Extracción de la data de OSM

Desde OpenSteetMaps, usando el paquete osmdata, extraemos la data correspondiente a diferentes atributos urbanos para el área de Barcelona.

Equipamientos y servicios

query_amenities <- opq(bbox = "Barcelona, Spain") %>%
  add_osm_feature(key = "amenity")
barcelona_amenities <- osmdata_sf(query_amenities)
amenities_points <- barcelona_amenities$osm_points
amenities_polygons <- barcelona_amenities$osm_polygons

Estaciones de metro

query_metro <- opq(bbox = "Barcelona, Spain") %>%
  add_osm_feature(key = "railway", value = "station")
barcelona_metro <- osmdata_sf(query_metro)
metro_stations <- barcelona_metro$osm_points

Estaciones de travía

query_tram_stops <- opq(bbox = "Barcelona, Spain") %>%
  add_osm_feature(key = "railway", value = "tram_stop")
barcelona_tram_stops <- osmdata_sf(query_tram_stops)
tram_stops <- barcelona_tram_stops$osm_points

Puntos turísticos

query_tourism <- opq(bbox = "Barcelona, Spain") %>%
  add_osm_feature(key = "tourism")
barcelona_tourism <- osmdata_sf(query_tourism)
tourism_points <- barcelona_tourism$osm_points
tourism_polygons <- barcelona_tourism$osm_polygons

Parques

query_parks <- opq(bbox = "Barcelona, Spain") %>%
  add_osm_feature(key = "leisure", value = "park")
barcelona_parks <- osmdata_sf(query_parks)
parks_polygons <- barcelona_parks$osm_polygons

Plazas

query_squares <- opq(bbox = "Barcelona, Spain") %>%
  add_osm_feature(key = "place", value = "square")
barcelona_squares <- osmdata_sf(query_squares)
squares_points <- barcelona_squares$osm_points
squares_polygons <- barcelona_squares$osm_polygons

Playas

query_beaches <- opq(bbox = "Barcelona, Spain") %>%
  add_osm_feature(key = "natural", value = "beach")
barcelona_beaches <- osmdata_sf(query_beaches)
beaches_polygons <- barcelona_beaches$osm_polygons

Edificios históricos

Edificios como la Casa Batllò o la Basílica de la Sagrada Familia no están incluídos dentro de los polígonos con key tourism. Sí como amenity, pero cuyos valores no son específicos a su valor patrimonial (en estos dos ejemplos, uno es apartment y el otro place_of_worship). En la columna historic se incluyen polígonos o nodos que representan sitios de valor histórico, en concreto tomaremos aquellos que poseen la categoría heritage. Podríamos hilar más fino y tomar la columna heritage:operator con el valor whc (World Heritage Centre, de UNESCO), o la columna importance con el valor international.

query_heritage <- opq(bbox = "Barcelona, Spain") %>%
  add_osm_feature(key = "historic", value = "heritage")
barcelona_heritage <- osmdata_sf(query_heritage)
heritage_polygons <- barcelona_heritage$osm_polygons
heritage_polygons$historic <- "heritage"

Procesamiento de datos

Función para procesar los puntos

process_points <- function(data, feature_type) {
  data <- st_transform(data, 4326)  # Transformar las coordenadas al sistema de referencia correcto (WGS84)
  
  coords <- st_coordinates(data)
  
  data_df <- cbind(data, coords)
  
  if (!"name" %in% colnames(data_df)) {
    data_df$name <- NA
  }
  if (!"amenity" %in% colnames(data_df)) {
    data_df$amenity <- NA
  }
  if (!"tourism" %in% colnames(data_df)) {
    data_df$tourism <- NA
  }
  if (!"historic" %in% colnames(data_df)) {
    data_df$historic <- NA
  }
  if (!"place" %in% colnames(data_df)) {
    data_df$place <- NA
  }
  if (!"type" %in% colnames(data_df)) {
    data_df$type <- NA
  }
  
  result <- data_df %>%
    st_drop_geometry() %>%  # Eliminar la geometría para obtener un dataframe simple
    select(osm_id, name, amenity, tourism, historic, place, X, Y) %>%  # Seleccionar columnas de interés
    rename(longitude = X, latitude = Y) %>%  # Renombrar columnas de coordenadas
    mutate(type = feature_type)  # Añadir columna del tipo de feature
  
  return(result)
}

Función para procesar los polígonos

process_polygons <- function(data, feature_type) {
  data <- st_transform(data, 4326)
  data <- st_centroid(data)  # Calcular los centroides
  coords <- st_coordinates(data)
  data_df <- cbind(data, coords)
  
  if (!"name" %in% colnames(data_df)) {
    data_df$name <- NA
  }
  if (!"tourism" %in% colnames(data_df)) {
    data_df$tourism <- NA
  }
  if (!"amenity" %in% colnames(data_df)) {
    data_df$amenity <- NA
  }
  if (!"historic" %in% colnames(data_df)) {
    data_df$historic <- NA
  }
  if (!"place" %in% colnames(data_df)) {
    data_df$place <- NA
  }
  if (!"type" %in% colnames(data_df)) {
    data_df$type <- NA
  }
  
  result <- data_df %>%
    st_drop_geometry() %>%
    select(osm_id, name, amenity, tourism, historic, place, X, Y) %>%
    rename(longitude = X, latitude = Y) %>%
    mutate(type = feature_type)
  
  return(result)
}

Procesar cada tipo de Punto

amenities_processed <- process_points(amenities_points, "amenity")
metro_processed <- process_points(metro_stations, "metro_station")
tram_processed <- process_points(tram_stops, "tram_stop")
tourism_processed <- process_points(tourism_points, "tourism")
#heritage_processed <- process_points(heritage_points, "historic")
squares_processed <- process_points(squares_points, "square")

Procesar cada tipo de Polígono

columns_to_keep <- c("name", "amenity", "tourism")
amenities_polygons_processed <- process_polygons(amenities_polygons, "amenity")

## Warning: st_centroid assumes attributes are constant over geometries

tourism_polygons_processed <- process_polygons(tourism_polygons, "tourism")

## Warning: st_centroid assumes attributes are constant over geometries

parks_processed <- process_polygons(parks_polygons, "park")

## Warning: st_centroid assumes attributes are constant over geometries

beaches_processed <- process_polygons(beaches_polygons, "beach")

## Warning: st_centroid assumes attributes are constant over geometries

heritage_polygons_processed <- process_polygons(heritage_polygons, "historic")

## Warning: st_centroid assumes attributes are constant over geometries

squares_polygons_processed <- process_polygons(squares_polygons, "square")

## Warning: st_centroid assumes attributes are constant over geometries

Combinar

all_points_combined <- bind_rows(
  amenities_processed,
  amenities_polygons_processed,
  metro_processed,
  tram_processed,
  tourism_processed,
  tourism_polygons_processed,
  parks_processed,
  beaches_processed,
  heritage_polygons_processed,
  squares_processed,
  squares_polygons_processed
)

Eliminamos IDs repetidos

Primero agrupamos por osm_id y luego dejamos un solo punto.

all_points_combined_grouped <- all_points_combined %>%
  group_by(osm_id) %>%
  filter(!is.na(name) | !is.na(amenity) | !is.na(tourism) | !is.na(type) | !is.na(historic) | !is.na(place)) %>%
  filter(osm_id != 1202318704) %>%  # Eliminamos un registro de beach que corresponde a Eurofitnes Can Dragó, gimnasio con piscina
  slice(1) %>%
  ungroup()

Incluir hitos patrimoniales que deja fuera el código

# Datos de los edificios
manual_buildings <- data.frame(
  osm_id = c(8974896, 9427554, 9194723),
  name = c("Casa Milà", "Casa Batlló", "Basílica de la Sagrada Família"),
  historic = "heritage",
  type = "historic",
  latitude = c(41.39158, 41.395277777778, 41.40369),
  longitude = c(2.1616666666667, 2.16492, 2.17433)
)

# Asegurarse de que all_points_combined_grouped tiene las columnas necesarias
required_columns <- c("osm_id", "name", "amenity", "tourism", "historic", "place", "longitude", "latitude", "type")

# Agregar las columnas faltantes si es necesario
for (col in required_columns) {
  if (!col %in% colnames(all_points_combined_grouped)) {
    all_points_combined_grouped[[col]] <- NA
  }
}

# Convertir osm_id en manual_buildings a character para que coincida con all_points_combined_grouped
manual_buildings$osm_id <- as.character(manual_buildings$osm_id)

# Asegurarse de que all_points_combined_grouped tiene las columnas necesarias
required_columns <- c("osm_id", "name", "amenity", "tourism", "historic", "place", "longitude", "latitude", "type")

# Agregar las columnas faltantes si es necesario
for (col in required_columns) {
  if (!col %in% colnames(all_points_combined_grouped)) {
    all_points_combined_grouped[[col]] <- NA
  }
}

# Seleccionar y ordenar las columnas en manual_buildings para que coincidan con all_points_combined_grouped
manual_buildings <- manual_buildings %>%
  mutate(amenity = NA, tourism = NA, place = NA) %>%
  select(all_of(required_columns))

# Agregar los nuevos datos
all_points_combined_grouped <- bind_rows(all_points_combined_grouped, manual_buildings)

Finalmente, cambiamos la categoría del hospital San Pau para que aparezca como edificio patrimonial. Una sección del hospital, la más moderna, se mantendrá como hospital, lo que nos sirve en caso de querer usar esos servicios.

all_points_combined_grouped$historic[all_points_combined_grouped$osm_id == "265391701"] <- "heritage"
all_points_combined_grouped$type[all_points_combined_grouped$osm_id == "265391701"] <- "historic"

Mapas

library(tmap)

## Warning: package 'tmap' was built under R version 4.2.3

## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')

library(sf)
tmap_mode("view")

## tmap mode set to interactive viewing

Mapas de turismo

Mapa de artwork

art_points <- all_points_combined_grouped %>%
  filter(tourism == "artwork")
art_points_sf <- st_as_sf(art_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(art_points_sf) +
  tm_dots(col = "darkred", size = 0.1, title = "Puntos de Artwork") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Artwork en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapa de atracciones

attraction_points <- all_points_combined_grouped %>%
  filter(tourism == "attraction")
attraction_points_sf <- st_as_sf(attraction_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(attraction_points_sf) +
  tm_dots(col = "gold", size = 0.1, title = "Puntos de atracciones") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Atracciones en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapa de galerías

gallery_points <- all_points_combined_grouped %>%
  filter(tourism == "information")
gallery_points_sf <- st_as_sf(gallery_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(gallery_points_sf) +
  tm_dots(col = "slateblue", size = 0.1, title = "Puntos de galerías") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Galerías en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapa de hostales

hostel_points <- all_points_combined_grouped %>%
  filter(tourism == "hostel")
hostel_points_sf <- st_as_sf(hostel_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(hostel_points_sf) +
  tm_dots(col = "tomato", size = 0.1, title = "Puntos de hostales") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Hostales en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapa de hoteles

hotel_points <- all_points_combined_grouped %>%
  filter(tourism == "hotel")
hotel_points_sf <- st_as_sf(hotel_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(hotel_points_sf) +
  tm_dots(col = "deeppink", size = 0.1, title = "Puntos de hoteles") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Hoteles en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapa de puntos de información

info_points <- all_points_combined_grouped %>%
  filter(tourism == "information")
info_points_sf <- st_as_sf(info_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(info_points_sf) +
  tm_dots(col = "steelblue", size = 0.1, title = "Puntos de información") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Puntos de información en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapa de museos

museos_points <- all_points_combined_grouped %>%
  filter(tourism == "museum")
museos_points_sf <- st_as_sf(museos_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(museos_points_sf) +
  tm_dots(col = "tan3", size = 0.1, title = "Puntos de museos") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Museos en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapa de miradores

picnic_points <- all_points_combined_grouped %>%
  filter(tourism == "picnic_site")
picnic_points_sf <- st_as_sf(picnic_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(picnic_points_sf) +
  tm_dots(col = "green", size = 0.1, title = "Puntos de sitios picnic") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Sitios de picnic en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapa de parques temáticos

tp_points <- all_points_combined_grouped %>%
  filter(tourism == "theme_park")
tp_points_sf <- st_as_sf(tp_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(tp_points_sf) +
  tm_dots(col = "orangered", size = 0.1, title = "Puntos de parques temáticos") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Parques temáticos en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapa de miradores

vp_points <- all_points_combined_grouped %>%
  filter(tourism == "viewpoint")
vp_points_sf <- st_as_sf(vp_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(vp_points_sf) +
  tm_dots(col = "darkcyan", size = 0.1, title = "Puntos de miradores") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Miradores en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapa del patrimonio de la UNESCO

hist_points <- all_points_combined_grouped %>%
  filter(historic == "heritage")
hist_points_sf <- st_as_sf(hist_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(hist_points_sf) +
  tm_dots(col = "gold2", size = 0.1, title = "Puntos patrimoniales") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Patrimonio de la UNESCO en Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapear playa

beach_points <- all_points_combined_grouped %>%
  filter(type == "beach")
# Asegúrate de que los datos están en formato sf
beach_points_sf <- st_as_sf(beach_points, coords = c("longitude", "latitude"), crs = 4326)

# Crear el mapa
tm_shape(beach_points_sf) +
  tm_dots(col = "blue", size = 0.1, title = "Beach Points") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Beaches in Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapear parques

park_points <- all_points_combined_grouped %>%
  filter(type == "park")

park_points_sf <- st_as_sf(park_points, coords = c("longitude", "latitude"), crs = 4326)

# Crear el mapa
tm_shape(park_points_sf) +
  tm_dots(col = "darkgreen", size = 0.1, title = "Park Points") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Parks in Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

Mapear hospitales (ejemplo de amenity)

Los puntos de amenity sí están solo como centroides.

hosp_points <- all_points_combined_grouped %>%
  filter(amenity == "hospital")
hosp_points_sf <- st_as_sf(hosp_points, coords = c("longitude", "latitude"), crs = 4326)

tm_shape(hosp_points_sf) +
  tm_dots(col = "lightblue", size = 0.1, title = "Hospital Points") +
  tm_basemap("OpenStreetMap") +
  tm_layout(title = "Hospitals in Barcelona",
            title.size = 1.5,
            legend.outside = TRUE,
            legend.outside.position = "bottom")

# 1. Instancias con type = beach
bcn_playa <- all_points_combined_grouped %>%
  filter(type == "beach") %>%
  select(type, longitude, latitude)

write.csv(bcn_playa, "bcn_playa.csv", row.names = FALSE)

# 2. Instancias con type = metro_station o tram_stop
bcn_metro <- all_points_combined_grouped %>%
  filter(type %in% c("metro_station", "tram_stop")) %>%
  select(type, longitude, latitude)

write.csv(bcn_metro, "bcn_metro.csv", row.names = FALSE)

# 3. Instancias con type = park
bcn_parques <- all_points_combined_grouped %>%
  filter(type == "park") %>%
  select(type, longitude, latitude)

write.csv(bcn_parques, "bcn_parques.csv", row.names = FALSE)

# 4. Instancias con historic = heritage o tourism = museum, o tourism = attraction
bcn_patrimonio <- all_points_combined_grouped %>%
  filter(historic == "heritage" | tourism == "museum" | tourism == "attraction") %>%
  select(type, longitude, latitude)

write.csv(bcn_patrimonio, "bcn_atractivos.csv", row.names = FALSE)

# 5. Instancias con amenity = bank
bcn_bancos <- all_points_combined_grouped %>%
  filter(amenity == "bank") %>%
  select(amenity, longitude, latitude)

write.csv(bcn_bancos, "bcn_bancos.csv", row.names = FALSE)

# 6. Instancias con amenity = nightclub
bcn_clubesnocturnos <- all_points_combined_grouped %>%
  filter(amenity == "nightclub") %>%
  select(amenity, longitude, latitude)

write.csv(bcn_clubesnocturnos, "bcn_clubesnocturnos.csv", row.names = FALSE)

# 7. Instancias con amenity = parking
bcn_parking <- all_points_combined_grouped %>%
  filter(amenity == "parking") %>%
  select(amenity, longitude, latitude)

write.csv(bcn_parking, "bcn_parking.csv", row.names = FALSE)

# 8. Instancias con amenity = bicycle_parking
bcn_parkingbici <- all_points_combined_grouped %>%
  filter(amenity == "bicycle_parking") %>%
  select(amenity, longitude, latitude)

write.csv(bcn_parkingbici, "bcn_parkingbici.csv", row.names = FALSE)

# 9. Instancias con name = Starbucks
bcn_starbucks <- all_points_combined_grouped %>%
  filter(name == "Starbucks") %>%
  select(name, longitude, latitude)

write.csv(bcn_starbucks, "bcn_starbucks.csv", row.names = FALSE)

# 10. Instancias con name = Santagloria
bcn_santagloria <- all_points_combined_grouped %>%
  filter(name == "Santagloria") %>%
  select(name, longitude, latitude)

write.csv(bcn_santagloria, "bcn_santagloria.csv", row.names = FALSE)

# 11. Instancias con name = Vivari
bcn_vivari <- all_points_combined_grouped %>%
  filter(name == "Vivari") %>%
  select(name, longitude, latitude)

write.csv(bcn_vivari, "bcn_vivari.csv", row.names = FALSE)

# 12. Instancias con name = Buenas Migas
bcn_buenasmigas <- all_points_combined_grouped %>%
  filter(name == "Buenas Migas") %>%
  select(name, longitude, latitude)

write.csv(bcn_buenasmigas, "bcn_buenasmigas.csv", row.names = FALSE)

# 13. Instancias con name = McDonald's
bcn_mcdonalds <- all_points_combined_grouped %>%
  filter(name == "McDonald's") %>%
  select(name, longitude, latitude)

write.csv(bcn_mcdonalds, "bcn_mcdonalds.csv", row.names = FALSE)

Equipamientos, transporte, hitos turísitcos, parques, plazas y playas de Barcelona

Juan Carraha

2024-07-10

Extracción de la data de OSM

Equipamientos y servicios

Estaciones de metro

Estaciones de travía

Puntos turísticos

Parques

Plazas

Playas

Edificios históricos

Procesamiento de datos

Función para procesar los puntos

Función para procesar los polígonos

Procesar cada tipo de Punto

Procesar cada tipo de Polígono

Combinar

Eliminamos IDs repetidos

Incluir hitos patrimoniales que deja fuera el código

Mapas

Mapas de turismo

Mapa de artwork

Mapa de atracciones

Mapa de galerías

Mapa de hostales

Mapa de hoteles

Mapa de puntos de información

Mapa de museos

Mapa de miradores

Mapa de parques temáticos

Mapa de miradores

Mapa del patrimonio de la UNESCO

Mapear playa

Mapear parques

Mapear hospitales (ejemplo de amenity)