China 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

China 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.

China Census Data, 2010, Admin-level 4

Folder: Census
File Name: CHN_2010_wgs84.shp
Source: China CDC, acquired by Gaughan, et al. for use in AsiaPop data products.
Description: These census data are 2010 China Country Population Census Data. Required fields for map production are ADMINID and ADMINPOP.
Class: polygon
Derived Covariates:
area, buff, zones,

class       : SpatialPolygonsDataFrame 
features    : 2925 
extent      : -2579239, 2095909, 925378, 6387627  (xmin, xmax, ymin, ymax)
coord. ref. : NA 
variables   : 52

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.

Random Forest for CHN Northeast


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: 14

          Mean of squared residuals: 0.17
                    % Var explained: 95

plot of chunk random_forest

Random Forest for CHN Northwest


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: 14

          Mean of squared residuals: 0.17
                    % Var explained: 95

plot of chunk random_forest

Random Forest for CHN Southeast


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: 14

          Mean of squared residuals: 0.17
                    % Var explained: 95

plot of chunk random_forest

Random Forest for CHN Southwest


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: 14

          Mean of squared residuals: 0.17
                    % Var explained: 95

plot of chunk random_forest

Covariate Metadata

Remotely-sensed, Classified Landcover

Folder: Landcover
File Name: chn_lc_full_corr.tif
Source: MDA GeoCover Landcover Product, 30m
Description: Landcover from the Landsat-derived MDA GeoCover product, reclassified to match AfriPop coding and eventually broken down into binary classifications by aggregated land cover type (see Linard, et al., 2010 and Gaughan, et al. 2013 for category information).
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  : 56689, 73971, 4193342019, 1  (nrow, ncol, ncell, nlayers)
resolution  : 0.00083, 0.00083  (x, y)
extent      : 73, 135, 6.3, 54  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : D:\Users\jnieves\Research\Population\Data\RF\data\CHN\Landcover\Derived\landcover.tif 
names       : landcover 
min values  :         0 
max values  :       240