189 lines
8.8 KiB
R
189 lines
8.8 KiB
R
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library(tidyverse)
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library(sf)
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#library(tmap)
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library(leaflet)
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library(RColorBrewer)
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library(tidycensus)
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library(htmltools)
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library(magick)
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library(htmlwidgets)
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library(MASS)
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library(raster)
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## Load TOPS data ----
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## To load TOPS data for the whole state for crashes involving bikes and pedestrians):
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## 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
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## Step 2 - include RACE1 and RACE2 for download in preferences
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## Step 3 - save the csv in the "data" directory as crash-data-download_2023.csv
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TOPS_data <- as.list(NULL)
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for (file in list.files(path = "data/TOPS/", pattern = "crash-data-download")) {
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message(paste("importing data from file: ", file))
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year <- substr(file, 21, 24)
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csv_run <- read_csv(file = paste0("data/TOPS/",file), col_types = cols(.default = "c"))
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csv_run["retreive_date"] <- file.info(file = paste0("data/TOPS/",file))$mtime
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TOPS_data[[file]] <- csv_run
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}
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rm(csv_run, file, year)
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TOPS_data <- bind_rows(TOPS_data)
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## clean up data ----
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TOPS_data <- TOPS_data %>%
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mutate(date = mdy(CRSHDATE),
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age1 = as.double(AGE1),
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age2 = as.double(AGE2),
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latitude = as.double(LATDECDG),
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longitude = as.double(LONDECDG)) %>%
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mutate(month = month(date, label = TRUE),
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year = as.factor(year(date)))
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retrieve_date <- max(TOPS_data %>% filter(year %in% max(year(TOPS_data$date), na.rm = TRUE)) %>% pull(retreive_date))
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# Injury Severity Index and Color -----
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injury_severity <- data.frame(InjSevName = c("Injury Severity Unknown", "No apparent injury", "Possible Injury", "Suspected Minor Injury","Suspected Serious Injury","Fatality"),
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code = c(NA, "O", "C", "B", "A", "K"),
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color = c("grey", "#fafa6e", "#edc346", "#d88d2d", "#bd5721", "#9b1c1c"))
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injury_severity_pal <- colorFactor(palette = injury_severity$color, levels = injury_severity$InjSevName)
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TOPS_data <- left_join(TOPS_data, injury_severity %>% dplyr::select(InjSevName, code), join_by(INJSVR1 == code)) %>%
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mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
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rename(InjSevName1 = InjSevName)
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TOPS_data <- left_join(TOPS_data, injury_severity %>% dplyr::select(InjSevName, code), join_by(INJSVR2 == code)) %>%
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mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
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rename(InjSevName2 = InjSevName)
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bike_roles <- c("BIKE", "O BIKE")
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ped_roles <- c("PED", "O PED", "PED NO")
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vuln_roles <- c(bike_roles, ped_roles)
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TOPS_data <- TOPS_data %>% mutate(ped_inj = ifelse(ROLE1 %in% vuln_roles,
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INJSVR1,
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ifelse(ROLE2 %in% vuln_roles,
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INJSVR2,
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NA)))
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TOPS_data <- left_join(TOPS_data, injury_severity %>% dplyr::select(InjSevName, code), join_by(ped_inj == code)) %>%
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mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
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rename(ped_inj_name = InjSevName)
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# bike or ped
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TOPS_data <- TOPS_data %>% mutate(vulnerable_role = ifelse(ROLE1 %in% bike_roles | ROLE2 %in% bike_roles,
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"Bicyclist",
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ifelse(ROLE1 %in% ped_roles | ROLE2 %in% ped_roles,
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"Pedestrian",
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NA)))
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# Race names
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race <- data.frame(race_name = c("Asian", "Black", "Indian","Hispanic","White"),
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code = c("A", "B", "I", "H", "W"))
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TOPS_data <- left_join(TOPS_data, race %>% dplyr::select(race_name, code), join_by(RACE1 == code)) %>% rename(race_name1 = race_name)
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TOPS_data <- left_join(TOPS_data, race %>% dplyr::select(race_name, code), join_by(RACE2 == code)) %>% rename(race_name2 = race_name)
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## make mutate TOPS_data
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TOPS_data <- TOPS_data %>%
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mutate(Year = year,
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PedestrianInjurySeverity = ped_inj_name,
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CrashDate = CRSHDATE,
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CrashTime = CRSHTIME,
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County = CNTYNAME,
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Street = ONSTR,
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CrossStreet = ATSTR) %>%
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mutate(PedestrianAge = ifelse(ROLE1 %in% vuln_roles, age1, age2))
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TOPS_geom <- st_as_sf(TOPS_data %>% filter(!is.na(latitude)), coords = c("longitude", "latitude"), crs = 4326)
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## load school locations ----
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WI_schools <- st_read(dsn = "data/Schools/WI_schools.gpkg")
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WI_schools <- WI_schools %>%
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filter(is.double(LAT),
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LAT > 0) %>%
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dplyr::select("SCHOOL", "DISTRICT", "SCHOOLTYPE", "COUNTY", "LAT", "LON")
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school_symbol <- makeIcon(iconUrl = "other/school_FILL0_wght400_GRAD0_opsz24.png",
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iconWidth = 24,
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iconHeight = 24,
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iconAnchorX = 12,
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iconAnchorY = 12)
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focus_columns <- c("PedestrianInjurySeverity", "CrashDate", "CrashTime", "County", "Street", "CrossStreet", "PedestrianAge", "Year", "vulnerable_role")
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focus_county <- "MILWAUKEE"
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WI_schools <- WI_schools %>% filter(COUNTY %in% str_to_title(focus_county))
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# generate map with leaflet ----
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Pedestrian_Crash_Data <- TOPS_data %>%
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filter(CNTYNAME == focus_county,
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!is.na(latitude)) %>%
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dplyr::select(all_of(c(focus_columns, "longitude", "latitude")))
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# generate density map ----
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crash_density <- kde2d(Pedestrian_Crash_Data$longitude, Pedestrian_Crash_Data$latitude, n = 200)
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crash_density <- raster(crash_density)
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crash_density <- cut(crash_density, breaks = 10)
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crash_density_poly <- rasterToPolygons(crash_density, dissolve = T)
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density_pal <- colorNumeric(palette = "YlOrRd", domain = c(min(crash_density_poly$layer, na.rm = TRUE), max(crash_density_poly$layer, na.rm = TRUE)))
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# add county census data ----
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census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40))
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county_populations <- get_estimates(geography = "county", year = 2022, product = "population", state = "Wisconsin", geometry = TRUE) %>%
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filter(variable == "POPESTIMATE") %>%
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mutate(County = str_to_upper(str_replace(NAME, " County, Wisconsin", "")))
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county_populations <- st_transform(county_populations, crs = 4326) %>% filter(County %in% focus_county)
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milwaukee_crash_map <-
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leaflet(options = leafletOptions(preferCanvas = TRUE)) %>%
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addProviderTiles(providers$Stadia.AlidadeSmooth) %>%
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addPolygons(data = county_populations,
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color = "black",
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weight = 1,
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fill = FALSE,
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group = "County Lines") %>%
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addPolygons(data = crash_density_poly,
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color = "black",
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weight = 0,
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opacity = 0.9,
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group = "Heat Map",
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fillColor = density_pal(crash_density_poly$layer))%>%
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addMarkers(data = WI_schools,
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lng=WI_schools$LON,
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lat = WI_schools$LAT,
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icon = school_symbol,
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label = lapply(paste0("<b>", WI_schools$SCHOOL, " School</b></br>",
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WI_schools$DISTRICT, " School District</br>",
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WI_schools$SCHOOLTYPE), htmltools::HTML),
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group = "Schools") %>%
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addCircleMarkers(data = Pedestrian_Crash_Data,
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lng=Pedestrian_Crash_Data$longitude,
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lat=Pedestrian_Crash_Data$latitude,
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fillColor=injury_severity_pal(Pedestrian_Crash_Data$PedestrianInjurySeverity),
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radius=4,
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stroke=TRUE,
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color = "black",
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weight = 1,
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fillOpacity = 0.8,
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label = lapply(paste0("<b>", str_to_title(replace_na(Pedestrian_Crash_Data$vulnerable_role, ""))," </b><br>",
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Pedestrian_Crash_Data$CrashDate, "</br>",
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Pedestrian_Crash_Data$PedestrianInjurySeverity, "</br>",
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replace_na(Pedestrian_Crash_Data$vulnerable_role, ""), " age: ", ifelse(!is.na(Pedestrian_Crash_Data$PedestrianAge), Pedestrian_Crash_Data$PedestrianAge, "unknown age")), htmltools::HTML),
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group = "Crash Points") %>%
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addLegend(position = "bottomleft", labels = injury_severity$InjSevName, colors = injury_severity$color, group = "Crash Points", title = "Injury Severity") %>%
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groupOptions(group = "Schools", zoomLevels = 15:20) %>%
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groupOptions(group = "Crash Points", zoomLevels = 13:20) %>%
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groupOptions(group = "County Lines", zoomLevels = 5:20) %>%
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groupOptions(group = "Heat Map", zoomLevels = 5:13)
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milwaukee_crash_map
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saveWidget(milwaukee_crash_map, file = "figures/dynamic_crash_maps/milwaukee_pedestrian_crash_map.html", selfcontained = TRUE)
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