| Title: | Plot Maps from Switzerland by Swiss Federal Statistical Office |
|---|---|
| Description: | At the Swiss Federal Statistical Office (SFSO), spatial maps of Switzerland are available free of charge as 'Cartographic bases for small-scale thematic mapping'. This package contains convenience functions to import ESRI (Environmental Systems Research Institute) shape files using the package 'sf' and to plot them easily and quickly without having to worry too much about the technical details. It contains utilities to combine multiple areas to one single polygon and to find neighbours for single regions. For any point on a map, a special locator can be used to determine to which municipality, district or canton it belongs. |
| Authors: | Andri Signorell [aut, cre], Juerg Guggenbuehl [ctb] |
| Maintainer: | Andri Signorell <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.99.3 |
| Built: | 2024-11-21 03:35:30 UTC |
| Source: | https://github.com/andrisignorell/bfsmaps |
This package contains convenience functions for plotting Switzerland maps distributed free of charge by the Swiss Federal Office of Statistics (SFSO). It uses the package 'sf' for reading and plotting ESRI (Environmental Systems Research Institute) shapefiles.
The generation of spatial images with maps normally requires several steps, which makes the handling for occasional users complex and confusing. Functions on a higher level of abstraction simplify the daily work. The purpose is to allow the user to get to the desired map as quickly and easily as possible.
The idea behind the functions is to load the specific map, assign the desired color to the regions and create the plot. The arguments are kept straightforward, what is needed is a vector with the specific ids of the regions and an equally sized vector for the colors.
There are specific functions for the most important spatial divisions in Switzerland.
Cantons can be plotted with PlotKant(), political municipalities with PlotPolg(), large regions with PlotGreg() and districts with PlotBezk().
Lakes and rivers in multiple categories can be added to existing images with AddLakes(), AddRivers() or AddWaters().
Before the maps can be drawn, a few preparations must be made:
download the maps following the link 'Swiss Federal Office of Statistics - Base maps' (below) and unzip them into a folder
declare the location as options(bfsMaps.base = "//path_to_my_maps/")
names and shortnames of the maps are stored in a file named 'maps.csv', which can be stored either in the 'bfsMaps.base' folder or alternatively in the packages installation folder.
An example file for the last map edition ('ThemaKart map boundaries - Set 2023') is included in the package and can be found in the packages .../extdata folder. If you are using a different edition, you have to adjust the file accordingly.
Andri Signorell <[email protected]>
Swiss Federal Office of Statistics - Base maps: https://www.bfs.admin.ch/bfs/de/home/statistiken/regionalstatistik/kartengrundlagen.html
Swiss Federal Office of Statistics - Spatial divisions: https://www.agvchapp.bfs.admin.ch/de/typologies/query
Official directory of towns and cities (PLZ): https://www.swisstopo.admin.ch/de/geodata/amtliche-verzeichnisse/ortschaftenverzeichnis.html
Swiss Premium Regions: https://www.priminfo.admin.ch/
# Note: # The examples can not be run without the map data being installed before! try( { # PlotKant simply tasks for the id and the color of the spatial region # labels can be directly placed PlotKant(id=c("ZH", "FR"), col=c("yellow","limegreen"), label=TRUE) PlotKant(id="GR", col="orange", label=TRUE, add=TRUE) AddLakes() title("Switzerland with some cantons") # mark the national border PlotCH(col=NA, add=TRUE, lwd=2) # The maps have all a general area and a vegetational area PlotKant(c("VS", "BE"), SetAlpha(c("yellow","limegreen"),.50), col.vf=c("yellow","limegreen"), label=TRUE) # The function returns the centroid points of the objects, which can be used # to label the plot afterwards xy <- PlotGreg(c(3,6), SetAlpha(c("plum1", "lightslateblue"),.50), col.vf=c("plum1", "lightslateblue"), labels=NA) AddLakes() BoxedText(xy$x, xy$y, labels = c("here", "there"), border=NA, col = SetAlpha("white", 0.8)) # Plot political communities PlotPolg(border="grey85" ) PlotBezk(border="grey55", add=TRUE ) PlotKant(border="black", lwd=1, add=TRUE) AddLakes() AddRivers() # Cantonal capitals points(sf::st_coordinates(GetMap("stkt.pnt")$geometry), pch=21, col="grey", bg="red") # Display vegetational area PlotCH(col="wheat3", col.vf="wheat", border="wheat3", main="CH Vegetation Area") AddRivers() AddLakes() PlotKant(col=NA, border="wheat4", add=TRUE, lwd=1) # Use extended spatial divisions (language regions) cols <- c("peachpuff2","gainsboro","honeydew3","lightgoldenrodyellow") PlotPolg(d.bfsrg$gem_id, col=cols[d.bfsrg$sprgeb_c], border="grey70", main="Language CH" ) PlotBezk(d.bfsrg$bezk_c, col=NA, border="grey40", add=TRUE) AddLakes(col="lightsteelblue1", border="lightskyblue" ) legend(x="topleft", legend=c("german", "french","italian","romanche"), bg="white", cex=0.8, fill= cols ) # Swiss premiumregions demonstrating combinations of polygons PlotCH(col="white", main="Premiumregions CH") plot(CombinePolg(id=d.bfsrg$gem_id, g=d.bfsrg$preg_c), col=c("white","olivedrab4","olivedrab3","olivedrab2"), add=TRUE) legend(x="topleft", fill=c("white","olivedrab4","olivedrab3","olivedrab2"), cex=0.8, legend=c("Region 0","Region 1","Region 2","Region 3") ) PlotKant(col=NA, border="grey40", add=TRUE) AddLakes() # Cities cols <- as.vector(sapply(c(hred, hblue, hyellow), SetAlpha, alpha=c(1, 0.7, 0.5))) old <- Mar(right=20) PlotPolg(id=d.bfsrg$gem_id, col=cols[as.numeric(d.bfsrg$gem_typ9_x)], border="grey70") AddLakes(col="grey90", border="grey50") PlotKant(add=TRUE, col=NA, border="grey30") legend(x=2854724, y=1292274, fill=cols, border=NA, box.col=NA, y.intersp=c(1,1,1, 1.1,1.05,1.05, 1.1,1.07,1.07), legend=StrTrunc(levels(d.bfsrg$gem_typ9_x), 50), xjust=0, yjust=1, cex=0.8, xpd=NA) par(mar=old) # Degree of urbanisation PlotPolg(col=SetAlpha(c(hred, hblue, hyellow), 0.8)[as.numeric(d.bfsrg$degurba_x)], main="Degree of Urbanisation 2022") PlotKant(add=TRUE, border="grey30") AddLakes(col = "grey90", border = "grey50") # get cantons' area area <- sf::st_area(GetMap("kant.map")) / 1E6 # plot cantons xy <- PlotKant(col=colorRampPalette(c("white", "steelblue"), space = "rgb")(720)[trunc(area)/10], main=expression(paste( "Cantons' area in ", km^2)) ) AddLakes(col="grey90", border="grey60") text(xy, labels=round(area,1), cex=0.7) kant.gr <- GetMap("kant.map") |> (\(.) .[.$name=="Graubünden", "geometry"])() # prepare plot plot(kant.gr, asp=1, axes=FALSE, xlab="", ylab="", main="Beautiful Grisons", col="steelblue", lwd=2) loctext <- function(x, y, text){ points(x, y, pch=15, col="lightgrey" ) text(x, y, text, adj=c(0,0.5), col="white", font=2) } # the new swiss coordinates LV95 are: x_new = x_old + 2e6, y_new = y_old + 1e6 loctext(2782783, 1185993," Davos") loctext(2761412, 1176112," Valbella") loctext(2784192, 1152424," St. Moritz") loctext(2714275, 1175027," Rabius") # Swiss metropolitan areas cols <- c("royalblue1","red","bisque3","yellow","orange","beige") # we have to prepare the background here, for some reasons... PlotCH(col="darkolivegreen1", border="grey", lwd=2, main="Swiss metropolitan areas") # require other map metr.map <- GetMap("metr.map") plot(metr.map$geometry, add=TRUE, border="grey60", col=cols) AddLakes(col="grey90", border="grey70") legend( x="topleft", legend=c("Ländliche Gemeinde", metr.map$name), fill=c("darkolivegreen1", cols), bg="white", cex=0.8, xpd=TRUE ) # We can find the neighbor cantons, here for the canton Glarus (id=8) nbs <- Neighbours(map=GetMap("kant.map"), id=8) PlotKant(id = c(8, nbs), col=c("steelblue", rep("grey80", length(nbs))), main="Find Neighbours") })# Note: # The examples can not be run without the map data being installed before! try( { # PlotKant simply tasks for the id and the color of the spatial region # labels can be directly placed PlotKant(id=c("ZH", "FR"), col=c("yellow","limegreen"), label=TRUE) PlotKant(id="GR", col="orange", label=TRUE, add=TRUE) AddLakes() title("Switzerland with some cantons") # mark the national border PlotCH(col=NA, add=TRUE, lwd=2) # The maps have all a general area and a vegetational area PlotKant(c("VS", "BE"), SetAlpha(c("yellow","limegreen"),.50), col.vf=c("yellow","limegreen"), label=TRUE) # The function returns the centroid points of the objects, which can be used # to label the plot afterwards xy <- PlotGreg(c(3,6), SetAlpha(c("plum1", "lightslateblue"),.50), col.vf=c("plum1", "lightslateblue"), labels=NA) AddLakes() BoxedText(xy$x, xy$y, labels = c("here", "there"), border=NA, col = SetAlpha("white", 0.8)) # Plot political communities PlotPolg(border="grey85" ) PlotBezk(border="grey55", add=TRUE ) PlotKant(border="black", lwd=1, add=TRUE) AddLakes() AddRivers() # Cantonal capitals points(sf::st_coordinates(GetMap("stkt.pnt")$geometry), pch=21, col="grey", bg="red") # Display vegetational area PlotCH(col="wheat3", col.vf="wheat", border="wheat3", main="CH Vegetation Area") AddRivers() AddLakes() PlotKant(col=NA, border="wheat4", add=TRUE, lwd=1) # Use extended spatial divisions (language regions) cols <- c("peachpuff2","gainsboro","honeydew3","lightgoldenrodyellow") PlotPolg(d.bfsrg$gem_id, col=cols[d.bfsrg$sprgeb_c], border="grey70", main="Language CH" ) PlotBezk(d.bfsrg$bezk_c, col=NA, border="grey40", add=TRUE) AddLakes(col="lightsteelblue1", border="lightskyblue" ) legend(x="topleft", legend=c("german", "french","italian","romanche"), bg="white", cex=0.8, fill= cols ) # Swiss premiumregions demonstrating combinations of polygons PlotCH(col="white", main="Premiumregions CH") plot(CombinePolg(id=d.bfsrg$gem_id, g=d.bfsrg$preg_c), col=c("white","olivedrab4","olivedrab3","olivedrab2"), add=TRUE) legend(x="topleft", fill=c("white","olivedrab4","olivedrab3","olivedrab2"), cex=0.8, legend=c("Region 0","Region 1","Region 2","Region 3") ) PlotKant(col=NA, border="grey40", add=TRUE) AddLakes() # Cities cols <- as.vector(sapply(c(hred, hblue, hyellow), SetAlpha, alpha=c(1, 0.7, 0.5))) old <- Mar(right=20) PlotPolg(id=d.bfsrg$gem_id, col=cols[as.numeric(d.bfsrg$gem_typ9_x)], border="grey70") AddLakes(col="grey90", border="grey50") PlotKant(add=TRUE, col=NA, border="grey30") legend(x=2854724, y=1292274, fill=cols, border=NA, box.col=NA, y.intersp=c(1,1,1, 1.1,1.05,1.05, 1.1,1.07,1.07), legend=StrTrunc(levels(d.bfsrg$gem_typ9_x), 50), xjust=0, yjust=1, cex=0.8, xpd=NA) par(mar=old) # Degree of urbanisation PlotPolg(col=SetAlpha(c(hred, hblue, hyellow), 0.8)[as.numeric(d.bfsrg$degurba_x)], main="Degree of Urbanisation 2022") PlotKant(add=TRUE, border="grey30") AddLakes(col = "grey90", border = "grey50") # get cantons' area area <- sf::st_area(GetMap("kant.map")) / 1E6 # plot cantons xy <- PlotKant(col=colorRampPalette(c("white", "steelblue"), space = "rgb")(720)[trunc(area)/10], main=expression(paste( "Cantons' area in ", km^2)) ) AddLakes(col="grey90", border="grey60") text(xy, labels=round(area,1), cex=0.7) kant.gr <- GetMap("kant.map") |> (\(.) .[.$name=="Graubünden", "geometry"])() # prepare plot plot(kant.gr, asp=1, axes=FALSE, xlab="", ylab="", main="Beautiful Grisons", col="steelblue", lwd=2) loctext <- function(x, y, text){ points(x, y, pch=15, col="lightgrey" ) text(x, y, text, adj=c(0,0.5), col="white", font=2) } # the new swiss coordinates LV95 are: x_new = x_old + 2e6, y_new = y_old + 1e6 loctext(2782783, 1185993," Davos") loctext(2761412, 1176112," Valbella") loctext(2784192, 1152424," St. Moritz") loctext(2714275, 1175027," Rabius") # Swiss metropolitan areas cols <- c("royalblue1","red","bisque3","yellow","orange","beige") # we have to prepare the background here, for some reasons... PlotCH(col="darkolivegreen1", border="grey", lwd=2, main="Swiss metropolitan areas") # require other map metr.map <- GetMap("metr.map") plot(metr.map$geometry, add=TRUE, border="grey60", col=cols) AddLakes(col="grey90", border="grey70") legend( x="topleft", legend=c("Ländliche Gemeinde", metr.map$name), fill=c("darkolivegreen1", cols), bg="white", cex=0.8, xpd=TRUE ) # We can find the neighbor cantons, here for the canton Glarus (id=8) nbs <- Neighbours(map=GetMap("kant.map"), id=8) PlotKant(id = c(8, nbs), col=c("steelblue", rep("grey80", length(nbs))), main="Find Neighbours") })
Add lakes and rivers to an already existing Switzerland map.
The lakes are defined in 2 categories 1 and 2, whereas category 1 contains the bigger ones, category 2 the smaller ones.
The rivers are defined in 5 categories 1:5, wheras category 1 contains the largest rivers, category 5 the smallest ones.
AddWaters(lakes = 1, rivers = 1:5, col = NULL, border = "lightskyblue3", lwd = 1, ...) AddLakes(categ = 1:2, col = "lightskyblue1", border = "lightskyblue3", lwd = 1, ...) AddRivers(categ = 1:5, col = "lightskyblue3", ...)AddWaters(lakes = 1, rivers = 1:5, col = NULL, border = "lightskyblue3", lwd = 1, ...) AddLakes(categ = 1:2, col = "lightskyblue1", border = "lightskyblue3", lwd = 1, ...) AddRivers(categ = 1:5, col = "lightskyblue3", ...)
categ |
category of the lakes ( |
lakes |
the category for the lakes |
rivers |
the category for the rivers |
col |
color of the lakes, defaults to |
border |
bordercolor of the lakes, defaults to |
lwd |
linewidth of border |
... |
the dots are passed to the plot command |
AddWaters() is a wrapper with sensible defaults. If the color is not provided it will be set to a less intense tint of the border.
Lakes are defined in the original files:
00_TOPO/K4_seenyyyymmdd/c_shp/k4seenyyyymmdd11_ch2007Poly.shp
00_TOPO/K4_seenyyyymmdd/c_shp/k4seenyyyymmdd22_ch2007Poly.shp
Rivers are defined in the files:
00_TOPO/K4_flusyyyymmdd/c_shp/k4flusyyyymmdd11_ch2007.shp
00_TOPO/K4_flusyyyymmdd/c_shp/k4flusyyyymmdd22_ch2007.shp
00_TOPO/K4_flusyyyymmdd/c_shp/k4flusyyyymmdd33_ch2007.shp
00_TOPO/K4_flusyyyymmdd/c_shp/k4flusyyyymmdd44_ch2007.shp
00_TOPO/K4_flusyyyymmdd/c_shp/k4flusyyyymmdd55_ch2007.shp
For accessing the meta data, we can simply use
lake <- GetMap("see1.map")
head(lake)
None
Andri Signorell <[email protected]
try({ PlotKant() AddWaters(lakes=1, rivers=1, border="grey") }) try({ PlotKant() AddLakes(categ=1) # adds the lakes of category 1 to the map AddRivers(categ=1:3) # adds the rivers of category 1:3 to the map })try({ PlotKant() AddWaters(lakes=1, rivers=1, border="grey") }) try({ PlotKant() AddLakes(categ=1) # adds the lakes of category 1 to the map AddRivers(categ=1:3) # adds the rivers of category 1:3 to the map })
Stamp the current plot in the lower right corner with the copyright of the BfS-maps:
"Kartengrundlage: (c) BFS, ThemaKart, 20xx"
This copyright is mandatory for all maps in public publications. The default coodinates are chosen by default in the bottomright corner of a Swiss map, but can be redefined by user.
BfSStamp(xy = NULL, year_n = getOption("bfsMaps.year", Year(Today())), txt = NULL, cex = 0.6, adj = c(1,0), ...)BfSStamp(xy = NULL, year_n = getOption("bfsMaps.year", Year(Today())), txt = NULL, cex = 0.6, adj = c(1,0), ...)
xy |
the coordinates for the text to be placed. |
year_n |
the year for the compulsory BfS copyright message. |
txt |
the text to be used. |
cex |
the character extension for the text (default is 0.6) |
adj |
one or two values in [0, 1] which specify the x (and optionally y) adjustment ('justification') of the labels, with 0 for left/bottom, 1 for right/top, and 0.5 for centered. On most devices values outside [0, 1] will also work. See below. |
... |
the dots are passed to the function |
The default value for the year can be entered as option in the .Rprofile file. bfsMaps.year=2022 would set the default to 2022.
None
Andri Signorell <[email protected]>
Stamp()
The function combines polygons to one single spatial polygon object, according to the ID vector that specifies which input polygons belong to which output polygon.
CombinePolygons(map, g) CombineKant(id, g, map = GetMap("kant.map")) CombinePolg(id, g, map = GetMap("polg.map"))CombinePolygons(map, g) CombineKant(id, g, map = GetMap("kant.map")) CombinePolg(id, g, map = GetMap("polg.map"))
map |
the map containing the regions to be combined. |
id |
the id of the cantons or communities to be aggregated. |
g |
a vector defining the assignment of the elements to the output polygons to be created. It may contain |
Returns an aggregated spatial polygons object named with the aggregated IDs values in their sorting order; see the ID values of the output object to view the order.
Juerg Guggenbuehl, Andri Signorell <[email protected]>
require(DescTools) try( { # Representation of the language areas in CH combined via cantons # by majority per canton tkt <- table(d.bfsrg$kt_c, d.bfsrg$sprgeb_x) grp <- unique(d.bfsrg$sprgeb_x)[apply(tkt, 1, which.max)] # combine and plot cantons plot(CombineKant(rownames(tkt), grp), col=SetAlpha(c(horange, hyellow, hecru), 0.8), border="grey40", main="Languages in CH") # copyright is mandatory for these SFSO maps BfSStamp() # waters make the maps more realistic ... AddLakes(col = "grey80", border = "grey40") })require(DescTools) try( { # Representation of the language areas in CH combined via cantons # by majority per canton tkt <- table(d.bfsrg$kt_c, d.bfsrg$sprgeb_x) grp <- unique(d.bfsrg$sprgeb_x)[apply(tkt, 1, which.max)] # combine and plot cantons plot(CombineKant(rownames(tkt), grp), col=SetAlpha(c(horange, hyellow, hecru), 0.8), border="grey40", main="Languages in CH") # copyright is mandatory for these SFSO maps BfSStamp() # waters make the maps more realistic ... AddLakes(col = "grey80", border = "grey40") })
The Swiss Federal Statistical Office (SFSO) produces, publishes and maintains various spatial divisions for Switzerland. A dataset for the year 2022 is part of the package.
Granularity is the community level.
data("d.bfsrg")data("d.bfsrg")
A data frame with 2148 observations on the following 27 variables.
gem_idcommunity id, a numeric vector
gemeinde_xcommunity name, factor with the names of the communities as levels (to ensure the correct order, if needed).
kt_ccanton id, numeric vector
kt_xcanton abbreviation, a factor with levels ZH BE LU UR SZ OW NW GL ZG FR SO BS BL SH AR AI SG GR AG TG TI VD VS NE GE JU
kt_bez_xa factor with levels Zuerich Bern Luzern Uri Schwyz Obwalden Nidwalden Glarus Zug Fribourg Solothurn Basel-Stadt Basel-Landschaft Schaffhausen Appenzell Ausserrhoden Appenzell Innerrhoden St. Gallen Graubuenden Aargau Thurgau Ticino Vaud Wallis Neuchatel Geneve Jura
bezk_ca numeric vector
bezk_xa factor with levels Bezirk Affoltern Bezirk Andelfingen Bezirk Buelach Bezirk Dielsdorf Bezirk Hinwil ...
greg_ca numeric vector
greg_xa factor with levels Region lemanique Espace Mittelland Nordwestschweiz Zuerich Ostschweiz Zentralschweiz Ticino
aggl_ca numeric vector
aggl_xa factor with levels keine Agglomerationsgemeinde und keine Kerngemeinde ausserhalb von Agglomerationen Winterthur Zuerich Bern
aggl_grp_ca numeric vector
aggl_grp_xa factor with levels keine Agglomerationszugehoerigkeit >= 500000 Einwohner/innen 250000 - 499999 Einwohner/innen 100000 - 249999 Einwohner/innen
stadt_char_ca numeric vector
stadt_char_xa factor with levels Laendliche Gemeinde ohne staedtischen Charakter Agglomerationskerngemeinde (Kernstadt) Agglomerationskerngemeinde (Hauptkern) Agglomerationskerngemeinde (Nebenkern) Agglomerationsguertelgemeinde Mehrfach orientierte Gemeinde Kerngemeinde ausserhalb Agglomerationen
stadtland_ca numeric vector
stadtland_xa factor with levels stadt agglo land
gem_typ9_ca numeric vector
gem_typ9_xa factor with 9 levels
gem_typ25_ca numeric vector
gem_typ25_xa factor with 25 levels, definig types of communities
degurba_ca numeric vector
degurba_xa factor with levels dense intermediate thin
sprgeb_ca numeric vector
sprgeb_xa factor with levels d f i r
preg_ca numeric vector containing the category of the premium region (0:3).
preg_xa factor with levels ZH1, ZH2, ZH3, BE1, BE2, ...
head(kt <- unique(d.bfsrg[,c("kt_c","kt_x","kt_bez_x")][order(d.bfsrg$kt_c),])) head(bezk <- unique(d.bfsrg[,c("bezk_c","bezk_x","kt_x")][order(d.bfsrg$bezk_c),]))head(kt <- unique(d.bfsrg[,c("kt_c","kt_x","kt_bez_x")][order(d.bfsrg$kt_c),])) head(bezk <- unique(d.bfsrg[,c("bezk_c","bezk_x","kt_x")][order(d.bfsrg$bezk_c),]))
Helperfunction to download the mapdata and unzip them into a user defined location.
DownloadBfSMaps(url = "https://dam-api.bfs.admin.ch/hub/api/dam/assets/21245514/master", path = paste0(path.expand("~"), "/MapData"))DownloadBfSMaps(url = "https://dam-api.bfs.admin.ch/hub/api/dam/assets/21245514/master", path = paste0(path.expand("~"), "/MapData"))
url |
the url for the data (might outdate and must then be redefined). |
path |
the path where the data are to be installed. |
It is convenient to not be forced to download the data by oneself. This function can be helpful.
the option entry is returned invisibly
Andri Signorell <[email protected]>
## Not run: DownloadBfSMaps() # enter the returned option options(bfsMaps.base="*** your map folder ***") library(bfsMaps) PlotKant("ZH", "blue") ## End(Not run)## Not run: DownloadBfSMaps() # enter the returned option options(bfsMaps.base="*** your map folder ***") library(bfsMaps) PlotKant("ZH", "blue") ## End(Not run)
GetMap directly looks up the path of a map based on a shortcut name or number, loads the map from this location and returns the object.
GetMap(name_x, basedir = getOption("bfsMaps.base", default = file.path(find.package("bfsMaps"), "extdata")), crs = 2056)GetMap(name_x, basedir = getOption("bfsMaps.base", default = file.path(find.package("bfsMaps"), "extdata")), crs = 2056)
name_x |
the name of a map, currently supported are |
basedir |
the root directory for the maps to reside. bfsMaps by default looks for the mapfiles in its install location in the extdata directory. The |
crs |
Coordinate reference system (default 2056) for the map, if not set to |
Loading the cards no longer causes performance problems and can thus be performed directly.
the map object
Andri Signorell <[email protected]>
try( { # use map containing Swiss metropolitan regions mymap <- GetMap("metr.map")$geometry PlotCH() plot(mymap, col=Pal("Helsana"), add=TRUE, border=NA) })try( { # use map containing Swiss metropolitan regions mymap <- GetMap("metr.map")$geometry PlotCH() plot(mymap, col=Pal("Helsana"), add=TRUE, border=NA) })
Abbreviations for Swiss Cantons in the correct order of BfS-ID. The motivation to define this constant is, that the ids in the official definition do not follow the alphabetic order of the canton names.
ktkt
The format is: Factor w/ 26 levels "ZH","BE","LU",..: 1 2 3 4 5 6 7 8 9 10 ...
Finding all directly adjacent neighbours of a regional unit is not trivial. For a list of regional units, this function searches for the corresponding Neighbours and returns the results as a list.
Neighbours(map, id = NULL)Neighbours(map, id = NULL)
map |
the name of the map |
id |
vector of ids for which the Neighbours are to be found. When it's left to |
A list of vectors of ids for the neighbours of each region in the map.
Andri Signorell <[email protected]>
try( { nbs <- Neighbours(GetMap("kant.map"), kt_id <- 18) PlotKant(c(kt_id, nbs), col=c("red", rep("green", length(nbs))) ) # works as well for communities and for vector of ids nbs <- Neighbours(GetMap("polg.map"), polg_id <- c(3851, 3352)) PlotPolg(c(polg_id, unlist(nbs)), col=c(rep("red", 2), rep("green", length(unlist(nbs))))) })try( { nbs <- Neighbours(GetMap("kant.map"), kt_id <- 18) PlotKant(c(kt_id, nbs), col=c("red", rep("green", length(nbs))) ) # works as well for communities and for vector of ids nbs <- Neighbours(GetMap("polg.map"), polg_id <- c(3851, 3352)) PlotPolg(c(polg_id, unlist(nbs)), col=c(rep("red", 2), rep("green", length(unlist(nbs))))) })
The function plots a map of Switzerland overlayed with different types of regions. Included are greater regions ('Grossregionen'), MS regions ('mobilité spatiale'), cantons, districts and political communities. The single regions can be given a defined color, whereas the color need not be defined for all.
The vegetational area is the spatial area where people live, excluding mountains and further uninhabitable area. The vegetational area can be drawn over an already existing map.
PlotGreg(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, col.vf = NA, border.vf = NA, labels = NULL, tmtxt = TRUE, add = FALSE, map_x = "greg.map", ...) PlotKant(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, col.vf = NA, border.vf = NA, labels = NULL, tmtxt = TRUE, add = FALSE, map_x = "kant.map", ...) PlotMSRe(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, col.vf = NA, border.vf = NA, labels = NULL, tmtxt = TRUE, add = FALSE, map_x = "msre.map", ...) PlotBezk(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, col.vf = NA, border.vf = NA, labels = NULL, tmtxt = TRUE, add = FALSE, map_x = "bezk.map", ...) PlotPolg(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, col.vf = NA, border.vf = NA, labels = NULL, tmtxt = TRUE, add = FALSE, map_x = "polg.map", ...)PlotGreg(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, col.vf = NA, border.vf = NA, labels = NULL, tmtxt = TRUE, add = FALSE, map_x = "greg.map", ...) PlotKant(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, col.vf = NA, border.vf = NA, labels = NULL, tmtxt = TRUE, add = FALSE, map_x = "kant.map", ...) PlotMSRe(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, col.vf = NA, border.vf = NA, labels = NULL, tmtxt = TRUE, add = FALSE, map_x = "msre.map", ...) PlotBezk(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, col.vf = NA, border.vf = NA, labels = NULL, tmtxt = TRUE, add = FALSE, map_x = "bezk.map", ...) PlotPolg(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, col.vf = NA, border.vf = NA, labels = NULL, tmtxt = TRUE, add = FALSE, map_x = "polg.map", ...)
id |
vector of region ids. All types of regions can be addressed via their numeric ID, cantons can additionally be identified with their abbreviation:
|
col |
vector of colors, defining the colors of the region area. |
pbg |
color for the plot background. |
main |
main title in the plot. |
border |
vector of colors for region borders. Default is |
lwd |
linewidth for region borders. |
col.vf |
vector of colors for the vegetational. If set to |
border.vf |
color of borders for the vegetational area. If set to |
labels |
optional labels to be placed in the map, by default the centroids of the map is used for that. |
tmtxt |
logical, should the copyright text be displayed. Default is |
add |
default |
map_x |
the name ot the path of a map to be used. This is convenient, if we want to plot a newer map with the logic of this function. |
... |
the dots are passed to the plot command. |
The different functions all use the same core code, but use different default maps. The default maps are named: "greg.map", "msre.map", "kant.map", "bezk.map" and "polg.map".
PlotGreg uses a map for Swiss regions (Grossregionen), as defined in greg.map@data. The regions are:
| 1 | Region lémanique |
| 2 | Espace Mittelland |
| 3 | Nordwestschweiz |
| 4 | Zürich |
| 5 | Ostschweiz |
| 6 | Zentralschweiz |
| 7 | Ticino |
The list of MS regions with names and ids can be found in d.bfsrg:unique(d.bfsrg[, c("ms_reg_c", "ms_reg_x", "ms_typ_c", "ms_typ_x")])
The abbreviations of the cantons are compiled in the variable kt. More details can be extracted fromunique(d.bfsrg[,c("kt_c", "kt_x", "kt_bez_x")])
Districts (german: 'Bezirke') are associations of communities. The district id internally consists of the canton nr (1 or 2 digits) and a 2-digits 'Bezirk-nr'. So is 'Hinwil' with the district nr '51' the 5th district in Zurich (canton '1').
The list of all districts is given in d.bfsrg: unique(d.bfsrg[, c("bezirk_c", "kt_c", "bezirk_x", "kt_x")])
The list of all political communities is given in d.bfsrg:d.bfsrg[,c("bfs_nr", "gemeinde_name_x", "kt_x")]
All the regions can also be accessd and plotted by manually loading the maps and use the generic plot function.
cant <- GetMap("kant.map")
plot(cant)
There are also dedicated maps for all regions, which contain only the coordinates of the regions' centroids. They can be accessed using according mapname with the extension .pnt, e.g. for the cantons GetMap("kant.pnt").
To simplify the description, the function returns the center coordinates. These can then be used with the function text().
A list containing x and y components which are the centroids of the plotted spatial units.
Andri Signorell <[email protected]>
# Note: # The examples can not be run without having the map data installed before! try( { # define the ids for the cantons and the according colors PlotKant(id=c("GR","ZH","VS"), col=c("lightgrey","lightblue","lightsalmon")) require(DescTools) # get some percentage values... some_p <- c(AG=0.48,AI=0.47,AR=0.4,BE=0.48,BL=0.44,BS=0.4,FR=0.48,GE=0.28,GL=0.51, GR=0.4,JU=0.61,LU=0.49,NE=0.54,NW=0.43,OW=0.58,SG=0.45,SH=0.36,SO=0.45, SZ=0.39,TG=0.47,TI=0.46,UR=0.4,VD=0.46,VS=0.45,ZG=0.41,ZH=0.41) # and a color ramp from white to hred cols <- colorRampPalette(c("white", hred))(100) PlotKant(id=names(some_p), col=FindColor(some_p, cols=cols), main="ECO in CH") ColorLegend(x="left", inset=-0.01, cols=cols, labels=formatC((seq(0, 1, .2)), digits=2, format="f"), width=12000, frame="grey", cex=0.8 ) # greater regions PlotGreg(col=colorRampPalette(c("blue", "white", "red"), space = "rgb")(7), main="Greater Regions CH") PlotGreg(id = c(2,4,7), col = c("bisque","darkolivegreen1","khaki"), main="Espace Mittelland, Zurich und Ticino") AddLakes(col="grey90", border="darkgrey") xy <- sf::st_coordinates((greg.pnt <- GetMap("greg.pnt"))$geometry[c(2,4,7)]) text(xy[,1], xy[,2], greg.pnt$name[c(2,4,7)], col="black") # plot the districts bezk.map <- GetMap("bezk.map") head(bezk.map) PlotBezk(id=311:316, col=colorRampPalette(c("red", "white", "blue"), space = "rgb")(5)) PlotBezk(id=bezk.map[[1]], col=rainbow(147), main="Districts in CH") cols <- c(y=rgb(255,247,174,max=255), o=rgb(251,208,124,max=255), v=rgb(228,201,224,max=255), b=rgb(211,230,246,max=255), g=rgb(215,233,205,max=255), r=rgb(244,182,156,max=255), p=rgb(255,248,236,max=255)) # display MS regions # start with a cantons map # start with a cantons map PlotKant(1:26,col=cols[c("g","g","o","r","v","b","y","g","y","o", "v","o","y","v","y","v","o","y","r","b", "v","y","b","r","v","b")], border="grey20", lwd=1, pbg=cols["p"], main="106 MS-Regions") # add the MS regions borders xy <- PlotMSRe(add=TRUE, border="grey60") # reoutline the cantons, as they have been overplotted in the step before PlotKant(1:26, add=TRUE, border="grey30", lwd=1) # add the waters AddLakes(1:2, col=rgb(235, 247, 253, max=255), border=rgb(0,166,235, max=255)) AddRivers(1:5, col=rgb(0, 166, 235, max=255)) # ... and finally add labels text(x=xy$x, y=xy$y, GetMap("msre.map")$id, cex=0.6) # plot political communities # plot only the first 10 elements PlotPolg(id=1:10, col=colorRampPalette(c("red", "white", "blue"), space = "rgb")(10)) # plot all communities PlotPolg(id=(map <- GetMap("polg.map"))$id, col=rainbow(nrow(map)), main="Political communities in CH") })# Note: # The examples can not be run without having the map data installed before! try( { # define the ids for the cantons and the according colors PlotKant(id=c("GR","ZH","VS"), col=c("lightgrey","lightblue","lightsalmon")) require(DescTools) # get some percentage values... some_p <- c(AG=0.48,AI=0.47,AR=0.4,BE=0.48,BL=0.44,BS=0.4,FR=0.48,GE=0.28,GL=0.51, GR=0.4,JU=0.61,LU=0.49,NE=0.54,NW=0.43,OW=0.58,SG=0.45,SH=0.36,SO=0.45, SZ=0.39,TG=0.47,TI=0.46,UR=0.4,VD=0.46,VS=0.45,ZG=0.41,ZH=0.41) # and a color ramp from white to hred cols <- colorRampPalette(c("white", hred))(100) PlotKant(id=names(some_p), col=FindColor(some_p, cols=cols), main="ECO in CH") ColorLegend(x="left", inset=-0.01, cols=cols, labels=formatC((seq(0, 1, .2)), digits=2, format="f"), width=12000, frame="grey", cex=0.8 ) # greater regions PlotGreg(col=colorRampPalette(c("blue", "white", "red"), space = "rgb")(7), main="Greater Regions CH") PlotGreg(id = c(2,4,7), col = c("bisque","darkolivegreen1","khaki"), main="Espace Mittelland, Zurich und Ticino") AddLakes(col="grey90", border="darkgrey") xy <- sf::st_coordinates((greg.pnt <- GetMap("greg.pnt"))$geometry[c(2,4,7)]) text(xy[,1], xy[,2], greg.pnt$name[c(2,4,7)], col="black") # plot the districts bezk.map <- GetMap("bezk.map") head(bezk.map) PlotBezk(id=311:316, col=colorRampPalette(c("red", "white", "blue"), space = "rgb")(5)) PlotBezk(id=bezk.map[[1]], col=rainbow(147), main="Districts in CH") cols <- c(y=rgb(255,247,174,max=255), o=rgb(251,208,124,max=255), v=rgb(228,201,224,max=255), b=rgb(211,230,246,max=255), g=rgb(215,233,205,max=255), r=rgb(244,182,156,max=255), p=rgb(255,248,236,max=255)) # display MS regions # start with a cantons map # start with a cantons map PlotKant(1:26,col=cols[c("g","g","o","r","v","b","y","g","y","o", "v","o","y","v","y","v","o","y","r","b", "v","y","b","r","v","b")], border="grey20", lwd=1, pbg=cols["p"], main="106 MS-Regions") # add the MS regions borders xy <- PlotMSRe(add=TRUE, border="grey60") # reoutline the cantons, as they have been overplotted in the step before PlotKant(1:26, add=TRUE, border="grey30", lwd=1) # add the waters AddLakes(1:2, col=rgb(235, 247, 253, max=255), border=rgb(0,166,235, max=255)) AddRivers(1:5, col=rgb(0, 166, 235, max=255)) # ... and finally add labels text(x=xy$x, y=xy$y, GetMap("msre.map")$id, cex=0.6) # plot political communities # plot only the first 10 elements PlotPolg(id=1:10, col=colorRampPalette(c("red", "white", "blue"), space = "rgb")(10)) # plot all communities PlotPolg(id=(map <- GetMap("polg.map"))$id, col=rainbow(nrow(map)), main="Political communities in CH") })
Simple map plot of Switzerland following the borders valid since 1848.
PlotCH(col = NA, main = "", col.vf = NA, border = "grey", border.vf = NA, lwd = 1, tmtxt = TRUE, add = FALSE, ...)PlotCH(col = NA, main = "", col.vf = NA, border = "grey", border.vf = NA, lwd = 1, tmtxt = TRUE, add = FALSE, ...)
col |
vector of colors, defining the colors of the cantons. |
main |
main title in the plot. |
col.vf |
defines a color for the vegetational area ("Vegetationsflaeche"). |
border |
color of map border. Default is |
border.vf |
color of borders for the vegetational area. Default is |
lwd |
linewidth for the border. Default is |
tmtxt |
logical, should the copyright text be displayed. Default is |
add |
default |
... |
the dots are passed to the plot command. |
The list of all cantons and their ids is given by d.bfsrg:
cantons <- unique(d.bfsrg[,c("kt_c", "kt_x", "kt_bez_x")])
A list containing x and y component of the centroid of the plotted spatial unit.
Andri Signorell <[email protected]>
PlotGreg, PlotKant, PlotBezk, PlotPolg, d.bfsrg
try( { PlotCH(col="lightgrey") AddLakes() # use the result to add a semitransparent label xy <- PlotCH(col.vf = "grey90", col="grey75", border="grey50", border.vf = NA) AddLakes() AddRivers() PlotCH(add=TRUE, col=NA) BoxedText(x=xy$x, y=xy$y, labels = "Visit\n Switzerland", cex=3, txt.col = "grey40", col=SetAlpha("white", 0.6), border=NA, ypad=0.5) # wawing flag ... PlotCH(col="red", main="Switzerland") sw <- 15000; xc <- 2671975; yc <- 1200600; ccol <- rgb(1,1,1,0.85) rect(xleft=xc-sw, ytop=yc-sw, xright=xc+sw, ybottom=yc+sw, col=ccol, border=NA) rect(xleft=(xc-2*sw)-sw, ytop=yc-sw, xright=(xc-2*sw)+sw, ybottom=yc+sw, col=ccol, border=NA) rect(xleft=(xc+2*sw)-sw, ytop=yc-sw, xright=(xc+2*sw)+sw, ybottom=yc+sw, col=ccol, border=NA) rect(xleft=xc-sw, ytop=(yc-2*sw)-sw, xright=xc+sw, ybottom=(yc-2*sw)+sw, col=ccol, border=NA) rect(xleft=xc-sw, ytop=(yc+2*sw)-sw, xright=xc+sw, ybottom=(yc+2*sw)+sw, col=ccol, border=NA) # using panel.first ensures that the borders are not hidden by waters PlotCH(col=NA, lwd=2, panel.first=AddLakes()) })try( { PlotCH(col="lightgrey") AddLakes() # use the result to add a semitransparent label xy <- PlotCH(col.vf = "grey90", col="grey75", border="grey50", border.vf = NA) AddLakes() AddRivers() PlotCH(add=TRUE, col=NA) BoxedText(x=xy$x, y=xy$y, labels = "Visit\n Switzerland", cex=3, txt.col = "grey40", col=SetAlpha("white", 0.6), border=NA, ypad=0.5) # wawing flag ... PlotCH(col="red", main="Switzerland") sw <- 15000; xc <- 2671975; yc <- 1200600; ccol <- rgb(1,1,1,0.85) rect(xleft=xc-sw, ytop=yc-sw, xright=xc+sw, ybottom=yc+sw, col=ccol, border=NA) rect(xleft=(xc-2*sw)-sw, ytop=yc-sw, xright=(xc-2*sw)+sw, ybottom=yc+sw, col=ccol, border=NA) rect(xleft=(xc+2*sw)-sw, ytop=yc-sw, xright=(xc+2*sw)+sw, ybottom=yc+sw, col=ccol, border=NA) rect(xleft=xc-sw, ytop=(yc-2*sw)-sw, xright=xc+sw, ybottom=(yc-2*sw)+sw, col=ccol, border=NA) rect(xleft=xc-sw, ytop=(yc+2*sw)-sw, xright=xc+sw, ybottom=(yc+2*sw)+sw, col=ccol, border=NA) # using panel.first ensures that the borders are not hidden by waters PlotCH(col=NA, lwd=2, panel.first=AddLakes()) })
Prepares the layout to plot a map and a dotplot side by side.
PlotMapDot(mar = c(5.1,4.1,0,1), oma = c(0,0,5,0), widths = c(2,0.8))PlotMapDot(mar = c(5.1,4.1,0,1), oma = c(0,0,5,0), widths = c(2,0.8))
mar |
defines the plot margins. |
oma |
defines the outer margins. We use that for displaying a title. |
widths |
a vector of values for the widths of two columns, the first for the map, the second for dotplot. Relative widths are specified with numeric values. Absolute widths (in centimetres) are specified with the lcm() function. Default is |
None
Andri Signorell <[email protected]>
require(DescTools) try( { yes_p <- c(ZH=0.465, BE=0.417, LU=0.376, UR=0.308, SZ=0.276, OW=0.273, NW=0.277, GL=0.324, ZG=0.344, FR=0.469, SO=0.352, BS=0.602, BL=0.414, SH=0.457, AR=0.325, AI=0.24, SG=0.365, GR=0.325, AG=0.347, TG=0.321, TI=0.446, VD=0.532, VS=0.329, NE=0.562, GE=0.601, JU=0.532) PlotMapDot() cols <- colorRampPalette( colors=c("red","white","green"), space ="rgb")(10) PlotKant(id=names(yes_p), col=FindColor(yes_p, cols=cols, min.x=0, max.x=1 ), main="", labels=TRUE) ColorLegend(x="left", width=10000, labels=paste(seq(0, 100, 10), "%", sep=""), cols=cols, cex=0.8, adj=c(1,0.5), frame="grey") x <- Sort(yes_p, decreasing=TRUE) opt <- DescToolsOptions(stamp=NULL) PlotDot(x, labels=gettextf("%s (%s)", names(x), Format(x, fmt="%", digits=1)), cex=0.9, xlim=c(0,1)) abline(v=0.5, col="grey") title(main="Volksinitiative 'Mehr bezahlbare Wohnungen' Abstimmung vom 09.02.2020", outer=TRUE) DescToolsOptions(opt) # reset the layout layout(1) })require(DescTools) try( { yes_p <- c(ZH=0.465, BE=0.417, LU=0.376, UR=0.308, SZ=0.276, OW=0.273, NW=0.277, GL=0.324, ZG=0.344, FR=0.469, SO=0.352, BS=0.602, BL=0.414, SH=0.457, AR=0.325, AI=0.24, SG=0.365, GR=0.325, AG=0.347, TG=0.321, TI=0.446, VD=0.532, VS=0.329, NE=0.562, GE=0.601, JU=0.532) PlotMapDot() cols <- colorRampPalette( colors=c("red","white","green"), space ="rgb")(10) PlotKant(id=names(yes_p), col=FindColor(yes_p, cols=cols, min.x=0, max.x=1 ), main="", labels=TRUE) ColorLegend(x="left", width=10000, labels=paste(seq(0, 100, 10), "%", sep=""), cols=cols, cex=0.8, adj=c(1,0.5), frame="grey") x <- Sort(yes_p, decreasing=TRUE) opt <- DescToolsOptions(stamp=NULL) PlotDot(x, labels=gettextf("%s (%s)", names(x), Format(x, fmt="%", digits=1)), cex=0.9, xlim=c(0,1)) abline(v=0.5, col="grey") title(main="Volksinitiative 'Mehr bezahlbare Wohnungen' Abstimmung vom 09.02.2020", outer=TRUE) DescToolsOptions(opt) # reset the layout layout(1) })
Plot premium regions in Switzerland.
PlotPremReg(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, labels = NULL, tmtxt = TRUE, add = FALSE, ...)PlotPremReg(id = NULL, col = NA, pbg = "white", main = "", border = "grey", lwd = 1, labels = NULL, tmtxt = TRUE, add = FALSE, ...)
id |
vector of region ids. The premium regions can be addressed via their their abbreviation:
|
col |
vector of colors, defining the colors of the region area. |
pbg |
color for the plot background. |
main |
main title in the plot. |
border |
vector of colors for region borders. Default is |
lwd |
linewidth for region borders. |
labels |
optional labels to be placed in the map, by default the centroids of the map is used for that. |
tmtxt |
logical, should the copyright text be displayed. Default is |
add |
default |
... |
the dots are passed to the plot command. |
A list containing x and y components which are the centroids of the plotted spatial units.
Andri Signorell <[email protected]>
# Note: # The examples can not be run without having the map data installed before! try( { preg_x <- sort(unique(d.bfsrg$preg_x)) PlotPremReg(id=preg_x, border="grey60", col=c("white","olivedrab4", "olivedrab3", "olivedrab2")[ StrVal(preg_x,as.numeric=T)+1], main="Prämienregionen CH") legend(x="topleft", fill=c("white","olivedrab4","olivedrab3","olivedrab2"), cex=0.8, legend=c("Region 0","Region 1","Region 2","Region 3") ) AddLakes() # plot all premium regions # find all regions d.bfsrg$preg_x <- paste0(d.bfsrg$kt_x, d.bfsrg$preg_c) preg <- unique(d.bfsrg$preg_x) cols <- c("white","darkolivegreen3", "darkolivegreen2", "darkolivegreen1") PlotPremReg(preg, cols[ZeroIfNA(StrVal(preg, as.numeric = T))+1], labels =NA) PlotKant(add=TRUE, border="grey55") AddLakes() # plot some selected premium regions PlotPremReg(c("ZH1", "GR2"), c("blue", "yellow"), labels=TRUE) PlotKant(add=TRUE, border="grey55") AddLakes() })# Note: # The examples can not be run without having the map data installed before! try( { preg_x <- sort(unique(d.bfsrg$preg_x)) PlotPremReg(id=preg_x, border="grey60", col=c("white","olivedrab4", "olivedrab3", "olivedrab2")[ StrVal(preg_x,as.numeric=T)+1], main="Prämienregionen CH") legend(x="topleft", fill=c("white","olivedrab4","olivedrab3","olivedrab2"), cex=0.8, legend=c("Region 0","Region 1","Region 2","Region 3") ) AddLakes() # plot all premium regions # find all regions d.bfsrg$preg_x <- paste0(d.bfsrg$kt_x, d.bfsrg$preg_c) preg <- unique(d.bfsrg$preg_x) cols <- c("white","darkolivegreen3", "darkolivegreen2", "darkolivegreen1") PlotPremReg(preg, cols[ZeroIfNA(StrVal(preg, as.numeric = T))+1], labels =NA) PlotKant(add=TRUE, border="grey55") AddLakes() # plot some selected premium regions PlotPremReg(c("ZH1", "GR2"), c("blue", "yellow"), labels=TRUE) PlotKant(add=TRUE, border="grey55") AddLakes() })
Locate a point in a Switzerland map and get the according community, district and canton.
SwissLocator()SwissLocator()
For each clicked and identified point the coordinates, the political community, the district and the canton will be returned.
x y bfs_nr community_x district_x kt_x
1014 536281.5 167176.3 2703 Riehen Kanton Basel-Stadt BS
1781 616565.2 268959.6 5136 Onsernone Distretto di Locarno TI
1962 690861.6 119006.1 5524 Goumoens-la-Ville District du Gros-de-Vaud VD
The result will also be stored for later use in the variable tkart$found.
Andri Signorell <[email protected]>
Loading maps and parsing their structure takes time. In order to avoid to load the maps multiple times in a session they're cached in this special environment after the first load in a session.
tkarttkart
The format is: <environment: 0x000001d443d516f8>