CODEWITHSUNDEEP
rggplot2shapefile
Flavio2f
Flavio2f

Reputation: 3

How to find city using latitude and longitude from shape file?

My problem: I would like to get city names from shapefile using many longitude and latitude data. Some lattitude and longitude will not necessarily be at the center of the polygon (city), but in some part of the polygon. I mean, I do not know which city this points belongs, this is what I want to know. In the shape file have the name of each polygon that belong at the respective City.

I know that Google and others API's are paid and/or limited. I prefere to get from shapefile.

[link to shapfile (cities) from São Paulo state in Brazil] (geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2020/UFs/SP/SP_Municipios_2020.zip)

Some points examples:

'coord <- data.frame( "lon" = c(-48.1766, -50.078,, -52.958, -51.3837, -50.2466, -47.0006, -47.8348, -45.0857), "lat" = c(-21.7946, -21.4209, -22.482, -20.8939, -20.2834, -21.4708, -24.4881, -23.4339))'

Upvotes: 0

Views: 526

Answers (1)

det
det

Reputation: 5232

For some reason library revgeo (reverse geocoding using Photon geocoder) is not working for me currently. You can try something like this:

library(tidyverse)
library(rjson)

coord %>%
  select(lon, lat) %>%
  imap(~str_c(.y, "=", .x)) %>%
  pmap(~str_c(c(...), collapse = "&")) %>%
  str_c("https://photon.komoot.io/reverse?", .) %>%
  map(~rjson::fromJSON(file = .x)) %>%
  map(~pluck(.x, "features", 1, "properties")) %>%
  map_dfr(~.x[c("country", "city", "county", "district", "state")]) %>%
  cbind(coord, .)

       lon      lat country          city                                                   county        district     state
1 -48.1766 -21.7946  Brasil    Araraquara            Região Geográfica Intermediária de Araraquara    Vila Melhado São Paulo
2 -50.0780 -21.4209  Brasil     Penápolis             Região Geográfica Intermediária de Araçatuba       Penápolis São Paulo
3 -52.9580 -22.4820  Brasil        Rosana   Região Geográfica Intermediária de Presidente Prudente          Rosana São Paulo
4 -51.3837 -20.8939  Brasil     Andradina             Região Geográfica Intermediária de Araçatuba  Vila Peliciari São Paulo
5 -50.2466 -20.2834  Brasil Fernandópolis Região Geográfica Intermediária de São José do Rio Preto         Coester São Paulo
6 -47.0006 -21.4708  Brasil        Mococa              Região Geográfica Intermediária de Campinas          Centro São Paulo
7 -47.8348 -24.4881  Brasil      Registro              Região Geográfica Intermediária de Sorocaba   Vila Paraguai São Paulo
8 -45.0857 -23.4339  Brasil       Ubatuba  Região Metropolitana do Vale do Paraíba e Litoral Norte Jardim Carolina São Paulo

Using shape file:

library(sf)

shp_df <- read_sf(data_path)
st_coord <- st_as_sf(coord, coords = c("lon", "lat"), crs = st_crs(shp_df))
coord$city <- shp_df[unlist(st_within(st_coord, shp_df)),]$NM_MUN

coord

       lon      lat          city
1 -48.1766 -21.7946    Araraquara
2 -50.0780 -21.4209     Penápolis
3 -52.9580 -22.4820        Rosana
4 -51.3837 -20.8939     Andradina
5 -50.2466 -20.2834 Fernandópolis
6 -47.0006 -21.4708        Mococa
7 -47.8348 -24.4881      Registro
8 -45.0857 -23.4339       Ubatuba

where data_path is path to SP_Municipios_2020.shp.

Upvotes: 0

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