Bilingual Town Names

Reason 1: Brussels, an official Bilingual region

In the above table it was obvious that the Brussels region has a much higher share of towns with two offical names: 68% versus the country average of 16%. Given Brussels status as bilingual that should not come as a surprise. I was actually more surprised to realize that there are still 6 that only have their original name: Anderlecht, Jette, Etterbeek, Evere, Ganshoren and Koekelberg.

#Checking the data on Brussels
popdata %>% 
  filter(Region=="Brussels agglomeration") %>% 
  summarise(NTowns=n(), N_SameName=n()-sum(DiffName), N_DiffName=sum(DiffName), 
            Prop_SameName =1-round(mean(DiffName),2), Prop_DiffName=round(mean(DiffName),2))

## # A tibble: 1 x 5
##   NTowns N_SameName N_DiffName Prop_SameName Prop_DiffName
##    <int>      <int>      <int>         <dbl>         <dbl>
## 1     19          6         13         0.320         0.680

#Adding a column to note down the reason for different names
reason_BXL <- popdata %>% 
  filter(Region=="Brussels agglomeration") %>% 
  filter(DiffName) %>%
  mutate(Reason = "Brussels")

popdata%>%
  filter(Region=="Brussels agglomeration") %>% 
  filter(DiffName) %>%
  arrange(desc(population))%>%
  select(-Region, -REFNIS) %>%
  knitr::kable()

Reason 2: Larger cities

Cities are generally more important and I would have guessed that most of our cities have two official names. By just looking at the difference in average population between towns that have two names (DiffName==TRUE) and those who don’t, there clearly is a skew towards higher population town.
A quick plot in ggplot confirms this to be true: grey shows all the towns in Belgium according to their population size on a logarithmic scale. I coloured those who have two names in green.

popdata %>%
  group_by(DiffName) %>% 
  summarise(mean=mean(population), median=median(population))

## # A tibble: 2 x 3
##   DiffName  mean median
##   <lgl>    <dbl>  <dbl>
## 1 F        14744  11383
## 2 T        42511  24701

#Plotting average town size of small and larger towns
ggplot()+
  geom_histogram(data=popdata, aes(x=population), fill="grey", alpha=0.6)+
  geom_histogram(data=subset(popdata, DiffName==TRUE), aes(x=population), fill="cadetblue4", alpha=1)+
  scale_x_log10()+
  labs(x= "Population", y="Number of towns", title="Size of towns with two official names amongst all towns in Belgium")

I took a shortcut to define our cities: the 10% highest populated towns. I got the cutoff value via quantile(popdata$population, probs=0.9): 34190.

#Proportion of Cities with different names
popdata %>% 
  filter(population > 34000) %>%
  summarise(NTowns=n(), N_SameName=n()-sum(DiffName), N_DiffName=sum(DiffName), 
            Prop_SameName =1-round(mean(DiffName),2), Prop_DiffName=round(mean(DiffName),2))

## # A tibble: 1 x 5
##   NTowns N_SameName N_DiffName Prop_SameName Prop_DiffName
##    <int>      <int>      <int>         <dbl>         <dbl>
## 1     60         27         33         0.450         0.550

#Adding a reason column 
reason_city <- popdata %>% 
  filter(population > 34000) %>%
  filter(Region != "Brussels agglomeration") %>% 
  filter(DiffName) %>% 
  mutate(Reason = "City")

#Ten largest cities outside Brusels
popdata%>%
  filter(population > 34000) %>% 
  filter(DiffName) %>%
  filter(Region != "Brussels agglomeration") %>% 
  arrange(desc(population)) %>%
  slice(1:10) %>%
  select(-REFNIS) %>%
  knitr::kable()

Reason 3: German speaking region (and towns with German language facilities)

After World War I, the peace treaty of Versailles listed the annexation of 9 German towns into Belgium as war compensation. They make up our third language region as German is still their main language today.
Given that German and Dutch are both German langauges and have a lot of similarities it would make sense that the Flemish would refer to the German town names, while the French have changed some of them.

#Listing the German communes and the two additional towns with german facilities
germanspeaking <- c("Eupen", "Kelmis", "Lontzen", "Raeren", "Amel", "Büllingen", 
                    "Burg-Reuland", "Bütgenbach", "Sankt Vith", "Malmedy", "Weismes")

#Proportion of Cities with different names
popdata %>% 
  filter(TownNL %in% germanspeaking) %>%
  summarise(NTowns=n(), N_SameName=n()-sum(DiffName), N_DiffName=sum(DiffName), 
            Prop_SameName =1-round(mean(DiffName),2), Prop_DiffName=round(mean(DiffName),2))

## # A tibble: 1 x 5
##   NTowns N_SameName N_DiffName Prop_SameName Prop_DiffName
##    <int>      <int>      <int>         <dbl>         <dbl>
## 1     11          5          6         0.450         0.550

#Adding a reason column 
reason_german <- popdata %>% 
  filter(TownNL %in% germanspeaking) %>%
  filter(DiffName) %>% 
  mutate(Reason = "German region")

#German towns with two official names
popdata %>% 
  filter(TownNL %in% germanspeaking) %>%
  filter(DiffName==TRUE) %>%
  select(-REFNIS) %>%
  knitr::kable(align="c")

Reason 4: Towns in Flanders or Wallonia with official language facilities

Always a topic for debate in Belgium: the towns with official language facilities. These are towns that belong to one region but they have some degree of bilingual facilities (it’s complicated!).

#Listing all towns with language facilities
faciliteiten <- c("Bever", "Drogenbos", "Herstappe", "Kraainem", "Linkebeek", 
                  "Mesen", "Ronse", "Sint-Genesius-Rode", "Spiere-Helkijn", 
                  "Voeren", "Wemmel", "Wezembeek-Oppem", "Edingen", 
                  "Komen-Waasten", "Moeskroen", "Vloesberg")

#Proportion of Cities with different names
popdata %>% 
  filter(TownNL %in% faciliteiten) %>%
  summarise(NTowns=n(), N_SameName=n()-sum(DiffName), N_DiffName=sum(DiffName), 
            Prop_SameName =1-round(mean(DiffName),2), Prop_DiffName=round(mean(DiffName),2))

## # A tibble: 1 x 5
##   NTowns N_SameName N_DiffName Prop_SameName Prop_DiffName
##    <int>      <int>      <int>         <dbl>         <dbl>
## 1     16          6         10         0.380         0.620

#Adding a reason column
reason_facilities <- popdata %>% 
  filter(TownNL %in% faciliteiten) %>%
  filter(DiffName) %>% 
  anti_join(reason_city) %>% 
  mutate(Reason = "Language facilities")

#Which towns have different names?
popdata %>% 
  filter(TownNL %in% faciliteiten) %>%
  filter(DiffName==TRUE) %>% 
  select(-REFNIS) %>%
  knitr::kable(align="c")

The language border is obviously another factor. Throughout history, many of these towns have changed region, so they inherited more than one name.
Finally, I wanted to make an “other reason” category, and bind all the reasons to my main data. Given the large size of this post already, I did all of this in the background.

A quick reason map:
The Brussels and German region are pretty obvious dots in the map, and equally obvious is our language border. Large cities are scattered across the whole of Belgium and many of the unidentied scattered dots also represent smaller cities (like Aarlen/Arlon or Temse/Tamise).
There is one other cluster of towns south west of Brussels starting from the language border but heading to the French border. That area used to be part of the medieval County of Flanders, where both Walloon and French towns often still carry a Flemish town name. Some of them got modernized but it seems quite a few kept their original name as well.