edited municipality maps, played around with census tract data

scripts/dynamic_crash_map.R

@@ -129,6 +129,7 @@ Pedestrian_Crash_Data <- TOPS_data %>%
   select(c(all_of(focus_columns), "longitude", "latitude"))
 
 County_Crash_Data <- Pedestrian_Crash_Data %>%
+  filter(PedestrianInjurySeverity %in% c("Fatality", "Suspected Serious Injury", "Suspected Minor Injury")) %>%
   group_by(County, Year) %>%
   summarise(TotalCrashes = n(),
             longitude = mean(longitude, na.rm = TRUE),
@@ -151,6 +152,35 @@ County_Crash_geom$CrashesPerPopulation[is.na(County_Crash_geom$CrashesPerPopulat
 
 county_pal <- colorNumeric(palette = "YlOrRd", domain = c(min(County_Crash_geom$CrashesPerPopulation, na.rm = TRUE), max(County_Crash_geom$CrashesPerPopulation, na.rm = TRUE)))
 
+# ---- census block data
+census_year <- 2020
+state <- "WI" 
+
+tract_data <- st_transform(get_decennial(
+  geography = "tract", 
+  variables = "P1_001N", # Total population variable for 2020 census
+  state = state, 
+  year = census_year, 
+  geometry = TRUE
+),
+crs = 4326)
+
+Census_Crash_geom <- st_join(tract_data, 
+        st_as_sf(Pedestrian_Crash_Data %>% filter(PedestrianInjurySeverity %in% c("Fatality", "Suspected Serious Injury", "Suspected Minor Injury"),
+                                                  latitude > 0), 
+                 coords = c("longitude", "latitude"), 
+                 crs = 4326), 
+        join = st_contains) %>%
+  group_by(GEOID, value) %>%
+  summarize(crash_count = n()) %>%
+  filter(value > 0) %>%
+  mutate(CrashesPerPopulation = crash_count/(value/100000))
+
+census_pal <- colorNumeric(palette = "YlOrRd", domain = c(min(Census_Crash_geom$CrashesPerPopulation, na.rm = TRUE), 3000))
+
+
+#---- make map
+
 #title style
 tag.map.title <- tags$style(HTML("
   .leaflet-control.map-title { 

scripts/municipality_maps.R

@@ -117,10 +117,12 @@ school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.
 
 #county_focus <- str_to_upper(unique(WI_schools %>% pull(CTY_DIST)))
 #county_focus <- c("DANE")
-county_focus <- c("DANE")
+county_focus <- "Dane"
 
-municipality_focus <- c("Monona", "Verona", "Fitchburg")
 municipality_geom <- st_read("data/WI_Cities,_Towns_and_Villages_January_2024.geojson")
+#municipality_focus <- c("Mcfarland")
+#municipality_focus <- c("Monona", "Fitchburg")
+municipality_focus <- municipality_geom %>% filter(CNTY_NAME == county_focus) %>% pull(MCD_NAME)
 
 for(municipality in municipality_focus) {
   
@@ -131,8 +133,10 @@ for(municipality in municipality_focus) {
   readRDS(file_drawer("index.rds"))
   file_drawer("index.rds")
   
+  municipality_filtered <- municipality_geom %>% filter(CNTY_NAME == county_focus, MCD_NAME == municipality) %>% pull(geometry)
+  
   # 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_poly <- st_transform(st_buffer(municipality_filtered, 1000), crs = 4326)
   bbox <- st_bbox(bbox_poly)
   bbox <- c(left = as.double(bbox[1]), 
             bottom = as.double(bbox[2]), 
@@ -185,7 +189,7 @@ for(municipality in municipality_focus) {
                shape = 23,
                size = 3) +
     scale_fill_manual(values = injury_severity$color, name = "Crash Severity") +
-    geom_sf(data = municipality_geom %>% filter(MCD_NAME == municipality),
+    geom_sf(data = municipality_filtered,
             inherit.aes = FALSE,
             color = 'black',
             fill = NA,