This answer is based on obtaining an origin-destination matrix between a grid of (roughly) equally distant points. This is a computer intensive operation not only because it requires a good number of apiAPI calls to mapping services, but also because the servers must calculate a matrix for each call. The number of required calls grows exponentially along the number of points in the grid.
To tackle this problem, I would suggest that you consider running on your local machine or on a local server a mapping server. Project OSRM offers a relatively simple, free, and open-source solution, enabling you to run an OpenStreetMap server into a Linux docker (https://github.com/Project-OSRM/osrm-backend). Having your own local mapping server will allow you to make as many API calls as you desire. R's osrm package allows you to interact with OpenStreetMaps' apis. includingAPIs, Including those placed to a local server.
BE <- raster::getData("GADM", country = "BEL", level = 1)
Bruxelles <- BE[BE$NAME_1 == "Bruxelles", ]
df_grid <- makegrid(Bruxelles, cellsize = 0.02) %>%
SpatialPoints() %>%
as.data.frame() %>% ## I convert the SpatialPoints object into a simple data.frame
rownames_to_columnas.data.frame() %>%
## create a unique id for each point in the data.frame
renamerownames_to_column(id) =%>% rowname,
lat = x2, lon = x1) # ## rename variables of the data.frame with more explanatory names.
options rename(osrm.serverid = "http://127.0.0.1:5000/"rowname, lat = x2, lon = x1)
## I point osrm.server to the OpenStreet docker running in my Linux machine. ...
### ... Do not run this if you are getting your data from OpenStreet public servers.
Distance_Tables <- osrmTableoptions(locosrm.server = df_grid"http://127.0.0.1:5000/")
## I obtain a list with distances (Origin Destination Matrix in ...
### ... minutes, origins and destinations)
Distance_Tables <- osrmTable(loc = df_grid)
OD_Matrix <- Distance_Tables$durations %>% ## Subsetsubset the previous list
and
as_data_frame() %>% ## ...convert the Origin Destination Matrix into a tibble
rownames_to_column as_data_frame() %>%
rename(origin_id = rowname rownames_to_column() %>%
## make sure we have an id column for the OD tibble
rename(origin_id = rowname) %>%
## transform the tibble into long/tidy format
gather(key = destination_id, value = distance_time, -origin_id) %>% #
transform the tibble into long/tidy format
left_join(df_grid, by = c("origin_id" = "id")) %>%
## set origin coordinates
rename(origin_lon = lon, origin_lat = lat) %>% ##
set origin coordinates
left_join(df_grid, by = c("destination_id" = "id")) %>%
## set destination coordinates
rename(destination_lat = lat, destination_lon = lon) ## set destination coordinates
## Obtain a nice looking road map of Brussels
Brux_map <- get_map(location = "bruxelles, belgique",
zoom = 11,
source = "google",
maptype = "roadmap")
ggmap(Brux_map) +
geom_point(aes(x = origin_lon, y = origin_lat),
data = OD_Matrix %>%
filter(destination_id == 42), ## Here I selected point_id 42 as the desired target, ...
## ... just because it is not far from the City Center.
filter(destination_id == 42),
size = 0.5) +
## Draw a diamond around point_id 42
geom_point(aes(x = origin_lon, y = origin_lat),
data = OD_Matrix %>%
filter(destination_id == 42, origin_id == 42),
shape = 5, size = 3) + ##
Draw a diamond## aroundCountour point_idmarking 42a distance of up to 8 minutes
geom_convexhull(alpha = 0.2,
fill = "blue",
geom_convexhull(alpha = 0.2,
fillcolour = "blue",
colour = "blue",
data = OD_Matrix %>%
filter(destination_id == 42,
distance_time <= 8), ##
Countour marking a distance of up to 8 minutes
aes(x = origin_lon, y = origin_lat)) +
## Countour marking a distance of up to 16 minutes
geom_convexhull(alpha = 0.2,
fill = "red",
colour = "red",
data = OD_Matrix %>%
filter(destination_id == 42,
distance_time <= 15), ##
Countour marking a distance of up to 16 minutes
aes(x = origin_lon, y = origin_lat))
I hope this help you to get your reverse Isochrones.
