Myanmar 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

Myanmar 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.

Myanmar Census Data, 2014, Admin-level 3

Folder: Census
File Name: MMR_2014_Census_Admin_lvl_3_hybrid.shp
Source: Republic of the Union of Myanmar Ministry of Immigration and Population
Description: These census data were acquired in July 2015 for use in WorldPop endeavors. To correct for population counts the Sittwe, Maungdaw, and Mrauk-U districts of Rakhine state were merged and therfore their input census resolution lay between admin lvl 1 and 2. Further, the townships of Momauk and Mansi were merged into the Bhamo district, of Kachin state, and had an input census resolution of admin lvl 2. Lastly, because of incompatibilities between the shapefile admin boundary data and the township level census data, areas corresponding to the Wa self-administered regions in the Shan state were merged into districts and input into the model at admin lvl 2. All other areas were input into the model as admin lvl 3 (township) data.
Class: polygon
Derived Covariates:
area, buff, zones,

class       : SpatialPolygonsDataFrame 
features    : 316 
extent      : 102470, 1035661, 1069650, 3158647  (xmin, xmax, ymin, ymax)
coord. ref. : NA 
variables   : 10

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 = TRUE) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 11

          Mean of squared residuals: 0.25
                    % Var explained: 94

plot of chunk random_forest

Covariate Metadata

Remotely-sensed, Classified Landcover

Folder: Landcover
File Name: mmr_lc_rc_nibb_built_rurb_maj_8bit.tif
Source: GlobCover, 300m
Description: Landcover from the GlobCover 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  : 22281, 11007, 245246967, 1  (nrow, ncol, ncell, nlayers)
resolution  : 100, 100  (x, y)
extent      : 59987, 1160687, 994787, 3222887  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=tmerc +lat_0=0 +lon_0=96 +k=0.9996 +x_0=500000 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : D:\Users\jnieves\Research\Population\Data\RF\data\MMR\Landcover\Derived\landcover.tif 
names       : landcover 
min values  :         0 
max values  :       240