added municipality script

scripts/municipality_maps.R

@@ -0,0 +1,211 @@
+library(tidyverse)
+library(ggmap)
+library(sf)
+library(osrm)
+library(smoothr)
+library(ggnewscale)
+library(RColorBrewer)
+library(magick)
+library(rsvg)
+library(parallel)
+library(tidycensus)
+
+## add data from WiscTransPortal Crash Data Retrieval Facility ----
+## query:  SELECT * 
+## FROM DTCRPRD.SUMMARY_COMBINED C 
+## WHERE C.CRSHDATE BETWEEN TO_DATE('2022-JAN','YYYY-MM') AND 
+## LAST_DAY(TO_DATE('2022-DEC','YYYY-MM')) AND 
+## (C.BIKEFLAG = 'Y' OR C.PEDFLAG = 'Y') 
+## ORDER BY C.DOCTNMBR
+
+## Load TOPS data ----
+## load TOPS data for the whole state (crashes involving bikes and pedestrians),
+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))
+
+# county index
+counties <- data.frame(name = c("Dane", "Milwaukee"),
+                       CNTYCODE = c(13, 40),
+                       COUNTY = c("DANE", "MILWAUKEE"))
+
+# Injury Severy Index and Color -------------------------------------------
+# injury severity index
+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"))
+
+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)
+# add bike or pedestrian roles ----
+
+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 %>% select(InjSevName, code), join_by(ped_inj == code)) %>% 
+  mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
+  rename(ped_inj_name = InjSevName)
+
+# bike or ped
+TOPS_data <- TOPS_data %>% mutate(vulnerable_role = ifelse(ROLE1 %in% bike_roles | ROLE2 %in% bike_roles, 
+                                                           "Bicyclist", 
+                                                           ifelse(ROLE1 %in% ped_roles | ROLE2 %in% ped_roles, 
+                                                                  "Pedestrian",
+                                                                  NA)))
+## load bike LTS networks
+bike_lts <- as.list(NULL)
+for(file in list.files("data/bike_lts")) {
+  county <- str_sub(file, 10, -9)
+  lts_run <- st_read(paste0("data/bike_lts/", file))
+  lts_run[["lts"]] <- as.factor(lts_run$LTS_F)
+  bike_lts[[county]] <- lts_run
+}
+bike_lts_scale <- data.frame(code = c(1, 2, 3, 4, 9),
+                             color = c("#1a9641",
+                                       "#a6d96a",
+                                       "#fdae61",
+                                       "#d7191c",
+                                       "#d7191c"))
+
+# register stadia API key ----
+register_stadiamaps(key = substr(read_file(file = "api_keys/stadia_api_key"), 1, 36))
+#options(ggmap.file_drawer = "basemaps")
+# dir.create(file_drawer(), recursive = TRUE, showWarnings = FALSE)
+# saveRDS(list(), file_drawer("index.rds"))
+#readRDS(file_drawer("index.rds"))
+#file_drawer("index.rds")
+
+# load census api key ----
+census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40))
+
+# load logo
+logo <- image_read(path = "other/BFW_Logo_180_x_200_transparent_background.png")
+school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.svg")
+
+## ---- generate charts/maps ----
+## set parameters of run
+
+#county_focus <- str_to_upper(unique(WI_schools %>% pull(CTY_DIST)))
+#county_focus <- c("DANE")
+county_focus <- c("DANE")
+
+municipality_focus <- c("Monona", "Verona", "Fitchburg")
+municipality_geom <- st_read("data/WI_Cities,_Towns_and_Villages_January_2024.geojson")
+
+for(municipality in municipality_focus) {
+  
+  message(paste("***", municipality))
+  options(ggmap.file_drawer = paste0("basemaps/municipalities/", municipality))
+  dir.create(file_drawer(), recursive = TRUE, showWarnings = FALSE)
+  saveRDS(list(), file_drawer("index.rds"))
+  readRDS(file_drawer("index.rds"))
+  file_drawer("index.rds")
+  
+  # create bounding box from school, 5km away.
+  bbox_poly <- st_transform(st_buffer(municipality_geom %>% filter(MCD_NAME == municipality) %>% pull(geometry), 1000), crs = 4326)
+  bbox <- st_bbox(bbox_poly)
+  bbox <- c(left = as.double(bbox[1]), 
+            bottom = as.double(bbox[2]), 
+            right = as.double(bbox[3]), 
+            top = as.double(bbox[4]))
+  
+  #get basemap
+  basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
+  
+  # generate map
+  ggmap(basemap) +
+    labs(title = paste0(
+      #        "Crashes between cars and youth (<18) pedestrians/bicyclists near ",
+      "Crashes between cars and all pedestrians/bicyclists in/near ",
+      municipality),
+      subtitle = paste0(min(year(TOPS_data$date), na.rm = TRUE), 
+                        " - ", 
+                        max(year(TOPS_data$date), na.rm = TRUE)),
+      caption = paste0("crash data from UW TOPS lab - retrieved ",
+                       strftime(retrieve_date, format = "%m/%Y"),
+                       " per direction of the WisDOT Bureau of Transportation Safety",
+                       "\nbasemap from StadiaMaps and OpenStreetMap Contributers"),
+      x = NULL,
+      y = NULL) +
+    theme(axis.text=element_blank(),
+          axis.ticks=element_blank(),
+          plot.caption = element_text(color = "grey")) +
+    
+    ## add bike lts
+    geom_sf(data = bike_lts[[county]] %>% st_intersection(bbox_poly),
+            inherit.aes = FALSE,
+            aes(color = lts)) +
+    scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
+    
+    # add crash locations
+    new_scale_fill() +
+    geom_point(data = TOPS_data %>%
+                 filter(ROLE1 %in% c("BIKE", "PED") 
+                        #                          & age1 < 18 
+                        | ROLE2 %in% c("BIKE", "PED") 
+                        #                          & age2 < 18
+                 ) %>%
+                 filter(longitude >= as.double(bbox[1]),
+                        latitude >= as.double(bbox[2]),
+                        longitude <= as.double(bbox[3]),
+                        latitude <= as.double(bbox[4])),
+               aes(x = longitude,
+                   y = latitude,
+                   fill = ped_inj_name),
+               shape = 23,
+               size = 3) +
+    scale_fill_manual(values = injury_severity$color, name = "Crash Severity") +
+    geom_sf(data = municipality_geom %>% filter(MCD_NAME == municipality),
+            inherit.aes = FALSE,
+            color = 'black',
+            fill = NA,
+            linewidth = 1) +
+    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/municipalities/",
+                       municipality,
+                       ".pdf"),
+         #title = paste0(municipality, " Youth Pedestrian/Bike crashes"),
+         title = paste0(municipality, " All Pedestrian/Bike crashes"),
+         device = pdf,
+         height = 8.5,
+         width = 11,
+         units = "in",
+         create.dir = TRUE)
+}

scripts/school_maps.R

@@ -149,7 +149,7 @@ county_focus <- c("DANE")
 school_type_focus <- c("High School")
 
 #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))
-district_focus <- c("Madison Metropolitan")
+district_focus <- c("Monona Grove")
 
 school_number <- length(unique(WI_schools %>% filter(CTY_DIST %in% str_to_title(county_focus),  
                                               SCHOOLTYPE %in% school_type_focus, 
@@ -317,8 +317,8 @@ for(district in district_focus) {
     # generate map
     ggmap(basemap) +
       labs(title = paste0(
-        "Crashes between cars and youth (<18) pedestrians/bicyclists near ",
+#        "Crashes between cars and youth (<18) pedestrians/bicyclists near ",
-#        "Crashes between cars and all pedestrians/bicyclists near ",
+        "Crashes between cars and all pedestrians/bicyclists near ",
         school_data %>% pull(SCHOOL_NAME),
         " School"),
            subtitle = paste0(school_data %>% pull(DISTRICT_NAME),
@@ -337,18 +337,18 @@ for(district in district_focus) {
             plot.caption = element_text(color = "grey")) +
       
       ## add bike lts
-      #geom_sf(data = bike_lts[[county]],
+      geom_sf(data = bike_lts[[county]],
-      #       inherit.aes = FALSE,
+             inherit.aes = FALSE,
-      #       aes(color = lts)) +
+             aes(color = lts)) +
-      #scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
+      scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
       
       # add crash locations
       new_scale_fill() +
       geom_point(data = TOPS_data %>%
                    filter(ROLE1 %in% c("BIKE", "PED") 
-                          & age1 < 18 
+#                          & age1 < 18 
                           | ROLE2 %in% c("BIKE", "PED") 
-                          & age2 < 18
+#                          & age2 < 18
                           ) %>%
                    filter(longitude >= as.double(bbox[1]),
                           latitude >= as.double(bbox[2]),
@@ -400,8 +400,8 @@ for(district in district_focus) {
                          "s/",
                          str_replace_all(school_data %>% pull(SCHOOL_NAME), "/", "-"), 
                          " School.pdf"),
-                                  
+           #title = paste0(school_data %>% pull(SCHOOL), " Youth Pedestrian/Bike crashes"),
-           title = paste0(school_data %>% pull(SCHOOL), " Youth Pedestrian/Bike crashes"),
+           title = paste0(school_data %>% pull(SCHOOL), " All Pedestrian/Bike crashes"),
            device = pdf,
            height = 8.5,
            width = 11,
@@ -425,7 +425,8 @@ for(school in WI_schools$district_school) {
                                                            str_replace_all(school_data %>% pull(SCHOOLTYPE), "/","-"), 
                                                            "s/",
                                                            str_replace_all(school_data %>% pull(SCHOOL_NAME), "/", "-"), 
-                                                           " School.pdf"))))
+                                                           #" School.pdf"))))
+                                                           " School_all.pdf"))))
   double_check[[school]] <- school_check
 }
 double_check <- bind_rows(double_check)