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Copy path002_Map_Cmpts_Residuals.R
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002_Map_Cmpts_Residuals.R
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#==========================#
# Map the Cmpts' Residuals
#==========================#
# if residuals exhibit spatial patterns,
# then justify further spatial modelling
install.packages("sp")
install.packages("raster")
install.packages("lattice", .Library)
install.packages("latticeExtra")
install.packages("rasterVis") # ref:https://gis.stackexchange.com/questions/342488/error-in-implementing-levelplot-in-r-for-a-categorical-raster-with-rat
install.packages("RColorBrewer")
library(raster)
library(sp)
library(lattice)
library(latticeExtra)
library(rasterVis)
library(RColorBrewer)
#---------------------#
# Rasterize data frame
#---------------------#
# ref:https://stackoverflow.com/questions/19627344/how-to-create-a-raster-from-a-data-frame-in-r
df_Cmpts_Res_wide_log_16 <- df_Cmpts_Res_wide_log %>%
filter(Year == 2016)
str(df_Cmpts_Res_wide_log_16)
# 'data.frame': 27384 obs. of 9 variables
Rst_Cmpt_Res_log_16 <- list()
df_Cmpt_Res_log_16 <- list()
NM <- paste(c("BC", "DU", "OM", "SS", "SU", "PM25"), "_Residuals_log", sep = "")
for (i in seq_along(NM)) {
df_Cmpt_Res_log_16[[i]] <- df_Cmpts_Res_wide_log_16 %>%
select(Lon, Lat, NM[i])
Rst_Cmpt_Res_log_16[[i]] <- rasterFromXYZ(df_Cmpt_Res_log_16[[i]])
}
Bk_Res_log_16 <- brick(Rst_Cmpt_Res_log_16[[1]], Rst_Cmpt_Res_log_16[[2]],
Rst_Cmpt_Res_log_16[[3]], Rst_Cmpt_Res_log_16[[5]],
Rst_Cmpt_Res_log_16[[4]], Rst_Cmpt_Res_log_16[[6]])
#class : RasterBrick
#dimensions : 186, 480, 89280, 6 (nrow, ncol, ncell, nlayers)
#resolution : 0.75, 0.75 (x, y)
#extent : -179.625, 180.375, -55.875, 83.625 (xmin, xmax, ymin, ymax)
#crs : NA
#source : memory
#names : BC_Residuals, DU_Residuals, OM_Residuals, SS_Residuals, SU_Residuals, PM25_Residuals
#min values : -3.510039, -5.210456, -2.779548, -3.233773, -2.921836, -2.457869
#max values : 4.026947, 7.231854, 3.832994, 3.050802, 4.886131, 3.515366
names(Bk_Res_log_16) <- c("BC_Residuals", "DU_Residuals",
"OM_Residuals", "SU_Residuals",
"SS_Residuals", "PM25_Residuals")
Bk_Res_log_16
#--------#
# Ticks
#--------#
tick_at_log <- c(-5, -3, -2, 0, 1, 3, 4, 7)
exp(tick_at_log)
# [1] 6.737947e-03 4.978707e-02
# [3] 1.353353e-01 1.000000e+00
# [5] 2.718282e+00 2.008554e+01
# [7] 5.459815e+01 1.096633e+03
labels_expback_total <- c(0.005, 0.05, 0.1, 1, 3, 5, 50, 1000)
#-------------#
# chose color
#-------------#
cols <- rev(brewer.pal(11, name = "RdBu"))
brewer.div <- colorRampPalette(cols, interpolate = "spline")
#------#
# plot
#------#
plt <- levelplot(Bk_Res_log_16, cuts = 499,
col.regions = brewer.div(500),
layout = c(2, 3),
colorkey = list(labels = list(labels = labels_expback_total),
width = 0.7))
plt + latticeExtra::layer(sp.polygons(WHO_map, col = "black", lwd = 0.05))
#----------#
# Quantile
#----------#
quantile(df_Cmpts_Res_wide_log$BC_Residuals_log)
# 0% 25% 50% 75% 100%
# -3.641150795 -0.656487485 -0.006686259 0.599218713 5.976576612
quantile(df_Cmpts_Res_wide_log$DU_Residuals_log)
# 0% 25% 50% 75% 100%
# -5.6810601 -0.8726389 -0.0434291 0.7836720 7.3503436
quantile(df_Cmpts_Res_wide_log$OM_Residuals_log)
# 0% 25% 50% 75% 100%
# -3.06485639 -0.58965103 0.01771825 0.55726757 4.53982242
quantile(df_Cmpts_Res_wide_log$SS_Residuals_log)
# 0% 25% 50% 75% 100%
# -3.36549056 -0.61581700 -0.03256249 0.61464714 3.25795270
quantile(df_Cmpts_Res_wide_log$SU_Residuals_log)
# 0% 25% 50% 75% 100%
# -3.16071072 -0.53046114 -0.05759097 0.51741907 4.90779859
quantile(df_Cmpts_Res_wide_log$PM25_Residuals_log)
# 0% 25% 50% 75% 100%
# -2.4578694 -0.4279401 -0.0403321 0.4013001 4.3661661
# -5, -0.5, -0.05, 0.4, 0.5, 0.5, 5, 7