Barcelona Neighborhoods

I have retrieved the geographical information of Barcelona neighbourhoods from the map stored in BAdatasetsSPatial. I will be using the bcn_neigh map.

bcn_neigh <- BCNNeigh |>
  select(c_barri, n_barri, area, perim, coord_x, coord_y)

Barcelona is administratively divided into ten districts and seventy-three neighbourhoods. In this map, i am presenting each neighbourhood with its code.

ggplot(bcn_neigh) +
  geom_sf(fill = "white") +
  geom_text(aes(coord_x, coord_y, label = c_barri), size = 3) +
  theme_void() +
  ggtitle(label = "Barcelona Neighborhoods")

And here is the official list of neighbourhoods in Barcelona.

bcn_neigh |>
  st_drop_geometry() |>
  select(c_barri, n_barri) |>
  rename(code = c_barri, name = n_barri) |>
  arrange(code) |>
  kbl() |>
  kable_minimal(full_width = FALSE)

Nightlife Data

The information abouth nightlife has been retrieved from the 2024 census of ground floor premises published in Open Data BCN. As the dataset includes latitude and longitude, it can be turned into a spatial object.

nightlife_2024 <- data_2024 |>
  select(nom_local, latitud, longitud, nom_barri, codi_barri, nom_via, porta)

nightlife_2024_sf <- nightlife_2024 |>
  st_as_sf(coords = c("longitud", "latitud"), crs = 4326, remove = FALSE)

nightlife_2024_sf

## Simple feature collection with 242 features and 7 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 2.11062 ymin: 41.37241 xmax: 2.200073 ymax: 41.43286
## Geodetic CRS:  WGS 84
## # A tibble: 242 × 8
##    nom_local                 latitud longitud nom_barri codi_barri nom_via porta
##  * <chr>                       <dbl>    <dbl> <chr>          <dbl> <chr>   <dbl>
##  1 LA MOLE BAR MUSICAL          41.4     2.12 Sants - …         17 TORNS      10
##  2 LA TRAVIESA TUSET            41.4     2.15 Sant Ger…         26 TUSET      10
##  3 LA CLAVE                     41.4     2.13 Sants - …         17 SUGRAN…    20
##  4 PEREJIL                      41.4     2.16 el Poble…         11 BLAI       10
##  5 AFTERLIFE ESPORTS GAMER …    41.4     2.14 Sant Ger…         26 MARIÀ …    10
##  6 APOLO                        41.4     2.17 el Poble…         11 NOU DE…    10
##  7 APOLO BAILE                  41.4     2.17 el Poble…         11 NOU DE…    20
##  8 EL GIARDINETTO               41.4     2.15 Sant Ger…         26 GRANAD…    10
##  9 APOLO CASINO                 41.4     2.17 el Poble…         11 NOU DE…    50
## 10 BAR SCHULTZ                  41.4     2.17 la Dreta…          7 BAILÈN     30
## # ℹ 232 more rows
## # ℹ 1 more variable: geometry <POINT [°]>

Checking Data Quality

The nightlife_2024_sf includes information about the neighbourhood in the nom_barri and codi_barri columns. Let’s check if the Raval venues are located into the Raval neighbourhood. To do so, I create:

• The nl_raval table of venues encoded as located at El Raval with codi_barri == 1.
• The sf_raval table, corresponding with the polygon of El Raval.

nl_raval <- nightlife_2024_sf |>
  filter(codi_barri == 1)

sf_raval <- bcn_neigh |>
  filter(c_barri == 1) 

Let’s plot both spatial objects together.

ggplot(sf_raval) +
  geom_sf() +
  geom_sf(data = nl_raval)

Some of the points fall outside the polygon (and maybe there are points inside the polygon incorrectly encoded too). We need to assign points to neighborhoods spatially using sf::st_join(). The result is stored in nightlife_2024_sf2.

nightlife_2024_sf2 <- st_join(nightlife_2024_sf, 
                              bcn_neigh |> select(c_barri, n_barri),
                              join = st_intersects)

In one case the spatial join has not found the neighborhood, so we keep it in the original neighborhood.

nightlife_2024_sf2 <- nightlife_2024_sf2 |>
  mutate(c_barri = ifelse(is.na(c_barri), codi_barri, c_barri),
         n_barri = ifelse(is.na(c_barri), nom_barri, n_barri))

The neighbourhood code assigned spatially is in c_barri and the code included in the nightlife dataset is in codi_barri. We can appreciate some differences:

nightlife_2024_sf2 |>
  filter(c_barri != codi_barri)

## Simple feature collection with 43 features and 9 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 2.127625 ymin: 41.37542 xmax: 2.195337 ymax: 41.43224
## Geodetic CRS:  WGS 84
## # A tibble: 43 × 10
##    nom_local               latitud longitud nom_barri   codi_barri nom_via porta
##  * <chr>                     <dbl>    <dbl> <chr>            <dbl> <chr>   <dbl>
##  1 ALIMENTACIÓ MONK           41.4     2.18 Sant Pere,…          4 ABAIXA…    10
##  2 BLING BLING                41.4     2.15 Sant Gerva…         26 TUSET      20
##  3 RATAFIA                    41.4     2.13 les Tres T…         24 RAFAEL…    20
##  4 ARENA XPERIENCE            41.4     2.17 la Dreta d…          7 RDA SA…    40
##  5 EL PATRÓN SHISHA LOUNGE    41.4     2.14 el Putxet …         27 MANUEL…    10
##  6 ARENA CHICAS               41.4     2.16 la Dreta d…          7 DIPUTA…    20
##  7 IMPERATOR                  41.4     2.16 la Vila de…         31 CÒRSEGA    10
##  8 SALA VIVALDI               41.4     2.15 Sant Antoni         10 LLANÇA     10
##  9 BEACH CLUB                 41.4     2.15 l'Antiga E…          8 VALÈNC…    10
## 10 SCOBIE'S IRISH PUB         41.4     2.17 la Dreta d…          7 RDA UN…    20
## # ℹ 33 more rows
## # ℹ 3 more variables: geometry <POINT [°]>, c_barri <dbl>, n_barri <chr>

Let’s check where are the points assigned to c_barri == 1.

nl2_raval <- nightlife_2024_sf2 |>
  filter(c_barri == 1)

ggplot(sf_raval) +
  geom_sf() +
  geom_sf(data = nl2_raval)

Seeing the results, I will be using the c_barri column to assign venues to neighbourhoods.

Metrics of Neighborhood Nightlife

I will be using two metrics to evaluate the intensity of nightlife in a neighbourhood:

• Number of venues.
• Venue density, measured in number of venues per square kilometer.

These metrics will be stored in the count_nl table. I am grouping by c_barri and count to obtain venues per neighbourhood.

count_nl <- nightlife_2024_sf2 |>
  st_drop_geometry() |>
  group_by(c_barri) |>
  summarise(venues = n(), .groups = "drop")
count_nl

## # A tibble: 35 × 2
##    c_barri venues
##      <dbl>  <int>
##  1       1     16
##  2       2     23
##  3       3      3
##  4       4     16
##  5       5      3
##  6       6      1
##  7       7     14
##  8       8     11
##  9       9      8
## 10      10      7
## # ℹ 25 more rows

Seeing the number of rows of count_nl, not all neighbourhoods have nightlife. I will pick the area of the incumbent neighbourhoods from bcn_neigh making a left_join() with count_nl as first table.

count_nl <- left_join(count_nl,
                      bcn_neigh |> 
                        st_drop_geometry() |>
                        select(c_barri, n_barri, area),
                      by = "c_barri")  

As the area is in square meters, I transform it in square kilometers and then compute density.

count_nl <- count_nl |>
  mutate(area = area/1e+06,
         density = venues/area)

The ten neighbourhoods with more nightlife venues are:

count_nl |>
  arrange(-venues) |>
  select(n_barri, venues, density) |>
  slice(1:10) |>
  kbl() |>
  kable_styling(full_width = FALSE)

And the top ten neighbourhoods by venue density of venues:

count_nl |>
  arrange(-density) |>
  select(n_barri, venues, density) |>
  slice(1:10) |>
  kbl() |>
  kable_styling(full_width = FALSE)

Choropleth Maps

I will present the results of above as choroplet maps. I need to add the values of count_nl to bcn_neigh. As we need to keep all the regions of the map, I use a left_join() with bcn_neigh as first table.

bcn_neigh <- left_join(bcn_neigh,
                       count_nl |> 
                         select(c_barri, venues, density),
                       by = "c_barri") 

Some neighborhoods have no venues, so I replace NA by zeros in venues and density.

bcn_neigh <- bcn_neigh |>
  mutate(venues= ifelse(is.na(venues), 0, venues),
         density = ifelse(is.na(density), 0, density))

Now I can do the choropleth of the number of venues. I have chosen a gradient fill color scale with low value white (for neighbourhoods with no venues) to red (maximum value of venues).

ggplot(bcn_neigh) +
  geom_sf(aes(fill = venues)) +
  scale_fill_gradient(low = "white", high = "red") +
  theme_void() +
  theme(legend.position = "none") +
  labs(title = "Number of Venues by Neighborhood (2024)",
          caption = "Source: Open Data BCN")

The venue density choropleth is built with the same principle, using orange instead of red.

ggplot(bcn_neigh) +
  geom_sf(aes(fill = density)) +
  scale_fill_gradient(low = "white", high = "orange") +
  theme_void() +
  theme(legend.position = "none") +
  labs(title = "Venue Density by Neighborhood (2024)",
          caption = "Source: Open Data BCN")

Conclusions

In this post, I have used a Barcelona neighbourhood map to analyze the intensity of nightlife across the city. I have used a spatial join to assign a neighbourhood to each venue, and I have calculated two metrics: the number of venues and density of venues for each neighbourhood. I have used this information to present a top ten ranking of neighbourhoods and a choropleth for each metric.

Results show two distinct geographical zones: the residential neighbourhood of Sant Gervasi-Galvany and downtown neighbourhoods. Public of Sant Gervasi–Galvany is predominantly middle- to upper-middle-class residents. Many patrons are locals from the district or nearby upscale areas (Sarrià, Tres Torres, Eixample). Nightlife here tends to be more oriented toward cocktail bars, lounges, and premium venues. The public of downtonw neighbourhoods like Barri Gòtic, El Raval, Poble Sec and Sant Pere, Santa Caterina i la Ribera is more socially mixed but heavily influenced by tourists, visitors, and young people from across the city. There is presence of international residents, students, and creative communities. In the Barri Gòtic especially, the nightlife public is tourism-driven, with a high proportion of short-term visitors.

References

• Census of premises on the ground floor intended for economic activity in the city of Barcelona https://opendata-ajuntament.barcelona.cat/data/en/dataset/cens-locals-planta-baixa-act-economica.

Session Info

## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Linux Mint 21.1
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## attached base packages:
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##  [1] kableExtra_1.4.0        BAdatasetsSpatial_0.1.0 sf_1.0-20              
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## [13] evaluate_1.0.3     grid_4.5.2         timechange_0.3.0   RColorBrewer_1.1-3
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