Syria 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. (In Press). 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

Syria 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.

Projected population figures (based on 2001 census data), 2011

Folder: Census
File Name: SYR_admin3_2011POP.shp
Source: OCHA UN, 2015
Description: Level 3 administrative unit boundaries and the corresponding population projected to 2011 were attained through the United Nations Office for the Coordination of Humanitarian Affairs (UN OCHA).
Class: polygon
Derived Covariates:
area, buff, zones,

class       : SpatialPolygonsDataFrame 
features    : 272 
extent      : 183239, 800944, 3577571, 4135155  (xmin, xmax, ymin, ymax)
coord. ref. : NA 
variables   : 24

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: 12

          Mean of squared residuals: 0.37
                    % Var explained: 83

plot of chunk random_forest

Covariate Metadata

Remotely-sensed, Classified Landcover, 2000

Folder: Landcover
File Name: SYR_RF_LC_INPUT.tif
Source: Geocover 2000 (30m), http://www.mdafederal.com/geocover
Description: Land cover from GeoCover 2000 coverage fused with Landsat-derived urban/rural built area classification to construct a single land cover dataset.
Class: raster
Derived Covariates:
prp011, cls011, dst011, prp040, cls040, dst040, prp130, cls130, dst130, prp140, cls140, dst140, prp150, cls150, dst150, prp160, cls160, dst160, prp190, cls190, dst190, prp200, cls200, dst200, prp210, cls210, dst210, prp230, cls230, dst230, prp240, cls240, dst240, prp250, cls250, dst250, prpBLT, clsBLT, dstBLT,

class       : RasterBrick 
dimensions  : 5609, 6202, 34787018, 1  (nrow, ncol, ncell, nlayers)
resolution  : 100, 100  (x, y)
extent      : 190809, 811009, 3574771, 4135671  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=utm +zone=37 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : C:\WorldPop\RFmap\data\SYR\Landcover\Derived\landcover.tif 
names       : landcover 
min values  :         0 
max values  :       240