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")) injury_severity_pal <- colorFactor(palette = injury_severity$color, levels = injury_severity$InjSevName) 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) # bike or ped TOPS_data <- TOPS_data %>% mutate(vulnerable_role = ifelse(ROLE1 %in% "BIKE" | ROLE2 %in% "BIKE", "Bicyclist", ifelse(ROLE1 %in% "PED" | ROLE2 %in% "PED", "Pedestrian", NA))) # 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 <- makeIcon(iconUrl = "other/school_FILL0_wght400_GRAD0_opsz24.png", iconWidth = 24, iconHeight = 24, iconAnchorX = 12, iconAnchorY = 12) focus_columns <- c("PedestrianInjurySeverity", "CrashDate", "CrashTime", "County", "Street", "CrossStreet", "PedestrianAge", "Year", "vulnerable_role") focus_county <- "DANE" # generate map with leaflet ---- Pedestrian_Crash_Data <- TOPS_data %>% # filter(CNTYNAME == focus_county) %>% select(c(all_of(focus_columns), "longitude", "latitude")) 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$div( tag.map.title, HTML(paste0("Pedestrians & Bicyclists involved in a crash
", min(year(TOPS_data$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$Stadia.AlidadeSmooth) %>% addPolygons(data = County_Crash_geom, color = "black", weight = 1, fill = FALSE, group = "Crash Points") %>% addMarkers(data = WI_schools, lng=WI_schools$LON, lat = WI_schools$LAT, icon = school_symbol, label = lapply(paste0("", WI_schools$SCHOOL, " School
", WI_schools$DISTRICT, " School District
", WI_schools$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$PedestrianInjurySeverity), radius=4, stroke=TRUE, color = "black", weight = 1, fillOpacity = 0.8, label = lapply(paste0("", str_to_title(Pedestrian_Crash_Data$vulnerable_role),"
", Pedestrian_Crash_Data$CrashDate, "
", Pedestrian_Crash_Data$PedestrianInjurySeverity, "
", Pedestrian_Crash_Data$vulnerable_role, " age: ", Pedestrian_Crash_Data$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$CrashesPerPopulation), fillOpacity = 0.6, label = lapply(paste0("", str_to_title(County_Crash_geom$County), " County
", "population: ", format(County_Crash_geom$value, nsmall=0, big.mark=","), "
", "average crashes per year: ", round(County_Crash_geom$MeanCrashes,0), "
", "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 = "Crashes/year
(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) wisconsin_crash_map saveWidget(wisconsin_crash_map, file = "figures/dynamic_crash_maps/wisconsin_pedestrian_crash_map.html", selfcontained = TRUE)