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626d69667e
...
c721c58a9a
@ -30,7 +30,7 @@ fig.height <- 6
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set.seed(1)
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set.seed(1)
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```
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```
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## School Location Data
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## GeoPackage Data
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```{r gpkg, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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```{r gpkg, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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WI_schools <- st_transform(st_read(dsn = "data/Schools/Wisconsin_Public_Schools_-5986231931870160084.gpkg"), crs = 4326)
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WI_schools <- st_transform(st_read(dsn = "data/Schools/Wisconsin_Public_Schools_-5986231931870160084.gpkg"), crs = 4326)
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@ -45,26 +45,6 @@ addresses <- read_csv(file="data/addresses/Addresses_Students_EastHS_2024_Geocod
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st_as_sf(coords=c("lon","lat"), crs=4326) # remember x=lon and y=lat
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st_as_sf(coords=c("lon","lat"), crs=4326) # remember x=lon and y=lat
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```
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```
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## Bike Level of Traffic Stress (LTS)
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```{r bikelts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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bike_lts <- st_read("data/bike_lts/bike_lts_DANE.geojson")
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# make lts attribute a factor
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bike_lts[["lts"]] <- as.factor(bike_lts$LTS_F)
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# remove segments with an LTS value of 9
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bike_lts <- bike_lts %>% filter(lts != 9)
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# set color scale
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bike_lts_scale <- data.frame(code = c(1, 2, 3, 4, 9),
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color = c("#1a9641",
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"#a6d96a",
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"#fdae61",
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"#d7191c",
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"#d7191c"))
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```
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# External sources configurations
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## Open Source Routing Machine (OSRM)
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## Open Source Routing Machine (OSRM)
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```{r osrm, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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```{r osrm, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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@ -95,9 +75,27 @@ walk_boundary_poly <- fill_holes(st_make_valid(osrmIsodistance(
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addresses_near <- st_intersection(addresses, walk_boundary_poly)
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addresses_near <- st_intersection(addresses, walk_boundary_poly)
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```
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```
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## Calculate walking routes for each student
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## Bike Level of Traffic Stress (LTS)
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```{r routes, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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```{r bikelts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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bike_lts <- st_read("data/bike_lts/bike_lts_DANE.geojson")
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# make lts attribute a factor
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bike_lts[["lts"]] <- as.factor(bike_lts$LTS_F)
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# remove segments with an LTS value of 9
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bike_lts <- bike_lts %>% filter(lts != 9)
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# set color scale
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bike_lts_scale <- data.frame(code = c(1, 2, 3, 4, 9),
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color = c("#1a9641",
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"#a6d96a",
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"#fdae61",
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"#d7191c",
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"#d7191c"))
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```
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## The Rest
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```{r therest, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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## calculate routes
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## calculate routes
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routes <- list(NULL)
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routes <- list(NULL)
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@ -108,12 +106,8 @@ for(i in addresses_near$number) {
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message(paste0("done - ", i, "of", max(addresses_near$number)))
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message(paste0("done - ", i, "of", max(addresses_near$number)))
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}
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}
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routes <- bind_rows(routes)
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routes <- bind_rows(routes)
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```
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## Combine routes with Bike LTS
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```{r routeslts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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## combine routes
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# Count the routes that intersect or overlap with each segment of the bike_tls network.
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# Count the routes that intersect or overlap with each segment of the bike_tls network.
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# The intersections have a buffer of 20m
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# The intersections have a buffer of 20m
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bike_lts_buffer <- st_buffer(st_intersection(bike_lts, walk_boundary_poly), 20)
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bike_lts_buffer <- st_buffer(st_intersection(bike_lts, walk_boundary_poly), 20)
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@ -121,19 +115,12 @@ bike_lts_buffer <- st_buffer(st_intersection(bike_lts, walk_boundary_poly), 20)
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bike_lts_buffer["student_use"] <- unlist(lapply(st_intersects(bike_lts_buffer, routes), length))
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bike_lts_buffer["student_use"] <- unlist(lapply(st_intersects(bike_lts_buffer, routes), length))
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bike_lts <- st_join(bike_lts, bike_lts_buffer %>% select(OBJECTID, student_use))
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bike_lts <- st_join(bike_lts, bike_lts_buffer %>% select(OBJECTID, student_use))
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```
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# Generate Maps
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## make maps
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## Load school and Bike Fed logo
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```{r logos, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# load logo
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# load logo
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logo <- image_read(path = "other/BFW_Logo_180_x_200_transparent_background.png")
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logo <- image_read(path = "other/BFW_Logo_180_x_200_transparent_background.png")
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school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.svg")
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school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.svg")
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```
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## Set boundaries and get basemap
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```{r basemap, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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bbox <- st_bbox(st_buffer(walk_boundary_poly, dist = 500))
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bbox <- st_bbox(st_buffer(walk_boundary_poly, dist = 500))
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bbox <- c(left = as.double(bbox[1]),
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bbox <- c(left = as.double(bbox[1]),
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@ -143,58 +130,7 @@ bbox <- c(left = as.double(bbox[1]),
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#get basemap
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#get basemap
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basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
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basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
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```
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## Generate map of addresses
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```{r mapaddresses, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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ggmap(basemap) +
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labs(title = paste0("Student homes at ",
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school_focus %>% pull(name)),
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x = NULL,
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y = NULL,
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color = NULL,
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fill = "How many students live there") +
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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")) +
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geom_hex(data = addresses %>% extract(geometry, into = c('Lat', 'Lon'), '\\((.*),(.*)\\)', conv = T),
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aes(x = Lat,
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y = Lon),
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alpha = 0.7) +
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scale_fill_distiller(palette = "YlOrRd", direction = "reverse") +
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geom_sf(data = walk_boundary_poly,
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inherit.aes = FALSE,
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aes(color = paste0(1.5, " mile walking boundary")),
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fill = NA,
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linewidth = 1) +
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scale_color_manual(values = "blue", name = NULL) +
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new_scale_color() +
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annotation_raster(school_symbol,
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# Position adjustments here using plot_box$max/min/range
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ymin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] - 0.001,
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ymax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] + 0.001,
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xmin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] - 0.0015,
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xmax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] + 0.0015) +
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geom_sf_label(data = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE),
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inherit.aes = FALSE,
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mapping = aes(label = school_focus %>% pull(name)),
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nudge_y = 0.0015,
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label.size = 0.04,
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size = 2)
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ggsave(file = paste0("figures/",
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school_focus %>% pull(name),
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" Addresses.pdf"),
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title = paste0(school_focus %>% pull(name), " Addresses"),
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device = pdf,
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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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## Generate map of routes
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```{r maproutes, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# generate map
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# generate map
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ggmap(basemap) +
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ggmap(basemap) +
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labs(title = paste0("Walking routes for students at ",
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labs(title = paste0("Walking routes for students at ",
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@ -242,10 +178,7 @@ ggsave(file = paste0("figures/",
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width = 11,
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width = 11,
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units = "in",
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units = "in",
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create.dir = TRUE)
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create.dir = TRUE)
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```
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## Generate map of routes with LTS
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```{r maprouteslts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# generate map
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# generate map
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ggmap(basemap) +
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ggmap(basemap) +
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labs(title = paste0("Walking routes for students at ",
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labs(title = paste0("Walking routes for students at ",
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@ -293,7 +226,49 @@ ggsave(file = paste0("figures/",
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units = "in",
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units = "in",
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create.dir = TRUE)
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create.dir = TRUE)
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ggmap(basemap) +
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labs(title = paste0("Student homes at ",
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school_focus %>% pull(name)),
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x = NULL,
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y = NULL,
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color = NULL,
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fill = "How many students live there") +
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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")) +
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geom_hex(data = addresses %>% extract(geometry, into = c('Lat', 'Lon'), '\\((.*),(.*)\\)', conv = T),
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aes(x = Lat,
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y = Lon),
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alpha = 0.7) +
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scale_fill_distiller(palette = "YlOrRd", direction = "reverse") +
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geom_sf(data = walk_boundary_poly,
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inherit.aes = FALSE,
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aes(color = paste0(1.5, " mile walking boundary")),
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fill = NA,
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linewidth = 1) +
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scale_color_manual(values = "blue", name = NULL) +
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new_scale_color() +
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annotation_raster(school_symbol,
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# Position adjustments here using plot_box$max/min/range
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ymin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] - 0.001,
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ymax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] + 0.001,
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xmin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] - 0.0015,
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xmax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] + 0.0015) +
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geom_sf_label(data = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE),
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inherit.aes = FALSE,
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mapping = aes(label = school_focus %>% pull(name)),
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nudge_y = 0.0015,
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label.size = 0.04,
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size = 2)
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ggsave(file = paste0("figures/",
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school_focus %>% pull(name),
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" Addresses.pdf"),
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title = paste0(school_focus %>% pull(name), " Addresses"),
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device = pdf,
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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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```
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