As of September 2016, disaggregated crash data is now published here: http://www.nzta.govt.nz/safety/safety-resources/road-safety-information-and-tools/disaggregated-crash-data/. It is somewhat different from the data provided in this package.
This package redistributes crash statistics already available from the New Zealand Transport Agency, but in a more convenient form.
It's a large package (over 20 megabytes, compressed).
The crashes dataset describes most facts about a crash. The datasets causes,
vehicles, and objects_struck describe facts that are in a many-to-one
relationship with crashes. They can be joined to the crashes dataset by the
common id column. The causes dataset can additionally be joined to the
vehicles dataset by the combination of the id and vehicle_id columns.
This is most useful when the resulting table is also joined to the crashes
dataset.
The data was last scraped from the NZTA website on 2015-09-30. At that time, the NZTA had published data up to 2015-03-10.
dim(crashes)## [1] 540888 32
dim(causes)## [1] 888072 7
dim(vehicles)## [1] 979930 3
dim(objects_struck)## [1] 261276 3
The NZTA, doesn't agree with itself about recent annual road tolls, and this dataset gives a third opinion.
crashes %>%
filter(severity == "fatal") %>%
group_by(year = year(date)) %>%
summarize(fatalities = sum(fatalities))## Source: local data frame [16 x 2]
##
## year fatalities
## (dbl) (int)
## 1 2000 462
## 2 2001 455
## 3 2002 405
## 4 2003 461
## 5 2004 435
## 6 2005 405
## 7 2006 393
## 8 2007 421
## 9 2008 366
## 10 2009 384
## 11 2010 375
## 12 2011 284
## 13 2012 308
## 14 2013 256
## 15 2014 279
## 16 2015 34
Crashes categorised as "fatal", "serious", "minor" or "non-injury", based on the casualties. If there are any fatalities, then the crash is a "fatal" crash, otherwise if there are any 'severe' injuries, the crash is a "serious" crash.
The definition of a 'severe' injury is not clear.
Minor and non-injury crashes are likely to be under-recorded since they often do not involve the police, who write most of the crash reports upon which these datasets are based.
A common mistake is to confuse the number of fatal crashes with the number of fatalities.
crashes %>% filter(severity == "fatal") %>% nrow## [1] 5042
sum(crashes$fatalities)## [1] 5723
Three columns of the crashes dataset describe the date and time of the crash
in the NZST time zone (Pacific/Auckland).
dategives the date without the timetimegives the time where this is available, and NA otherwise. Times are stored as date-times on the first of January, 1970.datetimegives the date and time in one value when both are available, and NA otherwise.dateis always available, howevertimeis not.
When aggregating by some function of the date, e.g. by year, then always start
from the date column unless you also need the time. This ensures against
accidentally discounting crashes where a time is not recorded.
crashes %>%
filter(is.na(time)) %>%
count(year = year(date)) %>%
ggplot(aes(year, n)) +
geom_line() +
ggtitle("Crashes missing\ntime-of-day information")
crashes %>%
filter(is.na(time)) %>%
count(year = year(date)) %>%
mutate(percent = n/sum(n)) %>%
ggplot(aes(year, percent)) +
geom_line() +
scale_y_continuous(labels = percent) +
ggtitle("Percent of crashes missing\ntime-of-day information")
99.9% of
crashes have coordinates. These have been converted from the NZTM projection to
the WGS84 projection for convenience with packages like ggmap.
Because New Zealand is tall and skinny, you can easily spot the main population centres with a simple histogram.
crashes %>%
ggplot(aes(northing)) +
geom_histogram(binwidth = .1)
There can be many vehicles in one crash, so vehicles are recorded in a separate
vehicles dataset that can be joined to crashes by the common id column.
crashes %>%
inner_join(vehicles, by = "id") %>%
count(vehicle) %>%
arrange(desc(n))## Source: local data frame [12 x 2]
##
## vehicle n
## (fctr) (int)
## 1 Car 728119
## 2 Van, ute 87927
## 3 SUV or 4x4 vehicle 48269
## 4 Truck 44305
## 5 Motorcycle 17733
## 6 NA 16996
## 7 Bicycle 15713
## 8 Bus 8066
## 9 Taxi or taxi van 6792
## 10 Moped 3594
## 11 Other or unknown 2043
## 12 School bus 373
There can be many objects struck in one crash, so these are recorded in a separate
objects_struck dataset that can be joined to crashes by the common id column.
Q: What are more fatal, trees or lamp posts?
crashes %>%
inner_join(objects_struck, by = "id") %>%
filter(object %in% c("Trees, shrubbery of a substantial nature"
, "Utility pole, includes lighting columns")
, severity != "non-injury") %>% # non-injury crashes are poorly recorded
count(object, severity) %>%
group_by(object) %>%
mutate(percent = n/sum(n)) %>%
select(-n) %>%
spread(severity, percent)## Source: local data frame [2 x 4]
##
## object fatal serious minor
## (fctr) (dbl) (dbl) (dbl)
## 1 Utility pole, includes lighting columns 0.04432701 0.2149482 0.7407248
## 2 Trees, shrubbery of a substantial nature 0.06742092 0.2459016 0.6866774
A: Trees (Don't worry, I know it's harder than that.)
Causes can be joined either to the crashes dataset (by the common id
column), or to the vehicles dataset (by both of the commont id and
vehicle_id) columns.
The main cause groups are given in the causes_category column.
crashes %>%
inner_join(causes, by = "id") %>%
group_by(cause_category, id) %>%
tally %>%
group_by(cause_category) %>%
summarize(n = n()) %>%
arrange(desc(n)) %>%
mutate(cause_category = factor(cause_category, levels = cause_category)) %>%
ggplot(aes(cause_category, n)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5))
That's odd -- where are speed, alcohol, and restraints? They're given in cause_subcategory.
causes %>%
filter(cause_subcategory == "Too fast for conditions") %>%
count(cause) %>%
arrange(desc(n))## Source: local data frame [8 x 2]
##
## cause n
## (fctr) (int)
## 1 Cornering 37861
## 2 On straight 10196
## 3 NA 7119
## 4 To give way at intersection 1658
## 5 At temporary speed limit 1010
## 6 At crash or emergency 55
## 7 Approaching railway crossing 44
## 8 When passing stationary school bus 37
There's nothing there about speed limit violations, because it's impossible to tell what speed a vehicle was going at when it crashed.
More worryingly, how is "Alcohol test below limit" a cause for a crash? Hopefully they filter those out when making policy decisions.
levels(causes$cause) <- # Wrap facet labels
str_wrap(levels(causes$cause), 13)
crashes %>%
inner_join(causes, by = "id") %>%
filter(cause_subcategory %in% c("Alcohol or drugs")) %>%
group_by(cause, id) %>%
tally %>%
group_by(cause) %>%
summarize(n = n()) %>% # This extra step deals with many causes per crash
arrange(desc(n)) %>%
mutate(cause= factor(cause, levels = cause)) %>%
ggplot(aes(cause, n)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5))
rm(causes) # Because we messed up the factor levelsThis time, join causes to both vehicles and crashes to assess the
drunken cyclist menace.
crashes %>%
filter(severity == "fatal") %>%
select(id) %>%
inner_join(vehicles, by = "id") %>%
filter(vehicle == "Bicycle") %>%
inner_join(causes, by = c("id", "vehicle_id")) %>%
count(cause) %>%
arrange(desc(n))## Source: local data frame [55 x 2]
##
## cause
## (fctr)
## 1 Behind when changing lanes position or direction (includes U-turns)
## 2 NA
## 3 When required to give way to traffic from another direction
## 4 Wandering or wobbling
## 5 At Give Way sign
## 6 Cyclist or M/cyclist wearing dark clothing
## 7 Driving or riding on footpath
## 8 On left without due care
## 9 When pulling out or moving to the right
## 10 At steady red light
## .. ...
## Variables not shown: n (int)
I think we all know what "Wandering or wobbling" means.