wisconsin_crashes/R/city_maps.R

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6.4 KiB
R

library(tidyverse)
library(ggmap)
library(sf)
library(osrm)
library(smoothr)
library(ggnewscale)
library(RColorBrewer)
library(magick)
library(rsvg)
library(parallel)
## 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"))
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)))
# Injury Severy Index and Color -----
injury_severity <- data.frame(InjSevName = c("No apparent injury", "Possible Injury", "Suspected Minor Injury","Suspected Serious Injury","Fatality"),
code = c("O", "C", "B", "A", "K"),
# color = c("#fafa6e", "#edc346", "#d88d2d", "#bd5721", "#9b1c1c"))
color = c("#fafa6e", "#edc346", "#d88d2d", "#d88d21", "#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)
TOPS_data <- TOPS_data %>% mutate(ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"),
INJSVR1,
ifelse(ROLE2 %in% c("BIKE", "PED"),
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$InjSevName)) %>%
rename(ped_inj_name = InjSevName)
# Race names
race <- data.frame(race_name = c("Asian", "Black", "Indian","Hispanic","White"),
code = c("A", "B", "I", "H", "W"))
TOPS_data <- left_join(TOPS_data, race %>% select(race_name, code), join_by(RACE1 == code)) %>% rename(race_name1 = race_name)
TOPS_data <- left_join(TOPS_data, race %>% select(race_name, code), join_by(RACE2 == code)) %>% rename(race_name2 = race_name)
logo <- image_read(path = "other/BFW_Logo_180_x_200_transparent_background.png")
## set tile server info
# register stadia API key ----
register_stadiamaps(key = substr(read_file(file = "api_keys/stadia_api_key"), 1, 36))
options(ggmap.file_drawer = "basemaps")
# dir.create(file_drawer(), recursive = TRUE, showWarnings = FALSE)
# saveRDS(list(), file_drawer("index.rds"))
readRDS(file_drawer("index.rds"))
file_drawer("index.rds")
## set parameters -----
focus_muni <- c("MILWAUKEE", "MADISON")
focus_inj <- c("A", "K")
focus_role <- c("BIKE", "PED")
focus_years <- c("2023")
## generate maps for focus city
for(muni in focus_muni) {
# create bounding box around crashes that happen in city.
muni_data <- TOPS_data %>% filter(MUNINAME %in% muni)
bbox <- c(left = min(muni_data$longitude, na.rm = TRUE),
bottom = min(muni_data$latitude, na.rm = TRUE),
right = max(muni_data$longitude, na.rm = TRUE),
top = max(muni_data$latitude, na.rm = TRUE))
#get basemap
basemap <- get_stadiamap(bbox = bbox, zoom = 12, maptype = "stamen_toner_lite")
# generate map
ggmap(basemap) +
labs(title = paste0("Crashes between pedestrians/bicyclists in ", str_to_title(muni)),
subtitle = paste0("that result in a severe injury or fatality | ",
focus_years),
caption = "data from Wisconsin DOT, UW TOPS Laboratory, and OpenStreetMap",
x = NULL,
y = NULL) +
theme(axis.text=element_blank(),
axis.ticks=element_blank()) +
## add bike lts
#geom_sf(data = bike_lts[[county]],
# inherit.aes = FALSE,
# aes(color = lts)) +
#scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
# add crash locations
new_scale_fill() +
geom_point(data = TOPS_data %>%
filter(ROLE1 %in% focus_role
& INJSVR1 %in% focus_inj
# & age1 < 18
| ROLE2 %in% focus_role
& INJSVR2 %in% focus_inj
# & age2 < 18
) %>%
filter(longitude >= as.double(bbox[1]),
latitude >= as.double(bbox[2]),
longitude <= as.double(bbox[3]),
latitude <= as.double(bbox[4])) %>%
filter(year %in% focus_years),
aes(x = longitude,
y = latitude,
fill = ped_inj_name),
shape = 21,
size = 2) +
scale_fill_manual(values = injury_severity %>% filter(code %in% focus_inj) %>% pull(color), name = "Crash Severity") +
annotation_raster(logo,
# Position adjustments here using plot_box$max/min/range
ymin = bbox['top'] - 0.25 * 0.16,
ymax = bbox['top'],
xmin = bbox['right'] + 0.25 * 0.05,
xmax = bbox['right'] + 0.25 * 0.20) +
coord_sf(clip = "off")
ggsave(file = paste0("figures/city_maps/",
str_to_title(muni),
".pdf"),
title = paste0(str_to_title(muni), " Pedestrian/Bike crashes"),
device = pdf,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
}