pct_2_abstr_exeter_example.r

####
####
#### 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 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(euclidean_distance = as.numeric(sf::st_length(exeter_commute_od))) %>%
  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)
# select 20% of buildings in each zone to reduce file size for this example
# remove this filter or increase the sampling to include more buildings
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")