Origin-destination data from Leeds, UK — od

This dataset represents movement between administrative 'MSOA' zones in Leeds, UK. The data was collected in the 2011 Census and reports the mode of travel to work.

od_leeds

Format

A data frame with 948 rows and 18 variables:

geo_code1: character Origin
geo_code2: character Destination
all: double Travel (all modes)
from_home: double Ppl working from home
light_rail: double N. travelling by mode
train: double N. travelling by mode
bus: double N. travelling by mode
taxi: double N. travelling by mode
motorbike: double N. travelling by mode
car_driver: double N. travelling by mode
car_passenger: double N. travelling by mode
bicycle: double N. travelling by mode
foot: double N. travelling by mode
other: double N. travelling by mode
geo_name1: character Name of origin zone
geo_name2: character Name of destination zone
la_1: character Local authority of origin
la_2: character Local authority of destination

Examples

dim(od_leeds)
#> [1] 948  18
b = seq(0, 2000, by = 100)
head(od_leeds)
#> # A tibble: 6 x 18
#>   geo_code1 geo_code2   all from_home light_rail train   bus  taxi motorbike
#>   <chr>     <chr>     <dbl>     <dbl>      <dbl> <dbl> <dbl> <dbl>     <dbl>
#> 1 E02002330 E02002330    66         0          0     0     0     0         1
#> 2 E02002330 E02002331   742         0          0     1     5     1         1
#> 3 E02002330 E02002334    50         0          0     0     3     1         0
#> 4 E02002330 E02006875   225         0          0     5    32     0         1
#> 5 E02002331 E02002331   849         0          0     0    34     1         6
#> 6 E02002331 E02002334    92         0          0     0     8     0         0
#> # … with 9 more variables: car_driver <dbl>, car_passenger <dbl>,
#> #   bicycle <dbl>, foot <dbl>, other <dbl>, geo_name1 <chr>, geo_name2 <chr>,
#> #   la_1 <chr>, la_2 <chr>
hist(od_leeds$all, breaks = b)
hist(od_leeds$foot, breaks = b)
hist(od_leeds$bicycle, breaks = b)