WorldPop Research Group
University of Southampton
14 September 2020
The peanutButter R package allows you to produce gridded population estimates from building footprints using the "peanut butter" method. This simple approach involves estimating the average household sizes for each settlement type (e.g. urban and rural) and then spreading those estimates evenly across buildings in each settlement type using high resolution maps of building footprints that are based on recent satellite imagery (Dooley and Tatem 2020, Ecopia.AI and Maxar Technologies 2020).
The peanutButter web application is available online from the WorldPop Applications website.
Code for the peanutButter package is openly available on GitHub: https://github.com/wpgp/peanutButter.
First, start a new R session. Then, install the peanutButter R package from WorldPop on GitHub:
devtools::install_github('wpgp/peanutButter')
library(peanutButter)You may be prompted to update some of your existing R packages. This is not required unless the peanutButter installation fails. You can avoid checking for package updates by adding the argument upgrade='never'. If needed, you can update individual packages that may be responsible for any peanutButter installation errors using install.packages('package_name'). Or, you can use devtools::install_github('wpgp/peanutButter', upgrade='ask') to update all of the packages that peanutButter depends on. In R Studio, you can also update all of your R packages by clicking "Tools > Check for Package Updates".
Note: When updating multiple packages, it may be necessary to restart your R session before each installation to ensure that packages being updated are not currently loaded in your R environment.
You can list vignettes that are available using: vignette(package='peanutButter')
See the vignette for the peanutButter::jelly shiny application using: vignette('jelly', package='peanutButter')
All of the source files used by peanutButter can be downloaded country-by-country through the web application at https://apps.worldpop.org/peanutButter.
There are two data sets describing building patterns (Dooley and Tatem 2020) that were derived from building footprints (Ecopia.AI and Maxar Technologies 2020):
There are also two source datasets that provide the proportion of population in each age-sex group for every ~100 m grid cell (WorldPop et al 2018, Pezullo et al 2017, Carioli et al in prep). The age-sex source files include:
peanutButter includes an R shiny application that allows you to produce rapid-response gridded population estimates from building footprints. The peanutButter application is available on the web at https://apps.worldpop.org/peanutButter.
You can run the application locally from your R console, but you will first need to download the source files from the web application. Then, run the Shiny application locally using:
peanutButter::jelly(srcdir="c:/local_source_directory")Version 0.3.0
Minor version update on 14 September 2020 to include advanced controls for filtering out buildings based on their area and to distribute population based on building area or building count. Modified default values to match population totals for the year 2020 from the United Nations World Population Prospects (2019; medium-variant projections).
Version 0.2.1
Patch on 9 July 2020 to include building footprints from additional countries for a total of 51 countries.
Version 0.2.0
Minor version update on 3 June 2020 to change terminology from "bottom-up" to "aggregate" and from "top-down" to "disaggregate".
Version 0.1.0
Initial beta release on 27 May 2020.
The peanutButter R package was developed by the WorldPop Research Group within the Department of Geography and Environmental Science at the University of Southampton. Funding was provided by the Bill and Melinda Gates Foundation (INV-002697). Ecopia.AI and Maxar Technologies (2020) provided high resolution building footprints based on recent satellite imagery. Gridded age-sex data were provided by the WorldPop Global High Resolution Population Denominators Project led by Alessandro Sorichetta with funding from the Bill and Melinda Gates Foundation (OPP1134076). Development of the peanutButter R package was led by Doug Leasure. Claire Dooley developed the source rasters of building counts and urban/rural settlements. Maksym Bondarenko maintains WorldPop's Shiny server. Professor Andy Tatem provides oversight of the WorldPop Research Group.
Leasure DR, Dooley CA, Bondarenko M, Tatem AJ. 2020. peanutButter: An R package to produce rapid-response gridded population estimates from building footprints, version 0.3.0. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00681
You are free to redistribute and modify the peanutButter software under the terms of the GNU General Public License v3.0 (GNU GPLv3). The source code is openly available on GitHub.
The source data for each country is available from the "Source Files" button in the application and the license terms for those datasets are included in the documentation with each download.
Carioli A, Pezzulo C, Hanspal S, Hilber T, Hornby G, Kerr D, Tejedor-Garavito N, Nilsen K, Pistolesi L, Adamo S, Mills J, Nieves JJ, Chamberlain H, Bondarenko M, Lloyd C, Ves N, Koper P, Yetman G, Gaughan A, Stevens F, Linard C, James W, Sorichetta A, and Tatem AJ. In prep. Population structure by age and sex: a multi-temporal subnational perspective.
Ecopia.AI and Maxar Technologies. 2020. Digitize Africa. Ecopia.AI and Maxar Technologies.
Dooley CA, Boo G, Leasure DR, and Tatem AJ. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. WorldPop, University of Southampton. Source of building footprints: Ecopia Vector Maps Powered by Maxar Satellite Imagery (C) 2020. doi:10.5258/SOTON/WP00677
Pezzulo C, Hornby GM, Sorichetta A, Gaughan AE, Linard C, Bird TJ, Kerr D, Lloyd CT, Tatem AJ. 2017. Sub-national mapping of population pyramids and dependency ratios in Africa and Asia. Sci. Data 4:170089 doi:10.1038/sdata.2017.89
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by the Bill and Melinda Gates Foundation (OPP1134076).