VGAM (version 1.1-12)

crashes: Crashes on New Zealand Roads in 2009

Description

A variety of reported crash data cross-classified by time (hour of the day) and day of the week, accumulated over 2009. These include fatalities and injuries (by car), trucks, motor cycles, bicycles and pedestrians. There are some alcohol-related data too.

Usage

data(crashi)
data(crashf)
data(crashtr)
data(crashmc)
data(crashbc)
data(crashp)
data(alcoff)
data(alclevels)

Format

Data frames with hourly times as rows and days of the week as columns. The alclevels dataset has hourly times and alcohol levels.

Mon, Tue, Wed, Thu, Fri, Sat, Sun

Day of the week.

0-30, 31-50, 51-80, 81-100, 101-120, 121-150, 151-200, 201-250, 251-300, 301-350, 350+

Blood alcohol level (milligrams alcohol per 100 millilitres of blood).

Details

Each cell is the aggregate number of crashes reported at each hour-day combination, over the 2009 calendar year. The rownames of each data frame is the start time (hourly from midnight onwards) on a 24 hour clock, e.g., 21 means 9.00pm to 9.59pm.

For crashes, chrashi are the number of injuries by car, crashf are the number of fatalities by car (not included in chrashi), crashtr are the number of crashes involving trucks, crashmc are the number of crashes involving motorcyclists, crashbc are the number of crashes involving bicycles, and crashp are the number of crashes involving pedestrians. For alcohol-related offences, alcoff are the number of alcohol offenders from breath screening drivers, and alclevels are the blood alcohol levels of fatally injured drivers.

References

Motor Vehicles Crashes in New Zealand 2009; Statistical Statement Calendar Year 2009. Ministry of Transport, NZ Government; Yearly Report 2010. ISSN: 1176-3949

See Also

rrvglm, rcim, grc.

Examples

if (FALSE)  plot(unlist(alcoff), type = "l", frame.plot = TRUE,
     axes = FALSE, col = "blue", bty = "o",
     main = "Alcoholic offenders on NZ roads, aggregated over 2009",
     sub  = "Vertical lines at midnight (purple) and noon (orange)",
     xlab = "Day/hour", ylab = "Number of offenders")
axis(1, at = 1 + (0:6) * 24 + 12, labels = colnames(alcoff))
axis(2, las = 1)
axis(3:4, labels = FALSE, tick = FALSE)
abline(v = sort(1 + c((0:7) * 24, (0:6) * 24 + 12)), lty = "dashed",
       col = c("purple", "orange")) 

# Goodmans RC models
if (FALSE) {
fitgrc1 <- grc(alcoff)  # Rank-1 model
fitgrc2 <- grc(alcoff, Rank = 2, Corner = FALSE, Uncor = TRUE)
Coef(fitgrc2)
}
if (FALSE)  biplot(fitgrc2, scaleA = 2.3, Ccol = "blue", Acol = "orange",
       Clabels = as.character(1:23), xlim = c(-1.3, 2.3),
       ylim = c(-1.2, 1))