Bangladesh 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

Bangladesh 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.

Bangladesh Census Data, 2011, Admin-level 5

Folder: Census
File Name: census_merged2.shp
Source: Bureau of Statistcs, Bangladesh, provided by Steven Rubinyi.
Description: These high spatial resolution census block shapefile was attained through the Bangladesh Bureau of Statistics for 2011. The tabular census data was joined by Steven Rubinyi with some help from WorldPop team. Tabular data for blocks in sixteen sub-districts located in the eastern part were missing. These blocks have been substituted with sub-district level data.
Class: polygon
Derived Covariates:
area, buff, zones,

class       : SpatialPolygonsDataFrame 
features    : 64502 
extent      : 298698, 777684, 2278472, 2946893  (xmin, xmax, ymin, ymax)
coord. ref. : NA 
variables   : 12

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 aggregated from 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 = F) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 30

          Mean of squared residuals: 0.47
                    % Var explained: 66

Covariate Metadata

Remotely-sensed, Classified Landcover

Folder: Landcover
File Name: Extract_tif31.tif
Source: http://www.esa-landcover-cci.org/
Description: Land cover information was combined from a GlobCover 2010 coverage and fused with Landsat-derived urban/rural built area classification to construct a single land cover dataset.
Class: raster
Derived Covariates:
cls011, dst011, cls040, dst040, cls130, dst130, cls140, dst140, cls150, dst150, cls160, dst160, cls190, dst190, cls200, dst200, cls210, dst210, cls230, dst230, cls240, dst240, cls250, dst250, clsBLT, dstBLT,

class       : RasterBrick 
dimensions  : 6738, 4862, 32760156, 1  (nrow, ncol, ncell, nlayers)
resolution  : 100, 100  (x, y)
extent      : 292899, 779099, 2274775, 2948575  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=tmerc +lat_0=0 +lon_0=90 +k=0.9996 +x_0=500000 +y_0=0 +a=6377276.345 +b=6356075.41314024 +towgs84=283.7,735.9,261.1,0,0,0,0 +units=m +no_defs 
data source : D:\Working_RF\data\BGD\Landcover\Derived\landcover.tif 
names       : landcover 
min values  :        11 
max values  :       210 

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  : 6877, 5237, 36014849, 1  (nrow, ncol, ncell, nlayers)
resolution  : 100, 100  (x, y)
extent      : 279270, 802970, 2271947, 2959647  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=tmerc +lat_0=0 +lon_0=90 +k=0.9996 +x_0=500000 +y_0=0 +a=6377276.345 +b=6356075.41314024 +units=m +no_defs 
data source : D:\Working_RF\data\BGD\Lights\Derived\lights.tif 
names       :  lights 
min values  : -0.0038 
max values  :     316