README.md

In nacnudus/nzcrash: New Zealand Motor Vehicle Crash Data

nzcrash

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).

Datasets

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.

Up-to-date-ness

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

Accuracy

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

Severity

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

Dates and times

Three columns of the crashes dataset describe the date and time of the crash in the NZST time zone (Pacific/Auckland).

• date gives the date without the time
• time gives the time where this is available, and NA otherwise. Times are stored as date-times on the first of January, 1970.
• datetime gives the date and time in one value when both are available, and NA otherwise. date is always available, however time is 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")

Location coordinates

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)

Vehicles

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

Objects struck

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

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 levels

This 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.

nacnudus/nzcrash documentation built on May 23, 2019, 12:04 p.m.