CRAN version

R/huff.R

@@ -117,7 +117,7 @@ huff <- function(knownpts,
 #' # grid defined by its resolution
 #' myhuff <- huff(knownpts = spatPts, varname = "Capacite",
 #'                typefct = "exponential", span = 750, beta = 2,
-#'                resolution = 50, mask = spatMask)
+#'                resolution = 100, mask = spatMask)
 #' # Create a raster of huff values
 #' myhuffraster <- rasterHuff(x = myhuff, mask = spatMask)
 #' plot(myhuffraster)
@@ -151,12 +151,10 @@ rasterHuff <- function(x, mask = NULL){
 #' # grid defined by its resolution
 #' myhuff <- huff(knownpts = spatPts, varname = "Capacite",
 #'                typefct = "exponential", span = 750, beta = 2,
-#'                resolution = 50, mask = spatMask)
+#'                resolution = 100, mask = spatMask)
 #' # Create a raster of huff values
 #' myhuffraster <- rasterHuff(x = myhuff, mask = spatMask)
-#' # Plot Huff values nicely
-#' plotHuff(x = myhuffraster)
-#' @import sp
+#' plotHuff(myhuffraster)
 #' @import raster
 #' @export
 plotHuff <- function(x, add = FALSE){

R/reilly.R

@@ -121,7 +121,7 @@ reilly <- function(knownpts,
 #' # grid defined by its resolution
 #' myreilly <- reilly(knownpts = spatPts, varname = "Capacite",
 #'                    typefct = "exponential", span = 750, beta = 2,
-#'                    resolution = 50, mask = spatMask)
+#'                    resolution = 100, mask = spatMask)
 #' # Create a raster of reilly values
 #' myreillyraster <- rasterReilly(x = myreilly, mask = spatMask)
 #' plot(myreillyraster, col = rainbow(18))
@@ -164,7 +164,7 @@ rasterReilly <- function(x ,mask = NULL){
 #' # grid defined by its resolution
 #' myreilly <- reilly(knownpts = spatPts, varname = "Capacite",
 #'                    typefct = "exponential", span = 750, beta = 2,
-#'                    resolution = 50, mask = spatMask)
+#'                    resolution = 100, mask = spatMask)
 #' # Create a raster of reilly values
 #' myreillyraster <- rasterReilly(x = myreilly, mask = spatMask)
 #' # Plot the raster nicely

R/stewart.R

@@ -120,7 +120,7 @@ stewart <- function(knownpts,
 #' # grid defined by its resolution
 #' mystewart <- stewart(knownpts = spatPts, varname = "Capacite",
 #'                      typefct = "exponential", span = 1000, beta = 3,
-#'                      resolution = 50, mask = spatMask)
+#'                      resolution = 100, mask = spatMask)
 #' # Create a raster of potentials values
 #' mystewartraster <- rasterStewart(x = mystewart, mask = spatMask)
 #' plot(mystewartraster)
@@ -164,7 +164,7 @@ rasterStewart <- function(x, mask = NULL){
 #' # grid defined by its resolution
 #' mystewart <- stewart(knownpts = spatPts, varname = "Capacite",
 #'                      typefct = "exponential", span = 1000, beta = 3,
-#'                      resolution = 50, mask = spatMask)
+#'                      resolution = 100, mask = spatMask)
 #' # Create a raster of potentials values
 #' mystewartraster <- rasterStewart(x = mystewart, mask = spatMask)
 #' # Plot stewart potentials nicely

vignettes/SpatialPosition.R

@@ -0,0 +1,70 @@
+## ---- fig.width=5, fig.height=5------------------------------------------
+library(SpatialPosition)
+data(spatData)
+
+# Compute potentials (accessibility)
+globalAccessibility <- stewart(knownpts = spatPts, varname = "Capacite",
+                               typefct = "exponential", span = 1000, beta = 3,
+                               resolution = 50,  
+                               mask = spatMask)
+
+# Create a raster
+rasterAccessibility <- rasterStewart(x = globalAccessibility, mask = spatMask)
+
+# Plot the raster
+par(mar = c(4,2,2,1))
+
+plotStewart(x = rasterAccessibility, add = FALSE, nclass = 6)
+
+# The function returns break values
+breakValues <- plotStewart(x = rasterAccessibility, add = FALSE, nclass = 6)
+
+# Create contour lines and add them to the plot
+contLines <- rasterToContour(x = rasterAccessibility, levels = breakValues)
+plot(contLines, add = TRUE)
+plot(spatMask, add = TRUE)
+
+mtext("Global Accessibility to Public Hospitals", side = 3,cex = 1.5)
+mtext(text = "Potential nb. of beds
+      distance function: exponential, span = 1 km, beta = 3",
+      side = 1, line = 1)   
+
+
+
+## ---- fig.width=5, fig.height=5------------------------------------------
+row.names(spatPts)
+catchReilly <- reilly(knownpts = spatPts, varname = "Capacite",
+                      typefct = "exponential", span = 750, beta = 2,
+                      resolution = 50, mask = spatMask)
+
+# Create a raster
+rasterCatch <- rasterReilly(x = catchReilly, mask = spatMask)
+
+par(mar = c(4,2,2,1))
+# Plot the raster and add the points
+plotReilly(x = rasterCatch)
+plot(spatPts, pch = 20, add = TRUE)
+
+mtext("Catchment Areas of Public Hospitals", side = 3,cex = 1.5)
+mtext(text = "distance function: exponential, span = 0.75 km, beta = 2",
+      side = 1, line = 0) 
+
+## ---- fig.width=5, fig.height=5------------------------------------------
+###
+catchHuff <- huff(knownpts = spatPts, varname = "Capacite",
+                  typefct = "exponential", span = 750, beta = 2,
+                  resolution = 50, mask = spatMask)
+
+# Create a raster
+rasterCatch <- rasterHuff(x = catchHuff, mask = spatMask)
+
+# Plot the raster and add the points
+par(mar = c(4,2,2,1))
+plotHuff(x = rasterCatch)
+plot(spatPts, pch = 20, col = "red", add = TRUE)
+
+mtext("Probabilistic Catchment Areas \nof Public Hospitals", 
+      side = 3,cex = 1.5, line=-1.5)
+mtext(text = "distance function: exponential, span = 0.75 km, beta = 2",
+      side = 1, line = 0) 
+

vignettes/StewartExample.R

@@ -0,0 +1,162 @@
+## ----regionalmap, fig.width=7, fig.height=6------------------------------
+library(cartography)
+library(SpatialPosition)
+data(nuts2006)
+
+# Compute the GDP per capita variable
+nuts3.df$gdpcap <- nuts3.df$gdppps2008 * 1000000 / nuts3.df$pop2008
+
+# Discretize of the variable
+bv <- quantile(nuts3.df$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)
+
+# 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)
+
+# Create a data frame of potential GDP per capita
+pot <- data.frame(id = nuts3.df$id, 
+                  gdpcap = gdppot$OUTPUT * 1000000 / poppot$OUTPUT, 
+                  stringsAsFactors = 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 regional potential of GDP per capita
+choroLayer(spdf = nuts3.spdf, df = pot, 
+           var = "gdpcap", 
+           legend.pos = "topright",
+           breaks = bv, 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)
+
+# 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)
+
+# Transform the regularly spaced SpatialPointsDataFrame to a raster
+popras <- rasterStewart(poppot)
+gdpras <- rasterStewart(gdppot)
+
+# Compute the GDP per capita 
+ras <- gdpras * 1000000 / popras
+
+# Create a SpatialPolygonsDataFrame from the raster
+pot.spdf <- rasterToContourPoly(r = ras, 
+                                breaks = bv, 
+                                mask = nuts0.spdf)
+
+# 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(spdf = pot.spdf, df = pot.spdf@data, var = "center", 
+           legend.pos = "topright",
+           breaks = bv, col = pal, add=T, 
+           border = "grey90", 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)
+