edited MilWALKeeWalks.Rmd
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1 changed files with 310 additions and 66 deletions
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@ -29,6 +29,8 @@ library(magick)
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library(rsvg)
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library(parallel)
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library(tidycensus)
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library(MASS)
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library(raster)
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```
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@ -56,8 +58,9 @@ year_max <- max(year(TOPS_data_filtered$date))
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```{r introCharts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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ggplot() +
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geom_col(data = TOPS_data_filtered %>%
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filter(year != year_max) %>%
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filter(!is.na(vulnerable_role)) %>%
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# filter(MUNINAME %in% "MILWAUKEE") %>%
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filter(! year %in% c(year_max)) %>%
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filter(! is.na(vulnerable_role)) %>%
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group_by(month, vulnerable_role) %>%
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summarize(total = n()),
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aes(x = month,
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@ -65,6 +68,7 @@ ggplot() +
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fill = vulnerable_role),
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position = position_dodge()) +
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geom_line(data = TOPS_data_filtered %>%
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# filter(MUNINAME %in% "MILWAUKEE") %>%
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filter(year == year_max) %>%
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filter(!is.na(vulnerable_role)) %>%
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group_by(month, vulnerable_role) %>%
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@ -80,8 +84,8 @@ ggplot() +
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labs(title = paste0("Crashes involved pedestrians and bicyclists"),
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subtitle = paste0(str_to_title(focus_county), " County"),
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x = "Month",
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y = "Average crashes per year",
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fill = paste0("Yearly average\n", year_min, " - ", year_max - 1),
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y = "Crashes per month",
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fill = paste0("Average\n", year_min, " - ", year_max - 1),
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color = year_max,
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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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@ -96,9 +100,10 @@ ggsave(filename = paste0("figures/MilWALKee_Walks/", "month_role.png"),
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ggplot() +
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geom_col(data = TOPS_data_filtered %>%
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# filter(MUNINAME %in% "MILWAUKEE") %>%
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filter(vulnerable_role == "Pedestrian",
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!is.na(ped_age)) %>%
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filter(year != year_max) %>%
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filter(! year %in% c(year_max)) %>%
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mutate(age = ifelse(ped_age <= 18, "child", "adult")) %>%
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group_by(month, age) %>%
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summarize(total = n()/((year_max - 1) - year_min + 1)),
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@ -107,25 +112,26 @@ ggplot() +
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fill = age),
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position = position_dodge()) +
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geom_line(data = TOPS_data_filtered %>%
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filter(year == year_max) %>%
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filter(vulnerable_role == "Pedestrian",
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!is.na(ped_age)) %>%
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mutate(age = ifelse(ped_age <= 18, "child", "adult")) %>%
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group_by(month, age, year) %>%
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summarize(total = n()),
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aes(x = month,
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y = total,
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color = age,
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group = age),
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linewidth = 1) +
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# filter(MUNINAME %in% "MILWAUKEE") %>%
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filter(year == year_max) %>%
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filter(vulnerable_role == "Pedestrian",
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!is.na(ped_age)) %>%
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mutate(age = ifelse(ped_age <= 18, "child", "adult")) %>%
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group_by(month, age, year) %>%
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summarize(total = n()),
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aes(x = month,
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y = total,
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color = age,
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group = age),
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linewidth = 1) +
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scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
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scale_fill_manual(values = c("deeppink1", "darkgoldenrod1")) +
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scale_color_manual(values = c("deeppink3", "darkgoldenrod3")) +
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labs(title = paste0("Crashes involved pedestrians"),
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subtitle = paste0(str_to_title(focus_county), " County"),
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x = "Month",
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y = "Crashes",
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fill = paste0("Yearly average\n", year_min, " - ", year_max - 1),
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y = "Crashes per month",
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fill = paste0("Average\n", year_min, " - ", year_max - 1),
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color = year_max,
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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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@ -140,7 +146,10 @@ ggsave(filename = paste0("figures/MilWALKee_Walks/", "month_age.png"),
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ggplot(data = TOPS_data_filtered %>%
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filter(vulnerable_role == "Pedestrian",
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month(date) <= 8) %>%
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month(date) <= 8,
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) %>%
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# filter(ped_age < 18,
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# MUNINAME %in% "MILWAUKEE") %>%
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group_by(year) %>%
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summarize(total = n())) +
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geom_col(aes(x = year,
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@ -150,7 +159,7 @@ ggplot(data = TOPS_data_filtered %>%
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labs(title = paste0("Crashes involved pedestrians"),
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subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"),
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x = NULL,
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y = "Crashes",
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y = "Crashes per year",
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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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@ -161,6 +170,35 @@ ggsave(filename = paste0("figures/MilWALKee_Walks/", "ped_years.png"),
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width = 11,
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units = "in",
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create.dir = TRUE)
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ggplot(data = TOPS_data_filtered %>%
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filter(vulnerable_role == "Pedestrian",
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month(date) <= 8) %>%
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mutate(mke_city = factor(ifelse(MUNINAME %in% "MILWAUKEE", "City of Milwaukee", "Other municipalities"),
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levels = c("Other municipalities", "City of Milwaukee"))) %>%
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group_by(year, mke_city) %>%
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summarize(total = n())) +
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geom_col(aes(x = year,
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y = total,
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fill = mke_city),
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position = position_dodge()) +
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scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
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labs(title = paste0("Crashes involved pedestrians"),
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subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"),
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x = NULL,
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y = "Crashes",
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fill = NULL,
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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/", "ped_years_MKEcity.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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```
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## Milwaukee maps
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@ -182,30 +220,24 @@ 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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census_tract_populations <- st_transform(get_decennial(
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year = 2020,
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geography = "block",
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variables = "P1_001N",
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state = "WI",
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county = focus_county,
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geometry = TRUE
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), crs = 4326)
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census_tract_crashes <- st_join(census_tract_populations, st_as_sf(TOPS_data_filtered %>% filter(!is.na(latitude)), coords = c("longitude", "latitude"), crs = 4326), join = st_contains) %>%
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group_by(GEOID) %>%
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summarise(count = n(), .groups = 'drop')
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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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hex_crashes <- st_join(hexgrid, st_as_sf(TOPS_data_filtered %>% filter(!is.na(latitude)), coords = c("longitude", "latitude"), crs = 4326), join = st_contains) %>%
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yearsforprior <- 8
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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(!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 * 5))) %>%
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filter(date >= (max(date) - (365 * yearsforprior))) %>%
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mutate(lastyear = ifelse((date <= max(date) - 365),
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"priorfive",
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"prior",
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"lastyear")) %>%
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group_by(ID, lastyear) %>%
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summarise(count = n(), .groups = 'drop') %>%
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@ -217,10 +249,45 @@ hex_crashes <- st_join(hexgrid, st_as_sf(TOPS_data_filtered %>% filter(!is.na(la
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hex_crashes <- st_as_sf(left_join(hexgrid, hex_crashes), crs = 4326)
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hex_crashes <- hex_crashes %>%
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mutate(lastyearchange = (lastyear - priorfive/5)/(priorfive/5))
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mutate(lastyearchange = (lastyear - prior/yearsforprior)/(prior/yearsforprior))
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hex_crashes_points <- st_centroid(hex_crashes)
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#hex_small_crashes
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hexgrid_small <- rowid_to_column(st_transform(st_as_sf(st_make_grid(st_transform(county_populations, crs = 32616),
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cellsize = 500,
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what = 'polygons',
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square = FALSE
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)), crs = 4326), "ID")
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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(!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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mutate(lastyear = ifelse((date <= max(date) - 365),
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"prior",
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"lastyear")) %>%
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group_by(ID, lastyear) %>%
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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 = lastyear, 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(lastyearchange = (lastyear - prior/yearsforprior)/(prior/yearsforprior))
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hex_crashes_small_points <- st_centroid(hex_crashes_small)
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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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@ -230,7 +297,7 @@ 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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basemap <- get_stadiamap(bbox = bbox, zoom = 13, maptype = "stamen_toner_lite")
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basemap <- get_stadiamap(bbox = bbox, zoom = 12, maptype = "stamen_toner_lite")
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# generate map with bubbles
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ggmap(basemap) +
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@ -247,7 +314,7 @@ 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 = "last 12 months\ncompared to previous") +
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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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@ -277,6 +344,168 @@ ggsave(file = paste0("figures/MilWALKee_Walks/",
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width = 11,
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units = "in",
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create.dir = TRUE)
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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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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 = "Crashes in the last 12 months\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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# add crash locations
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geom_sf(data = hex_crashes %>% mutate(lastyearchange = (lastyear - prior/yearsforprior)),
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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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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(-2, 2),
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# oob = scales::squish
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)
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ggsave(file = paste0("figures/MilWALKee_Walks/",
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"milwaukee_map_crashchange.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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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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ggmap(basemap) +
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labs(title = paste0("Crashes between cars and pedestrians\nselect areas of the county"),
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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 = "last 12 months\ncompared to previous") +
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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 crash locations
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geom_sf(data = hex_crashes %>% filter(ID %in% highlighted_areas),
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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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geom_sf_text(data = hex_crashes_points %>% filter(ID %in% highlighted_areas),
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inherit.aes = FALSE,
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aes(label = paste0(ifelse(lastyearchange > 0, "+",""),round(lastyearchange * 100, 0), "%")),
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size = 3) +
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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(-2, 2),
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oob = scales::squish,
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labels = scales::percent
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)
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ggsave(file = paste0("figures/MilWALKee_Walks/",
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"milwaukee_map_highlighted.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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bbox <- st_bbox(st_union(hexgrid %>% filter(ID %in% c(78, 85, 94))))
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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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basemap <- get_stadiamap(bbox = bbox, zoom = 14, maptype = "stamen_toner_lite")
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# Map of high increase areas
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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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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 %>% mutate(lastyearchange = (lastyear - prior/yearsforprior)),
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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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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(-2, 2),
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# oob = scales::squish
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name = "Crashes last year compared to previous average") +
|
||||
# add crash locations
|
||||
new_scale_fill() +
|
||||
geom_point(data = TOPS_data_filtered %>%
|
||||
filter(date >= max(TOPS_data_filtered$date) - 365) %>%
|
||||
#filter(ped_inj %in% c("K", "A")) %>%
|
||||
filter(vulnerable_role %in% "Pedestrian") %>%
|
||||
filter(longitude >= as.double(bbox[1]),
|
||||
latitude >= as.double(bbox[2]),
|
||||
longitude <= as.double(bbox[3]),
|
||||
latitude <= as.double(bbox[4])) %>%
|
||||
arrange(ped_inj_name),
|
||||
aes(x = longitude,
|
||||
y = latitude,
|
||||
fill = ped_inj_name),
|
||||
shape = 23,
|
||||
size = 3) +
|
||||
scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = "Crash Severity")
|
||||
|
||||
ggsave(file = paste0("figures/MilWALKee_Walks/",
|
||||
"milwaukee_map_zoomchange.png"),
|
||||
device = png,
|
||||
height = 8.5,
|
||||
width = 11,
|
||||
units = "in",
|
||||
create.dir = TRUE)
|
||||
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -300,7 +529,7 @@ TOPS_data <- TOPS_data %>% mutate(trickortreat = ifelse(date %in% trickortreatda
|
|||
ggplot(data = TOPS_data_filtered %>%
|
||||
# filter(ped_inj %in% c("K", "A", "B")) %>%
|
||||
# filter(ped_age <=18) %>%
|
||||
# filter(vulnerable_role == "Pedestrian") %>%
|
||||
filter(vulnerable_role == "Pedestrian") %>%
|
||||
mutate(mday = mday(date)) %>%
|
||||
mutate(date_yearagnostic = ymd(paste("2025", month, mday))) %>%
|
||||
group_by(date_yearagnostic, year, trickortreat) %>%
|
||||
|
|
@ -309,40 +538,55 @@ ggplot(data = TOPS_data_filtered %>%
|
|||
y = total,
|
||||
fill = trickortreat)) +
|
||||
scale_x_date(minor_breaks = "month", date_labels = "%b", expand = expansion(mult = c(0,0))) +
|
||||
scale_y_continuous(breaks = seq(0, 9, 3), labels = c(0, 3, 6, "")) +
|
||||
scale_fill_manual(values = c("black", "orange")) +
|
||||
facet_grid(year ~ .) +
|
||||
labs(title = paste0("Crashes involved pedestrians - Halloween"),
|
||||
labs(title = paste0("Car crashes involving pedestrians - Halloween"),
|
||||
subtitle = paste0(str_to_title(focus_county), " County | ", year_min, " - ", year_max),
|
||||
x = NULL,
|
||||
y = "Crashes",
|
||||
y = "Crashes per day",
|
||||
fill = "Days with\nTrick-or-Treating",
|
||||
caption = paste0("crash data from UW TOPS lab - retrieved ",
|
||||
strftime(retrieve_date, format = "%m/%Y"),
|
||||
"\nper direction of the WisDOT Bureau of Transportation Safety")) +
|
||||
theme(plot.caption = element_text(color = "grey"))
|
||||
ggsave(filename = paste0("figures/MilWALKee_Walks/", "halloween.png"),
|
||||
device = png,
|
||||
height = 8.5,
|
||||
width = 11,
|
||||
units = "in",
|
||||
create.dir = TRUE)
|
||||
|
||||
ggplot(data = TOPS_data_filtered %>%
|
||||
# filter(ped_inj %in% c("K", "A", "B")) %>%
|
||||
# filter(ped_age <=18) %>%
|
||||
mutate(wday = wday(date, label = TRUE)) %>%
|
||||
filter(wday(date) %in% c(1, 7)) %>%
|
||||
filter(vulnerable_role == "Pedestrian") %>%
|
||||
group_by(date, wday, trickortreat) %>%
|
||||
summarize(total = n()) %>%
|
||||
group_by(wday, trickortreat) %>%
|
||||
summarize(average = mean(total, na.rm = TRUE))) +
|
||||
geom_col(aes(x = wday,
|
||||
y = average,
|
||||
fill = trickortreat),
|
||||
position = position_dodge()) +
|
||||
scale_fill_manual(values = c("black", "orange")) +
|
||||
labs(title = paste0("Car crashes involving pedestrians - Halloween"),
|
||||
subtitle = paste0(str_to_title(focus_county), " County | ", year_min, " - ", year_max),
|
||||
x = NULL,
|
||||
y = "Crashes per day",
|
||||
fill = "Days with\nTrick-or-Treating",
|
||||
caption = paste0("crash data from UW TOPS lab - retrieved ",
|
||||
strftime(retrieve_date, format = "%m/%Y"),
|
||||
"\nper direction of the WisDOT Bureau of Transportation Safety")) +
|
||||
theme(plot.caption = element_text(color = "grey"))
|
||||
|
||||
|
||||
ggplot(data = TOPS_data_filtered %>%
|
||||
# filter(ped_inj %in% c("K", "A", "B")) %>%
|
||||
mutate(age = ifelse(ped_age <= 18, "child", "adult"))) +
|
||||
geom_bar(aes(x = month,
|
||||
fill = age),
|
||||
position = "fill")
|
||||
|
||||
ggplot(data = TOPS_data_filtered %>%
|
||||
# filter(ped_age <=18) %>%
|
||||
# filter(vulnerable_role == "Pedestrian") %>%
|
||||
mutate(age = ifelse(ped_age <= 18, "child", "adult")) %>%
|
||||
mutate(date_yearagnostic = ymd(paste("2025", month, mday(date)))) %>%
|
||||
group_by(date_yearagnostic, year, age, trickortreat) %>%
|
||||
summarize(total = n())) +
|
||||
# geom_vline(aes(xintercept = ymd("2025-10-31")),
|
||||
# linetype = "dashed",
|
||||
# alpha = 0.5) +
|
||||
geom_col(aes(x = date_yearagnostic,
|
||||
y = total,
|
||||
fill = trickortreat)) +
|
||||
scale_x_date(minor_breaks = "month", date_labels = "%b", expand = expansion(mult = c(0,0))) +
|
||||
scale_fill_manual(values = c("black", "orange")) +
|
||||
facet_grid(year ~ .)
|
||||
ggsave(filename = paste0("figures/MilWALKee_Walks/", "halloween_wday.png"),
|
||||
device = png,
|
||||
height = 8.5,
|
||||
width = 11,
|
||||
units = "in",
|
||||
create.dir = TRUE)
|
||||
|
||||
```
|
||||
Loading…
Add table
Add a link
Reference in a new issue