Skip to content
Python script (Jupyter notebook) for modeling of population densities
Jupyter Notebook Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
SRC
illustration
notebooks
.gitignore
LICENSE
README.md

README.md

Dasymetric mapping using GRASS GIS

This repository contains the computer code that supported the publication of our research[1]. It was used to create a gridded population product of the city of Dakar[2], using the Random Forest method as proposed by the WorldPop project [3].

Cite this code

Please use the following DOI for citing this code DOI

References

[1] Grippa, Taïs, Catherine Linard, Moritz Lennert, Stefanos Georganos, Nicholus Mboga, Sabine Vanhuysse, Assane Gadiaga, and Eléonore Wolff. “Improving Urban Population Distribution Models with Very-High Resolution Satellite Information.” Data 4, no. 1 (January 16, 2019): 13 https://doi.org/10.3390/data4010013.

[2] Taïs, Grippa. “Dakar Population Estimates at 100x100m Spatial Resolution - Grid Layer - Dasymetric Mapping.” Zenodo, December 24, 2018. https://doi.org/10.5281/zenodo.2525672.

[2] Stevens, Forrest R, Andrea E Gaughan, Catherine Linard, and Andrew J Tatem. “Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Other Ancillary Data.” Plos One, February 17, 2015. https://doi.org/10.1371/journal.pone.0107042.

Related code

This repository contain some pieces of code that are also available in this repository: https://github.com/tgrippa/Aggregate_polygons.

You can’t perform that action at this time.