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Gridded Population Maps Informed by Different Built Settlement Products

1
Geography and Geosciences, University of Louisville, Louisville, KY 40292, USA
2
CIESIN, Columbia University, Palisades, NY 10964, USA
3
WorldPop, Department Geography and Environment, University of Southampton, Southampton SO17 1B, UK
4
Flowminder Foundation, SE-11355 Stockholm, Sweden
*
Authors to whom correspondence should be addressed.
Received: 16 July 2018 / Revised: 15 August 2018 / Accepted: 27 August 2018 / Published: 4 September 2018
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. View Full-Text
Keywords: gridded population distribution; geography; built areas; remote sensing; geographic information systems; random forest; regression; binary dasymetric gridded population distribution; geography; built areas; remote sensing; geographic information systems; random forest; regression; binary dasymetric
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Reed, F.J.; Gaughan, A.E.; Stevens, F.R.; Yetman, G.; Sorichetta, A.; Tatem, A.J. Gridded Population Maps Informed by Different Built Settlement Products. Data 2018, 3, 33.

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