library(tidyverse) library(sf) library(tmap) ## 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")) 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))) # 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$InjSevName)) %>% rename(ped_inj_name = InjSevName) # Race names race <- data.frame(race_name = c("Asian", "Black", "Indian","Hispanic","White"), code = c("A", "B", "I", "H", "W")) TOPS_data <- left_join(TOPS_data, race %>% select(race_name, code), join_by(RACE1 == code)) %>% rename(race_name1 = race_name) TOPS_data <- left_join(TOPS_data, race %>% select(race_name, code), join_by(RACE2 == code)) %>% rename(race_name2 = race_name) ## make mutate TOPS_data TOPS_data <- TOPS_data %>% mutate(Year = year, PedestrianInjurySeverity = ped_inj_name, CrashDate = CRSHDATE, CrashTime = CRSHTIME, Street = ONSTR, CrossStreet = ATSTR) %>% mutate(PedestrianAge = ifelse(ROLE1 %in% c("BIKE", "PED"), age1, age2)) TOPS_geom <- st_as_sf(TOPS_data %>% filter(!is.na(latitude)), coords = c("longitude", "latitude"), crs = 4326) ## generate map ---- tmap_mode("view") focus_columns <- c("PedestrianInjurySeverity", "CrashDate", "CrashTime", "Street", "CrossStreet", "PedestrianAge") focus_county <- "DANE" Pedestrian_Crash_Data <- TOPS_geom %>% # filter(CNTYNAME == focus_county) %>% select(all_of(focus_columns)) tm_basemap("Stadia.AlidadeSmooth") + tm_shape(Pedestrian_Crash_Data) + tm_dots("PedestrianInjurySeverity", palette = injury_severity$color, popup.vars = focus_columns) tmap_save(file = "figures/dynamic_crash_maps/dynamic_crash_map.html")