Nepal 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

Nepal 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.

Nepal Census Data, 2011, Admin-level 5

Folder: Census
File Name: census.shp
Source: Central Bureau of Statistics of Nepal
Description: These census data were acquired in April 2014 for use in WorldPop endeavors. Required fields for map production are ADMINID and ADMINPOP
Class: polygon
Derived Covariates:
area, buff, zones,

class       : SpatialPolygonsDataFrame 
features    : 36036 
extent      : 115788, 916842, 2918485, 3370757  (xmin, xmax, ymin, ymax)
coord. ref. : NA 
variables   : 18

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 = F) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 22

          Mean of squared residuals: 0.39
                    % Var explained: 80

plot of chunk random_forestplot of chunk random_forestplot of chunk random_forest

Covariate Metadata

Remotely-sensed, ESA Landcover, 300m

Folder: Landcover
File Name: npl_lc_rc_nibb_rurb_maj_8bit.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, 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  : 5031, 8449, 42506919, 1  (nrow, ncol, ncell, nlayers)
resolution  : 100, 100  (x, y)
extent      : 400103, 1245003, 2901369, 3404469  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=utm +zone=44 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : D:\Working_RF\data\NPL\Landcover\Derived\landcover.tif 
names       : landcover 
min values  :         0 
max values  :       240 

plot of chunk covariate_reports


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 Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) which has a unique low-light imaging capability, developed for the detection of clouds using moonlight. In addition to moonlit clouds, the OLS also detects lights from human settlements, fires, gas flares, heavily lit fishing boats, lightning and the aurora.
Class: raster
Derived Covariates:
,

class       : RasterBrick 
dimensions  : 4882, 8449, 41248018, 1  (nrow, ncol, ncell, nlayers)
resolution  : 100, 100  (x, y)
extent      : 99472, 944372, 2901169, 3389369  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=tmerc +lat_0=0 +lon_0=84 +k=0.9996 +x_0=500000 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : D:\Working_RF\data\NPL\Lights\Derived\lights.tif 
names       : lights 
min values  :  -0.31 
max values  :    215 

plot of chunk covariate_reports


WorldClim/BioClim Mean Annual Temperature 1950-2000, 30 arc-second

Folder: Temp
File Name: DEFAULT: BIO1
Source: http://www.worldclim.org/current
Description: WorldClim/BioClim 1950-2000 mean annual precipitation (BIO12) and mean annual temperature (BIO1) estimates (Hijmans et al., 2005) were downloaded, mosaicked and subset to match the extent of our land cover data for the mapping of this region.
Class: raster
Derived Covariates:
,

class       : RasterBrick 
dimensions  : 4896, 8555, 41885280, 1  (nrow, ncol, ncell, nlayers)
resolution  : 100, 100  (x, y)
extent      : 89372, 944872, 2900469, 3390069  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=tmerc +lat_0=0 +lon_0=84 +k=0.9996 +x_0=500000 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : D:\Working_RF\data\NPL\Temp\Derived\temp.tif 
names       : temp 
min values  : -194 
max values  :  259 

plot of chunk covariate_reports


WorldClim/BioClim Mean Annual Precipitation 1950-2000, 30 arc-second

Folder: Precip
File Name: DEFAULT: BIO12
Source: http://www.worldclim.org/current
Description: WorldClim/BioClim 1950-2000 mean annual precipitation (BIO12) and mean annual temperature (BIO1) estimates (Hijmans et al., 2005) were downloaded, mosaicked and subset to match the extent of our land cover data for the mapping of this region.
Class: raster
Derived Covariates:
,

class       : RasterBrick 
dimensions  : 4896, 8555, 41885280, 1  (nrow, ncol, ncell, nlayers)
resolution  : 100, 100  (x, y)
extent      : 89372, 944872, 2900469, 3390069  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=tmerc +lat_0=0 +lon_0=84 +k=0.9996 +x_0=500000 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : D:\Working_RF\data\NPL\Precip\Derived\precip.tif 
names       : precip 
min values  :    186 
max values  :   4404