stats19 provides functions for downloading and formatting road crash data. Specifically, it enables access to the UK’s official road traffic casualty database, STATS19. (The name comes from the form used by the police to record car crashes and other incidents resulting in casualties on the roads.)
A full overview of STATS19 variables be found in a document provided by the UK’s Department for Transport (DfT).
The raw data is provided as a series of .csv files that contain
integers and which are stored in dozens of .zip files. Finding,
reading-in and formatting the data for research can be a time consuming
process subject to human error. stats19 speeds up these vital but
boring and error-prone stages of the research process with a single
function: get_stats19(). By allowing public access to properly
labelled road crash data, stats19 aims to make road safety research
more reproducible and accessible.
For transparency and modularity, each stage can be undertaken separately, as documented in the stats19 vignette.
Install and load the latest version with:
remotes::install_github("ropensci/stats19")library(stats19)
#> Data provided under OGL v3.0. Cite the source and link to:
#> www.nationalarchives.gov.uk/doc/open-government-licence/version/3/You can install the released version of stats19 from CRAN with:
install.packages("stats19")get_stats19() requires year and type parameters, mirroring the
provision of STATS19 data files, which are categorised by year (from
1979 onward) and type (with separate tables for crashes, casualties and
vehicles, as outlined below). The following command, for example, gets
crash data from 2017 (note: we follow the “crash not accident”
campaign of
RoadPeace
in naming crashes, although the DfT refers to the relevant tables as
‘accidents’ data):
crashes = get_stats19(year = 2017, type = "accident")
#> Files identified: dftRoadSafetyData_Accidents_2017.zip
#> http://data.dft.gov.uk.s3.amazonaws.com/road-accidents-safety-data/dftRoadSafetyData_Accidents_2017.zip
#> Data already exists in data_dir, not downloading
#> Data saved at ~/stats19-data/dftRoadSafetyData_Accidents_2017/Acc.csv
#> Reading in:
#> /home/robin/stats19-data/dftRoadSafetyData_Accidents_2017/Acc.csv
#> date and time columns present, creating formatted datetime columnWhat just happened? For the year 2017 we read-in crash-level
(type = "accident") data on all road crashes recorded by the police
across Great Britain. The dataset contains 33 columns (variables) for
129,982 crashes. We were not asked to download the file (by default you
are asked to confirm the file that will be downloaded). The contents of
this dataset, and other datasets provided by stats19, are outlined
below and described in more detail in the stats19
vignette.
We will see below how the function also works to get the corresponding
casualty and vehicle datasets for 2017. The package also allows STATS19
files to be downloaded and read-in separately, allowing more control
over what you download, and subsequently read-in, with
read_accidents(), read_casualties() and read_vehicles(), as
described in the vignette.
Data files can be downloaded without reading them in using the function
dl_stats19(). If there are multiple matches, you will be asked to
choose from a range of options. Providing just the year, for example,
will result in the following options:
dl_stats19(year = 2017)Multiple matches. Which do you want to download?
1: dftRoadSafetyData_vehicles.zip
2: dftRoadSafetyData_casualties.zip
3: dftRoadSafetyData_Accidents_2017.zip
Selection:
Enter an item from the menu, or 0 to exit
STATS19 data consists of 3 main tables:
- Accidents, the main table which contains information on the crash time, location and other variables (32 columns in total)
- Casualties, containing data on people hurt or killed in each crash (16 columns in total)
- Vehicles, containing data on vehicles involved in or causing each crash (23 columns in total)
The contents of each is outlined below.
Crash data was downloaded and read-in using the function
get_stats19(), as described above.
nrow(crashes)
#> [1] 129982
ncol(crashes)
#> [1] 33Some of the key variables in this dataset include:
crashes[c(7, 18, 23, 25)]
#> # A tibble: 129,982 x 4
#> accident_severity speed_limit pedestrian_crossing_human_… light_conditions
#> <chr> <int> <chr> <chr>
#> 1 Fatal 30 None within 50 metres Darkness - lights …
#> 2 Slight 30 None within 50 metres Darkness - lights …
#> 3 Slight 30 None within 50 metres Darkness - lights …
#> 4 Slight 30 None within 50 metres Darkness - lights …
#> 5 Serious 20 None within 50 metres Darkness - lights …
#> 6 Slight 30 None within 50 metres Darkness - lights …
#> 7 Slight 40 None within 50 metres Darkness - lights …
#> 8 Slight 30 Control by other authorise… Darkness - lights …
#> 9 Serious 50 None within 50 metres Darkness - lights …
#> 10 Serious 30 None within 50 metres Darkness - lights …
#> # … with 129,972 more rowsFor the full list of columns, run names(crashes) or see the
vignette.
As with crashes, casualty data for 2017 can be downloaded, read-in and
formatted as follows:
casualties = get_stats19(year = 2017, type = "casualties", ask = FALSE)
#> Files identified: dftRoadSafetyData_Casualties_2017.zip
#> http://data.dft.gov.uk.s3.amazonaws.com/road-accidents-safety-data/dftRoadSafetyData_Casualties_2017.zip
#> Data already exists in data_dir, not downloading
#> Data saved at ~/stats19-data/dftRoadSafetyData_Casualties_2017/Cas.csv
nrow(casualties)
#> [1] 170993
ncol(casualties)
#> [1] 16The results show that there were 170,993 casualties reported by the police in the STATS19 dataset in 2017, and 16 columns (variables). Values for a sample of these columns are shown below:
casualties[c(4, 5, 6, 14)]
#> # A tibble: 170,993 x 4
#> casualty_class sex_of_casualty age_of_casualty casualty_type
#> <chr> <chr> <int> <chr>
#> 1 Passenger Female 18 Car occupant
#> 2 Driver or rider Male 19 Motorcycle 50cc and under ri…
#> 3 Passenger Male 18 Motorcycle 50cc and under ri…
#> 4 Passenger Female 33 Car occupant
#> 5 Driver or rider Female 31 Car occupant
#> 6 Passenger Male 3 Car occupant
#> 7 Pedestrian Male 45 Pedestrian
#> 8 Driver or rider Male 14 Motorcycle 125cc and under r…
#> 9 Driver or rider Female 58 Car occupant
#> 10 Driver or rider Male 27 Car occupant
#> # … with 170,983 more rowsThe full list of column names in the casualties dataset is:
names(casualties)
#> [1] "accident_index" "vehicle_reference"
#> [3] "casualty_reference" "casualty_class"
#> [5] "sex_of_casualty" "age_of_casualty"
#> [7] "age_band_of_casualty" "casualty_severity"
#> [9] "pedestrian_location" "pedestrian_movement"
#> [11] "car_passenger" "bus_or_coach_passenger"
#> [13] "pedestrian_road_maintenance_worker" "casualty_type"
#> [15] "casualty_home_area_type" "casualty_imd_decile"Data for vehicles involved in crashes in 2017 can be downloaded, read-in and formatted as follows:
vehicles = get_stats19(year = 2017, type = "vehicles", ask = FALSE)
#> Files identified: dftRoadSafetyData_Vehicles_2017.zip
#> http://data.dft.gov.uk.s3.amazonaws.com/road-accidents-safety-data/dftRoadSafetyData_Vehicles_2017.zip
#> Data already exists in data_dir, not downloading
#> Data saved at ~/stats19-data/dftRoadSafetyData_Vehicles_2017/Veh.csv
nrow(vehicles)
#> [1] 238926
ncol(vehicles)
#> [1] 23The results show that there were 238,926 vehicles involved in crashes reported by the police in the STATS19 dataset in 2017, with 23 columns (variables). Values for a sample of these columns are shown below:
vehicles[c(3, 14:16)]
#> # A tibble: 238,926 x 4
#> vehicle_type journey_purpose_of_driv… sex_of_driver age_of_driver
#> <chr> <chr> <chr> <int>
#> 1 Car Not known Male 24
#> 2 Motorcycle 50cc and und… Not known Male 19
#> 3 Car Not known Male 33
#> 4 Car Not known Male 40
#> 5 Car Not known Not known -1
#> 6 Car Not known Male 35
#> 7 Car Not known Female 31
#> 8 Car Not known Female 37
#> 9 Car Not known Female 29
#> 10 Car Not known Male 78
#> # … with 238,916 more rowsThe full list of column names in the vehicles dataset is:
names(vehicles)
#> [1] "accident_index" "vehicle_reference"
#> [3] "vehicle_type" "towing_and_articulation"
#> [5] "vehicle_manoeuvre" "vehicle_location_restricted_lane"
#> [7] "junction_location" "skidding_and_overturning"
#> [9] "hit_object_in_carriageway" "vehicle_leaving_carriageway"
#> [11] "hit_object_off_carriageway" "first_point_of_impact"
#> [13] "was_vehicle_left_hand_drive" "journey_purpose_of_driver"
#> [15] "sex_of_driver" "age_of_driver"
#> [17] "age_band_of_driver" "engine_capacity_cc"
#> [19] "propulsion_code" "age_of_vehicle"
#> [21] "driver_imd_decile" "driver_home_area_type"
#> [23] "vehicle_imd_decile"An important feature of STATS19 data is that the “accidents” table
contains geographic coordinates. These are provided at ~10m resolution
in the UK’s official coordinate reference system (the Ordnance Survey
National Grid, EPSG code 27700). stats19 converts the non-geographic
tables created by format_accidents() into the geographic data form of
the sf package with the
function format_sf() as follows:
crashes_sf = format_sf(crashes)
#> 19 rows removed with no coordinatesThe note arises because NA values are not permitted in sf
coordinates, and so rows containing no coordinates are automatically
removed. Having the data in a standard geographic form allows various
geographic operations to be performed on it. The following code chunk,
for example, returns all crashes within the boundary of West Yorkshire
(which is contained in the object
police_boundaries,
an sf data frame containing all police jurisdictions in England and
Wales).
library(sf)
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 7.0.0
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
wy = filter(police_boundaries, pfa16nm == "West Yorkshire")
crashes_wy = crashes_sf[wy, ]
nrow(crashes_sf)
#> [1] 129963
nrow(crashes_wy)
#> [1] 4371This subsetting has selected the 4,371 crashes which occurred within West Yorkshire in 2017.
The three main tables we have just read-in can be joined by shared key variables. This is demonstrated in the code chunk below, which subsets all casualties that took place in Leeds, and counts the number of casualties by severity for each crash:
sel = casualties$accident_index %in% crashes_wy$accident_index
casualties_wy = casualties[sel, ]
cas_types = casualties_wy %>%
select(accident_index, casualty_type) %>%
mutate(n = 1) %>%
group_by(accident_index, casualty_type) %>%
summarise(n = sum(n)) %>%
tidyr::spread(casualty_type, n, fill = 0)
cas_types$Total = rowSums(cas_types[-1])
cj = left_join(crashes_wy, cas_types, by = "accident_index")What just happened? We found the subset of casualties that took place in
West Yorkshire with reference to the accident_index variable. Then we
used functions from the tidyverse package dplyr (and spread()
from tidyr) to create a dataset with a column for each casualty
type. We then joined the updated casualty data onto the crashes_wy
dataset. The result is a spatial (sf) data frame of crashes in Leeds,
with columns counting how many road users of different types were hurt.
The original and joined data look like this:
crashes_wy[1:2, c(1, 5)] %>% st_drop_geometry()
#> # A tibble: 2 x 2
#> accident_index accident_severity
#> * <chr> <chr>
#> 1 2017120009776 Slight
#> 2 2017120010412 Slight
cas_types[1:2, c("accident_index", "Cyclist")]
#> # A tibble: 2 x 2
#> # Groups: accident_index [2]
#> accident_index Cyclist
#> <chr> <dbl>
#> 1 2017120009776 0
#> 2 2017120010412 1
cj[1:2, c(1, 5, 34)] %>% st_drop_geometry()
#> # A tibble: 2 x 3
#> accident_index accident_severity `Bus or coach occupant (17 or more pass seat…
#> * <chr> <chr> <dbl>
#> 1 2017120009776 Slight 0
#> 2 2017120010412 Slight 0The join operation added a geometry column to the casualty data, enabling it to be mapped (for more advanced maps, see the vignette):
cex = cj$Total / 3
plot(cj["speed_limit"], cex = cex)
The spatial distribution of crashes in West Yorkshire clearly relates to the region’s geography. Crashes tend to happen on busy Motorway roads (with a high speed limit, of 70 miles per hour, as shown in the map above) and city centres, of Leeds and Bradford in particular. The severity and number of people hurt (proportional to circle width in the map above) in crashes is related to the speed limit.
STATS19 data can be used as the basis of road safety research. The map below, for example, shows the results of an academic paper on the social, spatial and temporal distribution of bike crashes in West Yorkshire, which estimated the number of crashes per billion km cycled based on commuter cycling as a proxy for cycling levels overall (more sophisticated measures of cycling levels are now possible thanks to new data sources) (Lovelace, Roberts, and Kellar 2016):
We can also explore seasonal trends in crashes by aggregating crashes by day of the year:
library(ggplot2)
crashes_dates = cj %>%
st_set_geometry(NULL) %>%
group_by(date) %>%
summarise(
walking = sum(Pedestrian),
cycling = sum(Cyclist),
passenger = sum(`Car occupant`)
) %>%
tidyr::gather(mode, casualties, -date)
ggplot(crashes_dates, aes(date, casualties)) +
geom_smooth(aes(colour = mode), method = "loess") +
ylab("Casualties per day")
#> `geom_smooth()` using formula 'y ~ x'
Different types of crashes also tend to happen at different times of day. This is illustrated in the plot below, which shows the times of day when people who were travelling by different modes were most commonly injured.
library(stringr)
crash_times = cj %>%
st_set_geometry(NULL) %>%
group_by(hour = as.numeric(str_sub(time, 1, 2))) %>%
summarise(
walking = sum(Pedestrian),
cycling = sum(Cyclist),
passenger = sum(`Car occupant`)
) %>%
tidyr::gather(mode, casualties, -hour)
ggplot(crash_times, aes(hour, casualties)) +
geom_line(aes(colour = mode))
Note that cycling manifests distinct morning and afternoon peaks (see Lovelace, Roberts, and Kellar 2016 for more on this).
The package has now been peer reviewed and is stable, and has been published in the Journal of Open Source Software (Lovelace et al. 2019). Please tell people about the package, link to it and cite it if you use it in your work.
Examples of how the package can been used for policy making include:
- Use of the package in a web app created by the library service of the UK Parliament. See commonslibrary.parliament.uk, screenshots of which from December 2019 are shown below, for details.
-
Use of methods taught in the stats19-training vignette by road safety analysts at Essex Highways and the Safer Essex Roads Partnership (SERP) to inform the deployment of proactive front-line police enforcement in the region (credit: Will Cubbin).
-
Mention of road crash data analysis based on the package in an article on urban SUVs. The question of how vehicle size and type relates to road safety is an important area of future research. A starting point for researching this topic can be found in the
stats19-vehiclesvignette, representing a possible next step in terms of how the data can be used.
There is much important research that needs to be done to help make the transport systems in many cities safer. Even if you’re not working with UK data, we hope that the data provided by stats19 data can help safety researchers develop new methods to better understand the reasons why people are needlessly hurt and killed on the roads.
The next step is to gain a deeper understanding of stats19 and the data it provides. Then it’s time to pose interesting research questions, some of which could provide an evidence-base in support policies that save lives (e.g. Sarkar, Webster, and Kumari 2018). For more on these next steps, see the package’s introductory vignette.
The stats19 package builds on previous work, including:
- code in the bikeR repo underlying an academic paper on collisions involving cyclists
- functions in stplanr for downloading Stats19 data
- updated functions related to the CyIPT project
Lovelace, Robin, Malcolm Morgan, Layik Hama, Mark Padgham, and M Padgham. 2019. “Stats19 A Package for Working with Open Road Crash Data.” Journal of Open Source Software 4 (33): 1181. https://doi.org/10.21105/joss.01181.
Lovelace, Robin, Hannah Roberts, and Ian Kellar. 2016. “Who, Where, When: The Demographic and Geographic Distribution of Bicycle Crashes in West Yorkshire.” Transportation Research Part F: Traffic Psychology and Behaviour, Bicycling and bicycle safety, 41, Part B. https://doi.org/10.1016/j.trf.2015.02.010.
Sarkar, Chinmoy, Chris Webster, and Sarika Kumari. 2018. “Street Morphology and Severity of Road Casualties: A 5-Year Study of Greater London.” International Journal of Sustainable Transportation 12 (7): 510–25. https://doi.org/10.1080/15568318.2017.1402972.