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Split up "The Rest" into chucks. Reordered some of the chunks.

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Ben Varick 2024-11-01 09:48:32 -05:00
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R/route_analysis.Rmd
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@ -30,7 +30,7 @@ fig.height <- 6
set.seed(1) set.seed(1)
``` ```
## GeoPackage Data ## School Location Data
```{r gpkg, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r gpkg, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
WI_schools <- st_transform(st_read(dsn = "data/Schools/Wisconsin_Public_Schools_-5986231931870160084.gpkg"), crs = 4326) WI_schools <- st_transform(st_read(dsn = "data/Schools/Wisconsin_Public_Schools_-5986231931870160084.gpkg"), crs = 4326)
@ -45,6 +45,26 @@ addresses <- read_csv(file="data/addresses/Addresses_Students_EastHS_2024_Geocod
st_as_sf(coords=c("lon","lat"), crs=4326) # remember x=lon and y=lat st_as_sf(coords=c("lon","lat"), crs=4326) # remember x=lon and y=lat
``` ```
## Bike Level of Traffic Stress (LTS)
```{r bikelts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
bike_lts <- st_read("data/bike_lts/bike_lts_DANE.geojson")
# make lts attribute a factor
bike_lts[["lts"]] <- as.factor(bike_lts$LTS_F)
# remove segments with an LTS value of 9
bike_lts <- bike_lts %>% filter(lts != 9)
# set color scale
bike_lts_scale <- data.frame(code = c(1, 2, 3, 4, 9),
color = c("#1a9641",
"#a6d96a",
"#fdae61",
"#d7191c",
"#d7191c"))
```
# External sources configurations
## Open Source Routing Machine (OSRM) ## Open Source Routing Machine (OSRM)
```{r osrm, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r osrm, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
@ -75,27 +95,9 @@ walk_boundary_poly <- fill_holes(st_make_valid(osrmIsodistance(
addresses_near <- st_intersection(addresses, walk_boundary_poly) addresses_near <- st_intersection(addresses, walk_boundary_poly)
``` ```
## Bike Level of Traffic Stress (LTS) ## Calculate walking routes for each student
```{r bikelts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r routes, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
bike_lts <- st_read("data/bike_lts/bike_lts_DANE.geojson")
# make lts attribute a factor
bike_lts[["lts"]] <- as.factor(bike_lts$LTS_F)
# remove segments with an LTS value of 9
bike_lts <- bike_lts %>% filter(lts != 9)
# set color scale
bike_lts_scale <- data.frame(code = c(1, 2, 3, 4, 9),
color = c("#1a9641",
"#a6d96a",
"#fdae61",
"#d7191c",
"#d7191c"))
```
## The Rest
```{r therest, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
## calculate routes ## calculate routes
routes <- list(NULL) routes <- list(NULL)
@ -106,8 +108,12 @@ for(i in addresses_near$number) {
message(paste0("done - ", i, "of", max(addresses_near$number))) message(paste0("done - ", i, "of", max(addresses_near$number)))
} }
routes <- bind_rows(routes) routes <- bind_rows(routes)
```
## Combine routes with Bike LTS
```{r routeslts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
## combine routes
# Count the routes that intersect or overlap with each segment of the bike_tls network. # Count the routes that intersect or overlap with each segment of the bike_tls network.
# The intersections have a buffer of 20m # The intersections have a buffer of 20m
bike_lts_buffer <- st_buffer(st_intersection(bike_lts, walk_boundary_poly), 20) bike_lts_buffer <- st_buffer(st_intersection(bike_lts, walk_boundary_poly), 20)
@ -115,12 +121,19 @@ bike_lts_buffer <- st_buffer(st_intersection(bike_lts, walk_boundary_poly), 20)
bike_lts_buffer["student_use"] <- unlist(lapply(st_intersects(bike_lts_buffer, routes), length)) bike_lts_buffer["student_use"] <- unlist(lapply(st_intersects(bike_lts_buffer, routes), length))
bike_lts <- st_join(bike_lts, bike_lts_buffer %>% select(OBJECTID, student_use)) bike_lts <- st_join(bike_lts, bike_lts_buffer %>% select(OBJECTID, student_use))
```
## make maps # Generate Maps
## Load school and Bike Fed logo
```{r logos, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# load logo # load logo
logo <- image_read(path = "other/BFW_Logo_180_x_200_transparent_background.png") logo <- image_read(path = "other/BFW_Logo_180_x_200_transparent_background.png")
school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.svg") school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.svg")
```
## Set boundaries and get basemap
```{r basemap, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
bbox <- st_bbox(st_buffer(walk_boundary_poly, dist = 500)) bbox <- st_bbox(st_buffer(walk_boundary_poly, dist = 500))
bbox <- c(left = as.double(bbox[1]), bbox <- c(left = as.double(bbox[1]),
@ -130,7 +143,58 @@ bbox <- c(left = as.double(bbox[1]),
#get basemap #get basemap
basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite") basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
```
## Generate map of addresses
```{r mapaddresses, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
ggmap(basemap) +
labs(title = paste0("Student homes at ",
school_focus %>% pull(name)),
x = NULL,
y = NULL,
color = NULL,
fill = "How many students live there") +
theme(axis.text=element_blank(),
axis.ticks=element_blank(),
plot.caption = element_text(color = "grey")) +
geom_hex(data = addresses %>% extract(geometry, into = c('Lat', 'Lon'), '\\((.*),(.*)\\)', conv = T),
aes(x = Lat,
y = Lon),
alpha = 0.7) +
scale_fill_distiller(palette = "YlOrRd", direction = "reverse") +
geom_sf(data = walk_boundary_poly,
inherit.aes = FALSE,
aes(color = paste0(1.5, " mile walking boundary")),
fill = NA,
linewidth = 1) +
scale_color_manual(values = "blue", name = NULL) +
new_scale_color() +
annotation_raster(school_symbol,
# Position adjustments here using plot_box$max/min/range
ymin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] - 0.001,
ymax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] + 0.001,
xmin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] - 0.0015,
xmax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] + 0.0015) +
geom_sf_label(data = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE),
inherit.aes = FALSE,
mapping = aes(label = school_focus %>% pull(name)),
nudge_y = 0.0015,
label.size = 0.04,
size = 2)
ggsave(file = paste0("figures/",
school_focus %>% pull(name),
" Addresses.pdf"),
title = paste0(school_focus %>% pull(name), " Addresses"),
device = pdf,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
```
## Generate map of routes
```{r maproutes, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# generate map # generate map
ggmap(basemap) + ggmap(basemap) +
labs(title = paste0("Walking routes for students at ", labs(title = paste0("Walking routes for students at ",
@ -178,7 +242,10 @@ ggsave(file = paste0("figures/",
width = 11, width = 11,
units = "in", units = "in",
create.dir = TRUE) create.dir = TRUE)
```
## Generate map of routes with LTS
```{r maprouteslts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# generate map # generate map
ggmap(basemap) + ggmap(basemap) +
labs(title = paste0("Walking routes for students at ", labs(title = paste0("Walking routes for students at ",
@ -226,49 +293,7 @@ ggsave(file = paste0("figures/",
units = "in", units = "in",
create.dir = TRUE) create.dir = TRUE)
ggmap(basemap) +
labs(title = paste0("Student homes at ",
school_focus %>% pull(name)),
x = NULL,
y = NULL,
color = NULL,
fill = "How many students live there") +
theme(axis.text=element_blank(),
axis.ticks=element_blank(),
plot.caption = element_text(color = "grey")) +
geom_hex(data = addresses %>% extract(geometry, into = c('Lat', 'Lon'), '\\((.*),(.*)\\)', conv = T),
aes(x = Lat,
y = Lon),
alpha = 0.7) +
scale_fill_distiller(palette = "YlOrRd", direction = "reverse") +
geom_sf(data = walk_boundary_poly,
inherit.aes = FALSE,
aes(color = paste0(1.5, " mile walking boundary")),
fill = NA,
linewidth = 1) +
scale_color_manual(values = "blue", name = NULL) +
new_scale_color() +
annotation_raster(school_symbol,
# Position adjustments here using plot_box$max/min/range
ymin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] - 0.001,
ymax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] + 0.001,
xmin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] - 0.0015,
xmax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] + 0.0015) +
geom_sf_label(data = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE),
inherit.aes = FALSE,
mapping = aes(label = school_focus %>% pull(name)),
nudge_y = 0.0015,
label.size = 0.04,
size = 2)
ggsave(file = paste0("figures/",
school_focus %>% pull(name),
" Addresses.pdf"),
title = paste0(school_focus %>% pull(name), " Addresses"),
device = pdf,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
``` ```