Calculating Geographic Distance With R

mapdist()

The ggmap package includes a function called mapdist() that computes the distance between two locations based on a “from” and a “to” argument. These parameters accept strings. What’s nice about this is that the two steps described above are combined in one. The names are geocoded and distances between the locations are computed via the Google API Distance Matrix. The mode can be set to “walking”, “bicycling” or “driving” and the output format configured to either “simple” (data frame) or “all” (list). But with its defaults, the function gives you easy access to the distance in miles (or meters or kilometers) and duration in minutes (or seconds or hours):

# load ggmap
library(ggmap)

# calculate distance between two place names
arena_dist <- mapdist(from = "Madison Square Garden New York, NY", to = "The Palace of Auburn Hills Auburn Hills, MI")

# output distance in miles
arena_dist$miles

# output "distance" in minutes
arena_dist$minutes

The method above works well for measuring a single distance between two locations. But there are some cases (including the one that inspired this post) that require measurements across many combinations of places. The code below achieves this by passing mapdist() into lapply():

# load ggmap
library(ggmap)

# create vector of place names
places_names <- c("Museum of Modern Art New York, NY",
                "Smithsonian Museum of American Art Washington, DC",
                "Brooklyn Museum Brooklyn, NY",
                "Walker Art Center Minneapolis, MN",
                "Fralin Museum of Art Charlottesville, VA")

# apply distance calculation to all combinations of place names
dist_list <- lapply(places_names, 
                    function(z) 
                        sapply(z, 
                               function(x) 
                                   mapdist(from=places_names, to=x)$miles)
                                   )

# view results as list
dist_list

# unlist results and convert to a "named" matrix format
dist_mat <- sapply(dist_list, unlist)

colnames(dist_mat) <- places_names

rownames(dist_mat) <- places_names

# view results as matrix
dist_mat

This is great … but there’s another but. The Google Maps API limits usage of its web services) to 250 queries per day. So say you have a list of 100 locations, and you want to compute a matrix of distances between all of them:

100 x 100 = 10000 > 2500

Bummer.

This is where a combination of the geocode() function from the ggmap package and the gdist() function from the Imap package is helpful.

geocode() + gdist()

There’s an easy workaround to the Google Maps API query limit: use the Google Maps API … just make fewer queries.

geocode() calculates the latitude and longitude of an input location name (as a string) via the Google Maps Geocoding API. Because it’s calculating the distance once for each individual location, the resulting query count for a list of 100 institutions is 100. And with latitude and longitude available, you can rely on other packages to calculate distance. gdist() from the Imap package computes the geodesic distance based on the latitude and longitude of the first location, and latitude and longitude of the second location. Because gdist() takes lon.1, lat.1, lon.2 and lat.2 the code can be a little bit messier. The example below geocodes the same place names as above, then computes all of the distances between them:

# create vector of place names
places_names <- c("Museum of Modern Art New York, NY",
                "Smithsonian Museum of American Art Washington, DC",
                "Brooklyn Museum Brooklyn, NY",
                "Walker Art Center Minneapolis, MN",
                "Fralin Museum of Art Charlottesville, VA")

# geocode place names
places_lat <- geocode(places_names, source="google")$lat
places_lon <- geocode(places_names, source="google")$lon

# create a data frame to store all variables
places_df <- data.frame(names = places_names,
                        lat = places_lat,
                        lon = places_lon)

# calculate geodesic distance with gdist() from Imap package

# load Imap
library(Imap)

# create an empty list
dist_list <- list()

# iterate through data frame placing calculated distance next to place place names
for (i in 1:nrow(places_df)) {
    
    dist_list[[i]] <- gdist(lon.1 = places_df$lon[i], 
                              lat.1 = places_df$lat[i], 
                              lon.2 = places_df$lon, 
                              lat.2 = places_df$lat, 
                              units="miles")
    
}

# view results as list
dist_list

# unlist results and convert to a "named" matrix format
dist_mat <- sapply(dist_list, unlist)

colnames(dist_mat) <- places_names

rownames(dist_mat) <- places_names

# view results as matrix
dist_mat

NB As of writing this blog post the ggmap package had been recently updated to Version 2.5.2, which sets the default source for the geocode() function set to “dsk”. I tried that method and received inaccurate latitude and longitude results. I would recommend using “source=‘google’” if you plan on using geocode() + gdist()

Alternative Solutions

Below is a list of a few possible alternatives to calculating geographic distances:

1. Enable pay-as-you-go billing for the Google Maps Distance Matrix API to expand query limit

2. Use the geosphere package(anybody else used this?)

3. Write your own great circle earth distance function