added seasonality and vehicle type graphs
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1 changed files with 121 additions and 2 deletions
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@ -708,7 +708,7 @@ TOPS_data <- TOPS_data %>% mutate(trickortreat = ifelse(date %in% trickortreatda
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```
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## Halloween
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```{r exploreHalloween, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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```{r Halloween, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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ggplot(data = TOPS_data_filtered %>%
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# filter(ped_inj %in% c("K", "A", "B")) %>%
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# filter(ped_age <=18) %>%
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@ -775,7 +775,7 @@ ggsave(filename = paste0("figures/MilWALKee_Walks/", "halloween_wday.png"),
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```
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## Trucks
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```{r exploreTrucks, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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```{r Trucks, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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TOPS_data_filtered %>%
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filter(ped_age < 18) %>%
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filter(nonvuln_vehicletype != "Other") %>%
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@ -809,6 +809,125 @@ ggsave(filename = paste0("figures/MilWALKee_Walks/", "trucks_injsev.png"),
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units = "in",
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create.dir = TRUE)
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```
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## seasonality
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```{r seasonality, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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TOPS_data_filtered %>%
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# filter(ped_inj %in% c("K", "A", "B")) %>%
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filter(ped_age >= 18) %>%
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filter(vulnerable_role == "Pedestrian",
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year != year_max,
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MUNINAME == "MILWAUKEE") %>%
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mutate(date_yearagnostic = ymd(paste("2025", month(date, label = FALSE), mday(date)))) %>%
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mutate(week = week(date_yearagnostic),
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weekdate = floor_date(date_yearagnostic, unit = "weeks", week_start = 3)) %>%
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group_by(weekdate) %>%
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summarize(average = n()/(year_max - year_min + 1)) %>%
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ggplot() +
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geom_col(aes(x = weekdate,
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y = average),
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fill = "darkorange3") +
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scale_x_date(minor_breaks = "month", date_labels = "%b", expand = expansion(mult = c(0,0))) +
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scale_y_continuous() +
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labs(title = paste0("Car crashes involving pedestrians - Adults"),
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subtitle = paste0("City of Milwaukee | ", year_min, " - ", year_max - 1),
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x = NULL,
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y = "Average crashes per week",
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fill = "Days with\nTrick-or-Treating",
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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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theme(plot.caption = element_text(color = "grey"))
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ggsave(filename = paste0("figures/MilWALKee_Walks/", "MKEadultweek.png"),
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device = png,
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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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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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ped_age >= 18,
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MUNINAME == "MILWAUKEE") %>%
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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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mutate(dateyearagnostic = ymd(paste("2025", month(date, label = FALSE), mday(date)))) %>%
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filter(dateyearagnostic >= ymd("2025-10-01"),
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dateyearagnostic <= ymd("2025-10-14")) %>%
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mutate(octoberfest = ifelse(dateyearagnostic <= ymd("2025-10-7"),
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"TRUE",
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"FALSE")) %>%
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group_by(ID, octoberfest) %>%
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summarise(count = n(), .groups = 'drop') %>%
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st_drop_geometry() %>%
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pivot_wider(id_cols = ID, names_from = octoberfest, values_from = count) %>%
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mutate(across(-ID, ~ replace_na(., 0))) %>%
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mutate(total = rowSums(dplyr::select(., -ID), na.rm = TRUE))
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hex_crashes_small <- st_as_sf(left_join(hexgrid_small, hex_crashes_small), crs = 4326)
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hex_crashes_small <- hex_crashes_small %>%
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mutate(octoberfestchange = `TRUE` - `FALSE`)
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# Map of high increase areas
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ggmap(basemap) +
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labs(title = paste0("Car crashes involving 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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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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"\nbasemap from StadiaMaps and OpenStreetMap Contributers"),
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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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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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#add hexagons
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new_scale_fill() +
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geom_sf(data = hex_crashes_small %>%
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filter(!is.na(octoberfestchange)),
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inherit.aes = FALSE,
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aes(fill = octoberfestchange),
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alpha = 0.5) +
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scale_size_area() +
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scale_fill_gradient2(
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low = "darkgreen",
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mid = "white",
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high = "red",
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midpoint = 0,
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# limits = c(-10, 10),
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# oob = scales::squish,
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name = "Crashes last year\ncompared to previous average")
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+
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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$date) - 365) %>%
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#filter(ped_inj %in% c("K", "A", "B")) %>%
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filter(date > ymd("2024-09-20"),
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date < ymd("2024-10-10")) %>%
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filter(vulnerable_role %in% "Pedestrian") %>%
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filter(longitude >= as.double(bbox[1]),
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latitude >= as.double(bbox[2]),
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longitude <= as.double(bbox[3]),
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latitude <= as.double(bbox[4])) %>%
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arrange(ped_inj_name),
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aes(x = longitude,
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y = latitude,
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fill = ped_inj_name),
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shape = 23,
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size = 3) +
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scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = paste0("Crashes ", previousyearstring))# + geom_sf(data = projects_2023, inherit.aes = FALSE)
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```
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