Libya 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 AfriPop, AsiaPop and AmeriPop.

plot of chunk predict_density

Libya 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.

Libyan Population Projections, 2018

Folder: Census
File Name: Lby_Adm2_Mantika.shp
Source: Bureau of Census and Statistics, Libya, 2016
Description: These district level data were provided via UN OCHA and are projected population counts by age group and sex for the year 2018.
Class: polygon
Derived Covariates:
area, buff, zones,

class       : SpatialPolygonsDataFrame 
nfeatures   : 22 
extent      : -361521, 1233644, 2168095, 3688100  (xmin, xmax, ymin, ymax)
coord. ref. : NA 
nvariables  : 20

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.

[1] "Random Forest model is a merged RF model using models from:"
TUN,


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: 7

          Mean of squared residuals: 0.26
                    % Var explained: 93

plot of chunk random_forest

Covariate Metadata

Remotely-sensed, Classified Landcover

Folder: Landcover
File Name: libya_landcover_corr.tif
Source: ESA CCI Landcover (300m), 2015
Description: Land cover information was combined from the ESA CCI 300m annual thematic land cover classification dataset corresponding to the year 2015. Landcover classification was collapsed to match WorldPop methods.
Class: raster
Derived Covariates:
cls011, dte011, cls040, dte040, cls130, dte130, cls140, dte140, cls150, dte150, cls160, dte160, cls190, dte190, cls200, dte200, cls210, dte210, cls230, dte230, cls240, dte240, cls250, dte250, clsBLT, dteBLT,

class       : RasterBrick 
dimensions  : 15458, 16581, 256309098, 1  (nrow, ncol, ncell, nlayers)
resolution  : 100, 100  (x, y)
extent      : -406129, 1251971, 2157029, 3702829  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=tmerc +lat_0=0 +lon_0=18 +k=0.9996 +x_0=500000 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : F:\RF\data\LBY\Landcover\Derived\landcover.tif 
names       : landcover 
min values  :        11 
max values  :       210