data-raw/exeter.r

In a-b-street/abstr: R Interface to the A/B Street Transport System Simulation Software

# ####
# ####
# #### AIM: Use PCT data to create scenario of change where commuting cycling levels increase and car journeys decrease which can be imported
# ####      into A/B Street city simulation software. This method should be fully reproducible for all other pct_regions.
# ####
# ####
#
# ####  LIBRARYS ####
# library(pct)
# library(sf)
# library(tidyverse)
# library(abstr)
#
# # for plotting
# # library(tmap)
# # library (mapview)
#
# ####    READ DATA ####
# devon_zones = pct::get_pct_zones(region = "devon", geography = "msoa") # get zone data
# exeter_zones = devon_zones %>% filter(lad_name == "Exeter") %>% select(geo_code) # filter for exeter
#
# exeter_commute_od = pct::get_pct_lines(region = "devon", geography = "msoa") %>% # get commute od data
#   filter(lad_name1 == "Exeter" &
#            lad_name2 == "Exeter") # filter for exeter
#
#
# ####    CLEAN DATA , CALCULATE EUCLIDEAN DISTANCE  & GENERATE SCENARIOS OF CHANGE ####
# exeter_commute_od = exeter_commute_od %>%
#   mutate(cycle_base = bicycle) %>%
#   mutate(walk_base = foot) %>%
#   mutate(transit_base = bus + train_tube) %>% # bunch of renaming -_-
#   mutate(drive_base = car_driver + car_passenger + motorbike + taxi_other) %>%
#   mutate(all_base = all) %>%
#   mutate(
#     # create new columns
#     pcycle_godutch_uptake = pct::uptake_pct_godutch_2020(distance = rf_dist_km, gradient = rf_avslope_perc),
#     cycle_godutch_additional = pcycle_godutch_uptake * drive_base,
#     cycle_godutch = cycle_base + cycle_godutch_additional,
#     pcycle_godutch = cycle_godutch / all_base,
#     drive__godutch = drive_base - cycle_godutch_additional,
#     across(c(drive__godutch, cycle_godutch), round, 0),
#     all_go_dutch = drive__godutch + cycle_godutch + transit_base + walk_base
#   ) %>%
#   select(
#     # select variables for new df
#     geo_code1,
#     geo_code2,
#     cycle_base,
#     drive_base,
#     walk_base,
#     transit_base,
#     all_base,
#     all_go_dutch,
#     drive__godutch,
#     cycle_godutch,
#     cycle_godutch_additional,
#     pcycle_godutch
#   )
#
# identical(exeter_commute_od$all_base, exeter_commute_od$all_go_dutch) # sanity check: make sure total remains the same (not a dynamic model where population change is factored in)
#
# ####    DOWNLOAD OSM BUILDING DATA ####
# osm_polygons = osmextract::oe_read(
#   "https://download.geofabrik.de/europe/great-britain/england/devon-latest.osm.pbf",
#   # download osm buildings for region using geofabrik
#   layer = "multipolygons"
# )
#
# building_types = c(
#   "yes",
#   "house",
#   "detached",
#   "residential",
#   "apartments",
#   "commercial",
#   "retail",
#   "school",
#   "industrial",
#   "semidetached_house",
#   "church",
#   "hangar",
#   "mobile_home",
#   "warehouse",
#   "office",
#   "college",
#   "university",
#   "public",
#   "garages",
#   "cabin",
#   "hospital",
#   "dormitory",
#   "hotel",
#   "service",
#   "parking",
#   "manufactured",
#   "civic",
#   "farm",
#   "manufacturing",
#   "floating_home",
#   "government",
#   "bungalow",
#   "transportation",
#   "motel",
#   "manufacture",
#   "kindergarten",
#   "house_boat",
#   "sports_centre"
# )
# osm_buildings  = osm_polygons %>%
#   dplyr::filter(building %in% building_types) %>%
#   dplyr::select(osm_way_id, name, building)
#
# osm_buildings_valid = osm_buildings[sf::st_is_valid(osm_buildings), ]
#
# exeter_osm_buildings_all = osm_buildings_valid[exeter_zones, ]
#
# #mapview(exeter_osm_buildings_all)
#
# # Filter down large objects for package -----------------------------------
# exeter_osm_buildings_all_joined = exeter_osm_buildings_all %>%
#   sf::st_join(exeter_zones)
#
# set.seed(2021)
# exeter_osm_buildings_sample = exeter_osm_buildings_all_joined %>%
#   dplyr::filter(!is.na(osm_way_id))
#
# exeter_osm_buildings_tbl = exeter_osm_buildings_all %>%
#   dplyr::filter(osm_way_id %in% exeter_osm_buildings_sample$osm_way_id)
#
#
# ####  LOGIC GATE ####
# # Logic gate for go_dutch scenario of change, where cycling levels increase to a proportion reflecting the Netherlands.
# #Switch to FALSE if you want census commuting OD
# go_dutch = TRUE
# if (go_dutch == TRUE) {
#   exeter_od = exeter_commute_od %>%
#     mutate(All = all_go_dutch) %>%
#     mutate(Bike = cycle_godutch) %>%
#     mutate(Transit = transit_base) %>%
#     mutate(Drive = drive_base) %>%
#     mutate(Walk = walk_base) %>%
#     select(geo_code1, geo_code2, All, Bike, Transit, Drive, Walk,geometry)
# } else {
#   exeter_od = exeter_commute_od %>%
#     mutate(All = all_base) %>%
#     mutate(Bike = cycle_base) %>%
#     mutate(Drive = drive_base) %>%
#     mutate(Transit = transit_base) %>%
#     mutate(Walk = walk_base) %>%
#     select(geo_code1, geo_code2, All, Bike, Transit, Drive, Walk, geometry)
# }
#
# ####  GENERATE A/B STREET SCENARIO ####
# output_sf = ab_scenario(
#   od = exeter_od,
#   zones = exeter_zones,
#   zones_d = NULL,
#   origin_buildings = exeter_osm_buildings_tbl,
#   destination_buildings = exeter_osm_buildings_tbl,
#   pop_var = 3,
#   time_fun = ab_time_normal,
#   output = "sf",
#   modes = c("Walk", "Bike", "Drive", "Transit")
# )
#
# # make map using tmap
# # tm_shape(output_sf) + tmap::tm_lines(col = "mode", lwd = .8, lwd.legeld.col = "black") +
# #   tm_shape(exeter_zones) + tmap::tm_borders(lwd = 1.2, col = "gray") +
# #   tm_text("geo_code", size = 0.6, col = "black") +
# #   tm_style("cobalt")
#
# #### SAVE JSON FILE ####
# output_json = ab_json(output_sf, time_fun = ab_time_normal, scenario_name = "Exeter Example")
# ab_save(output_json, f = "../../Desktop/exeter.json")
#
# #### COMMANDS FOR AB STREET
# # $ cargo run
# # $ cargo run --bin import_traffic -- --map=PATH/TO/MAP --input=/PATH/TO/JSON.json