route_analysis/cycling_route_analysis_brouter.Rmd

---
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


## 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 20m
bike_lts_buffer <- st_buffer(st_intersection(bike_lts, cycle_boundary_poly), 20)

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 20 m buffer)

```{r routeslts, eval = FALSE, 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 data frame
  result <- data.frame(
    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)
  )
  
  # Optional message for debugging/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, inherit.aes = FALSE, aes(color = route.lts, geometry = routes_lts$route.geometry))

addresses_near <- left_join(addresses_near, routes_lts, join_by("number"=="student_number"))
```

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")
```

## 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")
```

## 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 addresses with LTS
```{r mapaddresseslts, eval = FALSE, 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,
       color = "Average Bike Level of Traffic stress for route to school") +
  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 = addresses_near,
         inherit.aes = FALSE,
         aes(color = lts_average)) +
  scale_color_gradientn(colors = bike_lts_scale$color, name = "Average Bike Level of Traffic Stress\nfor route from that address", limits = c(1,4)) +
  annotation_raster(school_symbol,
                    # Position adjustments here using plot_box$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 - Traffic Stress_cycling.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()
```