This package contains functions to geocode locations and generate isochrones/isodistance polygons. It also allows for the estimation of population or employment captured within the isochrone.
devtools::install_github('chrisjb/RDistanceMatrix')
# for the examples below also install
devtools::install_github('chrisjb/basemapR')The isochrone method generates a polygon of the total area to which one
can travel from a given origin point. The origin point can be specified
as either an address string to be geocoded, or a data.frame with a
lat and lng column specifying the coordinates.
To use the mapbox method we need to get ourselves an API key and set it up on R. See section (6.2) for how to do this.
library(RDistanceMatrix)
battersea_isochrone <- make_isochrone(site = 'Battersea Power Station', time = 30, method = 'mapbox', mode= 'driving')By plotting our isochrone we can see that the mapbox method generates a pretty detailed polygon based on drive time from a given origin.
library(ggplot2)
library(basemapR)
ggplot() +
basemapR::base_map(bbox = sf::st_bbox(battersea_isochrone), increase_zoom = 2,basemap = 'google') +
geom_sf(data = battersea_isochrone, aes(fill = fillColor, color = color, alpha = opacity), show.legend = FALSE) ## please see attribution details: https://wikimediafoundation.org/wiki/Maps_Terms_of_Use
When to use method=mapbox: The mapbox method has the highest level of detail and we can see that it sticks quite rigidly to the road network. This should be used as the default option if we want drive/walking/cycling isochrones from a given origin.
When not to use method=mapbox: The mapbox method is less flexible
than the alternative of method=google but has the benefit of being
quick, easy and accurate. It does not support the creation of transit
isochrones which use the public transport network. It also does not work
in the reverse direction (direction='in') so cannot generate an
isochrone of origins that can travel to the destination site in a
given time. Finally, the mapbox method does not support drive times in
traffic so the isochrone can be seen as an ‘average’ drive time.
The google method is more flexible but requires a bit more set up. We
have a multiplier parameter to tune (see 1.2.1) and we will see that
while broadly similar to the mapbox output, it does not have quite the
same level of detail in it’s ability to follow the road network out to
its full extent.
The google method uses the google distance matrix API to calculate the
travel time to each of a detailed grid of points. The grid that we set
up must be larger than the possible travel time so we consider all
possible points. A multiplier parameter is used to ensure that the
grid is an appropriate size. A multiplier of 1.0 means that we can, on
average, travel 1km in 1 minute and so draws a grid of 10km x 10km for a
10 minute isochrone. The true multiplier will vary depending on the
geography with central London being much lower, and some areas being
higher than this.
To tune the parameter we should use method=google_guess. This method
uses a very small number of points in a grid to make an initial guess at
an isochrone. It returns a leaflet map with the grid and isochrone as
layers. A correctly tuned multiplier parameter should contain the
entire isochrone inside of the grid of points, if it doesn’t the
multiplier should be increased. The isochrone should also reach at least
one of the penultimate grid points to ensure we have a detailed enough
initial
guess.
make_isochrone(site = 'battersea power station', time = 30, method = 'google_guess', mode= 'driving', multiplier = 0.4)
Once we have a well calibrated multiplier parameter, the algorithm
will create a more detailed version of the isochrone by chainging the
method to method='google'. We have the choice of high, medium or
low detail. The former will use more of our API quota and cost us more
credits (see information on google api credits below). The default is
medium detail which should be sufficient for most
purposes.
battersea_isochrone_google <- make_isochrone(site = 'Battersea Power Station', time = 30, method = 'google', detail = 'med', mode= 'driving', multiplier = 0.4)## Geocoding: "Battersea Power Station" if you entered precise co-ordinates, please specify site as a data frame containing the columns "lat" and "lng"
## drawing initial isochrone...
## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0
## adding detail to initial isochrone...
## Trying URL: 1 of 4
## Trying URL: 2 of 4
## Trying URL: 3 of 4
## Trying URL: 4 of 4
## Google API elements used: 476 (£2.38 credits). Isochrone generated to accuracy of 509m
If we compare the results of our mapbox isochrone (red) with the google isochrone (blue), we see that the results are broadly similar but the google one is more generalised. The mapbox version does a better job at sticking to the road network and following the roads out as long as to their 30 minute extents. For this reason, we should prefer the mapbox version for tasks that can be accomplished with the mapbox API.
library(ggplot2)
library(basemapR)
ggplot() +
basemapR::base_map(bbox = sf::st_bbox(battersea_isochrone), increase_zoom = 2,basemap = 'google') +
geom_sf(data = battersea_isochrone, aes(fill = fillColor, color = color, alpha = opacity), show.legend = FALSE) +
geom_sf(data = battersea_isochrone_google, fill = 'blue', color = 'blue', alpha = 0.3)## please see attribution details: https://wikimediafoundation.org/wiki/Maps_Terms_of_Use
We can increase the detail of our google isochrone (costing us more credits):
battersea_isochrone_google_high <- make_isochrone(site = 'Battersea Power Station', time = 30, method = 'google', detail = 'high', mode= 'driving', multiplier = 0.4)## Geocoding: "Battersea Power Station" if you entered precise co-ordinates, please specify site as a data frame containing the columns "lat" and "lng"
## drawing initial isochrone...
## adding detail to initial isochrone...
## Trying URL: 1 of 7
## Trying URL: 2 of 7
## Trying URL: 3 of 7
## Trying URL: 4 of 7
## Trying URL: 5 of 7
## Trying URL: 6 of 7
## Trying URL: 7 of 7
## Google API elements used: 784 (£3.92 credits). Isochrone generated to accuracy of 382m
This gives us a more detailed isochrone, but we also get a few ‘islands’ and ‘holes’ where the algorithm found points that could be reached within 30minutes, but where there was a point in between which couldn’t (perhaps the point identified was in a park or otherwise off the road network).
library(ggplot2)
library(basemapR)
ggplot() +
basemapR::base_map(bbox = sf::st_bbox(battersea_isochrone), increase_zoom = 2,basemap = 'google') +
geom_sf(data = battersea_isochrone, aes(fill = fillColor, color = color, alpha = opacity), show.legend = FALSE) +
geom_sf(data = battersea_isochrone_google_high, fill = 'blue', color = 'blue', alpha = 0.3)## please see attribution details: https://wikimediafoundation.org/wiki/Maps_Terms_of_Use
With the google method we have the ability to reverse the direction
(what origins are there that can we leave from and arrive at the site
within x minutes?). We can also set the departure time to a peak hour to
get the isochrone accounting for traffic, or we can use mode=transit
to get an isochrone using public
transport.
battersea_isochrone_google_pt <- make_isochrone(site = 'Battersea Power Station', time = 30, method = 'google', detail = 'high', mode= 'transit', multiplier = 0.4)## Geocoding: "Battersea Power Station" if you entered precise co-ordinates, please specify site as a data frame containing the columns "lat" and "lng"
## drawing initial isochrone...
## adding detail to initial isochrone...
## Trying URL: 1 of 8
## Trying URL: 2 of 8
## Trying URL: 3 of 8
## Trying URL: 4 of 8
## Trying URL: 5 of 8
## Trying URL: 6 of 8
## Trying URL: 7 of 8
## Trying URL: 8 of 8
## Google API elements used: 807 (£4.035 credits). Isochrone generated to accuracy of 237m
With public transport (blue) we can’t get as far from Battersea Power station as we could by car (red).
ggplot() +
basemapR::base_map(bbox = sf::st_bbox(battersea_isochrone), increase_zoom = 2,basemap = 'google') +
geom_sf(data = battersea_isochrone_google, fill = "#bf4040", color = "#bf4040", alpha = .33, show.legend = FALSE) +
geom_sf(data = battersea_isochrone_google_pt, fill = 'blue', color = 'blue', alpha = 0.33)## please see attribution details: https://wikimediafoundation.org/wiki/Maps_Terms_of_Use
let’s see what happens with traffic. Note that the departing parameter
must be set to a date and time in the
future.
battersea_isochrone_google_traffic <- make_isochrone(site = 'Battersea Power Station', time = 30, method = 'google', detail = 'med', mode= 'driving', multiplier = 0.25,
departing = '2020-03-02 08:00:00')## Geocoding: "Battersea Power Station" if you entered precise co-ordinates, please specify site as a data frame containing the columns "lat" and "lng"
## drawing initial isochrone...
## adding detail to initial isochrone...
## Trying URL: 1 of 4
## Trying URL: 2 of 4
## Trying URL: 3 of 4
## Trying URL: 4 of 4
## Google API elements used: 486 (£4.86 credits). Isochrone generated to accuracy of 442m
In 8am traffic (blue) we can now only travel a bit further then by public transport (green).
ggplot() +
basemapR::base_map(bbox = sf::st_bbox(battersea_isochrone), increase_zoom = 2,basemap = 'google') +
geom_sf(data = battersea_isochrone_google, fill = "#bf4040", color = "#bf4040", alpha = .33, show.legend = FALSE) +
geom_sf(data = battersea_isochrone_google_traffic, fill = 'blue', color = 'blue', alpha = .33) +
geom_sf(data = battersea_isochrone_google_pt, fill = "green", color = "green", alpha = .33, show.legend = FALSE) ## please see attribution details: https://wikimediafoundation.org/wiki/Maps_Terms_of_Use
Is it better getting to battersea than from?
battersea_isochrone_google_traffic_inbound <- make_isochrone(site = 'Battersea Power Station', time = 30, direction = 'in',
method = 'google', detail = 'med', mode= 'driving', multiplier = 0.25,
departing = '2020-03-02 08:00:00')## Geocoding: "Battersea Power Station" if you entered precise co-ordinates, please specify site as a data frame containing the columns "lat" and "lng"
## drawing initial isochrone...
## Trying URL: 1 of 2
## adding detail to initial isochrone...
## Trying URL: 1 of 4
## Trying URL: 2 of 4
## Trying URL: 3 of 4
## Trying URL: 4 of 4
## Google API elements used: 475 (£4.75 credits). Isochrone generated to accuracy of 423m
Inbound travel time (green) seems to be broadly similar to outbound time (blue) in this case.
ggplot() +
basemapR::base_map(bbox = sf::st_bbox(battersea_isochrone), increase_zoom = 2,basemap = 'google') +
geom_sf(data = battersea_isochrone_google_traffic, fill = 'blue', color = 'blue', alpha = .33) +
geom_sf(data = battersea_isochrone_google_traffic_inbound, fill = "green", color = "green", alpha = .33, show.legend = FALSE)## please see attribution details: https://wikimediafoundation.org/wiki/Maps_Terms_of_Use
Only available with method=google.
Creates a simple features polygon of the area accessible to/from a given
location within a certain travel distance. Distances available by
driving, transit, walking or cycling.
As with make_isochrone, we can set the detail level detail = c('high', 'medium', 'low') to get a more/less accurate isodistance
polygon at the expense of more/less google API credits (see
below).
examples:
walk_radius <- make_isodistance('EC2R 8AH', distance = 2000, direction = 'out', mode = 'walking',)## Geocoding: "EC2R 8AH" if you entered precise co-ordinates, please specify site as a data frame containing the columns "lat" and "lng"
## Trying URL: 1 of 2
## Trying URL: 2 of 2
## Google API elements used: 214 (£1.07 credits). Isochrone generated to accuracy of 170m
ggplot() +
base_map(bbox = st_bbox(walk_radius), increase_zoom = 2,basemap = 'google') +
geom_sf(data = walk_radius, fill=NA)## please see attribution details: https://wikimediafoundation.org/wiki/Maps_Terms_of_Use
Uses the google distance matrix API to get the distance or time between a set of origins and destinations. Input is a data.frame with columns for origin and destination.
Examples: Single origin-destination:
od1 <- tibble::tibble(
origin = '51.5131,-0.09182',
destination = 'EC2R 8AH'
)
get_distance(od1, origin, destination, mode = 'transit')## Warning: Prefixing `UQ()` with the rlang namespace is deprecated as of rlang 0.3.0.
## Please use the non-prefixed form or `!!` instead.
##
## # Bad:
## rlang::expr(mean(rlang::UQ(var) * 100))
##
## # Ok:
## rlang::expr(mean(UQ(var) * 100))
##
## # Good:
## rlang::expr(mean(!!var * 100))
##
## This warning is displayed once per session.
## # A tibble: 1 x 4
## origin destination transit_distance transit_time
## <chr> <chr> <dbl> <dbl>
## 1 51.5131,-0.09182 EC2R 8AH 553 4.57
Multiple origin destination:
pcd_df <- tibble::tribble(
~ origin, ~destination,
"51.5131,-0.09182", 'EC2R 8AH',
"51.5037,-0.01715", 'E14 5AB',
" 51.5320,-0.12343", 'SE1 9SG',
"51.4447,-0.33749", 'SW1A 1AA'
)
get_distance(pcd_df, origin, destination)## # A tibble: 4 x 4
## origin destination driving_distance driving_time
## <chr> <chr> <dbl> <dbl>
## 1 "51.5131,-0.09182" EC2R 8AH 515 3.4
## 2 "51.5037,-0.01715" E14 5AB 867 3.33
## 3 " 51.5320,-0.12343" SE1 9SG 5747 24.0
## 4 "51.4447,-0.33749" SW1A 1AA 16895 40.2
Example with a dataframe of origins (lat lng) and a single destination;
df <- tibble::tribble(
~ lat, ~lng,
51.5131, -0.09182,
51.5037, -0.01715,
51.5320, -0.12343,
51.4447, -0.33749
)
origin_df <- mutate(df, origin = paste0(lat,',',lng))
get_distance(origin_df, origin, 'London Paddington')## # A tibble: 4 x 5
## lat lng origin driving_distance driving_time
## <dbl> <dbl> <chr> <dbl> <dbl>
## 1 51.5 -0.0918 51.5131,-0.09182 7877 26.9
## 2 51.5 -0.0172 51.5037,-0.01715 13839 39.3
## 3 51.5 -0.123 51.532,-0.12343 5231 20.0
## 4 51.4 -0.337 51.4447,-0.33749 17944 38.4
geocode uses the google geocoding API to geocode an address or set of
coordinates. geocode_mapbox uses the mapbox geocoding API to geocode
an address or set of coordinates.
Both require an API key to use for the respective APIs. See sections below on getting an API key.
library(RDistanceMatrix)
geocode(address = 'Ulverston, Cumbria')## lat lng type address
## 1 54.19514 -3.09267 APPROXIMATE Ulverston LA12, UK
By default the API will return multiple potential matches for our site.
geocode_mapbox(address = 'Bath Abbey, Bath, UK', return_all = T)## geocoding url: https://api.mapbox.com/geocoding/v5/mapbox.places/Bath%20Abbey,%20Bath,%20UK.json?access_token=SECRET
## lat lng type
## 1 51.38142 -2.358920 poi.180388701038
## 2 51.38081 -2.360889 address.4696521299330334
## 3 51.38619 -2.362608 poi.850403578315
## 4 51.38636 -2.363284 poi.283467888870
## 5 51.38417 -2.360052 poi.523986016019
Setting return_all = F can be useful when we want only the first
identified location returned. The first location tends to be the best
guess at the intended
address.
geocode_mapbox(address = 'Bath Abbey, Bath, UK', return_all = F)## geocoding url: https://api.mapbox.com/geocoding/v5/mapbox.places/Bath%20Abbey,%20Bath,%20UK.json?access_token=SECRET
## lat lng type
## 1 51.38142 -2.35892 poi.180388701038
We can always explore the locations returned in leaflet
library(leaflet)
bath <- geocode_mapbox(address = 'Bath Abbey, Bath, UK', return_all = T)
leaflet() %>%
addTiles() %>%
addAwesomeMarkers(data = bath, lng = ~lng, lat= ~lat, popup = ~type) %>%
addAwesomeMarkers(data = bath[1,], lng = ~lng, lat= ~lat, popup = 'best_guess',
icon = ~awesomeIcons('star',markerColor = 'red'))This function aims to estimate the population within an sf polygon. It
can be used in conjunction with make_isochrone or make_isodistance
which both return sf objects.
The function works by intersecting Lower Layer Super Output Areas (lsoas) with the input polygon. The population is then fetched from the NOMIS API for each LSOA that overlaps with our input polygon.
The dataset returned contains population data for each LSOA within our
boundary (population), the percentage of the LSOA that lies within our
boundary (overlap), and the estimated population that actually lies
within the boundary (population_within). The population_within
column assumes that population is evenly distributed throughout the LSOA
so is an estimate rather than a precise
figure.
iso <- make_isochrone(site = 'bath abbey, bath, uk', time = 30, method = 'mapbox', mode = 'driving')## Geocoding: "bath abbey, bath, uk" if you entered precise co-ordinates, please specify site as a data frame containing the columns "lat" and "lng"
## geocoding url: https://api.mapbox.com/geocoding/v5/mapbox.places/bath%20abbey,%20bath,%20uk.json?access_token=SECRET
## fetching isochrone from url: https://api.mapbox.com/isochrone/v1/mapbox/driving/-2.35892,51.381419?contours_minutes=30&polygons=true&access_token=SECRET
pop_all_ages <- get_population_within(iso, year ='latest',age = 'all')
glimpse(pop_all_ages)## Observations: 183
## Variables: 11
## $ date <dbl> 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018…
## $ geography_code <chr> "E01014370", "E01014371", "E01014372", "E01014…
## $ geography_name <chr> "Bath and North East Somerset 007A", "Bath and…
## $ geography_type <chr> "2011 super output areas - lower layer", "2011…
## $ gender_name <chr> "Total", "Total", "Total", "Total", "Total", "…
## $ age <chr> "All Ages", "All Ages", "All Ages", "All Ages"…
## $ age_type <chr> "Labour Market category", "Labour Market categ…
## $ population <dbl> 2037, 1933, 2057, 1717, 1535, 1261, 1475, 1388…
## $ record_count <dbl> 183, 183, 183, 183, 183, 183, 183, 183, 183, 1…
## $ overlap <dbl> 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00…
## $ population_within <dbl> 2037.00, 1933.00, 2057.00, 1717.00, 1535.00, 1…
We can also split by age group. Options include working,five_year
and sya (single year of age).
library(dplyr)
pop_working <- get_population_within(iso, year ='latest',age = 'working')
pop_working %>%
group_by(age) %>%
summarise(estimated_pop = sum(population_within))## # A tibble: 3 x 2
## age estimated_pop
## <chr> <dbl>
## 1 Aged 0 to 15 34949.
## 2 Aged 16 to 64 134067
## 3 Aged 65+ 41510.
This function aims to estimate the employment within an sf polygon. It
can be used in conjunction with make_isochrone or make_isodistance
which both return sf objects.
The function works by intersecting Lower Layer Super Output Areas (lsoas) with the input polygon. The employment data is then fetched from the NOMIS API using the Business Register and Employment Survey dataset.
The dataset returned contains employment data for each LSOA within our
boundary (employment), the percentage of the LSOA that lies within our
boundary (overlap), and the estimated employment that actually lies
within the boundary (employment_within). The employment_within
column assumes that employment is evenly distributed throughout the LSOA
so is an estimate rather than a precise
figure.
iso <- make_isochrone(site = 'Barrow-in-Furness, Cumrbia', time = 20, method = 'mapbox', mode = 'driving')## Geocoding: "Barrow-in-Furness, Cumrbia" if you entered precise co-ordinates, please specify site as a data frame containing the columns "lat" and "lng"
## geocoding url: https://api.mapbox.com/geocoding/v5/mapbox.places/Barrow-in-Furness,%20Cumrbia.json?access_token=SECRET
## fetching isochrone from url: https://api.mapbox.com/isochrone/v1/mapbox/driving/-3.2289,54.1113?contours_minutes=20&polygons=true&access_token=SECRET
emp_all_ind <- get_employment_within(iso, year ='latest',industry = 'all')
glimpse(emp_all_ind)## Observations: 56
## Variables: 12
## $ date <dbl> 2018, 2018, 2018, 2018, 2018, 2018, 2018,…
## $ geography_code <chr> "E01019138", "E01019139", "E01019140", "E…
## $ geography_name <chr> "Barrow-in-Furness 010A", "Barrow-in-Furn…
## $ geography_type <chr> "2011 super output areas - lower layer", …
## $ industry_code <dbl> 37748736, 37748736, 37748736, 37748736, 3…
## $ industry_name <chr> "Total", "Total", "Total", "Total", "Tota…
## $ employment_status_name <chr> "Employment", "Employment", "Employment",…
## $ employment <dbl> 350, 8000, 100, 2500, 350, 700, 300, 125,…
## $ obs_status_name <chr> "These figures exclude farm agriculture (…
## $ obs_status <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
## $ overlap <dbl> 0.83, 1.00, 1.00, 1.00, 1.00, 1.00, 0.40,…
## $ employment_within <dbl> 290.5, 8000.0, 100.0, 2500.0, 350.0, 700.…
If we set the type option to ‘employees’ rather than ‘employment’, we
can split by part-time/full-time employees.
library(dplyr)
pt_ft <- get_employment_within(iso, year ='latest',type = 'employees', split = TRUE)
pt_ft %>%
group_by(employment_status_name) %>%
summarise(estimated_emp = sum(employment_within)) %>%
tidyr::pivot_wider(names_from = employment_status_name, values_from = estimated_emp) %>%
mutate(full_time_equiv = 0.5*`Part-time employees` + `Full-time employees`)## # A tibble: 1 x 3
## `Full-time employees` `Part-time employees` full_time_equiv
## <dbl> <dbl> <dbl>
## 1 20681. 9324. 25343
We can also get the industry break down of employees in the area.
Options include: * all for no industry breakdown * broad for broad
groups * sections for sections * 2digit for two digit SIC codes
library(dplyr)
emp_broad <- get_employment_within(iso, year ='latest',industry = 'broad')
emp_broad %>%
group_by(industry_id, industry_name) %>%
summarise(estimated_emp = sum(employment_within)) ## # A tibble: 18 x 3
## # Groups: industry_id [18]
## industry_id industry_name estimated_emp
## <chr> <chr> <dbl>
## 1 1 Agriculture, forestry & fishing (A) 21.7
## 2 10 Information & communication (J) 437.
## 3 11 Financial & insurance (K) 325.
## 4 12 Property (L) 181.
## 5 13 Professional, scientific & technical (M) 1538.
## 6 14 Business administration & support services (N) 772.
## 7 15 Public administration & defence (O) 961.
## 8 16 Education (P) 2501.
## 9 17 Health (Q) 4717.
## 10 18 Arts, entertainment, recreation & other servi… 984.
## 11 2 Mining, quarrying & utilities (B,D and E) 404.
## 12 3 Manufacturing (C) 9214.
## 13 4 Construction (F) 1136.
## 14 5 Motor trades (Part G) 480.
## 15 6 Wholesale (Part G) 416.
## 16 7 Retail (Part G) 3810.
## 17 8 Transport & storage (inc postal) (H) 876.
## 18 9 Accommodation & food services (I) 1889.
- Create a Google account
- Head to the google cloud console and log in
- Collect free trial credits: At the time of writing google are offering $300 in free credits to use over 12 months. You will have to submit billing info to collect this, but you will need to do this in a later step anyway.
- Create a project: Select project > New project
- Select our new project: Select project >
- Go to the API library (APIs and services > Dashboard > Library)
- Search for GeoCoding API and enable
- Search for Distance Matrix API and enable
- enable billing: to use the APIs you must enable billing. In the main nav bar on the left navigate to billing and link billing account. If you didn’t sign up for the free trial in step 3, you will need to create a billing account here.
- get your API key: on the left nav navigate to APIs & Services > Credentials > Create Credentials > API KEY
- Copy this API key and set it in our RStudio environment using
set_google_api('<your api key>')
Setting the API key in this way should mean that the API key is always accessible by the RDistanceMatrix package in every new R session. If the API key cannot be found after closing and opening a new R session, we can set it manually using:
usethis::edit_r_environ()
and paste in the line: google_api_key = ‘’
The documentation explains how requests made to the distance matrix API are priced. At the time of writing you get $200 worth of free API usage each month. This equates to 40,000 elements each month using the standard API request, or 20,000 elements using the advanced API request (the advanced API is used if we set a departure time for time in traffic).
To avoid going over the monthly limit, pay attention to the messages
that are output from the make_isochrone and make_isodistance
functions. The functions will tell you how many API credits we used
after each request, and will warn us before making requests worth over
$10 in credit. Never reveal your API key to anyone.
What is an element? An element is one origin-destination request. A typical isochrone will use anywhere between 100 and 1000 origin-destination queries to determine the extents of the isochrone polygon.
If you’re unsure how much usage you have left for the month, visit the APIs and services dashboard for your project, click on our API (Distance Matrix API) and you can view how many elements used each day over the past month.
If you’re worried about going over the free allowance, it’s possible to set up billing alerts so google will email you when you are at, say, 50%, and 90% of your free credit limit.
Head to the cloud console billing dashboard and in the menu you should see Budgets & alerts. Create a budget with an alert to email us at set percentages of our budget. Set the target amount to £200 or the amount of the free allowance, and untick include credits in cost. (you could also set the target amount to 0 and tick include credits in cost but the UI won’t be quite as informative).
- sign up for mapbox
- Head to your account page
- Scroll down to see an option to create an api key
- Copy this API key and set it in our RStudio environment using
set_mapbox_api('<your api key>')
No billing details are required at the time of writing so no need to worry about going over quotas. The free quota is very generous and allows 100,000 isochrones to be made free of charge.
Setting the API key in this way should mean that the API key is always accessible by the RDistanceMatrix package in every new R session. If the API key cannot be found after closing and opening a new R session, we can set it manually using:
usethis::edit_r_environ()
and paste in the line: mapbox_api_key = ‘’
- Sign up for a NOMIS account
- Once signed in, click on your name to reveal the account menu and click on ‘account summary’
- Scroll down and click ‘NOMIS API’ in the menu to the left
- Your ‘unique id’ should be displayed here. This is your API key.
- Copy this API key and set it in our RStudio environment using
set_nomis_api('<your api key>')
Setting the API key in this way should mean that the API key is always accessible by the RDistanceMatrix package in every new R session. If the API key cannot be found after closing and opening a new R session, we can set it manually using:
usethis::edit_r_environ()
and paste in the line: nomis_api_key = ‘’