2024-04-02 12:36:56 -05:00
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library(tidyverse)
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library(ggmap)
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library(sf)
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library(osrm)
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library(smoothr)
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library(ggnewscale)
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library(RColorBrewer)
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library(magick)
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library(rsvg)
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library(parallel)
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## add data from WiscTransPortal Crash Data Retrieval Facility ----
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## query: SELECT *
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## FROM DTCRPRD.SUMMARY_COMBINED C
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## WHERE C.CRSHDATE BETWEEN TO_DATE('2022-JAN','YYYY-MM') AND
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## LAST_DAY(TO_DATE('2022-DEC','YYYY-MM')) AND
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## (C.BIKEFLAG = 'Y' OR C.PEDFLAG = 'Y')
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## ORDER BY C.DOCTNMBR
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## Load TOPS data ----
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## load TOPS data for the whole state (crashes involving bikes and pedestrians),
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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))$mtime
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2024-04-02 12:36:56 -05:00
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TOPS_data[[file]] <- csv_run
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}
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rm(csv_run, file, year)
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TOPS_data <- bind_rows(TOPS_data)
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## clean up data
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TOPS_data <- TOPS_data %>%
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mutate(date = mdy(CRSHDATE),
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age1 = as.double(AGE1),
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age2 = as.double(AGE2),
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latitude = as.double(LATDECDG),
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longitude = as.double(LONDECDG)) %>%
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mutate(month = month(date, label = TRUE),
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year = as.factor(year(date)))
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2024-04-04 12:15:32 -05:00
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retrieve_date <- max(TOPS_data %>% filter(year %in% max(year(TOPS_data$date), na.rm = TRUE)) %>% pull(retreive_date))
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2024-04-02 12:36:56 -05:00
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# county index
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counties <- data.frame(name = c("Dane", "Milwaukee"),
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CNTYCODE = c(13, 40),
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COUNTY = c("DANE", "MILWAUKEE"))
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2024-06-04 15:21:14 -05:00
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2024-04-02 12:36:56 -05:00
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# Injury Severy Index and Color -------------------------------------------
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# injury severity index
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2024-06-04 15:21:14 -05:00
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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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TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR1 == code)) %>%
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mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
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rename(InjSevName1 = InjSevName)
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TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR2 == code)) %>%
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mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
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rename(InjSevName2 = InjSevName)
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# add bike or pedestrian roles ----
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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 %>% 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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2024-06-04 15:21:14 -05:00
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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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# ---- add additional data
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## add school enrollment data
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enrollment <- read_csv(file = "data/Schools/Enrollement_2022-2023/enrollment_by_gradelevel_certified_2022-23.csv",
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col_types = "ccccccccccccciid")
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enrollment_wide <-
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enrollment %>%
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mutate(district_school = paste0(DISTRICT_CODE, SCHOOL_CODE),
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variable_name = paste0(GROUP_BY, "__", GROUP_BY_VALUE)) %>%
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mutate(variable_name = str_replace_all(variable_name, "[ ]", "_")) %>%
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pivot_wider(id_cols = c(district_school, GRADE_LEVEL, SCHOOL_NAME, DISTRICT_NAME, GRADE_GROUP, CHARTER_IND), names_from = variable_name, values_from = PERCENT_OF_GROUP) %>%
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group_by(district_school, SCHOOL_NAME, DISTRICT_NAME, GRADE_GROUP, CHARTER_IND) %>%
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summarise_at(vars("Disability__Autism":"Migrant_Status__[Data_Suppressed]"), mean, na.rm = TRUE)
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district_info <- data.frame(name = c("Madison Metropolitan", "Milwaukee"),
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code = c("3269","3619"),
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walk_boundary_hs = c(1.5, 2),
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walk_boundary_ms = c(1.5, 2),
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walk_boundary_es = c(1.5, 1))
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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 <- left_join(WI_schools %>% mutate(district_school = paste0(SDID, SCH_CODE)),
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enrollment_wide,
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join_by(district_school))
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## load bike LTS networks
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bike_lts <- as.list(NULL)
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for(file in list.files("data/bike_lts")) {
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county <- str_sub(file, 10, -9)
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lts_run <- st_read(paste0("data/bike_lts/", file))
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lts_run[["lts"]] <- as.factor(lts_run$LTS_F)
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bike_lts[[county]] <- lts_run
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}
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bike_lts_scale <- data.frame(code = c(1, 2, 3, 4, 9),
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color = c("#1a9641",
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"#a6d96a",
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"#fdae61",
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"#d7191c",
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"#d7191c"))
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# register stadia API key ----
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register_stadiamaps(key = substr(read_file(file = "api_keys/stadia_api_key"), 1, 36))
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2024-04-03 11:11:37 -05:00
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#options(ggmap.file_drawer = "basemaps")
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# dir.create(file_drawer(), recursive = TRUE, showWarnings = FALSE)
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# saveRDS(list(), file_drawer("index.rds"))
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#readRDS(file_drawer("index.rds"))
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#file_drawer("index.rds")
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# load census api key ----
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#census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40))
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# load logo
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logo <- image_read(path = "other/BFW_Logo_180_x_200_transparent_background.png")
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school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.svg")
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## ---- generate charts/maps ----
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## set parameters of run
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2024-04-03 11:11:37 -05:00
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#county_focus <- str_to_upper(unique(WI_schools %>% pull(CTY_DIST)))
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#county_focus <- c("DANE")
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county_focus <- c("DANE")
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#school_type_focus <- unique(WI_schools %>% filter(CTY_DIST %in% str_to_title(county_focus)) %>% pull(SCHOOLTYPE))
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school_type_focus <- c("High School")
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2024-04-03 11:11:37 -05:00
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#district_focus <- unique(WI_schools %>% filter(CTY_DIST %in% str_to_title(county_focus), SCHOOLTYPE %in% school_type_focus, !is.na(DISTRICT_NAME)) %>% pull(DISTRICT_NAME))
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district_focus <- c("Madison Metropolitan")
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school_number <- length(unique(WI_schools %>% filter(CTY_DIST %in% str_to_title(county_focus),
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SCHOOLTYPE %in% school_type_focus,
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DISTRICT_NAME %in% district_focus) %>%
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pull(district_school)))
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## * generate county charts ----
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for(county in county_focus) {
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message(county)
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TOPS_data %>%
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filter(CNTYNAME %in% county) %>%
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2024-04-09 09:17:26 -05:00
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filter(ROLE1 %in% vuln_roles & age1 < 18 | ROLE2 %in% vuln_roles & age2 < 18) %>%
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group_by(year) %>% summarise(count = n_distinct(DOCTNMBR)) %>%
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ggplot() +
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geom_col(aes(x = year,
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y = count),
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fill = "darkred") +
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scale_y_continuous(expand = expansion(mult = c(0,0.07))) +
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labs(title = paste0("Pedestrians/bicyclists under 18 years old hit by cars in ",
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str_to_title(county),
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" County"),
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x = "Year",
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y = "Number of crashes",
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caption = paste0("crash data from UW TOPS lab - retrieved ",
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strftime(retrieve_date, format = "%m/%Y"),
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" per direction of the WisDOT Bureau of Transportation Safety",
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"\nbasemap from StadiaMaps and OpenStreetMap Contributers"))
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2024-04-02 12:36:56 -05:00
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ggsave(file = paste0("figures/school_maps/Crash Maps/",
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str_to_title(county),
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" County/_",
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str_to_title(county),
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" County_year.pdf"),
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title = paste0(county, " County Youth Pedestrian/Bike crashes"),
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device = pdf,
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height = 8.5,
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width = 11,
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units = "in",
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create.dir = TRUE)
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# # generate map for county
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# county_data <- WI_schools %>% filter(CTY_DIST %in% str_to_title(county))
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# bbox <- st_bbox(st_transform(st_buffer(county_data %>% pull(geom), dist = 4000), crs = 4326))
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# bbox <- c(left = as.double(bbox[1]), bottom = as.double(bbox[2]), right = as.double(bbox[3]), top = as.double(bbox[4]))
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#
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# #get basemap
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# basemap <- get_stadiamap(bbox = bbox, zoom = 12, maptype = "stamen_toner_lite")
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#
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# # generate map
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# ggmap(basemap) +
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# labs(title = paste0("Crashes between cars and youth (under 18) pedestrians/bicyclists in ",
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# str_to_title(county),
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# " County"),
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# subtitle = paste0(min(year(TOPS_data$date), na.rm = TRUE), " - ", max(year(TOPS_data$date), na.rm = TRUE)),
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# caption = "data from Wisconsin DOT, UW TOPS Laboratory, Wisconsin DPI, and OpenStreetMap",
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# x = NULL,
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# y = NULL) +
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# theme(axis.text=element_blank(),
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# axis.ticks=element_blank()) +
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#
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# # add crash heatmap
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# # stat_density_2d(data = TOPS_data %>%
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# # filter(ROLE1 %in% c("BIKE", "PED") & age1 < 18 | ROLE2 %in% c("BIKE", "PED") & age2 < 18),
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# # inherit.aes = FALSE,
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# # geom = "polygon",
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# # aes(fill = after_stat(level),
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# # x = longitude,
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# # y = latitude),
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# # alpha = 0.2,
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# # color = NA,
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# # na.rm = TRUE,
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# # bins = 12,
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# # n = 300) +
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# # scale_fill_distiller(type = "div", palette = "YlOrRd", guide = "none", direction = 1) +
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#
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# # add crashes
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# new_scale_color() +
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# geom_point(data = TOPS_data %>%
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# filter(ROLE1 %in% c("BIKE", "PED") & age1 < 18 | ROLE2 %in% c("BIKE", "PED") & age2 < 18) %>%
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# filter(longitude >= as.double(bbox[1]),
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# latitude >= as.double(bbox[2]),
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# longitude <= as.double(bbox[3]),
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# latitude <= as.double(bbox[4])),
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# aes(x = longitude,
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# y = latitude,
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# color = InjSevName),
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# shape = 18,
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# size = 1) +
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# scale_color_manual(values = injury_severity$color, name = "Crash Severity")
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#
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# # add school location
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# # new_scale_color() +
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# # geom_sf(data = st_transform(WI_schools, crs = 4326),
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# # inherit.aes = FALSE,
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# # aes(color = "school"),
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# # size = 2,
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# # shape = 0) +
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# # scale_color_manual(values = "black", name = NULL)
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#
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# ggsave(file = paste0("figures/school_maps/Crash Maps/",
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# str_to_title(county), " County/_",
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# str_to_title(county), " County.pdf"),
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# title = paste0(str_to_title(county), " County Youth Pedestrian/Bike crashes"),
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# device = pdf,
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# height = 8.5,
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# width = 11,
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# units = "in",
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# create.dir = TRUE)
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}
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# * generate individual school maps ----
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options(osrm.server = "http://127.0.0.1:5000/")
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options(osrm.profile = "walk")
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i <- 0
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#districts_done <- read_csv(file = "other/districts_done.csv")
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#district_focus <- district_focus[! district_focus %in% districts_done$district]
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for(district in district_focus) {
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message(paste("***", district, "School District |"))
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2024-04-03 11:11:37 -05:00
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options(ggmap.file_drawer = paste0("basemaps/districts/", district))
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dir.create(file_drawer(), recursive = TRUE, showWarnings = FALSE)
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saveRDS(list(), file_drawer("index.rds"))
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readRDS(file_drawer("index.rds"))
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file_drawer("index.rds")
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2024-04-02 12:36:56 -05:00
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for(school in WI_schools %>%
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filter(DISTRICT_NAME %in% district,
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SCHOOLTYPE %in% school_type_focus,
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!st_is_empty(geom)) %>%
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pull(district_school)) {
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school_data <- WI_schools %>% filter(district_school == school)
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i <- i + 1
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message(paste(school_data %>% pull(SCHOOL_NAME), "-", district, "School District", "-", school_data %>% pull(CTY_DIST), "County |", i, "/", school_number, "|", round(i/school_number*100, 2), "%"))
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#find walk boundary distance for school
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if(length(which(district_info$name == district)) > 0) {
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ifelse((school_data %>% pull(SCHOOLTYPE)) %in% "High School",
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walk_boundary_mi <- district_info$walk_boundary_hs[district_info$name == district],
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ifelse((school_data %>% pull(SCHOOLTYPE)) %in% c("Junior High School", "Middle School"),
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walk_boundary_mi <- district_info$walk_boundary_ms[district_info$name == district],
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ifelse((school_data %>% pull(SCHOOLTYPE)) %in% c("Combined Elementary/Secondary School", "Elementary School"),
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walk_boundary_mi <- district_info$walk_boundary_es[district_info$name == district],
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walk_boundary <- 2)))
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} else {
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walk_boundary_mi <- 2
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}
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walk_boundary_m <- walk_boundary_mi * 1609
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walk_boundary_poly <- fill_holes(st_make_valid(osrmIsodistance(
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loc = st_transform(school_data %>% pull(geom), crs = 4326),
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breaks = c(walk_boundary_m),
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res = 80)
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), units::set_units(1, km^2))
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# create bounding box from school, 5km away.
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bbox <- st_bbox(st_transform(st_buffer(school_data %>% pull(geom), dist = walk_boundary_m + 500), crs = 4326))
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bbox <- c(left = as.double(bbox[1]),
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bottom = as.double(bbox[2]),
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right = as.double(bbox[3]),
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top = as.double(bbox[4]))
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#get basemap
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2024-04-03 11:11:37 -05:00
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basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
|
2024-04-02 12:36:56 -05:00
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# generate map
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ggmap(basemap) +
|
2024-06-04 15:21:14 -05:00
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labs(title = paste0(
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"Crashes between cars and youth (<18) pedestrians/bicyclists near ",
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# "Crashes between cars and all pedestrians/bicyclists near ",
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school_data %>% pull(SCHOOL_NAME),
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" School"),
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2024-04-02 12:36:56 -05:00
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subtitle = paste0(school_data %>% pull(DISTRICT_NAME),
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" School District | ",
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min(year(TOPS_data$date), na.rm = TRUE),
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" - ",
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max(year(TOPS_data$date), na.rm = TRUE)),
|
2024-04-04 12:15:32 -05:00
|
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|
caption = paste0("crash data from UW TOPS lab - retrieved ",
|
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|
|
strftime(retrieve_date, format = "%m/%Y"),
|
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|
|
" per direction of the WisDOT Bureau of Transportation Safety",
|
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|
|
"\nbasemap from StadiaMaps and OpenStreetMap Contributers"),
|
2024-04-02 12:36:56 -05:00
|
|
|
x = NULL,
|
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|
|
y = NULL) +
|
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|
|
theme(axis.text=element_blank(),
|
2024-04-03 11:11:37 -05:00
|
|
|
axis.ticks=element_blank(),
|
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|
|
plot.caption = element_text(color = "grey")) +
|
2024-04-02 12:36:56 -05:00
|
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|
|
|
|
|
## add bike lts
|
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|
|
#geom_sf(data = bike_lts[[county]],
|
|
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|
|
# inherit.aes = FALSE,
|
|
|
|
|
# aes(color = lts)) +
|
|
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|
|
#scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
|
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|
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|
|
|
|
# add crash locations
|
|
|
|
|
new_scale_fill() +
|
|
|
|
|
geom_point(data = TOPS_data %>%
|
|
|
|
|
filter(ROLE1 %in% c("BIKE", "PED")
|
|
|
|
|
& age1 < 18
|
|
|
|
|
| ROLE2 %in% c("BIKE", "PED")
|
|
|
|
|
& age2 < 18
|
|
|
|
|
) %>%
|
|
|
|
|
filter(longitude >= as.double(bbox[1]),
|
|
|
|
|
latitude >= as.double(bbox[2]),
|
|
|
|
|
longitude <= as.double(bbox[3]),
|
|
|
|
|
latitude <= as.double(bbox[4])),
|
|
|
|
|
aes(x = longitude,
|
|
|
|
|
y = latitude,
|
2024-06-04 15:21:14 -05:00
|
|
|
fill = ped_inj_name),
|
2024-04-02 12:36:56 -05:00
|
|
|
shape = 23,
|
|
|
|
|
size = 3) +
|
|
|
|
|
scale_fill_manual(values = injury_severity$color, name = "Crash Severity") +
|
|
|
|
|
# add walk boundary
|
|
|
|
|
new_scale_color() +
|
|
|
|
|
new_scale_fill() +
|
|
|
|
|
geom_sf(data = walk_boundary_poly,
|
|
|
|
|
inherit.aes = FALSE,
|
|
|
|
|
aes(color = paste0(walk_boundary_mi, " mile walking boundary")),
|
|
|
|
|
fill = NA,
|
|
|
|
|
linewidth = 1) +
|
|
|
|
|
scale_color_manual(values = "black", name = NULL) +
|
|
|
|
|
# add school location
|
|
|
|
|
# geom_sf(data = st_transform(school_data, crs = 4326), inherit.aes = FALSE) +
|
|
|
|
|
annotation_raster(school_symbol,
|
|
|
|
|
# Position adjustments here using plot_box$max/min/range
|
|
|
|
|
ymin = as.double((st_transform(school_data, crs = 4326) %>% pull(geom))[[1]])[2] - 0.001,
|
|
|
|
|
ymax = as.double((st_transform(school_data, crs = 4326) %>% pull(geom))[[1]])[2] + 0.001,
|
|
|
|
|
xmin = as.double((st_transform(school_data, crs = 4326) %>% pull(geom))[[1]])[1] - 0.0015,
|
|
|
|
|
xmax = as.double((st_transform(school_data, crs = 4326) %>% pull(geom))[[1]])[1] + 0.0015) +
|
|
|
|
|
geom_sf_label(data = st_transform(school_data, crs = 4326),
|
|
|
|
|
inherit.aes = FALSE,
|
|
|
|
|
mapping = aes(label = paste(SCHOOL_NAME, "School")),
|
|
|
|
|
nudge_y = 0.0015,
|
|
|
|
|
label.size = 0.04,
|
|
|
|
|
size = 2) +
|
|
|
|
|
annotation_raster(logo,
|
|
|
|
|
# Position adjustments here using plot_box$max/min/range
|
|
|
|
|
ymin = bbox['top'] - (bbox['top']-bbox['bottom']) * 0.16,
|
|
|
|
|
ymax = bbox['top'],
|
|
|
|
|
xmin = bbox['right'] + (bbox['right']-bbox['left']) * 0.05,
|
|
|
|
|
xmax = bbox['right'] + (bbox['right']-bbox['left']) * 0.20) +
|
|
|
|
|
coord_sf(clip = "off")
|
|
|
|
|
|
|
|
|
|
ggsave(file = paste0("figures/school_maps/Crash Maps/",
|
|
|
|
|
str_to_title(school_data %>% pull(CTY_DIST)),
|
|
|
|
|
" County/",
|
|
|
|
|
school_data %>% pull(DISTRICT_NAME),
|
|
|
|
|
" School District/",
|
|
|
|
|
str_replace_all(school_data %>% pull(SCHOOLTYPE), "/","-"),
|
|
|
|
|
"s/",
|
|
|
|
|
str_replace_all(school_data %>% pull(SCHOOL_NAME), "/", "-"),
|
|
|
|
|
" School.pdf"),
|
|
|
|
|
|
|
|
|
|
title = paste0(school_data %>% pull(SCHOOL), " Youth Pedestrian/Bike crashes"),
|
|
|
|
|
device = pdf,
|
|
|
|
|
height = 8.5,
|
|
|
|
|
width = 11,
|
|
|
|
|
units = "in",
|
|
|
|
|
create.dir = TRUE)
|
|
|
|
|
}
|
2024-06-04 15:21:14 -05:00
|
|
|
# districts_done <- bind_rows(districts_done, data.frame(district = c(district)))
|
|
|
|
|
# write_csv(districts_done, file = "other/districts_done.csv")
|
2024-04-02 12:36:56 -05:00
|
|
|
}
|
|
|
|
|
|
|
|
|
|
# double check that all schools have a map ----
|
|
|
|
|
double_check <- list(NULL)
|
|
|
|
|
for(school in WI_schools$district_school) {
|
|
|
|
|
school_data <- WI_schools %>% filter(district_school %in% school)
|
|
|
|
|
school_check <- data.frame(district_school = c(school),
|
|
|
|
|
exists = c(file.exists(paste0("figures/school_maps/Crash Maps/",
|
|
|
|
|
str_to_title(school_data %>% pull(CTY_DIST)),
|
|
|
|
|
" County/",
|
|
|
|
|
school_data %>% pull(DISTRICT_NAME),
|
|
|
|
|
" School District/",
|
|
|
|
|
str_replace_all(school_data %>% pull(SCHOOLTYPE), "/","-"),
|
|
|
|
|
"s/",
|
|
|
|
|
str_replace_all(school_data %>% pull(SCHOOL_NAME), "/", "-"),
|
|
|
|
|
" School.pdf"))))
|
|
|
|
|
double_check[[school]] <- school_check
|
|
|
|
|
}
|
|
|
|
|
double_check <- bind_rows(double_check)
|
|
|
|
|
unique(WI_schools %>%
|
|
|
|
|
filter(district_school %in% (double_check %>%
|
|
|
|
|
filter(exists == FALSE) %>%
|
|
|
|
|
pull(district_school)),
|
|
|
|
|
!st_is_empty(geom)) %>%
|
|
|
|
|
pull(DISTRICT_NAME))
|
