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0209cf76f2 ... ba4fb8b6c8

5 changed files with 17 additions and 55 deletions
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6
figures/dynamic_crash_maps/dynamic_crash_map.html
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File diff suppressed because one or more lines are too long

8
scripts/crash_summary_charts.R
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@ -13,7 +13,6 @@ 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))$mtime
TOPS_data[[file]] <- csv_run
}
rm(csv_run, file, year)
@ -29,8 +28,6 @@ TOPS_data <- TOPS_data %>%
mutate(month = month(date, label = TRUE),
year = as.factor(year(date)))
retrieve_date <- max(TOPS_data %>% filter(year %in% max(year(TOPS_data$date), na.rm = TRUE)) %>% pull(retreive_date))
# 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"),
@ -124,10 +121,7 @@ TOPS_data %>%
x = "Year",
y = "Total crashes per year per 100,000 residents",
color = "County",
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")) +
caption = "data from UW TOPS lab\nretrieved 3/2024 per direction of the WisDOT Bureau of Transportation Safety") +
theme(plot.caption = element_text(color = "grey"))
ggsave(file = paste0("figures/crash_summaries/counties_year.pdf"),

24
scripts/dynamic_crash_map.R
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@ -16,11 +16,11 @@ 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))$mtime
TOPS_data[[file]] <- csv_run
}
rm(csv_run, file, year)
TOPS_data <- bind_rows(TOPS_data)
## clean up data ----
TOPS_data <- TOPS_data %>%
mutate(date = mdy(CRSHDATE),
@ -31,9 +31,6 @@ TOPS_data <- TOPS_data %>%
mutate(month = month(date, label = TRUE),
year = as.factor(year(date)))
retrieve_date <- max(TOPS_data %>% filter(year %in% max(year(TOPS_data$date), na.rm = TRUE)) %>% pull(retreive_date))
# 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"),
@ -141,10 +138,7 @@ tag.map.title <- tags$style(HTML("
"))
title <- tags$div(
tag.map.title, HTML(paste0("Pedestrians & Bicyclists involved in a crash</br>",
min(year(TOPS_data$date), na.rm = TRUE),
" - ",
max(year(TOPS_data$date), na.rm = TRUE)))
tag.map.title, HTML("Pedestrians & Bicyclists hit by cars</br>2017-2023")
)
tag.map.subtitle <- tags$style(HTML("
@ -162,14 +156,12 @@ tag.map.subtitle <- tags$style(HTML("
"))
subtitle <- tags$div(
tag.map.subtitle, HTML(paste0("data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"),
"</br>per direction of the WisDOT Bureau of Transportation Safety"))
tag.map.subtitle, HTML("data from UW TOPS lab - retrieved 3/2024</br>per direction of the WisDOT Bureau of Transportation Safety")
)
leaflet() %>%
# addControl(title, position = "topleft", className="map-title") %>%
# addControl(subtitle, position = "bottomleft", className="map-subtitle") %>%
addControl(title, position = "topleft", className="map-title") %>%
addControl(subtitle, position = "bottomleft", className="map-subtitle") %>%
addProviderTiles(providers$Stadia.AlidadeSmooth) %>%
addCircleMarkers(data = Pedestrian_Crash_Data,
lng=Pedestrian_Crash_Data$longitude,
@ -180,9 +172,6 @@ leaflet() %>%
color = "black",
weight = 1,
fillOpacity = 0.8,
label = lapply(paste0("<b>", Pedestrian_Crash_Data$CrashDate, "</b></br>",
Pedestrian_Crash_Data$PedestrianInjurySeverity, "</br>",
"pedestrian age: ", Pedestrian_Crash_Data$PedestrianAge), htmltools::HTML),
group = "Crash Points") %>%
addLegend(position = "bottomleft", labels = injury_severity$InjSevName, colors = injury_severity$color, group = "Crash Points", title = "Injury Severity") %>%
groupOptions(group = "Crash Points", zoomLevels = 10:20) %>%
@ -199,9 +188,6 @@ leaflet() %>%
color = "black",
weight = 1,
fillOpacity = 0.7,
label = lapply(paste0("<b>", str_to_title(County_Crash_Data$County), " County</b></br>",
"mean pedestrian crashes/year: ", round(County_Crash_Data$MeanCrashes,0), "</br>",
"crashes/100,000 residents: ", round(County_Crash_Data$CrashesPerPopulation,0)), htmltools::HTML),
group = "Counties") %>%
addLegend(position = "bottomleft", pal = county_pal, values = County_Crash_Data$CrashesPerPopulation, group = "Counties", title = "Mean Crashes/Year in County</br>(per 100,000 residents)") %>%
# addLegendSize(position = "bottomright", color = "black", shape = "circle", values = County_Crash_Data$value.y, group = "Counties", title = "Population of County") %>%

15
scripts/school_maps.R
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@ -20,11 +20,10 @@ library(parallel)
## 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")) {
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))$mtime
TOPS_data[[file]] <- csv_run
}
rm(csv_run, file, year)
@ -40,8 +39,6 @@ TOPS_data <- TOPS_data %>%
mutate(month = month(date, label = TRUE),
year = as.factor(year(date)))
retrieve_date <- max(TOPS_data %>% filter(year %in% max(year(TOPS_data$date), na.rm = TRUE)) %>% pull(retreive_date))
# county index
counties <- data.frame(name = c("Dane", "Milwaukee"),
CNTYCODE = c(13, 40),
@ -146,10 +143,7 @@ for(county in county_focus) {
" County"),
x = "Year",
y = "Number of crashes",
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"))
caption = "data from UW TOPS Laboratory")
ggsave(file = paste0("figures/school_maps/Crash Maps/",
str_to_title(county),
" County/_",
@ -298,10 +292,7 @@ for(district in district_focus) {
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"),
caption = "crash data from UW TOPS lab - retrieved 3/2024 per direction of the WisDOT Bureau of Transportation Safety\nbasemap from StadiaMaps and OpenStreetMap Contributers",
x = NULL,
y = NULL) +
theme(axis.text=element_blank(),

17
scripts/school_maps_parallel.R
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@ -20,14 +20,13 @@ library(parallel)
## 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")) {
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))$mtime
TOPS_data[[file]] <- csv_run
}
rm(csv_run, file, year)
rm(csv_run)
TOPS_data <- bind_rows(TOPS_data)
## clean up data
@ -40,8 +39,6 @@ TOPS_data <- TOPS_data %>%
mutate(month = month(date, label = TRUE),
year = as.factor(year(date)))
retrieve_date <- max(TOPS_data %>% filter(year %in% max(year(TOPS_data$date), na.rm = TRUE)) %>% pull(retreive_date))
# county index
counties <- data.frame(name = c("Dane", "Milwaukee"),
CNTYCODE = c(13, 40),
@ -150,10 +147,7 @@ for(county in county_focus) {
labs(title = paste0("Pedestrians/bicyclists under 18 years old hit by cars in ",
str_to_title(county),
" County"),
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"),
caption = "crash data from UW TOPS lab - retrieved 3/2024 per direction of the WisDOT Bureau of Transportation Safety",
x = "Year",
y = "Number of crashes")
ggsave(file = paste0("~/temp/wi_crashes/figures/crash_maps/Crash Maps/",
@ -301,10 +295,7 @@ generate_school_maps <- function(district) {
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"),
caption = "crash data from UW TOPS lab - retrieved 3/2024 per direction of the WisDOT Bureau of Transportation Safety\nbasemap from StadiaMaps and OpenStreetMap Contributers",
x = NULL,
y = NULL) +
theme(axis.text=element_blank(),