In soukhova/AccessPack: Spatial Availability Function and Sample Datasets including Real-World 2016 TTS Data and Trips To Work Hypothetical Toy Data

In this notebook we process a cross-tabulation of origin and destinations by people employed full-time in the Greater Golden Horseshoe (GGH) in 2016.

The source data is in file tts-2016-OD-FT-Employment.txt, which was retrieved from the Transportation Tomorrow Survey Data Retrieval System on October 28, 2021.

The centroids of all work zones (origins) and full-time (FT) employment zones (destinations) are calculated.

Preliminaries

Load packages:

library(readxl)
library(sf)
library(tidyverse)
library(units)

Traffic Analysis Zones

Read the boundaries of the traffic analysis zones:

ggh_taz <- st_read("TTS16-data-inputs/OD-by-FT-Employment/tts06_83_region.shp")

Project object:

ggh_taz <- ggh_taz %>%
  st_transform(crs = 32617)

Prepare traffic analysis zones:

ggh_taz <- ggh_taz %>%
  transmute(GTA06 = as.character(GTA06),
            AREA = st_area(geometry) %>% 
              set_units(km^2) %>% 
              drop_units())

Extract the IDs for all TAZ:

ghta_taz_id <- ggh_taz$GTA06

Origin destination matrices for work and shop

Read trip tables. The query was for "trips" by zone of residence, zone of work, and trip purpose (Work):

od_ft <- read_delim(file = "TTS16-data-inputs/OD-by-FT-Employment/tts-2016-OD-FT-Employment.txt", 
                      delim = "\t", 
                      col_names = FALSE)

Find the position in the table where the information on work trips begins:

idx <- which(od_ft$X1 == "TABLE    : emp_stat (Full time)")

Slice the table to obtain the work trips (remove heading information):

od_ft <- od_ft %>% 
  slice((idx+2):n())

Separate the zone identifiers and the trips, and convert to numeric:

od_ft <- od_ft %>%
  separate(X1, into = c("Zones", "Persons"), sep = " (?=[^ ]+$)") %>%
  mutate(Zones = str_trim(Zones, side = "both")) %>%
  separate(Zones, into = c("Origin", "Destination"), sep = " (?=[^ ]+$)") %>%
  mutate(Origin = str_trim(Origin),
         Destination = str_trim(Destination),
         Persons = as.numeric(Persons))

The resulting object od_ft has the number of individuals who are employed full-time by place of residence (Origin) and place of work (Destination) using the GTA06 zoning system.

Workers and Jobs

The od table can be used to calculate the workers and jobs by traffic analysis zone:

workers_ft <- od_ft %>%
  group_by(Origin) %>%
  summarize(workers = sum(Persons),
            .groups = "drop") %>%
  transmute(GTA06 = Origin,
            workers)

jobs_ft <- od_ft %>%
  group_by(Destination) %>%
  summarize(jobs = sum(Persons),
            .groups = "drop") %>%
  transmute(GTA06 = Destination,
            jobs)

Join workers and jobs to simple features object:

ggh_taz <- ggh_taz %>%
  # Join workers
  left_join(workers_ft,
            by = "GTA06") %>%
  # Join jobs
  left_join(jobs_ft,
            by = "GTA06") %>%
  # Replace NAs with zeros
  mutate(workers = replace_na(workers, 0),
         jobs = replace_na(jobs, 0))

Plot worker population in the ggh:

ggplot() +
  geom_sf(data = ggh_taz,
          aes(fill = workers),
          color = NA) +
  scale_fill_fermenter(direction = 1)

Plot job distribution in the ggh:

ggplot() +
  geom_sf(data = ggh_taz,
          aes(fill = jobs),
          color = NA) +
  scale_fill_fermenter(direction = 1)

Creating input for travel-time OD calculations

Create centroids for each feature in ggh_taz:

ggh_taz_cent <- st_centroid(ggh_taz) 

Attach the centroid point geometry to the origin object (workers) and the destination object (jobs)

#workers (origins)
workers_ft_cent <- workers_ft %>% merge(ggh_taz_cent %>% select(GTA06), by="GTA06") %>% st_sf

work_origins <- cbind(workers_ft_cent, st_coordinates(st_transform(workers_ft_cent, crs = 4326))) %>%
  rename(lon = "X", lat = "Y", id = "GTA06") %>% as.data.frame()  %>%
  select(id, lon, lat) %>% as.data.frame()

#jobs (destinations)
jobs_ft_cent <- jobs_ft %>% merge(ggh_taz_cent %>% select(GTA06), by="GTA06") %>% st_sf

job_destinations <- cbind(jobs_ft_cent, st_coordinates(st_transform(jobs_ft_cent, crs = 4326))) %>%
  rename(lon = "X", lat = "Y", id = "GTA06") %>% as.data.frame()  %>%
  select(id, lon, lat) 

saving inputs for OD travel time calculations

save("work_origins", file = "TTS16-data-inputs/Travel-Time-Calculations/inputs/work_origins.Rdata")
save("job_destinations", file = "TTS16-data-inputs/Travel-Time-Calculations/inputs/job_destinations.Rdata")

saving inputs for later processing

save("ggh_taz", file = "TTS16-data-inputs/OD-by-FT-Employment/ggh_taz.Rdata")
save("od_ft", file = "TTS16-data-inputs/OD-by-FT-Employment/od_ft.Rdata")

soukhova/AccessPack documentation built on March 20, 2022, 6:23 p.m.