library(tidyverse)
library(ggmap)
library(sf)
library(osrm)
library(smoothr)
library(ggnewscale)
library(RColorBrewer)
library(magick)
library(rsvg)
library(parallel)
library(tidycensus)
## add data from WiscTransPortal Crash Data Retrieval Facility ----
## query: SELECT *
## FROM DTCRPRD.SUMMARY_COMBINED C
## WHERE C.CRSHDATE BETWEEN TO_DATE('2022-JAN','YYYY-MM') AND
## LAST_DAY(TO_DATE('2022-DEC','YYYY-MM')) AND
## (C.BIKEFLAG = 'Y' OR C.PEDFLAG = 'Y')
## ORDER BY C.DOCTNMBR
## Load TOPS data ----
## load TOPS data for the whole state (crashes involving bikes and pedestrians),
TOPS_data <- as.list(NULL)
for (file in list.files(path = "data/TOPS/", pattern = "crash-data-download")) {
message(paste("importing data from file: ", file))
year <- substr(file, 21, 24)
csv_run <- read_csv(file = paste0("data/TOPS/",file), col_types = cols(.default = "c"))
csv_run["retreive_date"] <- file.info(file = paste0("data/TOPS/",file))$mtime
TOPS_data[[file]] <- csv_run
}
rm(csv_run, file, year)
TOPS_data <- bind_rows(TOPS_data)
## clean up data
TOPS_data <- TOPS_data %>%
mutate(date = mdy(CRSHDATE),
age1 = as.double(AGE1),
age2 = as.double(AGE2),
latitude = as.double(LATDECDG),
longitude = as.double(LONDECDG)) %>%
mutate(month = month(date, label = TRUE),
year = as.factor(year(date)))
retrieve_date <- max(TOPS_data %>% filter(year %in% max(year(TOPS_data$date), na.rm = TRUE)) %>% pull(retreive_date))
# county index
counties <- data.frame(name = c("Dane", "Milwaukee"),
CNTYCODE = c(13, 40),
COUNTY = c("DANE", "MILWAUKEE"))
# Injury Severy Index and Color -------------------------------------------
# injury severity index
injury_severity <- data.frame(InjSevName = c("Injury severity unknown", "No apparent injury", "Possible Injury", "Suspected Minor Injury","Suspected Serious Injury","Fatality"),
code = c(NA, "O", "C", "B", "A", "K"),
color = c("grey", "#fafa6e", "#edc346", "#d88d2d", "#bd5721", "#9b1c1c"))
TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR1 == code)) %>%
mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
rename(InjSevName1 = InjSevName)
TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR2 == code)) %>%
mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
rename(InjSevName2 = InjSevName)
# add bike or pedestrian roles ----
bike_roles <- c("BIKE", "O BIKE")
ped_roles <- c("PED", "O PED", "PED NO")
vuln_roles <- c(bike_roles, ped_roles)
TOPS_data <- TOPS_data %>% mutate(ped_inj = ifelse(ROLE1 %in% vuln_roles,
INJSVR1,
ifelse(ROLE2 %in% vuln_roles,
INJSVR2,
NA)))
TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(ped_inj == code)) %>%
mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
rename(ped_inj_name = InjSevName)
# bike or ped
TOPS_data <- TOPS_data %>% mutate(vulnerable_role = ifelse(ROLE1 %in% bike_roles | ROLE2 %in% bike_roles,
"Bicyclist",
ifelse(ROLE1 %in% ped_roles | ROLE2 %in% ped_roles,
"Pedestrian",
NA)))
## load bike LTS networks
bike_lts <- as.list(NULL)
for(file in list.files("data/bike_lts")) {
county <- str_sub(file, 10, -9)
lts_run <- st_read(paste0("data/bike_lts/", file))
lts_run[["lts"]] <- as.factor(lts_run$LTS_F)
bike_lts[[county]] <- lts_run
}
bike_lts_scale <- data.frame(code = c(1, 2, 3, 4, 9),
color = c("#1a9641",
"#a6d96a",
"#fdae61",
"#d7191c",
"#d7191c"))
# register stadia API key ----
register_stadiamaps(key = substr(read_file(file = "api_keys/stadia_api_key"), 1, 36))
#options(ggmap.file_drawer = "basemaps")
# dir.create(file_drawer(), recursive = TRUE, showWarnings = FALSE)
# saveRDS(list(), file_drawer("index.rds"))
#readRDS(file_drawer("index.rds"))
#file_drawer("index.rds")
# load census api key ----
census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40))
# load logo
logo <- image_read(path = "other/BFW_Logo_180_x_200_transparent_background.png")
school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.svg")
## ---- generate charts/maps ----
## set parameters of run
#county_focus <- str_to_upper(unique(WI_schools %>% pull(CTY_DIST)))
#county_focus <- c("DANE")
county_focus <- "Rock"
municipality_geom <- st_read("data/WI_Cities,_Towns_and_Villages_January_2024.geojson")
municipality_focus <- c("Evansville")
#municipality_focus <- c("Monona", "Fitchburg")
#municipality_focus <- municipality_geom %>% filter(CNTY_NAME == county_focus) %>% pull(MCD_NAME)
for(municipality in municipality_focus) {
message(paste("***", municipality))
options(ggmap.file_drawer = paste0("basemaps/municipalities/", municipality))
dir.create(file_drawer(), recursive = TRUE, showWarnings = FALSE)
saveRDS(list(), file_drawer("index.rds"))
readRDS(file_drawer("index.rds"))
file_drawer("index.rds")
municipality_filtered <- municipality_geom %>% filter(CNTY_NAME == county_focus, MCD_NAME == municipality) %>% pull(geometry)
# create bounding box from school, 5km away.
bbox_poly <- st_transform(st_buffer(municipality_filtered, 1000), crs = 4326)
bbox <- st_bbox(bbox_poly)
bbox <- c(left = as.double(bbox[1]),
bottom = as.double(bbox[2]),
right = as.double(bbox[3]),
top = as.double(bbox[4]))
#get basemap
basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
# generate map
ggmap(basemap) +
labs(title = paste0(
# "Crashes between cars and youth (<18) pedestrians/bicyclists near ",
"Crashes between cars and pedestrians & bicyclists in ",
municipality),
subtitle = paste0(min(year(TOPS_data$date), na.rm = TRUE),
" - ",
max(year(TOPS_data$date), na.rm = TRUE)),
caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"),
" per direction of the WisDOT Bureau of Transportation Safety",
"\nbasemap from StadiaMaps and OpenStreetMap Contributers"),
x = NULL,
y = NULL) +
theme(axis.text=element_blank(),
axis.ticks=element_blank(),
plot.caption = element_text(color = "grey")) +
## add bike lts
geom_sf(data = bike_lts[[county]] %>% st_intersection(bbox_poly),
inherit.aes = FALSE,
aes(color = lts)) +
scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
# add crash locations
new_scale_fill() +
geom_point(data = TOPS_data %>%
filter(ROLE1 %in% c("BIKE", "PED")
# & age1 < 18
| ROLE2 %in% c("BIKE", "PED")
# & age2 < 18
) %>%
filter(longitude >= as.double(bbox[1]),
latitude >= as.double(bbox[2]),
longitude <= as.double(bbox[3]),
latitude <= as.double(bbox[4])),
aes(x = longitude,
y = latitude,
fill = ped_inj_name),
shape = 23,
size = 3) +
scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = "Crash Severity") +
geom_sf(data = municipality_filtered,
inherit.aes = FALSE,
color = 'black',
fill = NA,
linewidth = 1) +
annotation_raster(logo,
# Position adjustments here using plot_box$max/min/range
ymin = bbox['top'] - (bbox['top']-bbox['bottom']) * 0.16,
ymax = bbox['top'],
xmin = bbox['right'] + (bbox['right']-bbox['left']) * 0.05,
xmax = bbox['right'] + (bbox['right']-bbox['left']) * 0.15) +
coord_sf(clip = "off")
ggsave(file = paste0("figures/municipalities/",
municipality,
".pdf"),
#title = paste0(municipality, " Youth Pedestrian/Bike crashes"),
title = paste0(municipality, " All Pedestrian/Bike crashes"),
device = pdf,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
}