added municipality script
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data/WI_Cities,_Towns_and_Villages_January_2024.geojson
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data/WI_Cities,_Towns_and_Villages_January_2024.geojson
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figures/municipalities/Monona.pdf
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figures/municipalities/Monona.pdf
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figures/municipalities/Verona.pdf
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figures/municipalities/Verona.pdf
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scripts/municipality_maps.R
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scripts/municipality_maps.R
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@ -0,0 +1,211 @@
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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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library(tidycensus)
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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))$mtime
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TOPS_data[[file]] <- csv_run
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}
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rm(csv_run, file, year)
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TOPS_data <- bind_rows(TOPS_data)
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## clean up data
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TOPS_data <- TOPS_data %>%
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mutate(date = mdy(CRSHDATE),
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age1 = as.double(AGE1),
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age2 = as.double(AGE2),
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latitude = as.double(LATDECDG),
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longitude = as.double(LONDECDG)) %>%
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mutate(month = month(date, label = TRUE),
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year = as.factor(year(date)))
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retrieve_date <- max(TOPS_data %>% filter(year %in% max(year(TOPS_data$date), na.rm = TRUE)) %>% pull(retreive_date))
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# 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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# Injury Severy Index and Color -------------------------------------------
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# injury severity index
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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$InjSevName)) %>%
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rename(ped_inj_name = InjSevName)
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# bike or ped
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TOPS_data <- TOPS_data %>% mutate(vulnerable_role = ifelse(ROLE1 %in% bike_roles | ROLE2 %in% bike_roles,
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"Bicyclist",
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ifelse(ROLE1 %in% ped_roles | ROLE2 %in% ped_roles,
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"Pedestrian",
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NA)))
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## load bike LTS networks
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bike_lts <- as.list(NULL)
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for(file in list.files("data/bike_lts")) {
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county <- str_sub(file, 10, -9)
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lts_run <- st_read(paste0("data/bike_lts/", file))
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lts_run[["lts"]] <- as.factor(lts_run$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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#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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#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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municipality_focus <- c("Monona", "Verona", "Fitchburg")
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municipality_geom <- st_read("data/WI_Cities,_Towns_and_Villages_January_2024.geojson")
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for(municipality in municipality_focus) {
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message(paste("***", municipality))
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options(ggmap.file_drawer = paste0("basemaps/municipalities/", municipality))
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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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# create bounding box from school, 5km away.
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bbox_poly <- st_transform(st_buffer(municipality_geom %>% filter(MCD_NAME == municipality) %>% pull(geometry), 1000), crs = 4326)
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bbox <- st_bbox(bbox_poly)
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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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basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
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# generate map
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ggmap(basemap) +
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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 in/near ",
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municipality),
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subtitle = paste0(min(year(TOPS_data$date), na.rm = TRUE),
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" - ",
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max(year(TOPS_data$date), na.rm = TRUE)),
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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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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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plot.caption = element_text(color = "grey")) +
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## add bike lts
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geom_sf(data = bike_lts[[county]] %>% st_intersection(bbox_poly),
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inherit.aes = FALSE,
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aes(color = lts)) +
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scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
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# add crash locations
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new_scale_fill() +
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geom_point(data = TOPS_data %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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# & age1 < 18
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| ROLE2 %in% c("BIKE", "PED")
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# & age2 < 18
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) %>%
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filter(longitude >= as.double(bbox[1]),
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latitude >= as.double(bbox[2]),
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longitude <= as.double(bbox[3]),
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latitude <= as.double(bbox[4])),
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aes(x = longitude,
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y = latitude,
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fill = ped_inj_name),
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shape = 23,
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size = 3) +
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scale_fill_manual(values = injury_severity$color, name = "Crash Severity") +
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geom_sf(data = municipality_geom %>% filter(MCD_NAME == municipality),
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inherit.aes = FALSE,
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color = 'black',
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fill = NA,
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linewidth = 1) +
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annotation_raster(logo,
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# Position adjustments here using plot_box$max/min/range
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ymin = bbox['top'] - (bbox['top']-bbox['bottom']) * 0.16,
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ymax = bbox['top'],
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xmin = bbox['right'] + (bbox['right']-bbox['left']) * 0.05,
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xmax = bbox['right'] + (bbox['right']-bbox['left']) * 0.20) +
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coord_sf(clip = "off")
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ggsave(file = paste0("figures/municipalities/",
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municipality,
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".pdf"),
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#title = paste0(municipality, " Youth Pedestrian/Bike crashes"),
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title = paste0(municipality, " All 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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@ -149,7 +149,7 @@ county_focus <- c("DANE")
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school_type_focus <- c("High School")
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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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district_focus <- c("Monona Grove")
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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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@ -317,8 +317,8 @@ for(district in district_focus) {
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# generate map
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ggmap(basemap) +
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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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# "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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subtitle = paste0(school_data %>% pull(DISTRICT_NAME),
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@ -337,18 +337,18 @@ for(district in district_focus) {
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plot.caption = element_text(color = "grey")) +
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## add bike lts
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#geom_sf(data = bike_lts[[county]],
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# inherit.aes = FALSE,
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# 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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geom_sf(data = bike_lts[[county]],
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inherit.aes = FALSE,
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aes(color = lts)) +
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scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
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# add crash locations
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new_scale_fill() +
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geom_point(data = TOPS_data %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& age1 < 18
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# & age1 < 18
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| ROLE2 %in% c("BIKE", "PED")
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& age2 < 18
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# & age2 < 18
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) %>%
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filter(longitude >= as.double(bbox[1]),
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latitude >= as.double(bbox[2]),
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@ -400,8 +400,8 @@ for(district in district_focus) {
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"s/",
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str_replace_all(school_data %>% pull(SCHOOL_NAME), "/", "-"),
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" School.pdf"),
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title = paste0(school_data %>% pull(SCHOOL), " Youth Pedestrian/Bike crashes"),
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#title = paste0(school_data %>% pull(SCHOOL), " Youth Pedestrian/Bike crashes"),
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title = paste0(school_data %>% pull(SCHOOL), " All Pedestrian/Bike crashes"),
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device = pdf,
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height = 8.5,
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width = 11,
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@ -425,7 +425,8 @@ for(school in WI_schools$district_school) {
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str_replace_all(school_data %>% pull(SCHOOLTYPE), "/","-"),
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"s/",
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str_replace_all(school_data %>% pull(SCHOOL_NAME), "/", "-"),
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" School.pdf"))))
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#" School.pdf"))))
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" School_all.pdf"))))
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double_check[[school]] <- school_check
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}
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double_check <- bind_rows(double_check)
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