examples/smooth_map.R

In mtennekes/oldtmaptools: Old Thematic Map Tools

\dontrun{
if (require(tmap)) {
    # set mode to plotting mode
    current.mode <- tmap_mode("plot")

    ####################################
    ## Already smoothed raster
    ####################################
    vol <- raster::raster(t(volcano[, ncol(volcano):1]), xmn=0, xmx=870, ymn=0, ymx=610)
    vol_smooth <- smooth_map(vol, smooth.raster = FALSE, nlevels = 10)

    tm_shape(vol_smooth$polygons) +
    	tm_fill("level", palette=terrain.colors(11), title="Elevation") +
    	tm_shape(vol_smooth$iso) +
    	tm_iso(col = "black", size = .7, fontcolor="black") +
    	tm_layout("Maunga Whau volcano (Auckland)", title.position=c("left", "bottom"),
    	    inner.margins=0) +
    	tm_legend(width=.13, position=c("right", "top"), bg.color="gray80", frame = TRUE)


    ####################################
    ## Smooth polygons
    ####################################
    data(NLD_muni)

    NLD_muni$population_dens <- as.vector(calc_densities(NLD_muni, "population"))

    qtm(NLD_muni, fill="population_dens")

    NLD_smooth <- smooth_map(NLD_muni, var = "population_dens")

    qtm(NLD_smooth$raster, style="grey")
    qtm(NLD_smooth$polygons, format="NLD")

    ####################################
    ## Smooth points
    ####################################

    # Approximate world population density as spatial points, one for each 1 million people,
    # in the following way. Each metropolitan area of x million people will be represented
    # by x dots. The remaining population per country will be represented by dots that are
    # sampled across the country.
    create_dot_per_1mln_people <- function() {
    	data(World, metro)
    	metro_eck <- set_projection(metro, projection = "eck4")

    	# aggregate metropolitan population per country
    	metro_per_country <- tapply(metro_eck$pop2010, INDEX = list(metro_eck$iso_a3), FUN=sum)
    	metro_per_country_in_World <- metro_per_country[names(metro_per_country) %in% World$iso_a3]

    	# assign to World shape
    	World$pop_metro <- 0
    	World$pop_metro[match(names(metro_per_country_in_World), World$iso_a3)] <-
    		metro_per_country_in_World

    	# define population density other than metropolitan areas
    	World$pop_est_dens_non_metro <- (World$pop_est - World$pop_metro) / World$area

    	# generate dots for metropolitan areas (1 dot = 1mln people)
    	repeats <- pmax(1, metro_eck$pop2010 %/% 1e6)
    	ids <- unlist(mapply(rep, 1:nrow(metro_eck), repeats, SIMPLIFY = FALSE))

    	metro_dots <- metro_eck[ids, ]


    	# sample population dots from non-metropolitan areas (1 dot = 1mln people)
    	World_pop <- sample_dots(World, vars="pop_est_dens_non_metro", w = 1e6,
    							 npop = 7.3e9 - nrow(metro_dots)*1e6)

    	# combine

    	c(st_geometry(World_pop), st_geometry(metro_dots))
    }

    World_1mln_dots <- create_dot_per_1mln_people()


    # dot map
    tm_shape(World_1mln_dots) + tm_dots()

    # create smooth map
    World_list <- smooth_map(World_1mln_dots, cover = World, weight=1e6)

    # plot smooth raster map
    qtm(World_list$raster, style="grey")

    # plot smooth raster map
    qtm(World, bbox="India") + qtm(World_list$iso)

    # plot kernel density map
    qtm(World_list$polygons, style="grey", format="World")

    ####################################
    ## Smooth raster
    ####################################
    data(land)

    land_smooth <- smooth_map(land, var="trees", cover.type = "smooth")

    qtm(land, raster="trees")
    qtm(land_smooth$raster)
    qtm(land_smooth$polygons, format="World", style="grey")

    # reset current mode
    tmap_mode(current.mode)
}
}