capcarb
The goal of capcarb is to provide a home for reproducible analysis and open data for the analysis of embodied carbon, known as capital carbon or CapCarb, associated with transport systems.
Installation
Install from GitHub with:
# install.packages("devtools")
devtools::install_github("creds2/capcarb")library(capcarb)
library(tidyverse)
library(tmap)
tmap_mode("view")Generating input data
There was no readily available boundary data for the TfN region, so it was constructed as follows, using open data from the Propensity to Cycle Tool (PCT):
regions = pct::pct_regions
# mapview::mapview(regions)
tfn_zone_text = "manc|yorks|lanc|chesh|liv|north-e|north-y|cumbria|humb"
regions_tfn = regions[grepl(pattern = tfn_zone_text, ignore.case = TRUE, x = regions$region_name), ]
# mapview::mapview(regions_tfn)
tfn_region = sf::st_union(regions_tfn)
plot(tfn_region, lwd = 4)
plot(regions, add = TRUE, border = "grey", col = "NA")
tfn_region_27700 = sf::st_transform(tfn_region, 27700)
Motorway data was accessed as follows (after pre-cleaning Ordnance Survey data):
roads_uk = readRDS("~/hd/data/os/Download_mastermap-roads-2019_1483661.Rds")
names(roads_uk) [1] "gml_id" "identifier"
[3] "beginLifespanVersion" "localId"
[5] "namespace" "fictitious"
[7] "validFrom" "reasonForChange"
[9] "roadClassification" "routeHierarchy"
[11] "formOfWay" "trunkRoad"
[13] "primaryRoute" "roadClassificationNumber"
[15] "operationalState" "provenance"
[17] "length" "length_uom"
[19] "matchStatus" "startGradeSeparation"
[21] "endGradeSeparation" "averageWidth"
[23] "averageWidth_uom" "minimumWidth"
[25] "minimumWidth_uom" "confidenceLevel"
[27] "inDirection" "inDirection_uom"
[29] "inOppositeDirection" "inOppositeDirection_uom"
[31] "cycleFacility" "wholeLink"
[33] "roadStructure" "alternateIdentifier|ThematicIdentifier|identifier"
[35] "identifierScheme" "roadName"
[37] "alternateName" "geometry"
table(roads_uk$roadClassification) A Road B Road Classified Unnumbered Motorway
440770 215962 361711 16710
Not Classified Unclassified Unknown
363169 2150266 1563113
motorways_uk = roads_uk[roads_uk$roadClassification == "Motorway", ]
motorways_tfn = motorways_uk[tfn_region_27700, ]
saveRDS(motorways_tfn, "motorways_tfn.Rds")Roads in Leeds
roads_leeds = readRDS("roads_leeds.RDS")
aggregate(roads_leeds$length, by = list(roads_leeds$roadClassification), sum)
#> Group.1 x
#> 1 A Road 413116.75
#> 2 B Road 85369.64
#> 3 Classified Unnumbered 178685.63
#> 4 Motorway 202262.91
#> 5 Not Classified 283781.92
#> 6 Unclassified 2339655.83
#> 7 Unknown 613394.89
roads_leeds %>%
sf::st_drop_geometry() %>%
group_by(roadClassification) %>%
summarise(length_km = sum(length) / 1000) %>%
ggplot() +
geom_bar(aes(roadClassification, length_km), stat = "identity")
#> `summarise()` ungrouping output (override with `.groups` argument)
Motorways in the TfN region
motorways_tfn = readRDS("motorways_tfn.Rds")
tm_shape(tfn_region_27700) + tm_borders() +
tm_shape(motorways_tfn) + tm_lines()
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 7.0.0
The data is quite detailed, as shown below:
a58m = motorways_tfn %>%
dplyr::filter(roadClassificationNumber == "A58(M)")
tm_shape(a58m) + tm_lines("averageWidth", palette = "viridis", lwd = 2)