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: ind_admin_l2.shp
Source: Indian Census, 2011, provided from Andrea
Description: These high spatial resolution census block data were attained through in-country partners for 2011.
area, buff, zones,
class : SpatialPolygonsDataFrame features : 5967 extent : -814591, 2126033, 765644, 3933281 (xmin, xmax, ymin, ymax) coord. ref. : NA variables : 27
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: 12 Mean of squared residuals: 0.21 % Var explained: 86
File Name: esa_2010_ind1.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 : 33085, 32664, 1080688440, 1 (nrow, ncol, ncell, nlayers) resolution : 100, 100 (x, y) extent : -927715, 2338685, 746560, 4055060 (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\IND\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 : 33085, 32664, 1080688440, 1 (nrow, ncol, ncell, nlayers) resolution : 100, 100 (x, y) extent : -927715, 2338685, 746560, 4055060 (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\IND\Lights\Derived\lights.tif names : lights min values : -0.4 max values : 2764
File Name: DEFAULT: BIO1
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 : RasterBrick dimensions : 33604, 35973, 1208836692, 1 (nrow, ncol, ncell, nlayers) resolution : 100, 100 (x, y) extent : -1111315, 2485985, 730360, 4090760 (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\IND\Temp\Derived\temp.tif names : temp min values : -216 max values : 293