of Population Data in Supporting SDG Assessments: A Case Study from Deqing County, China.” ISPRS International Journal of Geo-Information 8 (8): 356. doi:10.3390/ijgi8080356.
   Web of Science ®Google Scholar
• Ranchin, T., M. Trolliet, L. Ménard, and L. Wald. 2020. “Which Variables are Essential for Renewable Energies?” International Journal of Digital Earth 13 (2): 253–261. doi:10.1080/17538947.2019.1679267.
   Web of Science ®Google Scholar
• Randall, D. A., T. D. Ringler, R. P. Heikes, P. W. Jones, and J. R. Baumgardner. 2002. “Climate Modeling with Spherical Geodesic Grids.” Computing in Science & Engineering 4 (5): 32–41. doi:10.1109/MCISE.2002.1032427.
   Web of Science ®Google Scholar
• Reyers, B., M. Stafford-Smith, K. K. Erb, R. J. Scholes, and O. Selomane. 2017. “Essential Variables Help to Focus Sustainable Development Goals Monitoring.” Current Opinion in Environmental Sustainability 26-27: 97–105. doi:10.1016/j.cosust.2017.05.003.
   Web of Science ®Google Scholar
• Rosina, K., P. Hurbánek, and M. Cebecauer. 2017. “Using OpenStreetMap to Improve Population Grids in Europe.” Cartography and Geographic Information Science 44 (2): 139–151. doi:10.1080/15230406.2016.1192487.
   Web of Science ®Google Scholar
• Sabesan, A., K. Abercrombie, A. R. Ganguly, B. Bhaduri, E. A. Bright, and P. R. Coleman. 2007. “Metrics for the Comparative Analysis of Geospatial Datasets with Applications to High-Resolution Grid-Based Population Data.” GeoJournal 69 (1-2): 81–91. doi:10.1007/s10708-007-9103-y.
   Google Scholar
• Sahr, K., D. White, and A. J. Kimerling. 2003. “Geodesic Discrete Global Grid Systems.” Cartography and Geographic Information Science 30 (2): 121–134. doi:10.1559/152304003100011090.
   Google Scholar
• Salvatore, M., F. Pozzi, E. Ataman, B. Huddleston, and M. Bloise. 2005. “Mapping Global Urban and Rural Population Distributions.” Environment and Natural Resources Series, No. 24 – FAO. Rome, Italy: Food and Agriculture Organization of the United Nations.
   Google Scholar
• Schiavina, M., M. Melchiorri, C. Corbane, A. J. Florczyk, S. Freire, M. Pesaresi, and T. Kemper. 2019. “Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data.” Sustainability 11 (20): 5674. doi:10.3390/su11205674.
   Web of Science ®Google Scholar
• Scholz, J., M. Andorfer, and M. Mittlboeck. 2013. “Spatial Accuracy Evaluation of Population Density Grid Disaggregations with Corine Landcover.” In Geographic Information Science at the Heart of Europe. Lecture Notes in Geoinformation and Cartography, edited by D. Vandenbroucke, B. Bucher, and J. Crompvoets, 267–283. Cham: Springer. doi:10.1007/978-3-319-00615-4_15.
   Google Scholar
• Schroeder, J. P. 2017. “Hybrid Areal Interpolation of Census Counts from 2000 Blocks to 2010 Geographies.” Computers, Environment and Urban Systems 62: 53–63. doi:10.1016/j.compenvurbsys.2016.10.001.
   PubMed Web of Science ®Google Scholar
• Selomane, O., B. Reyers, R. Biggs, H. Tallis, and S. Polasky. 2015. “Towards Integrated Social-Ecological Sustainability Indicators: Exploring the Contribution and Gaps in Existing Global Data.” Ecological Economics 118: 140–146. doi:10.1016/j.ecolecon.2015.07.024.
   Web of Science ®Google Scholar
• Sémécurbe, F., C. Tannier, and S. G. Roux. 2016. “Spatial Distribution of Human Population in France: Exploring the Modifiable Areal Unit Problem Using Multifractal Analysis.” Geographical Analysis 48 (3): 292–313. doi:10.1111/gean.12099.
   Web of Science ®Google Scholar
• Semenov-Tian-Shansky, B. 1928. “Russia: Territory and Population: A Perspective on the 1926 Census.” Geographical Review 18 (4): 616–640. doi:10.2307/207951.
   Google Scholar
• Shelestov, A., A. Kolotii, T. Borisova, O. Turos, G. Milinevsky, I. Gomilko, T. Bulanay, et al. 2020. “Essential Variables for Air Quality Estimation.” International Journal of Digital Earth 13 (2): 278–298. doi:10.1080/17538947.2019.1620881.
   Web of Science ®Google Scholar
• Šimbera, J. 2020. “Neighborhood Features in Geospatial Machine Learning: The Case of Population Disaggregation.” Cartography and Geographic Information Science 47 (1): 79–94. doi:10.1080/15230406.2019.1618201.
   Web of Science ®Google Scholar
• Sinha, P., A. E. Gaughan, F. R. Stevens, J. J. Nieves, A. Sorichetta, and A. J. Tatem. 2019. “Assessing the Spatial Sensitivity of a Random Forest Model: Application in Gridded Population Modeling.” Computers, Environment and Urban Systems 75: 132–145. doi:10.1016/j.compenvurbsys.2019.01.006.
   Web of Science ®Google Scholar
• Small, C., and J. E. Cohen. 2004. “Continental Physiography, Climate and the Global Distribution of Human Population.” Current Anthropology 45 (2): 269–277. doi:10.1086/382255.
   Web of Science ®Google Scholar
• Smith, A., P. D. Bates, O. Wing, C. Sampson, N. Quinn, and J. Neal. 2019. “New Estimates of Flood Exposure in Developing Countries Using High-Resolution Population Data.” Nature Communications 10 (1): 1814. doi:10.1038/s41467-019-09282-y.
   PubMed Web of Science ®Google Scholar
• Snow, R. W., C. A. Guerra, A. M. Noor, H. Y. Myint, and S. I. Hay. 2005. “The Global Distribution of Clinical Episodes of Plasmodium Falciparum Malaria.” Nature 434 (7030): 214–217. doi:10.1038/nature03342.
   PubMed Web of Science ®Google Scholar
• Sridharan, H., and F. Qiu. 2013. “A Spatially Disaggregated Areal Interpolation Model Using Light Detection and Ranging-Derived Building Volumes.” Geographical Analysis 45 (3): 238–258. doi:10.1111/gean.12010.
   Web of Science ®Google Scholar
• Stafford-Smith, M., D. Griggs, O. Gaffney, F. Ullah, B. Reyers, N. Kanie, B. Stigson, P. Shrivastava, M. Leach, and D. O’Connell. 2017. “Integration: The Key to Implementing the Sustainable Development Goals.” Sustainability Science 12 (6): 911–919. doi:10.1007/s11625-016-0383-3.
   PubMed Web of Science ®Google Scholar
• Stevens, F. R., A. E. Gaughan, C. Linard, and A. J. Tatem. 2015. “Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data.” PLoS ONE 10 (2): e0107042. doi:10.1371/journal.pone.0107042.
   PubMed Web of Science ®Google Scholar
• Su, M. D., M. C. Lin, H. I. Hsieh, B. W. Tsai, and C. H. Lin. 2010. “Multi-layer Multi-Class Dasymetric Mapping to Estimate Population Distribution.” Science of The Total Environment 408 (20): 4807–4816. doi:10.1016/j.scitotenv.2010.06.032.
   PubMed Web of Science ®Google Scholar
• Sutton, P., D. Roberts, C. Elvidge, and H. Melj. 1997. “A Comparison of Nighttime Satellite Imagery and Population Density for the Continental United States.” Photogrammetric Engineering & Remote Sensing 63 (11): 1303–1313.
   Web of Science ®Google Scholar
• Tatem, A. J. 2014. “Mapping the Denominator: Spatial Demography in the Measurement of Progress.” International Health 6 (3): 153–155. doi:10.1093/inthealth/ihu057.
   PubMed Web of Science ®Google Scholar
• Tatem, A. J. 2017. “WorldPop, Open Data for Spatial Demography.” Scientific Data 4 (1): 170004. doi:10.1038/sdata.2017.4.
   PubMed Web of Science ®Google Scholar
• Tatem, A. J., A. M. Noor, C. von Hagen, A. D. Gregorio, and S. I. Hay. 2007. “High Resolution Population Maps for Low Income Nations: Combining Land Cover and Census in East Africa.” PLoS One 2 (12): e1298. doi:10.1371/journal.pone.0001298.
   PubMed Web of Science ®Google Scholar
• Thomson, D. R., D. A. Rhoda, A. J. Tatem, and M. C. Castro. 2020. “Gridded Population Survey Sampling: A Systematic Scoping Review of the Field and Strategic Research Agenda.” International Journal of Health Geographics 19 (1): 34. doi:10.1186/s12942-020-00230-4.
   PubMed Web of Science ®Google Scholar
• Tian, Y. Z., T. X. Yue, L. F. Zhu, and N. Clinton. 2005. “Modeling Population Density Using Land Cover Data.” Ecological Modelling 189 (1-2): 72–88. doi:10.1016/j.ecolmodel.2005.03.012.
   Web of Science ®Google Scholar
• Tiecke, T. 2016. “Open Population Datasets and Open Challenges.” Accessed August 1, 2020. https://engineering.fb.com/connectivity/open-population-datasets-and-open-challenges/.
   Google Scholar
• Tiecke, T. G., X. M. Liu, A. Zhang, A. Gros, N. Li, G. Yetman, T. Kilic, et al. 2017. “Mapping the World Population One Building at a Time.” arXiv 1712.05839.
   Google Scholar
• Tobler, W. R. 1979. “Smooth Pycnophylactic Interpolation for Geographical Regions.” Journal of the American Statistical Association 74 (367): 519–530. doi:10.1080/01621459.1979.10481647.
   PubMed Web of Science ®Google Scholar
• Tobler, W., and Z. T. Chen. 1986. “A Quadtree for Global Information Storage.” Geographical Analysis 18 (4): 360–371. doi:10.1111/j.1538-4632.1986.tb00108.x.
   Web of Science ®Google Scholar
• Tobler, W., U. Deichmann, J. Gottsegen, and K. Maloy. 1997. “World Population in a Grid of Spherical Quadrilaterals.” International Journal of Population Geography 3 (3): 203–225. doi:10.1002/(SICI)1099-1220(199709)3:3<203::AID-IJPG68>3.0.CO;2-C.
   PubMedGoogle Scholar
• UN (United Nations). 2015. Transforming Our World: the 2030 Agenda for Sustainable Development. New York, NY.
   Google Scholar
• UN (United Nations). 2019. “World Population Prospects 2019: Highlights (ST/ESA/SER.A/423).” Department of Economic and Social Affairs, Population Division.
   Google Scholar
• UN (United Nations). 2020a. “Global Indicator Framework for the Sustainable Development Goals and Targets of the 2030 Agenda for Sustainable Development.” Accessed October 18, 2020. https://unstats.un.org/sdgs/indicators/indicators-list/.
   Google Scholar
• UN (United Nations). 2020b. “IAEG-SDGs Tier Classification for Global SDG Indicators.” Accessed October 18, 2020. https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/.
   Google Scholar
• UN (United Nations) World Data Forum. 2018. “Hybrid Census to Generate Spatially-Disaggregated Population Estimates.” Accessed September 6, 2021. https://unstats.un.org/unsd/undataforum/blog/hybrid-census-to-generate-spatially-disaggregated-population-estimates/.
   Google Scholar
• Ural, S., E. Hussain, and J. Shan. 2011. “Building Population Mapping with Aerial Imagery and GIS Data.” International Journal of Applied Earth Observation and Geoinformation 13 (6): 841–852. doi:10.1016/j.jag.2011.06.004.
   Web of Science ®Google Scholar
• Wak, G., M. Bangha, D. Azongo, A. Oduro, and S. Kwankye. 2017. “Data Reliability: Comparison between Census and Health and Demographic Surveillance System (HDSS) Outputs for Kassena-Nankana East and West Districts, Ghana.” Population Review 56 (1): 31–45. doi:10.1353/prv.2017.0001.
   Web of Science ®Google Scholar
• Wang, L. Y., H. Fan, and Y. K. Wang. 2019. “An Estimation of Housing Vacancy Rate Using NPP-VIIRS Night-Time Light Data and OpenStreetMap Data.” International Journal of Remote Sensing 40 (22): 8566–8588. doi:10.1080/01431161.2019.1615655.
   Web of Science ®Google Scholar
• Wardrop, N. A., W. C. Jochem, T. J. Bird, H. R. Chamberlain, D. Clarke, D. Kerr, L. Bengtsson, S. Juran, V. Seaman, and A. J. Tatem. 2018. “Spatially Disaggregated Population Estimates in the Absence of National Population and Housing Census Data.” Proceedings of the National Academy of Sciences 115 (14): 3529–3537. doi:10.1073/pnas.1715305115.
   PubMed Web of Science ®Google Scholar
• Woodcock, C. E., and A. H. Strahler. 1987. “The Factor of Scale in Remote Sensing.” Remote Sensing of Environment 21 (3): 311–332. doi:10.1016/0034-4257(87)90015-0.
   Web of Science ®Google Scholar
• Wright, J. K. 1936. “A Method of Mapping Densities of Population: With Cape Cod as an Example.” Geographical Review 26 (1): 103–110. doi:10.2307/209467.
   Google Scholar
• Wu, C. S., and A. T. Murray. 2005. “A Cokriging Method for Estimating Population Density in Urban Areas.” Computers, Environment and Urban Systems 29 (5): 558–579. doi:10.1016/j.compenvurbsys.2005.01.006.
   Google Scholar
• Wu, S. S., X. M. Qiu, and L. Wang. 2005. “Population Estimation Methods in GIS and Remote Sensing: A Review.” GIScience & Remote Sensing 42 (1): 80–96. doi:10.2747/1548-1603.42.1.80.
   Google Scholar
• Yang, X. C., T. T. Ye, N. Z. Zhao, Q. Chen, W. Z. Yue, J. G. Qi, B. Zeng, and P. Jia. 2019. “Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data.” Remote Sensing 11 (5): 574. doi:10.3390/rs11050574.
   Web of Science ®Google Scholar
• Yao, Y., X. P. Liu, X. Li, J. B. Zhang, Z. T. Liang, K. Mai, and Y. T. Zhang. 2017. “Mapping Fine-Scale Population Distributions at the Building Level by Integrating Multisource Geospatial big Data.” International Journal of Geographical Information Science 31 (6): 1220–1244. doi:10.1080/13658816.2017.1290252.
   Web of Science ®Google Scholar
• Ye, T. T., N. Z. Zhao, X. C. Yang, Z. T. Ouyang, X. P. Liu, Q. Chen, K. J. Hu, et al. 2019. “Improved Population Mapping for China Using Remotely Sensed and Points-of-Interest Data Within a Random Forests Model.” Science of The Total Environment 658: 936–946. doi:10.1016/j.scitotenv.2018.12.276.
   PubMed Web of Science ®Google Scholar
• Yu, B. L., T. Lian, Y. X. Huang, S. J. Yao, X. Y. Ye, Z. Q. Chen, C. S. Yang, and J. P. Wu. 2019. “Integration of Nighttime Light Remote Sensing Images and Taxi GPS Tracking Data for Population Surface Enhancement.” International Journal of Geographical Information Science 33 (4): 687–706. doi:10.1080/13658816.2018.1555642.
   Web of Science ®Google Scholar
• Yu, W. Y., N. A. Wardrop, R. Bain, and J. A. Wright. 2017. “Integration of Population Census and Water Point Mapping Data—A Case Study of Cambodia, Liberia and Tanzania.” International Journal of Hygiene and Environmental Health 220 (5): 888–899. doi:10.1016/j.ijheh.2017.04.006.
   PubMed Web of Science ®Google Scholar
• Yue, T. X., Y. A. Wang, J. Y. Liu, S. P. Chen, D. S. Qiu, X. Z. Deng, M. L. Liu, Y. Z. Tian, and B. P. Su. 2005. “Surface Modelling of Human Population Distribution in China.” Ecological Modelling 181 (4): 461–478. doi:10.1016/j.ecolmodel.2004.06.042.
   Web of Science ®Google Scholar
• Zhou, Y. Y., S. J. Smith, C. D. Elvidge, K. G. Zhao, A. Thomson, and M. Imhoff. 2014. “A Cluster-Based Method to Map Urban Area from DMSP/OLS Nightlights.” Remote Sensing of Environment 147: 173–185. doi:10.1016/j.rse.2014.03.004.
   Web of Science ®Google Scholar
• Zhuo, L., T. Ichinose, J. Zheng, J. Chen, P. J. Shi, and X. Li. 2009. “Modelling the Population Density of China at the Pixel Level Based on DMSP/OLS Non-Radiance-Calibrated Night-Time Light Images.” International Journal of Remote Sensing 30 (4): 1003–1018. doi:10.1080/01431160802430693.
   Web of Science ®Google Scholar