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## ----regionalmap, fig.width=7, fig.height=6-----------------------------------
library(cartography)
library(sp)
library(sf)
library(SpatialPosition)
data(nuts2006)
# Compute the GDP per capita variable
nuts3.df$gdpcap <- nuts3.df$gdppps2008 * 1000000 / nuts3.df$pop2008
# Discretize the variable
bv <- quantile(nuts3.df$gdpcap, seq(from = 0, to = 1, length.out = 9))
# Draw the map
opar <- par(mar = c(0,0,1.2,0))
# Set a color palette
pal <- carto.pal(pal1 = "wine.pal", n1 = 8)
# Draw the basemap
plot(nuts0.spdf, add = F, border = NA, bg = "#cdd2d4")
plot(world.spdf, col = "#f5f5f3ff", border = "#a9b3b4ff", add = TRUE)
# Map the regional GDP per capita
choroLayer(spdf = nuts3.spdf, df = nuts3.df,
var = "gdpcap",
legend.pos = "topright",
breaks = bv, col = pal,
border = NA,
legend.title.txt = "GDP per capita",
legend.values.rnd = -2,
add = TRUE)
plot(nuts0.spdf, add = TRUE, lwd = 0.5, border = "grey30")
plot(world.spdf, col = NA, border = "#7DA9B8", add = TRUE)
# Set a layout
layoutLayer(title = "Wealth Inequality in Europe",
sources = "Basemap: UMS RIATE, 2015 - Data: Eurostat, 2008",
author = "T. Giraud, 2015")
par(opar)
## ----regionalmappot, fig.width=7, fig.height=6--------------------------------
# Create a distance matrix between units
mat <- CreateDistMatrix(knownpts = nuts3.spdf,
unknownpts = nuts3.spdf)
# Merge the data frame and the SpatialPolygonsDataFrame
nuts3.spdf@data <- nuts3.df[match(nuts3.spdf$id, nuts3.df$id),]
# Compute the potentials of population per units
# function = exponential, beta = 2, span = 75 km
poppot <- stewart(knownpts = nuts3.spdf,
unknownpts = nuts3.spdf,
matdist = mat,
varname = "pop2008",
typefct = "exponential",
beta = 2,
span = 75000,
returnclass = "sf")
# Compute the potentials of GDP per units
# function = exponential, beta = 2, span = 75 km
gdppot <- stewart(knownpts = nuts3.spdf,
unknownpts = nuts3.spdf,
matdist = mat,
varname = "gdppps2008",
typefct = "exponential",
beta = 2,
span = 75000,
returnclass = "sf")
# Create a data frame of potential GDP per capita
pot <- data.frame(id = nuts3.df$id,
gdpcap = gdppot$OUTPUT * 1000000 / poppot$OUTPUT,
stringsAsFactors = FALSE)
# Discretize the variable
bv2 <- c(min(pot$gdpcap), bv[2:8], max(pot$gdpcap))
# Draw the map
par <- par(mar = c(0,0,1.2,0))
# Draw the basemap
plot(nuts0.spdf, add = F, border = NA, bg = "#cdd2d4")
plot(world.spdf, col = "#f5f5f3ff", border = "#a9b3b4ff", add = TRUE)
# Map the regional potential of GDP per capita
choroLayer(spdf = nuts3.spdf, df = pot,
var = "gdpcap",
legend.pos = "topright",
breaks = bv2, col = pal,
border = NA,
legend.title.txt = "Potential\nGDP per capita",
legend.values.rnd = -2, add = TRUE)
plot(nuts0.spdf, add=T, lwd = 0.5, border = "grey30")
plot(world.spdf, col = NA, border = "#7DA9B8", add=T)
# Set a text to explicit the function parameters
text(x = 6271272, y = 3743765,
labels = "Distance function:\n- type = exponential\n- beta = 2\n- span = 75 km",
cex = 0.8, adj = 0, font = 3)
# Set a layout
layoutLayer(title = "Wealth Inequality in Europe",
sources = "Basemap: UMS RIATE, 2015 - Data: Eurostat, 2008",
author = "T. Giraud, 2015")
par(opar)
## ----smoothedmappot, fig.width=7, fig.height=6--------------------------------
# Compute the potentials of population on a regular grid (50km span)
# function = exponential, beta = 2, span = 75 km
poppot <- stewart(knownpts = nuts3.spdf,
varname = "pop2008",
typefct = "exponential",
span = 75000,
beta = 2,
resolution = 50000,
mask = nuts0.spdf,
returnclass = "sf")
# Compute the potentials of GDP on a regular grid (50km span)
# function = exponential, beta = 2, span = 75 km
gdppot <- stewart(knownpts = nuts3.spdf,
varname = "gdppps2008",
typefct = "exponential",
span = 75000,
beta = 2,
resolution = 50000,
mask = nuts0.spdf,
returnclass = "sf")
# Create the ratio variable
poppot$OUTPUT2 <- gdppot$OUTPUT * 1e6 / poppot$OUTPUT
# Create an isopleth layer
pot <- isopoly(x = poppot, var = "OUTPUT2",
breaks = bv,
mask = nuts0.spdf,
returnclass = "sf")
# Get breaks values
bv3 <- sort(c(unique(pot$min), max(pot$max)), decreasing = FALSE)
# Draw the map
par <- par(mar = c(0,0,1.2,0))
# Draw the basemap
plot(nuts0.spdf, add = F, border = NA, bg = "#cdd2d4")
plot(world.spdf, col = "#f5f5f3ff", border = "#a9b3b4ff", add = TRUE)
# Map the potential GDP per Capita
choroLayer(x = pot, var = "center",
legend.pos = "topright",
breaks = bv3, col = pal, add=T,
border = NA, lwd = 0.2,
legend.title.txt = "Potential\nGDP per capita",
legend.values.rnd = -2)
plot(nuts0.spdf, add=T, lwd = 0.5, border = "grey30")
plot(world.spdf, col = NA, border = "#7DA9B8", add=T)
# Set a text to explicit the function parameters
text(x = 6271272, y = 3743765,
labels = "Distance function:\n- type = exponential\n- beta = 2\n- span = 75 km",
cex = 0.8, adj = 0, font = 3)
# Set a layout
layoutLayer(title = "Wealth Inequality in Europe",
sources = "Basemap: UMS RIATE, 2015 - Data: Eurostat, 2008",
author = "T. Giraud, 2015")
par(opar)
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