---
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)
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_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)

```{r osrm, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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}
# Prepare the URL query
url <- "http://127.0.0.1:17777/brouter"
profile <- "trekking"  # choose appropriate profile
```

## 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 <- 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))

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$NCES_CODE) %>% select(LAT, LON)
for(i in addresses_near %>% arrange(number) %>% pull(number)) {
  query <- paste0(
  url, 
  "?lonlats=",
  (addresses_near %>% filter(number == i) %>% pull(point) %>% str_split(., ","))[[1]][1], ",", 
  (addresses_near %>% filter(number == i) %>% pull(point) %>% str_split(., ","))[[1]][2], "|", 
  school_focus_location$LON, ",", school_focus_location$LAT,
  "&profile=", profile,
  "&alternativeidx=0&format=geojson"
)
  response <- GET(query)
 routes[[i]] <- st_read(content <- content(response, as = "text"), quiet = TRUE)
 
 
  message(paste0("done - ", i, " of ", max(addresses_near$number)))
}

routes <- bind_rows(routes)
```

Notes:
- _osrmRoute_ is the primary function used above.


## Combine routes with Bike LTS
```{r routeslts, 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 <- st_join(bike_lts, bike_lts_buffer %>% select(OBJECTID, student_use))
```

Notes:


# 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)

```

# Appendix

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