Malawi Population Map Metadata Report

Prediction Weighting Layer Used in Population Redistribution

The data presented below represent the predicted number of people per ~100 m pixel as estimated using the random forest (RF) model as described in Stevens, et al. (2015). The following pages contain a description of the RF model and its covariates, their sources and any metadata collected for each covariate. The prediction weighting layer is used to dasymetrically redistribute the census counts and project counts to match estimated populations based on UN estimates for the final population maps provided by WorldPop.

Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLOS ONE, 10(2), e0107042. doi:10.1371/journal.pone.0107042

plot of chunk predict_density

Malawi Census Data and Observed Population Density

These data are the population density values used to estimate the RF model used to create the prediction weighting layer you see above. Values represent population density as measured by people per hectare and calculated from population counts within each census unit. These values are used as the dependent variable during model estimation.

Malawi Census, 2008

Folder: Census
File Name: MWI_adm3_2008_12666.shp
Source: Malawi Census, 2008, provided from Andy Tatem
Description: These high spatial resolution census block data were attained through in-country partners for 2008.
Class: polygon
Derived Covariates:
area, buff, zones,

class       : SpatialPolygonsDataFrame 
features    : 12666 
extent      : 464484, 814001, 8105168, 8964939  (xmin, xmax, ymin, ymax)
coord. ref. : NA 
variables   : 25

plot of chunk census_data

Random Forest Model and Diagnostics

These output and figures outline the estimated RF model that is used to predict the population density weighting layer. The model is fitted to the population density values for the preceding census data using covariates aggregatedfrom the ancillary data sources summarized following the model diagnostics.


Call:
 randomForest(x = x_data, y = y_data, ntree = popfit$ntree, mtry = popfit$mtry,      nodesize = length(y_data)/1000, importance = TRUE, proximity = TRUE) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 16

          Mean of squared residuals: 0.35
                    % Var explained: 83

plot of chunk random_forest

Covariate Metadata

Suomi NPP VIIRS-Derived 2012 Lights at Night, 15 arc-second

Folder: Lights
File Name: DEFAULT: VIIRS 2012
Source: http://ngdc.noaa.gov/eog/viirs/download_viirs_ntl.html
Description: These 'Lights at Night' data were derived from imagery collected by the Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) sensor. Data were collected in 2012 on moonless nights and though background noise associated with fires, gas-flares, volcanoes or aurora have not been removed it represents the best-available data for night-time light production.
Class: raster
Derived Covariates:
,

class       : RasterBrick 
dimensions  : 8613, 3567, 30722571, 1  (nrow, ncol, ncell, nlayers)
resolution  : 100, 100  (x, y)
extent      : 463703, 820403, 8103924, 8965224  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=utm +zone=36 +south +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : D:\WorldPop\data\MWI\Lights\Derived\lights.tif 
names       : lights 
min values  : -0.047 
max values  :    619