library(tidyverse) library(sf) #library(tmap) library(leaflet) library(RColorBrewer) library(tidycensus) library(htmltools) library(magick) library(htmlwidgets) library(MASS) library(raster) ## 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 Severity Index and Color ----- 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")) injury_severity_pal <- colorFactor(palette = injury_severity$color, levels = injury_severity$InjSevName) TOPS_data <- left_join(TOPS_data, injury_severity %>% dplyr::select(InjSevName, code), join_by(INJSVR1 == code)) %>% mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>% rename(InjSevName1 = InjSevName) TOPS_data <- left_join(TOPS_data, injury_severity %>% dplyr::select(InjSevName, code), join_by(INJSVR2 == code)) %>% mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>% rename(InjSevName2 = InjSevName) 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 %>% dplyr::select(InjSevName, code), join_by(ped_inj == code)) %>% mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>% rename(ped_inj_name = InjSevName) # bike or ped TOPS_data <- TOPS_data %>% mutate(vulnerable_role = ifelse(ROLE1 %in% bike_roles | ROLE2 %in% bike_roles, "Bicyclist", ifelse(ROLE1 %in% ped_roles | ROLE2 %in% ped_roles, "Pedestrian", NA))) # Race names race <- data.frame(race_name = c("Asian", "Black", "Indian","Hispanic","White"), code = c("A", "B", "I", "H", "W")) TOPS_data <- left_join(TOPS_data, race %>% dplyr::select(race_name, code), join_by(RACE1 == code)) %>% rename(race_name1 = race_name) TOPS_data <- left_join(TOPS_data, race %>% dplyr::select(race_name, code), join_by(RACE2 == code)) %>% rename(race_name2 = race_name) ## make mutate TOPS_data TOPS_data <- TOPS_data %>% mutate(Year = year, PedestrianInjurySeverity = ped_inj_name, CrashDate = CRSHDATE, CrashTime = CRSHTIME, County = CNTYNAME, Street = ONSTR, CrossStreet = ATSTR) %>% mutate(PedestrianAge = ifelse(ROLE1 %in% vuln_roles, age1, age2)) TOPS_geom <- st_as_sf(TOPS_data %>% filter(!is.na(latitude)), coords = c("longitude", "latitude"), crs = 4326) ## load school locations ---- WI_schools <- st_read(dsn = "data/Schools/WI_schools.gpkg") WI_schools <- WI_schools %>% filter(is.double(LAT), LAT > 0) %>% dplyr::select("SCHOOL", "DISTRICT", "SCHOOLTYPE", "COUNTY", "LAT", "LON") school_symbol <- makeIcon(iconUrl = "other/school_FILL0_wght400_GRAD0_opsz24.png", iconWidth = 24, iconHeight = 24, iconAnchorX = 12, iconAnchorY = 12) focus_columns <- c("PedestrianInjurySeverity", "CrashDate", "CrashTime", "County", "Street", "CrossStreet", "PedestrianAge", "Year", "vulnerable_role") focus_county <- "MILWAUKEE" WI_schools <- WI_schools %>% filter(COUNTY %in% str_to_title(focus_county)) # generate map with leaflet ---- Pedestrian_Crash_Data <- TOPS_data %>% filter(CNTYNAME == focus_county, !is.na(latitude)) %>% dplyr::select(all_of(c(focus_columns, "longitude", "latitude"))) # generate density map ---- crash_density <- kde2d(Pedestrian_Crash_Data$longitude, Pedestrian_Crash_Data$latitude, n = 200) crash_density <- raster(crash_density) crash_density <- cut(crash_density, breaks = 10) crash_density_poly <- rasterToPolygons(crash_density, dissolve = T) density_pal <- colorNumeric(palette = "YlOrRd", domain = c(min(crash_density_poly$layer, na.rm = TRUE), max(crash_density_poly$layer, na.rm = TRUE))) # add county census data ---- census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40)) county_populations <- get_estimates(geography = "county", year = 2022, product = "population", state = "Wisconsin", geometry = TRUE) %>% filter(variable == "POPESTIMATE") %>% mutate(County = str_to_upper(str_replace(NAME, " County, Wisconsin", ""))) county_populations <- st_transform(county_populations, crs = 4326) %>% filter(County %in% focus_county) milwaukee_crash_map <- leaflet(options = leafletOptions(preferCanvas = TRUE)) %>% addProviderTiles(providers$Stadia.AlidadeSmooth) %>% addPolygons(data = county_populations, color = "black", weight = 1, fill = FALSE, group = "County Lines") %>% addPolygons(data = crash_density_poly, color = "black", weight = 0, opacity = 0.9, group = "Heat Map", fillColor = density_pal(crash_density_poly$layer))%>% addMarkers(data = WI_schools, lng=WI_schools$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$PedestrianInjurySeverity), radius=4, stroke=TRUE, color = "black", weight = 1, fillOpacity = 0.8, label = lapply(paste0("", str_to_title(replace_na(Pedestrian_Crash_Data$vulnerable_role, "")),"
", Pedestrian_Crash_Data$CrashDate, "
", Pedestrian_Crash_Data$PedestrianInjurySeverity, "
", replace_na(Pedestrian_Crash_Data$vulnerable_role, ""), " age: ", ifelse(!is.na(Pedestrian_Crash_Data$PedestrianAge), Pedestrian_Crash_Data$PedestrianAge, "unknown age")), htmltools::HTML), group = "Crash Points") %>% addLegend(position = "bottomleft", labels = injury_severity$InjSevName, colors = injury_severity$color, group = "Crash Points", title = "Injury Severity") %>% groupOptions(group = "Schools", zoomLevels = 15:20) %>% groupOptions(group = "Crash Points", zoomLevels = 13:20) %>% groupOptions(group = "County Lines", zoomLevels = 5:20) %>% groupOptions(group = "Heat Map", zoomLevels = 5:13) milwaukee_crash_map saveWidget(milwaukee_crash_map, file = "figures/dynamic_crash_maps/milwaukee_pedestrian_crash_map.html", selfcontained = TRUE)