route_analysis/R/route_analysis.Rmd

---
title: "East High Active Travel"
output:
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    toc: true
    toc_depth: 5
    toc_float:
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---

# 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)
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 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://127.0.0.1:5000/")
options(osrm.profile = "walk")
```

## 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

## Subset Addresses Within 1.5 Miles

```{r walkBoundary, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
walk_boundary_m <- 1.5 * 1609 ## walk boundary
school_focus <- data.frame(name = c("East High School"), NCES_CODE = c("550852000925")) ## school focus

walk_boundary_poly <- fill_holes(st_make_valid(osrmIsodistance(
  loc = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE),
  breaks = c(walk_boundary_m),
  res = 80)
), units::set_units(1, km^2))

addresses_near <- st_intersection(addresses, walk_boundary_poly)
```

## Calculate walking routes for each student

```{r routes, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}

## calculate routes
routes <- list(NULL)
for(i in addresses_near$number) {
 routes[[i]] <- osrmRoute(
      src = addresses_near %>% filter(number == i),
      dst = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE))
  message(paste0("done - ", i, "of", max(addresses_near$number)))
}
routes <- bind_rows(routes)
```


## 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, walk_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))
```

# Generate 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(walk_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 = 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
ggmap(basemap) +
  labs(title = paste0("Walking routes for students at ",
    school_focus %>% pull(name)),
    subtitle = "only showing routes within the 1.5 walk boundary",
    x = NULL,
    y = NULL,
    color = NULL,
    linewidth = "Potential student walkers") +
  theme(axis.text=element_blank(),
        axis.ticks=element_blank(),
        plot.caption = element_text(color = "grey")) +
  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() +
  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.pdf"),
       title = paste0(school_focus %>% pull(name), " Walking Routes"),
       device = pdf,
       height = 8.5,
       width = 11,
       units = "in",
       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
ggmap(basemap) +
  labs(title = paste0("Walking routes for students at ",
                      school_focus %>% pull(name)),
       subtitle = "only showing routes within the walk boundary",
       x = NULL,
       y = NULL,
       color = NULL,
       linewidth = "Potential student walkers") +
  theme(axis.text=element_blank(),
        axis.ticks=element_blank(),
        plot.caption = element_text(color = "grey")) +
  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() +
  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.pdf"),
       title = paste0(school_focus %>% pull(name), " Walking 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()
```