I am trying to reverse geocode a large data-set (around 100k). I used the revgeocode
function from the package ggmap
. I got the result for 1 entry
48 Grand View Terrace, San Francisco,
CA 94114, USA
48 Grand View Terrace Eureka Valley San Francisco
San Francisco County California United States
postal_code postal_code_suffix
, but I need to automate the process and use it for the entire data-set.
I tried
r <- lapply(revgeocode(location = (c(z$lon),c(z$lat)),
output = "more",
messaging = FALSE, sensor = FALSE, override_limit = FALSE,
client = "", signature = ""))
and got the errors for unexpected ',' in each step.
I tried to write the following loop too
r <- for(i in 1:10){
revgeocode(location = ("z$lon", "z$lat"),output = "more", messaging = FALSE, sensor = FALSE, override_limit = FALSE,client = "", signature = "")}
and got similar errors
Please provide some material or helpful links that will help me to write the loop for reverse geocoding. How to verify the authenticity of the data?
To perform reverse geocoding (obtaining addresses from geographic coordinates), we can use the reverse_geocode() function. The arguments are similar to the geocode() function, but now we specify the input data columns with the lat and long arguments.
Yes, the Geoapify Reverse Geocoding Tool is Free! You can upload and process multiple locations. No coding and registration required. You can also use Geoapify Reverse Geocoding API and process up to 3000 addresses per day for FREE.
Reverse geocoding results will be imprecise as well. Administrative area geocoding provides the lowest level of geocoding accuracy to connect an area with a location such as a state or province. It is not possible to connect state or provincial level coordinates to a specific address.
Based on this answer, you could create new variables in your data.frame
We use mapply()
to process your coordinates and return the results in a list res
.
res <- mapply(FUN = function(lon, lat) {
revgeocode(c(lon, lat), output = "more")
},
df$lon, df$lat
)
Then, we use rbindlist()
from data.table
to convert the list into a data.frame
(with fill = TRUE
since not all elements of res
have the same lenghts i.e. some results do not return a street_number
and a postal_code
) and cbind()
it to the original data
cbind(df, data.table::rbindlist(res, fill = TRUE))
Update
Following up on your comment, if you want to process more than 2500 queries, you could subscribe to Google Maps APIs Premium Plan to unlock higher quotas. Then you can pass on your credentials to revgeocode()
using the signature
and client
parameter.
As per mentionned in the documentation:
Upon purchasing your Google Maps APIs Premium Plan license, you will receive a welcome email from Google that contains your client ID and your private cryptographic key.
Your client ID is used to access the special features of Google Maps APIs Premium Plan. All client IDs begin with a gme- prefix. Pass your client ID as the value of the client parameter. A unique digital signature is generated using your private cryptographic key. Pass this signature as the value of the signature parameter.
You can see how it works under the hood by examining the revgeocode()
source and see how the URL is constructed:
sensor4url <- paste("&sensor=", sensor, sep = "")
client4url <- paste("&client=", client, sep = "")
signature4url <- paste("&signature=", signature, sep = "")
url_string <- paste("http://maps.googleapis.com/maps/api/geocode/json?latlng=",
loc4url, sensor4url, sep = "")
if (userType == "business") {
url_string <- paste(url_string, client4url, signature4url,
sep = "")
}
Data
df <- structure(list(lat = c(32.31, 32.19, 34.75, 35.09, 35.35, 34.74 ), lon =
c(119.827, 119.637, 119.381, 119.364, 119.534, 119.421 )), .Names =
c("lat", "lon"), row.names = c(21L, 32L, 37L, 48L, 50L, 89L), class = "data.frame")
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