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route_analysis/cycling_route_analysis_brouter.Rmd
syounkin 14a097fe9e Moved basemap chunk 
Code in routeslts called basemap before it was defined. To correct this the basemap chunk was moved up in the script.
2024-11-06 16:48:21 -06:00

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---
title: "East High Cycling Routes"
output:
html_document:
toc: true
toc_depth: 5
toc_float:
collapsed: false
smooth_scroll: true
editor_options:
chunk_output_type: console
---
# Input Data & Configuration
## Libraries
```{r libs, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
date()
rm(list=ls())
library(tidyverse)
library(ggmap)
library(sf)
library(osrm)
library(smoothr)
library(magick)
library(ggnewscale)
library(rsvg)
library(httr)
library(jsonlite)
library(parallel)
fig.height <- 6
set.seed(1)
```
## School Location Data
```{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 <- WI_schools %>% mutate(geom = SHAPE)
```
## Addresses Data
```{r addresses, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
addresses <- read_csv(file="data/addresses/Addresses_Students_EastHS_2024_GeocodeResults.csv") %>%
filter(lat > 0) %>%
st_as_sf(coords=c("lon","lat"), crs=4326)
```
(Remember that 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_transform(st_read("data/bike_lts/bike_lts_DANE.geojson"), crs = 4326)
# make lts attribute a factor
bike_lts[["lts"]] <- as.factor(bike_lts$LTS_F)
# remove segments with an LTS value of 9
bike_lts <- bike_lts %>% filter(lts != 9)
# set color scale
bike_lts_scale <- data.frame(code = c(1, 2, 3, 4, 9),
color = c("#1a9641",
"#a6d96a",
"#fdae61",
"#d7191c",
"#d7191c"))
```
# External sources configurations
## Open Source Routing Machine (OSRM)
```{r osrm, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# Set url and profile of OSRM server
options(osrm.server = "http://127.0.0.1:5001/")
options(osrm.profile = "bike")
```
## Brouter options
```{r brouter, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# Set url and profile of brouter server
brouter_url <- "http://127.0.0.1:17777/brouter"
brouter_profile <- "safety"
```
## Stadia Maps API Key
```{r stadiamaps, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
register_stadiamaps(key = substr(read_file(file = "api_keys/stadia_api_key"), 1, 36))
```
# Analysis
```{r analysisPreamble, eval = TRUE, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
radius <- 3 # miles
levels <- c(1)
res <- 100
threshold <- 1
```
## Subset Addresses Within `r radius` Miles
```{r cycleBoundary, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
cycle_boundary_m <- radius*1609
school_focus <- data.frame(name = c("East High School"), NCES_CODE = c("550852000925"))
#school_focus <- data.frame(name = c("IMAP"), NCES_CODE = c("550008203085"))
cycle_boundary_poly <- st_transform(fill_holes(st_make_valid(osrmIsodistance(
loc = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE),
# breaks = c(cycle_boundary_m),
breaks = cycle_boundary_m*levels,
res = res)
), units::set_units(threshold, km^2)), crs = 4326)
addresses_near <- st_intersection(addresses, cycle_boundary_poly)
```
Notes:
- _osrmIsoDistance_ is the primary function in the above chunk.
- This function computes areas that are reachable within a given road
distance from a point and returns the reachable regions as
polygons. These areas of equal travel distance are called isodistances.
- Input is a point represented as an sf object (extended
data.frame-like objects with a simple feature list column) could be
other classes, e.g., vector of coods, data.frame of lat tand
long. etc.
- Arguments to osrmIsodistances used here are breaks and res
- breaks: a numeric vector of break values to define isodistance areas, in meters.
- res: number of points used to compute isodistances, one side of the
square grid, the total number of points will be res*res. Increase res to obtain more detailed isodistances.
- _fill\_holes_ is also used with a threshold of `r threshold` km^2.
- _st\_intersection_ is also used on sf objects (simple features?)
## Calculate Routes
```{r routes, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
routes <- list(NULL)
school_focus_location <- WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% select(LAT, LON)
for(i in addresses_near %>% arrange(number) %>% pull(number)) {
query <- paste0(
brouter_url,
"?lonlats=",
(addresses_near %>% filter(number == i) %>% pull(point) %>% str_split(., ","))[[1]][1], ",",
(addresses_near %>% filter(number == i) %>% pull(point) %>% str_split(., ","))[[1]][2], "|",
school_focus_location$LON, ",", school_focus_location$LAT,
"&profile=", brouter_profile,
"&alternativeidx=0&format=geojson"
)
response <- GET(query)
route_run <- st_read(content <- content(response, as = "text"), quiet = TRUE)
route_run[["student_number"]] <- i
routes[[i]] <- route_run
message(paste0("done - ", i, " of ", max(addresses_near$number)))
}
routes <- st_transform(bind_rows(routes), crs = 4326)
```
Notes:
- this queries the brouter server to get routes
## Set boundaries and get basemap
```{r basemap, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
bbox <- st_bbox(st_buffer(cycle_boundary_poly, dist = 500))
bbox <- c(left = as.double(bbox[1]),
bottom = as.double(bbox[2]),
right = as.double(bbox[3]),
top = as.double(bbox[4]))
#get basemap
basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
```
## Combine routes with Bike LTS
```{r ltscount, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# Count the routes that intersect or overlap with each segment of the bike_tls network.
# The intersections have a buffer of 10m
bike_lts_buffer <- st_buffer(st_intersection(bike_lts, cycle_boundary_poly), 10)
bike_lts_buffer["student_use"] <- unlist(lapply(st_intersects(bike_lts_buffer, routes), length))
bike_lts <- left_join(bike_lts, as.data.frame(bike_lts_buffer %>% select(OBJECTID, student_use)), by = "OBJECTID")
```
Notes: for each segment in bike_lts, this counts how many student's calculated routes intersect with it (within a 10 m buffer)
```{r routeslts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
getLTSForRoute <- function(i) {
# Filter the routes for the current student number
current_route <- routes %>% filter(student_number == i)
# Find intersecting OBJECTIDs
intersecting_ids <- relevant_buffer$OBJECTID[lengths(st_intersects(relevant_buffer, current_route)) > 0]
# Filter relevant segments to calculate max and average lts
relevant_segments <- bike_lts_buffer %>% filter(OBJECTID %in% intersecting_ids)
# find all the segments of relevant_buffer that the current route passes through
current_route_lts_intersection <- st_intersection(current_route, relevant_segments)
# calculate segment length in meters
current_route_lts_intersection$"segment_length" <- as.double(st_length(current_route_lts_intersection))
# Return the result as a list
result <- list(
student_number = i
, lts_max = max(current_route_lts_intersection$LTS_F)
, lts_average = weighted.mean(current_route_lts_intersection$LTS_F, current_route_lts_intersection$segment_length)
, lts_1_dist = sum(current_route_lts_intersection %>% filter(LTS_F == 1) %>% pull(LTS_F))
, lts_2_dist = sum(current_route_lts_intersection %>% filter(LTS_F == 2) %>% pull(LTS_F))
, lts_3_dist = sum(current_route_lts_intersection %>% filter(LTS_F == 3) %>% pull(LTS_F))
, lts_4_dist = sum(current_route_lts_intersection %>% filter(LTS_F == 4) %>% pull(LTS_F))
, route = as.data.frame(current_route_lts_intersection)
)
# Message for progress
message(paste0("done - ", i))
return(result)
}
# Start with routes_lts as a NULL list
routes_lts <- list(NULL)
# Pre-filter the bike_lts_buffer for relevant student use
relevant_buffer <- bike_lts_buffer %>% filter(student_use > 0)
# routes_lts <- lapply(head(addresses_near %>% arrange(number) %>% pull(number)),
# getLTSForRoute)
# system.time(routes_lts <- lapply(head(addresses_near %>% arrange(number) %>% pull(number)),
# getLTSForRoute))
routes_lts <- mclapply(addresses_near %>% arrange(number) %>% pull(number),
getLTSForRoute,
mc.cores = detectCores() / 2,
mc.cleanup = TRUE,
mc.preschedule = TRUE,
mc.silent = FALSE)
# for(i in addresses_near %>% arrange(number) %>% pull(number)) {
# lts_segments <- bike_lts_buffer$OBJECTID[st_intersects(bike_lts_buffer, routes %>% filter(student_number == i), sparse = FALSE)]
# lts_max <- max(bike_lts_buffer %>% filter(OBJECTID %in% lts_segments) %>% pull(LTS_F), na.rm = TRUE)
# lts_average <- mean(bike_lts_buffer %>% filter(OBJECTID %in% lts_segments) %>% pull(LTS_F), na.rm = TRUE)
# routes_lts[[i]] <- data.frame("student_number" = c(i), "lts_max" = c(lts_max), "lts_average" = c(lts_average))
# message(paste0("done - ", i, " of ", max(addresses_near$number)))
# }
routes_lts <- bind_rows(routes_lts)
ggmap(basemap) +
geom_sf(data = routes_lts %>% filter(student_number == 6), inherit.aes = FALSE,
aes(color = route.lts,
geometry = route.geometry),
linewidth = 2) +
scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress")
# Join the data with the addresses data
addresses_near <- left_join(addresses_near,
routes_lts %>%
select(c("student_number", "lts_max", "lts_average", "lts_1_dist", "lts_2_dist", "lts_3_dist", "lts_4_dist")),
join_by("number"=="student_number"),
multiple = "any")
# add supplemental analysis
addresses_near <- addresses_near %>% mutate(lts_34_dist = lts_3_dist + lts_4_dist)
```
Notes: for each student's route, this finds which bike_lts segment it intersects with and calculates a max and an average level of traffic stress (LTS). This takes a while, so a parallelized it. There's probably a more efficient way to do this calculation.
# Make Maps
## Load school and Bike Fed logo
```{r logos, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# load logo
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")
```
## Generate map of addresses
```{r mapaddresses, eval = TRUE, echo = FALSE, 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 = cycle_boundary_poly,
inherit.aes = FALSE,
aes(color = paste0(radius, " mile cycling 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_cycling.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 = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# generate map
ggmap(basemap) +
labs(title = paste0("Cycling routes for students at ",
school_focus %>% pull(name)),
subtitle = paste0("only showing routes within the ", radius, " mile cycling boundary"),
x = NULL,
y = NULL,
color = NULL,
linewidth = "Potential student cyclists") +
theme(axis.text=element_blank(),
axis.ticks=element_blank(),
plot.caption = element_text(color = "grey")) +
geom_sf(data = cycle_boundary_poly,
inherit.aes = FALSE,
aes(color = paste0(radius, " mile cycling boundary")),
fill = NA,
linewidth = 1) +
scale_color_manual(values = "blue", name = NULL) +
new_scale_color() +
geom_sf(data = bike_lts %>% filter(!is.na(student_use), student_use > 3),
inherit.aes = FALSE,
aes(linewidth = student_use),
color = "mediumvioletred",
fill = NA) +
scale_linewidth_continuous(range = c(0, 3)) +
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),
" Routes_cycling.pdf"),
title = paste0(school_focus %>% pull(name), " Cycling Routes"),
device = pdf,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
```
## Generate map of routes with LTS
```{r maprouteslts, eval = TRUE, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# generate map
ggmap(basemap) +
labs(title = paste0("Cycling routes for students at ",
school_focus %>% pull(name)),
subtitle = "only showing routes within the cycling boundary",
x = NULL,
y = NULL,
color = NULL,
linewidth = "Potential student cyclists") +
theme(axis.text=element_blank(),
axis.ticks=element_blank(),
plot.caption = element_text(color = "grey")) +
geom_sf(data = cycle_boundary_poly,
inherit.aes = FALSE,
aes(color = paste0(radius, " mile cycling boundary")),
fill = NA,
linewidth = 1) +
scale_color_manual(values = "blue", name = NULL) +
new_scale_color() +
geom_sf(data = bike_lts %>% filter(!is.na(student_use), student_use > 0),
inherit.aes = FALSE,
aes(color = lts,
linewidth = student_use)) +
scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
scale_linewidth_continuous(range = c(0, 3)) +
annotation_raster(school_symbol,
# Position adjustments here using plot_box$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$NCES_CODE),
inherit.aes = FALSE,
mapping = aes(label = school_focus %>% pull(name)),
nudge_y = 0.0015,
label.size = 0.04,
size = 2)
ggsave(file = paste0("figures/",
school_focus %>% pull(name),
" Routes - Traffic Stress_cycling.pdf"),
title = paste0(school_focus %>% pull(name), " Cycling Routes - Traffic Stress"),
device = pdf,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
```
## Generate map of routes with LTS
```{r mapaddresseslts, eval = TRUE, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# generate map
ggmap(basemap) +
labs(title = paste0("Level of Traffic stress for biking for students at ",
school_focus %>% pull(name)),
subtitle = "only showing routes within the cycling boundary",
x = NULL,
y = NULL) +
theme(axis.text=element_blank(),
axis.ticks=element_blank(),
plot.caption = element_text(color = "grey")) +
geom_sf(data = cycle_boundary_poly,
inherit.aes = FALSE,
aes(color = paste0(radius, " mile cycling boundary")),
fill = NA,
linewidth = 1) +
scale_color_manual(values = "blue", name = NULL) +
new_scale_color() +
geom_sf(data = routes_lts %>% filter(route$student_use >= 4),
inherit.aes = FALSE,
aes(geometry = route$geometry,
color = route$lts,
linewidth = route$student_use)) +
#scale_color_gradientn(colors = bike_lts_scale$color, name = "Length of high stress travel on route from that address", limits = c(1,4)) +
scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
#scale_color_distiller(palette = "YlOrRd", direction = "reverse") +
scale_linewidth_continuous(range = c(0, 3)) +
annotation_raster(school_symbol,
# Position adjustments here using plot_box$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),
" Routes - Traffic Stress_cycling_new.pdf"),
title = paste0(school_focus %>% pull(name), " Student Addresses - Cycling Traffic Stress"),
device = pdf,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
```
# Appendix
```{r chunklast, eval = TRUE, echo = TRUE, results = "show", warning = TRUE, error = TRUE, message = TRUE}
date()
sessionInfo()
```
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