edited MilWALKeeWalks.Rmd
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1 changed files with 38 additions and 20 deletions
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@ -220,22 +220,27 @@ county_populations <- get_estimates(geography = "county",
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filter(variable == "POPESTIMATE") %>%
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mutate(County = str_to_upper(str_replace(NAME, " County, Wisconsin", "")))
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county_populations <- st_transform(county_populations, crs = 4326) %>% filter(County %in% focus_county)
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```
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## make hex grids and join with crash data ----
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```{r hexgrids, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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hexgrid <- rowid_to_column(st_transform(st_as_sf(st_make_grid(st_transform(county_populations, crs = 32616),
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cellsize = 3000,
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what = 'polygons',
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square = FALSE
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)), crs = 4326), "ID")
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yearsforprior <- 8
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yearsforprior <- 7
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hex_crashes <- st_join(hexgrid,
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st_as_sf(TOPS_data_filtered %>%
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filter(vulnerable_role %in% "Pedestrian") %>%
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#filter(ped_inj %in% c("A", "K", "B")) %>%
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filter(!is.na(latitude)),
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coords = c("longitude", "latitude"),
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crs = 4326),
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join = st_contains) %>%
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filter(!is.na(year)) %>%
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filter(date >= (max(date) - (365 * yearsforprior))) %>%
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filter(date > (max(date) - (365 * (yearsforprior + 1)))) %>%
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mutate(lastyear = ifelse((date <= max(date) - 365),
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"prior",
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"lastyear")) %>%
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@ -264,12 +269,13 @@ hexgrid_small <- rowid_to_column(st_transform(st_as_sf(st_make_grid(st_transform
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hex_crashes_small <- st_join(hexgrid_small,
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st_as_sf(TOPS_data_filtered %>%
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filter(vulnerable_role %in% "Pedestrian") %>%
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#filter(ped_inj %in% c("A", "K", "B")) %>%
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filter(!is.na(latitude)),
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coords = c("longitude", "latitude"),
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crs = 4326),
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join = st_contains) %>%
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filter(!is.na(year)) %>%
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filter(date >= (max(date) - (365 * yearsforprior))) %>%
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filter(date > (max(date) - (365 * (yearsforprior + 1)))) %>%
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mutate(lastyear = ifelse((date <= max(date) - 365),
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"prior",
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"lastyear")) %>%
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@ -290,6 +296,17 @@ hex_crashes_small_points <- st_centroid(hex_crashes_small)
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```
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```{r previousyearstring, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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previousyearstring <- paste0(format(max(TOPS_data_filtered$date, na.rm = TRUE) - 365 + 1, "%m/%Y"), " - ", format(max(TOPS_data_filtered$date, na.rm = TRUE), "%m/%Y"))
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```
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```{r importprojects, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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projects_2024 <- read_sf(dsn = "data/MilWALKeeWalks/projects_2024.kml")
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projects_2023 <- read_sf(dsn = "data/MilWALKeeWalks/projects_2023.kml")
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```
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```{r MilwaukeeMaps, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# get basemap
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bbox <- st_bbox(county_populations)
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@ -304,9 +321,9 @@ ggmap(basemap) +
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labs(title = paste0("Crashes between cars and pedestrians"),
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subtitle = paste0(str_to_title(focus_county),
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" County | ",
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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)),
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year(min(TOPS_data_filtered$date, na.rm = TRUE)),
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" - ",
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year(max(TOPS_data_filtered$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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"\nper direction of the WisDOT Bureau of Transportation Safety",
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@ -314,7 +331,7 @@ ggmap(basemap) +
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x = NULL,
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y = NULL,
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size = paste0("Total crashes"),
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fill = paste0("last 12 months\ncompared to previous\n", yearsforprior, " years")) +
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fill = paste0(previousyearstring, "\ncompared to\nprevious years")) +
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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", size = 8)) +
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@ -349,9 +366,7 @@ ggmap(basemap) +
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labs(title = paste0("Crashes between cars and pedestrians"),
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subtitle = paste0(str_to_title(focus_county),
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" County | ",
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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)),
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previousyearstring),
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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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"\nper direction of the WisDOT Bureau of Transportation Safety",
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@ -359,7 +374,7 @@ ggmap(basemap) +
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x = NULL,
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y = NULL,
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size = paste0("Total crashes"),
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fill = "Crashes in the last 12 months\ncompared to previous average") +
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fill = "Crashes in the last year\ncompared to previous average") +
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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", size = 8)) +
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@ -376,7 +391,7 @@ ggmap(basemap) +
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midpoint = 0,
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# limits = c(-2, 2),
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# oob = scales::squish
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)
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) # + geom_sf(data = projects_2023, inherit.aes = FALSE)
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ggsave(file = paste0("figures/MilWALKee_Walks/",
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"milwaukee_map_crashchange.png"),
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@ -390,7 +405,8 @@ highlighted_areas <- hex_crashes %>%
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mutate(lastyearchange = (lastyear - prior/yearsforprior)) %>%
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filter(prior > 100,
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(lastyearchange > 5 |
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lastyearchange < -1)) %>% pull(ID)
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lastyearchange < -2)) %>% pull(ID)
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highlighted_areas <- c(62, 69, 78, 85)
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ggmap(basemap) +
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labs(title = paste0("Crashes between cars and pedestrians\nselect areas of the county"),
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@ -425,7 +441,7 @@ ggmap(basemap) +
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mid = "white",
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high = "red",
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midpoint = 0,
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limits = c(-2, 2),
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limits = c(-1, 1),
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oob = scales::squish,
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labels = scales::percent
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)
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@ -438,7 +454,7 @@ ggsave(file = paste0("figures/MilWALKee_Walks/",
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units = "in",
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create.dir = TRUE)
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bbox <- st_bbox(st_union(hexgrid %>% filter(ID %in% c(78, 85, 94))))
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bbox <- st_bbox(st_union(hexgrid %>% filter(ID %in% highlighted_areas)))
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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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@ -466,7 +482,9 @@ ggmap(basemap) +
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plot.caption = element_text(color = "grey", size = 8)) +
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#add hexagons
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new_scale_fill() +
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geom_sf(data = hex_crashes_small %>% mutate(lastyearchange = (lastyear - prior/yearsforprior)),
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geom_sf(data = hex_crashes_small %>%
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mutate(lastyearchange = (lastyear - prior/yearsforprior)) %>%
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filter(!is.na(lastyearchange)),
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inherit.aes = FALSE,
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aes(fill = lastyearchange),
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alpha = 0.5) +
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@ -478,12 +496,12 @@ ggmap(basemap) +
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midpoint = 0,
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# limits = c(-2, 2),
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# oob = scales::squish
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name = "Crashes last year compared to previous average") +
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name = "Crashes last year\ncompared to previous average") +
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# add crash locations
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new_scale_fill() +
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geom_point(data = TOPS_data_filtered %>%
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filter(date >= max(TOPS_data_filtered$date) - 365) %>%
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#filter(ped_inj %in% c("K", "A")) %>%
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filter(date > max(TOPS_data_filtered$date) - 365) %>%
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#filter(ped_inj %in% c("K", "A", "B")) %>%
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filter(vulnerable_role %in% "Pedestrian") %>%
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filter(longitude >= as.double(bbox[1]),
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latitude >= as.double(bbox[2]),
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@ -495,7 +513,7 @@ ggmap(basemap) +
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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 = setNames(injury_severity$color, injury_severity$InjSevName), name = "Crash Severity")
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scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = paste0("Crashes ", previousyearstring))# + geom_sf(data = projects_2023, inherit.aes = FALSE)
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ggsave(file = paste0("figures/MilWALKee_Walks/",
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"milwaukee_map_zoomchange.png"),
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