233 lines
11 KiB
R
233 lines
11 KiB
R
library(tidyverse)
|
|
library(sf)
|
|
#library(tmap)
|
|
library(leaflet)
|
|
library(RColorBrewer)
|
|
library(tidycensus)
|
|
library(htmltools)
|
|
library(magick)
|
|
library(htmlwidgets)
|
|
|
|
|
|
## Load TOPS data ----
|
|
## To load TOPS data for the whole state for crashes involving bikes and pedestrians):
|
|
## Step 1 - download csv from the TOPS Data Retrieval Tool with the query: SELECT * FROM DTCRPRD.SUMMARY_COMBINED C WHERE C.CRSHDATE BETWEEN TO_DATE('2023-JAN','YYYY-MM') AND LAST_DAY(TO_DATE('2023-DEC','YYYY-MM')) AND (C.BIKEFLAG = 'Y' OR C.PEDFLAG = 'Y') ORDER BY C.DOCTNMBR
|
|
## Step 2 - include RACE1 and RACE2 for download in preferences
|
|
## Step 3 - save the csv in the "data" directory as crash-data-download_2023.csv
|
|
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))
|
|
|
|
|
|
# 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"))
|
|
|
|
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$InjSevName)) %>%
|
|
rename(ped_inj_name = InjSevName)
|
|
|
|
# Race names
|
|
race <- data.frame(race_name = c("Asian", "Black", "Indian","Hispanic","White"),
|
|
code = c("A", "B", "I", "H", "W"))
|
|
|
|
TOPS_data <- left_join(TOPS_data, race %>% select(race_name, code), join_by(RACE1 == code)) %>% rename(race_name1 = race_name)
|
|
TOPS_data <- left_join(TOPS_data, race %>% select(race_name, code), join_by(RACE2 == code)) %>% rename(race_name2 = race_name)
|
|
|
|
## make mutate TOPS_data
|
|
TOPS_data <- TOPS_data %>%
|
|
mutate(Year = year,
|
|
PedestrianInjurySeverity = ped_inj_name,
|
|
CrashDate = CRSHDATE,
|
|
CrashTime = CRSHTIME,
|
|
County = CNTYNAME,
|
|
Street = ONSTR,
|
|
CrossStreet = ATSTR) %>%
|
|
mutate(PedestrianAge = ifelse(ROLE1 %in% c("BIKE", "PED"), age1, age2))
|
|
|
|
|
|
TOPS_geom <- st_as_sf(TOPS_data %>% filter(!is.na(latitude)), coords = c("longitude", "latitude"), crs = 4326)
|
|
|
|
## load school locations ----
|
|
WI_schools <- st_read(dsn = "data/Schools/WI_schools.gpkg")
|
|
WI_schools <- WI_schools %>%
|
|
filter(is.double(LAT),
|
|
LAT > 0) %>%
|
|
select("SCHOOL", "DISTRICT", "SCHOOLTYPE", "LAT", "LON")
|
|
|
|
school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.svg")
|
|
|
|
## add county borders ----
|
|
CountyBoundaries <- read_sf("data/WI_County_Boundaries_24K.geojson")
|
|
|
|
focus_columns <- c("PedestrianInjurySeverity", "CrashDate", "CrashTime", "County", "Street", "CrossStreet", "PedestrianAge", "Year")
|
|
focus_county <- "DANE"
|
|
|
|
## generate map with tmap ----
|
|
# tmap_mode("view")
|
|
#
|
|
# Pedestrian_Crash_Data <- TOPS_geom %>%
|
|
# # filter(CNTYNAME == focus_county) %>%
|
|
# select(all_of(focus_columns))
|
|
#
|
|
# tm_basemap("Stadia.AlidadeSmooth") +
|
|
# tm_shape(Pedestrian_Crash_Data) +
|
|
# tm_dots("PedestrianInjurySeverity", palette = injury_severity$color, popup.vars = focus_columns)
|
|
#
|
|
# tmap_save(file = "figures/dynamic_crash_maps/dynamic_crash_map.html")
|
|
|
|
|
|
# generate map with leaflet ----
|
|
Pedestrian_Crash_Data <- TOPS_data %>%
|
|
# filter(CNTYNAME == focus_county) %>%
|
|
select(c(all_of(focus_columns), "longitude", "latitude"))
|
|
|
|
injury_severity_pal <- colorFactor(palette = injury_severity$color, domain = injury_severity$InjSevName)
|
|
|
|
County_Crash_Data <- Pedestrian_Crash_Data %>%
|
|
group_by(County, Year) %>%
|
|
summarise(TotalCrashes = n(),
|
|
longitude = mean(longitude, na.rm = TRUE),
|
|
latitude = mean(latitude, na.rm = TRUE)) %>%
|
|
group_by(County) %>%
|
|
summarise(MeanCrashes = mean(TotalCrashes, na.rm = TRUE),
|
|
longitude = mean(longitude, na.rm = TRUE),
|
|
latitude = mean(latitude, na.rm = TRUE))
|
|
|
|
# add population census data
|
|
census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40))
|
|
county_populations <- get_estimates(geography = "county", year = 2022, product = "population", state = "Wisconsin", geometry = TRUE) %>%
|
|
filter(variable == "POPESTIMATE") %>%
|
|
mutate(County = str_to_upper(str_replace(NAME, " County, Wisconsin", "")))
|
|
county_populations <- st_transform(county_populations, crs = 4326)
|
|
County_Crash_geom <- left_join(county_populations, County_Crash_Data, join_by("County"))
|
|
County_Crash_geom <- County_Crash_geom %>%
|
|
mutate(CrashesPerPopulation = MeanCrashes/(value/100000))
|
|
County_Crash_geom$CrashesPerPopulation[is.na(County_Crash_geom$CrashesPerPopulation)] <- 0
|
|
|
|
county_pal <- colorNumeric(palette = "YlOrRd", domain = c(min(County_Crash_geom$CrashesPerPopulation, na.rm = TRUE), max(County_Crash_geom$CrashesPerPopulation, na.rm = TRUE)))
|
|
|
|
#title style
|
|
tag.map.title <- tags$style(HTML("
|
|
.leaflet-control.map-title {
|
|
transform: translate(-50%,20%);
|
|
position: fixed !important;
|
|
left: 50%;
|
|
text-align: center;
|
|
padding-left: 10px;
|
|
padding-right: 10px;
|
|
background: rgba(255,255,255,0.75);
|
|
font-weight: bold;
|
|
font-size: 28px;
|
|
}
|
|
"))
|
|
|
|
title <- tags$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.subtitle <- tags$style(HTML("
|
|
.leaflet-control.map-subtitle {
|
|
transform: translate(0%,20%);
|
|
position: fixed !important;
|
|
left: 90%;
|
|
text-align: center;
|
|
padding-left: 10px;
|
|
padding-right: 10px;
|
|
background: rgba(255,255,255,0.75);
|
|
font-weight: regular;
|
|
font-size: 12px;
|
|
}
|
|
"))
|
|
|
|
wisconsin_crash_map <-
|
|
leaflet(options = leafletOptions(preferCanvas = TRUE)) %>%
|
|
# addControl(title, position = "topleft", className="map-title") %>%
|
|
# addControl(subtitle, position = "bottomleft", className="map-subtitle") %>%
|
|
addProviderTiles(providers$Stadia.AlidadeSmooth) %>%
|
|
addMarkers(data = WI_schools,
|
|
lng=WI_schools$LON,
|
|
lat = WI_schools$LAT,
|
|
label = lapply(paste0("<b>", WI_schools$SCHOOL, " School</b></br>",
|
|
WI_schools$DISTRICT, " School District</br>",
|
|
WI_schools$SCHOOLTYPE), htmltools::HTML),
|
|
group = "Schools") %>%
|
|
addCircleMarkers(data = Pedestrian_Crash_Data,
|
|
lng=Pedestrian_Crash_Data$longitude,
|
|
lat=Pedestrian_Crash_Data$latitude,
|
|
fillColor=injury_severity_pal(Pedestrian_Crash_Data$PedestrianInjurySeverity),
|
|
radius=4,
|
|
stroke=TRUE,
|
|
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$color, group = "Crash Points", title = "Injury Severity") %>%
|
|
addCircleMarkers(data = County_Crash_geom,
|
|
lng=County_Crash_geom$longitude,
|
|
lat=County_Crash_geom$latitude,
|
|
#fillColor=county_pal(County_Crash_geom$CrashesPerPopulation),
|
|
radius=County_Crash_geom$value/20000,
|
|
stroke = TRUE,
|
|
color = "black",
|
|
weight = 1,
|
|
fillOpacity = 0.5,
|
|
group = "Counties") %>%
|
|
addPolygons(data = County_Crash_geom,
|
|
color = "black",
|
|
weight = 1,
|
|
fillColor=county_pal(County_Crash_geom$CrashesPerPopulation),
|
|
fillOpacity = 0.6,
|
|
label = lapply(paste0("<b>", str_to_title(County_Crash_geom$County), " County</b></br>",
|
|
"population: ", format(County_Crash_geom$value, nsmall=0, big.mark=","), "<br>",
|
|
"average crashes per year: ", round(County_Crash_geom$MeanCrashes,0), "</br>",
|
|
"average crashes/year per 100k residents: ", round(County_Crash_geom$CrashesPerPopulation,0)), htmltools::HTML),
|
|
group = "Counties") %>%
|
|
addLegend(position = "bottomleft", pal = county_pal, values = County_Crash_geom$CrashesPerPopulation, group = "Counties", title = "Circle size = county population<br><br>Color = Crashes/year</br>(normalized per 100k residents)") %>%
|
|
# addLegendSize(position = "bottomright", color = "black", shape = "circle", values = County_Crash_geom$value, group = "Counties", title = "Total crashes") %>%
|
|
groupOptions(group = "Schools", zoomLevels = 13:20) %>%
|
|
groupOptions(group = "Crash Points", zoomLevels = 10:20) %>%
|
|
groupOptions(group ="Counties", zoomLevels = 1:9)
|
|
|
|
saveWidget(wisconsin_crash_map, file = "figures/dynamic_crash_maps/wisconsin_crash_map.html", selfcontained = TRUE)
|
|
|
|
|