2 Extracting data from OpenStreetMap

2.1 Installing OpenStreetMap packages

Filtering and extracting contents from OpenStreetMap dump files is facilitated by dedicated utilities. In this project, Osmconvert and Osmfilter have been used to filter relevant data.

# install osmfilter
if (file.exists("osmfilter")==FALSE) {
  system(command = "wget -O - http://m.m.i24.cc/osmfilter.c |cc -x c - -O3 -o osmfilter")
}

# install osmconvert
if (file.exists(file.path("osmconvert64"))==FALSE) {
  download.file(url = "http://m.m.i24.cc/osmconvert64", destfile = file.path("osmconvert64"))
  system(command = "chmod +x osmconvert64")
}

2.2 Download OpenStreetMap data

It is possible to download all available data for selected countries from online repositories.

dir.create(path = "data", showWarnings = FALSE)
dir.create(path = file.path("data", "pbf"), showWarnings = FALSE)


for (i in countries) {
  if (file.exists(file.path("data", "pbf", paste0(i, "-latest.osm.pbf")))==FALSE) {
    download.file(url = paste0("http://download.geofabrik.de/europe/", i, "-latest.osm.pbf"),
                  destfile = file.path("data", "pbf", paste0(i, "-latest.osm.pbf")))
  }
}

2.3 Keep only street names

Through the above-mentioned utilities, all names of street found in the region have been extracted from the dump and converted to .cvs format. This has been accomplished by filtering only the names of objects recorded under the key highway in OpenStreetMap, which according to OpenStreetMap’s wiki and in spite of its name, “is the main key used for identifying any kind of road, street or path.”

At this stage, only streets have been included in the filter, but other OpenStreetMap categories may include relevant data, e.g. Tag:place=square, or Tag:leisure=park.

# convert to a format that can be read by osmfilter, and remove author data to reduce file size
dir.create(path = file.path("data", "o5m"), showWarnings = FALSE)

for (i in countries) {
  if (file.exists(file.path("data", "o5m", paste0(i, "-latest.o5m")))==FALSE) {
    system(paste0('./osmconvert64 data/pbf/', i, 
                  '-latest.osm.pbf --drop-version --out-o5m -o=data/o5m/', i,
                  '-latest.o5m'))
  }
}

# filter only streets
dir.create(path = file.path("data", "o5m-streets"), showWarnings = FALSE)

for (i in countries) {
  if (file.exists(file.path("data", "o5m-streets", paste0(i, "-streets.o5m")))==FALSE) {
    system(paste0('./osmfilter data/o5m/', i, '-latest.o5m --keep="highway=*" --drop-version > ', 'data/o5m-streets/', i, '-streets.o5m'))
  }
}

# export to csv only street type, name, and lon/lat

dir.create(path = file.path("data", "csv-streets"), showWarnings = FALSE)

for (i in countries) {
  if (file.exists(file.path("data", "csv-streets", paste0(i, "-streets.csv")))==FALSE) {
    system(paste0('./osmconvert64 data/o5m-streets/', i,
                  '-streets.o5m --all-to-nodes --csv="@id @lat @lon highway name" > data/csv-streets/', i,
                  '-streets.csv', " --csv-separator='; '"))
  }
}

2.4 Keep only street names including reference to Tito

Once all street names have been extracted, only street names inlcuding reference to Tito have been kept for further analysis.

Criteria for filters:

ends with ‘Tito’
ends with ‘Tita’
includes ‘Titov’
includes ‘Тито’ (and accordingly, also all ‘Титов’ etc., but excluding “Ктито*’)

StreetsSummary <- data_frame(Country = countries, TotalStreets = as.integer(NA), TitoStreets = as.integer(NA))

FindTito <- "Tito$|Tita$|Titov|Титов|(?<!К)Тито"
tito_all <- data_frame()

for (i in countries) {
  # Import from csv
  temp_streets <- read_delim(file = file.path("data", "csv-streets", paste0(i, "-streets.csv")),
                             delim = "; ",
                             col_names = FALSE, 
                             locale = locale(decimal_mark = "."),
                             trim_ws = TRUE)
  # Store total number of streets for each country
  StreetsSummary$TotalStreets[StreetsSummary$Country==i] <- nrow(temp_streets)
  # Filter only Tito's streets
  temp_tito <- temp_streets %>% 
    filter(is.na(X5)==FALSE) %>% 
    filter(stringr::str_detect(string = X5, pattern = stringr::regex(pattern = FindTito, ignore_case = TRUE))) %>%
    transmute(lat = X2, lon = X3, streetname = X5)  %>%
    # remove duplicate location with which have similar coordinates (same first decimal)
    mutate(lonShort = format(lon, digit = 3), latShort = format(lat, digit = 3)) %>%
    distinct(lonShort, latShort, .keep_all = TRUE) %>% 
    select(-lonShort, -latShort) %>% 
    mutate(country = i)
  # Store number of Tito's streets in given country
  StreetsSummary$TitoStreets[StreetsSummary$Country==i] <- nrow(temp_tito)
  # Merge table for each country
  tito_all <- bind_rows(tito_all, temp_tito)
}

tito_all <- tito_all %>% distinct()

# tito_all <- tito_all %>% 
#   mutate(lonShort = format(lon, digit = 4), latShort = format(lat, digit = 4)) %>%
#   distinct(lonShort, latShort, .keep_all = TRUE) %>% 
#   select(-lonShort, -latShort)

ExportData(data = tito_all, filename = "OSM_tito_all")

The data are available as a spreadsheet in .csv, .xlsx, and as a data frame in R’s .rds format.

2.5 Preliminary results based on OpenStreetMap

As appears from the following sections, these data include substantial inaccuracies. They are reported here as preliminary results based on OpenStreetMap. See the following sections for more accurate data.

2.5.1 Number of streets dedicated to Tito

StreetsSummary %>%
  arrange(TitoStreets) %>% 
  mutate(Country = toupper(Country)) %>% 
  mutate(Country = forcats::fct_inorder(Country)) %>% 
  ggplot(aes(x = Country, y = TitoStreets, label = TitoStreets)) +
  geom_col() +
  scale_y_continuous(name = "", limits = c(0, 30)) +
  scale_x_discrete(name = "") +
  geom_text(hjust = -0.1) +
  coord_flip() +
  theme_minimal() +
  labs(title = "Number of streets dedicated to Tito",
       subtitle = "Based on OpenStreetMaps data")

ShowTable(data = StreetsSummary %>% arrange(desc(TitoStreets)))

Country	TotalStreets	TitoStreets
croatia	3873902	25
macedonia	739505	24
bosnia-herzegovina	2077207	19
slovenia	3231464	14
serbia	2423391	14
montenegro	713605	7
kosovo	672647	0

2.6 Putting streets dedicated to Tito (according to OpenStreetMap) on a map

# Setting the coordinates
wb <- c(left = 12, bottom = 40, right = 26, top = 48)

# Preparing the canvas, and forcing cache
if (file.exists(file.path("temp", "mapTonerLite.rds"))==FALSE) {
  saveRDS(object = ggmap::get_stamenmap(bbox = wb, zoom = 6, maptype = "toner-lite") %>% ggmap(), file = file.path("temp", "mapTonerLite.rds"))
}

mapTonerLite <- readRDS(file = file.path("temp", "mapTonerLite.rds"))

OSM_TitoTonerLight <-  mapTonerLite + geom_point(data=tito_all, aes(x=lon, y=lat), color="red", size=2, alpha=0.5) +
  labs(x = '', y = '') + 
  labs(title = "Streets and squares dedicated to Tito", 
       subtitle = paste("based on OpenStreetMap"))

OSM_TitoTonerLight

ExportGraph(graph = OSM_TitoTonerLight, filename = "OSM_TitoTonerLight")

This image is available for download in .png, .svg, and as an object in R’s .rds format.

# Preparing the canvas  
dir.create(path = "temp", showWarnings = FALSE)

## forcing cache to prevent reloading map from Google Maps each time
if (file.exists(file.path("temp", "mapG.rds"))==FALSE) {
  saveRDS(object = get_googlemap("Sarajevo", scale = 2, zoom = 6), file = file.path("temp", "mapG.rds"))
}

mapG <- readRDS(file = file.path("temp", "mapG.rds"))

ggmap(mapG) + 
  geom_point(data=tito_all, aes(x=lon, y=lat), color="red", size=2, alpha=0.5) +
  labs(x = '', y = '') +
  labs(title = "Streets and squares dedicated to Tito", 
       subtitle = paste("Based on OpenStreetMap data"))

ExportGraph(filename = "OSM_TitoGmaps")

This image is available for download in .png, .svg, and as an object in R’s .rds format.

# # Preparing the canvas
# mapWatercolor <- get_stamenmap(bbox = wb, zoom = 7, maptype = "watercolor") %>% ggmap()
# # Adding the dots
# mapWatercolor + geom_point(data=tito_all, aes(x=lon, y=lat), color="red", size=2, alpha=0.5) +
#   labs(x = '', y = '') + ggtitle("Streets dedicated to Tito")
# ggsave("TitoWatercolor.png")

2.7 Alternative density maps

Testing alternative visualisations.

mapTonerLite + 
  stat_density2d(
    aes(x = lon, y = lat, fill = ..level..,  alpha = ..level..),
    size = 2, bins = 4, data = tito_all,
    geom = "polygon"
)

2.8 Summary of all relevant street names found on OpenStreetMap

The following table presents all versions of street names including reference to Tito found in each of the countries included in the analysis.

tito_all_streetname <- tito_all %>% select(country, streetname) %>% group_by(country, streetname) %>% count() %>% arrange(country, desc(n), streetname)

ShowTable(data = tito_all_streetname)

country	streetname	n
bosnia-herzegovina	Maršala Tita	11
bosnia-herzegovina	Titova	4
bosnia-herzegovina	Titova ili Put Oficirske Škole	1
bosnia-herzegovina	Trg maršala Tita	1
bosnia-herzegovina	Ul Maršala Tita	1
bosnia-herzegovina	ul. Maršala Tita	1
croatia	Maršala Tita	6
croatia	Ulica Maršala Tita	4
croatia	Obala Maršala Tita	2
croatia	Titov trg	2
croatia	Hodaliste marsala tita	1
croatia	Josipa Broza Tita	1
croatia	Obala Josipa Broza Tita	1
croatia	Obala m. Tita	1
croatia	Poljana maršala Tita	1
croatia	Trg J. B. Tita	1
croatia	Trg Josipa Broza Tita	1
croatia	Trg maršala Tita	1
croatia	Trg Maršala Tita	1
croatia	Ulica Josipa Broza Tita	1
croatia	Ulica Josipa Broza-Tita	1
macedonia	Маршал Тито	14
macedonia	bul. Marsal Tito	1
macedonia	Marsal Tito	1
macedonia	Marshal Tito	1
macedonia	ul. Marshal Tito	1
macedonia	Кеј Маршал Тито	1
macedonia	Титова Митровачка	1
macedonia	Титовелешка	1
macedonia	Титово Ужице	1
macedonia	ул. Маршал Тито	1
macedonia	Улица Маршал Тито	1
montenegro	Marsala Tita	2
montenegro	Gjergj Kastrioti - Skënderbeu / Maršal Tito	1
montenegro	Josipa Broza Tita	1
montenegro	Maršala Tita	1
montenegro	Titove Korenice	1
montenegro	trg Maršala Tita	1
serbia	Maršala Tita	5
serbia	Титоградска	4
serbia	Aleja Maršala Tita	1
serbia	Marsala Tita	1
serbia	Ulica Marsala Tita	1
serbia	Тито Маршал	1
serbia	Титова	1
slovenia	Titova ulica	3
slovenia	Cesta maršala Tita	2
slovenia	Titova cesta	2
slovenia	Trg maršala Tita	2
slovenia	Titov most	1
slovenia	Titov trg	1
slovenia	Titov trg / Piazza Tito	1
slovenia	Titova - Nasipna	1
slovenia	Ulica Josipa Broza-Tita	1

ExportData(data = tito_all_streetname, filename = "tito_all_streetname")

The data are available as a spreadsheet in .csv, .xlsx, and as a data frame in R’s .rds format.