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.
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.
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.
area, buff, zones,
class : SpatialPolygonsDataFrame features : 64502 extent : 298698, 777684, 2278472, 2946893 (xmin, xmax, ymin, ymax) coord. ref. : NA variables : 12
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
File Name: Extract_tif31.tif
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.
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
File Name: DEFAULT: VIIRS 2012
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 : 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