Journal
DATA
Volume 3, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/data3030033
Keywords
gridded population distribution; geography; built areas; remote sensing; geographic information systems; random forest; regression; binary dasymetric
Funding
- Bill & Melinda Gates Foundation [OPP1134076]
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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. Dataset: doi:10.5258/SOTON/WP00643 Dataset License: CC-BY-4.0
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