route_analysis/route_to_school.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)
makePlots <- TRUE
source("./R/functions.R")
```

# 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

## Create Bikeable Region Using OSRM

```{r boundary, eval = TRUE, echo = TRUE, results = "show", warning = TRUE, error = TRUE, message = TRUE}
WI_schools <- st_transform(st_read(dsn = "data/Schools/Wisconsin_Public_Schools_-5986231931870160084.gpkg"), crs = 4326)
WI_schools <- WI_schools %>% mutate(geom = SHAPE)

school_focus <- data.frame(name = c("East High School"), NCES_CODE = c("550852000925"))
#school_focus <- data.frame(name = c("IMAP"), NCES_CODE = c("550008203085"))
school_location <- WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE)

radius <- 3 # miles
levels <- c(1)
res <- 100
threshold <- units::set_units(1, km^2)

cycle_boundary_m <- radius*1609

cycle_boundary_poly <- osrmIsodistance( loc = school_location, breaks = cycle_boundary_m, res = res )

cycle_boundary_poly <- st_make_valid(cycle_boundary_poly)

cycle_boundary_poly <- fill_holes(cycle_boundary_poly, threshold)

cycle_boundary_poly <- st_transform(cycle_boundary_poly, crs = 4326)

saveRDS(cycle_boundary_poly, "./R/data/cycle_boundary_poly.rds")
```

# Create Grid Over Bikeable Region

```{r grid, eval = TRUE, echo = TRUE, results = "show", warning = TRUE, error = TRUE, message = TRUE}
cellsize <- 5e-3
grid <- st_intersection(cycle_boundary_poly, st_make_grid(cycle_boundary_poly, cellsize = cellsize, what = "polygons", square = FALSE))
```

# Compute Routes from Cell Centroids to School with brouter

```{r routes, eval = TRUE, echo = TRUE, results = "show", warning = TRUE, error = TRUE, message = TRUE}
grid_pts <- st_centroid(grid)
grid_coods <- st_coordinates(grid_pts)
school_focus_location <- WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% select(LAT, LON)

routes <- list(NULL)
for(i in 1:nrow(grid_coods) ) {
  query <- paste0(
    brouter_url,
    "?lonlats=", grid_coods[i,1], ",",grid_coods[i,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
}

routes <- st_transform(bind_rows(routes), crs = 4326)
```

Notes:

- What does `st_transform(bind_rows(routes), crs = 4326)` do?


# Generate Map for Total Time

## Set boundaries and get basemap
```{r basemap, eval = makePlots, 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]))

basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
```

## Route Characteristic Map

```{r sandbox3, eval = makePlots, echo = TRUE, results = "show", warning = TRUE, error = TRUE, message = TRUE}
track.length.vec <- routes %>% pull(track.length)
grid <- cbind(grid, track.length = as.numeric(track.length.vec)/1609)

total.time.vec <- routes %>% pull(total.time)
grid <- cbind(grid, total.time = as.numeric(total.time.vec)/60)

total.energy.vec <- routes %>% pull(total.energy)
grid <- cbind(grid, total.energy = as.numeric(total.energy.vec))

gg1 <- ggmap(basemap) + geom_sf(data = grid, aes(fill = total.time), inherit.aes = FALSE)

ggsave(gg1, filename = "./figures/route-characteristics.pdf", width = 11, height = 8, units = "in")

gg1
```
## Routes Map

```{r sandbox3b, eval = makePlots, echo = TRUE, results = "show", warning = TRUE, error = TRUE, message = TRUE}
gg2 <- ggmap(basemap) + geom_sf(data = routes, aes(color = "red"), inherit.aes = FALSE)
ggsave(gg2, filename = "./figures/routes.pdf", width = 11, height = 8, units = "in")
gg2
```

# Available Route Data

## Investigatioin of Messages Data

```{r sandbox4, eval = TRUE, echo = TRUE, results = "show", warning = TRUE, error = TRUE, message = TRUE}
x.vec <- c()
for(j in 1:nrow(routes)){
    foobar <- routeChar(routes[j, "messages"])
    x.vec <- c(x.vec, foobar)
}

new.df <- cbind(grid, T.cycleway = x.vec)

gg3 <- ggmap(basemap) + geom_sf(data = new.df, aes(fill= T.cycleway/60), inherit.aes = FALSE)
ggsave(gg3, filename = "./figures/routes.pdf", width = 11, height = 8, units = "in")
gg3
```

# Message Data?

What information can we pull out of the messages data?

```{r sandbox5, eval = TRUE, echo = TRUE, results = "show", warning = TRUE, error = TRUE, message = TRUE}
routes[1,"messages"]
```


```{r chunklast, eval = TRUE, echo = TRUE, results = "show", warning = TRUE, error = TRUE, message = TRUE}
date()
sessionInfo()
```