Gridded Population Maps Informed by Different Built Settlement Products

• Published in: Data (Basel) Vol. 3; no. 3; p. 33
• Main Authors: Reed, Fennis J, Gaughan, Andrea E, Stevens, Forrest R, Yetman, Greg, Sorichetta, Alessandro, Tatem, Andrew J
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.09.2018
• Subjects: binary dasymetric; built areas; Climate change; Construction; Data Descriptor; Datasets; geographic information systems; geography; gridded population distribution; Land settlement; Model accuracy; Population growth; Population statistics; random forest; regression; Remote sensing; Spatial distribution; United States > US; Asia; Africa; built areas; geography; regression; geographic information systems; remote sensing; gridded population distribution; random forest; binary dasymetric
• ISSN: 2306-5729; 2306-5729
• DOI: 10.3390/data3030033

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.