Structured geocoding — structured • photon

Geocode a set of place information such as street, house number, or post code. Structured geocoding is generally more accurate but requires more information than unstructured geocoding.

Note that structured geocoding must be specifically enabled when building a Nominatim database. It is generally not available on komoot's public API and on pre-built search indices through download_searchindex. See vignette("nominatim-import", package = "photon") for details. You can use the helper function has_structured_support() to check if the current API supports structured geocoding.

Usage

structured(
  .data,
  limit = 3,
  lang = "en",
  bbox = NULL,
  osm_tag = NULL,
  layer = NULL,
  locbias = NULL,
  locbias_scale = NULL,
  zoom = NULL,
  progress = interactive()
)

has_structured_support()

Arguments

.data: Dataframe or list containing structured information on a place to geocode. Can contain the columns street, housenumber, postcode, city, district, county, state, and countrycode. At least one of these columns must be present in the dataframe. Note that countries must be passed as ISO-2 country codes.
limit: Number of results to return. Defaults to 3.
lang: Language of the results.
bbox: Any object that can be parsed by st_bbox. Results must lie within this bbox.
osm_tag: Character string giving an OSM tag to filter the results by. See details.
layer: Character string giving a layer to filter the results by. Can be one of "house", "street", "locality", "district", "city", "county", "state", "country", or "other".
locbias: Numeric vector of length 2 or any object that can be coerced to a length-2 numeric vector (e.g. a list or sfg object). Specifies a location bias for geocoding in the format c(lon, lat). Geocoding results are biased towards this point. The radius of the bias is controlled through zoom and the weight of place prominence through location_bias_scale.
locbias_scale: Numeric vector specifying the importance of prominence in locbias. A higher prominence scale gives more weight to important places. Defaults to 0.2.
zoom: Numeric specifying the radius for which the locbias is effective. Corresponds to the zoom level in OpenStreetMap. The exact relation to locbias is \(0.25\text{ km} \cdot 2^{(18 - \text{zoom})}\). Defaults to 16.
progress: If TRUE, shows a progress bar for longer queries.

Value

An sf dataframe or tibble containing the following columns:

idx: Internal ID specifying the index of the texts parameter.
osm_type: Type of OSM element, one of N (node), W (way), R (relation), or P (polygon).
osm_id: OpenStreetMap ID of the matched element.
country: Country of the matched place.
city: City of the matched place.
osm_key: OpenStreetMap key.
countrycode: ISO2 country code.
housenumber: House number, if applicable.
postcode: Post code, if applicable.
locality: Locality, if applicable.
street: Street, if applicable.
district: District name, if applicable.
osm_value: OpenStreetMap tag value.
name: Place name.
type: Layer type as described for the layer parameter.
extent: Boundary box of the match.

Details

Filtering by OpenStreetMap tags follows a distinct syntax explained on https://github.com/komoot/photon. In particular:

Include places with tag: key:value
Exclude places with tag: !key:value
Include places with tag key: key
Include places with tag value: :value
Exclude places with tag key: !key
Exclude places with tag value: :!value

Examples

if (FALSE) { # \dontrun{
# structured() requires an OpenSearch instance with structured support
# the following code will not work off the shelf
# refer to vignette("nominatim-import") for details
dir <- file.path(tempdir(), "photon")
photon <- new_photon(dir, opensearch = TRUE)
photon$import(password = "psql_password", structured = TRUE)
photon$start()

# check if structured() is supported
has_structured_support()

# structured() works on dataframes containing structurized data
place_data <- data.frame(
  housenumber = c(NA, "77C", NA),
  street = c("Falealilli Cross Island Road", "Main Beach Road", "Le Mafa Pass Road"),
  state = c("Tuamasaga", "Tuamasaga", "Atua")
)
structured(place_data, limit = 1)

# countries must be specified as iso2 country codes
structured(data.frame(countrycode = "ws"))

# traditional parameters from geocode() can also be used but are much more niche
structured(data.frame(city = "Apia"), layer = "house") # matches nothing

# structured geocoding can discern small differences in places
safune <- data.frame(
  city = c("Safune", "Safune"),
  state = c("Gaga'ifomauga", "Tuamasaga")
)
structured(safune, limit = 1)
} # }