How to draw a map of arbitrary contiguous regions, or visualizing the spread of COVID-19 in the Greater Region
R
Introduction
I was able to blog during the year 2020 without mentioning the ongoing pandemic once. It’s not that I made any conscious effort not to talk about it, but I did not really want to do something that had already been done a 1000 times. This changed this year, when I wanted to look at the spread of COVID-19, not only in the Grand-Duchy of Luxembourg, the country I live in, but also among our neighbours. You see, the Grand-Duchy of Luxembourg is like an island, but instead of being surrounded by water, it’s surrounded by Belgians, Germans and Frenchmen. Many of them commute every day to Luxembourg to work, and even though they technically don’t live inside the country, many aspects of their lives happen inside Luxembourguish borders. Their children might even come to school here, and sometimes they live so close by the border, that they can catch Luxembourguish public transportation in their towns. 200k commuters from Belgium, Germany and France work here every day. That’s half our workforce! So that’s why I thought that it would make sense to look at the spread of the disease at the level of the so-called Greater Region. This Greater Region is made up of the Grand-Duchy of Luxembourg, the Provinces of Liège and Luxembourg in Belgium (hence why I keep writing the Grand-Duchy of Luxembourg to refer to the country, and the Province of Luxembourg to refer to the Belgian province of the same name), and two German Länders, the Saarland and the Rhineland-Palatinate. Confused? Welcome to Europe, where supranational institutions literally have to have a page entitled Do not get confused so that citizens don’t get lost (we still do).
So the Greater Region is not a state, but facilitates collaboration between the regions comprising it. To me, technically a citizen of the Greater Region, it feels like there was a want to peacefully correct for the randomness of history, where German-speaking regions ended up in both France and Belgium, and where Belgium and Luxembourg, well, somehow became independent countries.
Anyways, what I wanted to do was to first of all get the COVID-19 daily cases data for each of these
regions. I did that, and even created a package called {covidGrandeRegion} hosted
here that makes it very easy to download the
latest data for the Greater Region. I will write another blog post about it, I have something
in mind that I wanted to try for some time, and this was the first step.
Then I thought that adding a function that would create a map could also be nice. And this is
where the technical aspect of this blog post starts.
The problems to map the Greater Region
So how do you draw a map for an arbitrary landmass like the Greater Region? I wanted to draw the
maps using {echarts4r}, and there’s a very easy guide you can read.
If you want to draw a map for one, or several, countries, this guide is all you need. But I wanted
a map with only parts of France, Belgium and Germany. The only complete country was Luxembourg.
So the first problem was how to get only parts of a country. The second problem, is that I had
daily covid cases for the lowest administrative levels for France (which are Départements),
Belgium (the Provinces) and Germany (Land- and Stadtkreise). But for the Grand-Duchy of Luxembourg,
there’s only data at the level of the country. So this would be another problem. How to draw a map
with unequal levels of precision?
One final problem: the names of the administrative divisions in my covid datasets are not the same
than the ones that get downloaded if you follow the guide I linked before. So I had to rename
them as well.
The solutions
Let’s first start by following the guide, so loading the packages, and getting the maps I need:
library(echarts4r)
library(sp)
library(raster)
library(geojsonio)france_dep <- getData("GADM", country = "FRANCE", level = 2)
ger_kreise <- getData("GADM", country = "GERMANY", level = 2)
be_province <- getData("GADM", country = "BELGIUM", level = 2)The above lines of code load the required packages, and download the maps for France, Belgium and Germany with the required administrative level I need. I’ll leave Luxembourg for last.
Let’s take a look at what type of object we’re dealing with:
class(france_dep)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"So it seems to be something like a data frame, but probably more complex. Looking for some help online, I saw that you can coerce it to a data frame:
as.data.frame(be_province)## GID_0 NAME_0 GID_1 NAME_1 NL_NAME_1 GID_2 NAME_2
## 1 BEL Belgium BEL.1_1 Bruxelles <NA> BEL.1.1_1 Bruxelles
## 2 BEL Belgium BEL.2_1 Vlaanderen <NA> BEL.2.1_1 Antwerpen
## 3 BEL Belgium BEL.2_1 Vlaanderen <NA> BEL.2.2_1 Limburg
## 4 BEL Belgium BEL.2_1 Vlaanderen <NA> BEL.2.3_1 Oost-Vlaanderen
## 5 BEL Belgium BEL.2_1 Vlaanderen <NA> BEL.2.4_1 Vlaams Brabant
## 6 BEL Belgium BEL.2_1 Vlaanderen <NA> BEL.2.5_1 West-Vlaanderen
## 7 BEL Belgium BEL.3_1 Wallonie <NA> BEL.3.1_1 Brabant Wallon
## 8 BEL Belgium BEL.3_1 Wallonie <NA> BEL.3.2_1 Hainaut
## 9 BEL Belgium BEL.3_1 Wallonie <NA> BEL.3.3_1 Liège
## 10 BEL Belgium BEL.3_1 Wallonie <NA> BEL.3.4_1 Luxembourg
## 11 BEL Belgium BEL.3_1 Wallonie <NA> BEL.3.5_1 Namur
## VARNAME_2
## 1 Brussel Hoofstadt|Brusselse Hoofdstedelijke Gewest|Brüssel|Bruxelas|Région de Bruxelles-Capitale|Brussels|Bruselas
## 2 Amberes|Antuérpia|Antwerp|Anvers|Anversa
## 3 Limbourg|Limburgo
## 4 Flandres Oriental|Fiandra Orientale|Flandes Oriental|Flandre orientale|East Flanders|Ost Flandern
## 5 Brabant Flamand|Brabante Flamenco|Brabante Flamengo|Flemish Brabant
## 6 Fiandra Occidentale|Flandes Occidental|Flandre occidentale|Flandres Ocidental|West Flandern|West Flanders
## 7 Waals Brabant|Walloon Brabant
## 8 Henegouwen|Hennegau
## 9 Luik|Liegi|Lieja|Lüttich
## 10 Lussemburgo|Luxemburg|Luxemburgo
## 11 Namen
## NL_NAME_2 TYPE_2 ENGTYPE_2 CC_2 HASC_2
## 1 <NA> Hoofdstedelijk Gewest|Région Capitale Capital Region <NA> BE.BU
## 2 <NA> Provincie Province <NA> BE.AN
## 3 <NA> Provincie Province <NA> BE.LI
## 4 <NA> Provincie Province <NA> BE.OV
## 5 <NA> Provincie Province <NA> BE.VB
## 6 <NA> Provincie Province <NA> BE.WV
## 7 <NA> Province Provincie <NA> BE.BW
## 8 <NA> Province Provincie <NA> BE.HT
## 9 <NA> Province Provincie <NA> BE.LG
## 10 <NA> Province Provincie <NA> BE.LX
## 11 <NA> Province Provincie <NA> BE.NAWe’re not going to convert them to data frames however; but this is an interesting clue; these SpatialPolygonsDataFrame
objects share common methods with data frames. What this means is that we can use the usual,
base R way of manipulating these objects.
So to get only the French départements I need, I can slice them like so:
lorraine <- france_dep[`%in%`(france_dep$NAME_2, c("Meurthe-et-Moselle", "Meuse", "Moselle", "Vosges")),]Same for the German kreise, here I select the Länder which are a higher administrative division than the Kreise, which makes it faster (so I don’t need to type all the 40+ Kreise):
ger_kreise <- ger_kreise[`%in%`(ger_kreise$NAME_1, c("Rheinland-Pfalz", "Saarland")),]For Germany, many Kreise had a name which was different than on my covid data, so I had to rename them. So here again, the base R way of doing things works:
ger_kreise$NAME_2[ger_kreise$NAME_2 == "Eifelkreis Bitburg-Prüm"] <- "Bitburg-Prüm"
ger_kreise$NAME_2[ger_kreise$NAME_2 == "St. Wendel"] <- "Sankt Wendel"
ger_kreise$NAME_2[ger_kreise$NAME_2 == "Altenkirchen (Westerwald)"] <- "Altenkirchen"
ger_kreise$NAME_2[ger_kreise$NAME_2 == "Neustadt an der Weinstraße"] <- "Neustadt a.d.Weinstraße"
ger_kreise$NAME_2[ger_kreise$NAME_2 == "Landau in der Pfalz"] <- "Landau i.d.Pfalz"
ger_kreise$NAME_2[ger_kreise$NAME_2 == "Ludwigshafen am Rhein"] <- "Ludwigshafen"
ger_kreise$NAME_2[ger_kreise$NAME_2 == "Frankenthal (Pfalz)"] <- "Frankenthal"Finally, I do the same for Belgium, and rename their province of Luxembourg, which was simply called “Luxembourg”, to “Province de Luxembourg”:
be_wallonia <- be_province[be_province$NAME_1 == "Wallonie", ]
be_wallonia$NAME_2[be_wallonia$NAME_2 == "Luxembourg"] <- "Province de Luxembourg"I rename the province because the Grand-Duchy of Luxembourg is also only called “Luxembourg” in the data, and this would cause issues when mapping.
Now, comes Luxembourg. As I’ve written above, I only have data at the level of the country, so I download the country map:
lu_map_0 <- getData("GADM", country = "LUXEMBOURG", level = 0)Let’s also see how it looks like as a data frame:
as.data.frame(lu_map_0)## GID_0 NAME_0
## 1 LUX LuxembourgUnlike the previous SpatialPolygonsDataFrames, there are much less columns and this will cause
an issue. Indeed, in order to have a single SpatialPolygonsDataFrame object to draw my map,
I will need to combine them. This will be very easy, by simple using the rbind() function.
Again, simply using base R functions. However, this only works if the data frames have the same
columns. Another issue, is that I will be using the names of the regions which are in the SpatialPolygonsDataFrames’
column called NAME_2, but for Luxembourg, the name of the region (in this case the whole country)
is in the column called NAME_0. So I need to add this columns to the SpatialPolygonsDataFrame
object for Luxembourg:
lu_map_0$GID_1 <- NA
lu_map_0$NAME_1 <- NA
lu_map_0$NL_NAME_1 <- NA
lu_map_0$GID_2 <- NA
lu_map_0$NAME_2 <- "Luxembourg"
lu_map_0$VARNAME_2 <- NA
lu_map_0$NL_NAME_2 <- NA
lu_map_0$TYPE_2 <- NA
lu_map_0$ENGTYPE_2 <- NA
lu_map_0$CC_2 <- NA
lu_map_0$HASC_2 <- NAAaaand… that’s it! Wasn’t that hard, but a bit convoluted nonetheless. Now I can bind all
the SpatialPolygonsDataFrame objects in one and use that for mapping:
grande_region <- do.call(rbind, list(lorraine, ger_kreise, be_wallonia, lu_map_0))
as.data.frame(grande_region)## GID_0 NAME_0 GID_1 NAME_1 NL_NAME_1 GID_2
## 76 FRA France FRA.6_1 Grand Est <NA> FRA.6.7_1
## 77 FRA France FRA.6_1 Grand Est <NA> FRA.6.8_1
## 78 FRA France FRA.6_1 Grand Est <NA> FRA.6.9_1
## 70 FRA France FRA.6_1 Grand Est <NA> FRA.6.10_1
## 99 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.1_1
## 110 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.2_1
## 121 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.3_1
## 129 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.4_1
## 130 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.5_1
## 131 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.6_1
## 132 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.7_1
## 133 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.8_1
## 134 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.9_1
## 100 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.10_1
## 101 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.11_1
## 102 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.12_1
## 104 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.14_1
## 103 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.13_1
## 105 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.15_1
## 106 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.16_1
## 107 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.17_1
## 108 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.18_1
## 111 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.20_1
## 109 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.19_1
## 112 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.21_1
## 113 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.22_1
## 114 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.23_1
## 115 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.24_1
## 116 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.25_1
## 117 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.26_1
## 118 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.27_1
## 119 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.28_1
## 120 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.29_1
## 122 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.30_1
## 124 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.32_1
## 123 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.31_1
## 125 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.33_1
## 126 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.34_1
## 127 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.35_1
## 128 DEU Germany DEU.11_1 Rheinland-Pfalz <NA> DEU.11.36_1
## 135 DEU Germany DEU.12_1 Saarland <NA> DEU.12.1_1
## 136 DEU Germany DEU.12_1 Saarland <NA> DEU.12.2_1
## 137 DEU Germany DEU.12_1 Saarland <NA> DEU.12.3_1
## 138 DEU Germany DEU.12_1 Saarland <NA> DEU.12.4_1
## 139 DEU Germany DEU.12_1 Saarland <NA> DEU.12.5_1
## 140 DEU Germany DEU.12_1 Saarland <NA> DEU.12.6_1
## 7 BEL Belgium BEL.3_1 Wallonie <NA> BEL.3.1_1
## 8 BEL Belgium BEL.3_1 Wallonie <NA> BEL.3.2_1
## 9 BEL Belgium BEL.3_1 Wallonie <NA> BEL.3.3_1
## 10 BEL Belgium BEL.3_1 Wallonie <NA> BEL.3.4_1
## 11 BEL Belgium BEL.3_1 Wallonie <NA> BEL.3.5_1
## 1 LUX Luxembourg <NA> <NA> <NA> <NA>
## NAME_2 VARNAME_2
## 76 Meurthe-et-Moselle <NA>
## 77 Meuse <NA>
## 78 Moselle Lothringen
## 70 Vosges <NA>
## 99 Ahrweiler <NA>
## 110 Altenkirchen <NA>
## 121 Alzey-Worms <NA>
## 129 Bad Dürkheim <NA>
## 130 Bad Kreuznach <NA>
## 131 Bernkastel-Wittlich <NA>
## 132 Birkenfeld <NA>
## 133 Cochem-Zell <NA>
## 134 Donnersbergkreis <NA>
## 100 Bitburg-Prüm <NA>
## 101 Frankenthal <NA>
## 102 Germersheim <NA>
## 104 Kaiserslautern <NA>
## 103 Kaiserslautern (Kreisfreie Stadt) <NA>
## 105 Koblenz <NA>
## 106 Kusel <NA>
## 107 Landau i.d.Pfalz <NA>
## 108 Ludwigshafen <NA>
## 111 Mainz <NA>
## 109 Mainz-Bingen <NA>
## 112 Mayen-Koblenz <NA>
## 113 Neustadt a.d.Weinstraße <NA>
## 114 Neuwied <NA>
## 115 Pirmasens <NA>
## 116 Rhein-Hunsrück-Kreis <NA>
## 117 Rhein-Lahn-Kreis <NA>
## 118 Rhein-Pfalz-Kreis <NA>
## 119 Speyer <NA>
## 120 Südliche Weinstraße <NA>
## 122 Südwestpfalz <NA>
## 124 Trier <NA>
## 123 Trier-Saarburg <NA>
## 125 Vulkaneifel <NA>
## 126 Westerwaldkreis <NA>
## 127 Worms <NA>
## 128 Zweibrücken <NA>
## 135 Merzig-Wadern <NA>
## 136 Neunkirchen <NA>
## 137 Regionalverband Saarbrücken <NA>
## 138 Saarlouis <NA>
## 139 Saarpfalz-Kreis <NA>
## 140 Sankt Wendel <NA>
## 7 Brabant Wallon Waals Brabant|Walloon Brabant
## 8 Hainaut Henegouwen|Hennegau
## 9 Liège Luik|Liegi|Lieja|Lüttich
## 10 Province de Luxembourg Lussemburgo|Luxemburg|Luxemburgo
## 11 Namur Namen
## 1 Luxembourg <NA>
## NL_NAME_2 TYPE_2 ENGTYPE_2 CC_2 HASC_2
## 76 <NA> Département Department 54 FR.MM
## 77 <NA> Département Department 55 FR.MS
## 78 <NA> Département Department 57 FR.MO
## 70 <NA> Département Department 88 FR.VG
## 99 <NA> Landkreis District 07131 DE.RP.AR
## 110 <NA> Landkreis District 07132 DE.RP.AT
## 121 <NA> Landkreis District 07331 DE.RP.AW
## 129 <NA> Landkreis District 07332 DE.RP.BD
## 130 <NA> Landkreis District 07133 DE.RP.BK
## 131 <NA> Landkreis District 07231 DE.RP.BW
## 132 <NA> Landkreis District 07134 DE.RP.BR
## 133 <NA> Landkreis District 07135 DE.RP.CZ
## 134 <NA> Landkreis District 07333 DE.RP.DN
## 100 <NA> Landkreis District 07232 DE.RP.EB
## 101 <NA> Kreisfreie Stadt District 07311 DE.RP.FA
## 102 <NA> Landkreis District 07334 DE.RP.GR
## 104 <NA> Landkreis District 07335 DE.RP.KL
## 103 <NA> Kreisfreie Stadt District 07312 DE.RP.KL
## 105 <NA> Kreisfreie Stadt District 07111 DE.RP.KO
## 106 <NA> Landkreis District 07336 DE.RP.KU
## 107 <NA> Kreisfreie Stadt District 07313 DE.RP.LP
## 108 <NA> Kreisfreie Stadt District 07314 DE.RP.LR
## 111 <NA> Kreisfreie Stadt District 07315 DE.RP.MI
## 109 <NA> Landkreis District 07339 DE.RP.MB
## 112 <NA> Landkreis District 07137 DE.RP.MK
## 113 <NA> Kreisfreie Stadt District 07316 DE.RP.NW
## 114 <NA> Landkreis District 07138 DE.RP.NU
## 115 <NA> Kreisfreie Stadt District 07317 DE.RP.PR
## 116 <NA> Landkreis District 07140 DE.RP.RH
## 117 <NA> Landkreis District 07141 DE.RP.RN
## 118 <NA> Landkreis District 07338 DE.RP.RZ
## 119 <NA> Kreisfreie Stadt District 07318 DE.RP.SE
## 120 <NA> Landkreis District 07337 DE.RP.SW
## 122 <NA> Landkreis District 07340 DE.RP.SD
## 124 <NA> Kreisfreie Stadt District 07211 DE.RP.TI
## 123 <NA> Landkreis District 07235 DE.RP.TS
## 125 <NA> Landkreis District 07233 DE.RP.VL
## 126 <NA> Landkreis District 07143 DE.RP.WS
## 127 <NA> Kreisfreie Stadt District 07319 DE.RP.WR
## 128 <NA> Kreisfreie Stadt District 07320 DE.RP.ZE
## 135 <NA> Landkreis District 10042 DE.SL.MW
## 136 <NA> Landkreis District 10043 DE.SL.NU
## 137 <NA> Landkreis District 10041 DE.SL.SB
## 138 <NA> Landkreis District 10044 DE.SL.SA
## 139 <NA> Landkreis District 10045 DE.SL.SP
## 140 <NA> Landkreis District 10046 DE.SL.SW
## 7 <NA> Province Provincie <NA> BE.BW
## 8 <NA> Province Provincie <NA> BE.HT
## 9 <NA> Province Provincie <NA> BE.LG
## 10 <NA> Province Provincie <NA> BE.LX
## 11 <NA> Province Provincie <NA> BE.NA
## 1 <NA> <NA> <NA> <NA> <NA>And now I can continue following the tutorial from the {echarts4r} website, by converting this
SpatialPolygonsDataFrame object for the Greater Region into a geojson file which can now be
used to draw maps! You can take a look at the final result here.
I don’t post the code to draw the map here, because it would require some more tinkering by
joining the COVID data. But you can find my raw script here
(lines 51 to 61) or you could also take a look at the draw_map() function from the package
I made, which you can find here.
I really like the end result, {echarts4r} is really a fantastic package!
Stay tuned part 2 of the project, which will deal with machine learning.
Hope you enjoyed! If you found this blog post useful, you might want to follow me on twitter for blog post updates and buy me an espresso or paypal.me, or buy my ebook on Leanpub. You can also watch my videos on youtube. So much content for you to consoom!
