479 lines
19 KiB
Text
479 lines
19 KiB
Text
---
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title: "East High Cycling Routes"
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output:
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html_document:
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toc: true
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toc_depth: 5
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toc_float:
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collapsed: false
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smooth_scroll: true
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editor_options:
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chunk_output_type: console
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---
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# Input Data & Configuration
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## Libraries
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```{r libs, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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date()
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rm(list=ls())
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library(tidyverse)
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library(ggmap)
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library(sf)
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library(osrm)
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library(smoothr)
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library(magick)
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library(ggnewscale)
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library(rsvg)
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library(httr)
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library(jsonlite)
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library(parallel)
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fig.height <- 6
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set.seed(1)
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runTLS <- TRUE
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logo <- image_read(path = "other/BFW_Logo_180_x_200_transparent_background.png")
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school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.svg")
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```
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## School Location Data
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```{r gpkg, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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WI_schools <- st_transform(st_read(dsn = "data/Schools/Wisconsin_Public_Schools_-5986231931870160084.gpkg"), crs = 4326)
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WI_schools <- WI_schools %>% mutate(geom = SHAPE)
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```
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## Addresses Data
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```{r addresses, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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addresses <- read_csv(file="data/addresses/Addresses_Students_EastHS_2024_GeocodeResults.csv") %>%
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filter(lat > 0) %>%
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st_as_sf(coords=c("lon","lat"), crs=4326)
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```
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(Remember that x = lon and y = lat.)
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## Bike Level of Traffic Stress (LTS)
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```{r bikelts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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bike_lts <- st_transform(st_read("data/bike_lts/bike_lts_DANE.geojson"), crs = 4326)
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# make lts attribute a factor
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bike_lts[["lts"]] <- as.factor(bike_lts$LTS_F)
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# remove segments with an LTS value of 9
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bike_lts <- bike_lts %>% filter(lts != 9)
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# set color scale
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bike_lts_scale <- data.frame(code = c(1, 2, 3, 4, 9),
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color = c("#1a9641",
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"#a6d96a",
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"#fdae61",
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"#d7191c",
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"#d7191c"))
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```
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# External sources configurations
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## Open Source Routing Machine (OSRM)
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```{r osrm, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# Set url and profile of OSRM server
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options(osrm.server = "http://127.0.0.1:5001/")
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options(osrm.profile = "bike")
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```
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## Brouter options
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```{r brouter, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# Set url and profile of brouter server
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brouter_url <- "http://127.0.0.1:17777/brouter"
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brouter_profile <- "safety"
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```
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## Stadia Maps API Key
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```{r stadiamaps, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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register_stadiamaps(key = substr(read_file(file = "api_keys/stadia_api_key"), 1, 36))
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```
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# Analysis
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```{r analysisPreamble, eval = TRUE, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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radius <- 3 # miles
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levels <- c(1)
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res <- 100
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threshold <- 1
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```
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## Subset Addresses Within `r radius` Miles
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```{r cycleBoundary, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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cycle_boundary_m <- radius*1609
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school_focus <- data.frame(name = c("East High School"), NCES_CODE = c("550852000925"))
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#school_focus <- data.frame(name = c("IMAP"), NCES_CODE = c("550008203085"))
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cycle_boundary_poly <- st_transform(fill_holes(st_make_valid(osrmIsodistance(
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loc = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE),
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# breaks = c(cycle_boundary_m),
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breaks = cycle_boundary_m*levels,
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res = res)
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), units::set_units(threshold, km^2)), crs = 4326)
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addresses_near <- st_intersection(addresses, cycle_boundary_poly)
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```
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Notes:
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- _osrmIsoDistance_ is the primary function in the above chunk.
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- This function computes areas that are reachable within a given road
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distance from a point and returns the reachable regions as
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polygons. These areas of equal travel distance are called isodistances.
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- Input is a point represented as an sf object (extended
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data.frame-like objects with a simple feature list column) could be
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other classes, e.g., vector of coods, data.frame of lat tand
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long. etc.
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- Arguments to osrmIsodistances used here are breaks and res
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- breaks: a numeric vector of break values to define isodistance areas, in meters.
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- res: number of points used to compute isodistances, one side of the
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square grid, the total number of points will be res*res. Increase res to obtain more detailed isodistances.
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- _fill\_holes_ is also used with a threshold of `r threshold` km^2.
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- _st\_intersection_ is also used on sf objects (simple features?)
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## Calculate Routes
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```{r routes, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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routes <- list(NULL)
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school_focus_location <- WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% select(LAT, LON)
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for(i in addresses_near %>% arrange(number) %>% pull(number)) {
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query <- paste0(
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brouter_url,
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"?lonlats=",
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(addresses_near %>% filter(number == i) %>% pull(point) %>% str_split(., ","))[[1]][1], ",",
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(addresses_near %>% filter(number == i) %>% pull(point) %>% str_split(., ","))[[1]][2], "|",
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school_focus_location$LON, ",", school_focus_location$LAT,
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"&profile=", brouter_profile,
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"&alternativeidx=0&format=geojson"
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)
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response <- GET(query)
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route_run <- st_read(content <- content(response, as = "text"), quiet = TRUE)
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route_run[["student_number"]] <- i
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routes[[i]] <- route_run
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message(paste0("done - ", i, " of ", max(addresses_near$number)))
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}
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routes <- st_transform(bind_rows(routes), crs = 4326)
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```
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Notes:
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- this queries the brouter server to get routes
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## Set boundaries and get basemap
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```{r basemap, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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bbox <- st_bbox(st_buffer(cycle_boundary_poly, dist = 500))
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bbox <- c(left = as.double(bbox[1]),
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bottom = as.double(bbox[2]),
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right = as.double(bbox[3]),
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top = as.double(bbox[4]))
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#get basemap
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basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
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```
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Notes:
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- This chunk retrieves the base map from Stadia Maps (API key required)
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## Combine routes with Bike LTS
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```{r ltscount, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# Count the routes that intersect or overlap with each segment of the bike_tls network.
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# The intersections have a buffer of 10m
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bike_lts_buffer <- st_buffer(st_intersection(bike_lts, cycle_boundary_poly), 10)
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bike_lts_buffer["student_use"] <- unlist(lapply(st_intersects(bike_lts_buffer, routes), length))
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bike_lts <- left_join(bike_lts, as.data.frame(bike_lts_buffer %>% select(OBJECTID, student_use)), by = "OBJECTID")
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```
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Notes:
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- for each segment in bike_lts, this counts how many student’s
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calculated routes intersect with it (within a 10 m buffer)
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```{r functions, eval = runTLS, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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source("./R/functions.R")
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```
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```{r routeslts, eval = runTLS, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# Start with routes_lts as a NULL list
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routes_lts <- list(NULL)
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# Pre-filter the bike_lts_buffer for relevant student use
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relevant_buffer <- bike_lts_buffer %>% filter(student_use > 0)
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routes_lts <- mclapply(addresses_near %>% arrange(number) %>% pull(number),
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getLTSForRoute,
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mc.cores = detectCores() / 2,
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mc.cleanup = TRUE,
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mc.preschedule = TRUE,
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mc.silent = FALSE)
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routes_lts <- bind_rows(routes_lts)
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```
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Notes:
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- for each student's route, this finds which bike_lts segment it
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intersects with and calculates a max and an average level of traffic
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stress (LTS). This takes a while, so a parallelized it. There's
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probably a more efficient way to do this calculation.
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- see ./R/functions.R for defintion of getLTSForRoute()
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# Make Maps
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## Generate map with LTS data
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```{r maplts, eval = runTLS, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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ggmap(basemap) +
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geom_sf(data = routes_lts %>% filter(student_number == 6), inherit.aes = FALSE,
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aes(color = route$lts,
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geometry = route$geometry),
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linewidth = 2) +
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scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress")
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# Join the data with the addresses data
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addresses_near <- left_join(addresses_near,
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routes_lts %>%
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select(c("student_number", "lts_max", "lts_average", "lts_1_dist", "lts_2_dist", "lts_3_dist", "lts_4_dist")),
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join_by("number"=="student_number"),
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multiple = "any")
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# add supplemental analysis
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addresses_near <- addresses_near %>% mutate(lts_34_dist = lts_3_dist + lts_4_dist)
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```
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## Generate map of addresses
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```{r mapaddresses, eval = TRUE, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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ggmap(basemap) +
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labs(title = paste0("Student homes at ",
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school_focus %>% pull(name)),
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x = NULL,
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y = NULL,
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color = NULL,
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fill = "How many students live there") +
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theme(axis.text=element_blank(),
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axis.ticks=element_blank(),
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plot.caption = element_text(color = "grey")) +
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geom_hex(data = addresses %>% extract(geometry, into = c('Lat', 'Lon'), '\\((.*),(.*)\\)', conv = T),
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aes(x = Lat,
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y = Lon),
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alpha = 0.7) +
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scale_fill_distiller(palette = "YlOrRd", direction = "reverse") +
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geom_sf(data = cycle_boundary_poly,
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inherit.aes = FALSE,
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aes(color = paste0(radius, " mile cycling boundary")),
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fill = NA,
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linewidth = 1) +
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scale_color_manual(values = "blue", name = NULL) +
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new_scale_color() +
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annotation_raster(school_symbol,
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# Position adjustments here using plot_box$max/min/range
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ymin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] - 0.001,
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ymax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] + 0.001,
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xmin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] - 0.0015,
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xmax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] + 0.0015) +
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geom_sf_label(data = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE),
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inherit.aes = FALSE,
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mapping = aes(label = school_focus %>% pull(name)),
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nudge_y = 0.0015,
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label.size = 0.04,
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size = 2)
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ggsave(file = paste0("figures/",
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school_focus %>% pull(name),
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" Addresses_cycling.pdf"),
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title = paste0(school_focus %>% pull(name), " Addresses"),
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device = pdf,
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height = 8.5,
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width = 11,
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units = "in",
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create.dir = TRUE)
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```
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## Generate map of routes
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```{r maproutes, eval = TRUE, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# generate map
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ggmap(basemap) +
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labs(title = paste0("Cycling routes for students at ",
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school_focus %>% pull(name)),
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subtitle = paste0("only showing routes within the ", radius, " mile cycling boundary"),
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x = NULL,
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y = NULL,
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color = NULL,
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linewidth = "Potential student cyclists") +
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theme(axis.text=element_blank(),
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axis.ticks=element_blank(),
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plot.caption = element_text(color = "grey")) +
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geom_sf(data = cycle_boundary_poly,
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inherit.aes = FALSE,
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aes(color = paste0(radius, " mile cycling boundary")),
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fill = NA,
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linewidth = 1) +
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scale_color_manual(values = "blue", name = NULL) +
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new_scale_color() +
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geom_sf(data = bike_lts %>% filter(!is.na(student_use), student_use > 3),
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inherit.aes = FALSE,
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aes(linewidth = student_use),
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color = "mediumvioletred",
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fill = NA) +
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scale_linewidth_continuous(range = c(0, 3)) +
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annotation_raster(school_symbol,
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# Position adjustments here using plot_box$max/min/range
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ymin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] - 0.001,
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ymax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] + 0.001,
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xmin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] - 0.0015,
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xmax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] + 0.0015) +
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geom_sf_label(data = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE),
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inherit.aes = FALSE,
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mapping = aes(label = school_focus %>% pull(name)),
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nudge_y = 0.0015,
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label.size = 0.04,
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size = 2)
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ggsave(file = paste0("figures/",
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school_focus %>% pull(name),
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" Routes_cycling.pdf"),
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title = paste0(school_focus %>% pull(name), " Cycling Routes"),
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device = pdf,
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height = 8.5,
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width = 11,
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units = "in",
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create.dir = TRUE)
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```
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## Generate map of routes with LTS (1)
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```{r maprouteslts, eval = runTLS, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# generate map
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ggmap(basemap) +
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labs(title = paste0("Cycling routes for students at ",
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school_focus %>% pull(name)),
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subtitle = "only showing routes within the cycling boundary",
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x = NULL,
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y = NULL,
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color = NULL,
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linewidth = "Potential student cyclists") +
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theme(axis.text=element_blank(),
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axis.ticks=element_blank(),
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plot.caption = element_text(color = "grey")) +
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geom_sf(data = cycle_boundary_poly,
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inherit.aes = FALSE,
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aes(color = paste0(radius, " mile cycling boundary")),
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fill = NA,
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linewidth = 1) +
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scale_color_manual(values = "blue", name = NULL) +
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new_scale_color() +
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geom_sf(data = bike_lts %>% filter(!is.na(student_use), student_use > 0),
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inherit.aes = FALSE,
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aes(color = lts,
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linewidth = student_use)) +
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scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
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scale_linewidth_continuous(range = c(0, 3)) +
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annotation_raster(school_symbol,
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# Position adjustments here using plot_box$max/min/range
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ymin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] - 0.001,
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ymax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[2] + 0.001,
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xmin = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] - 0.0015,
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xmax = as.double((WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% pull(geom))[[1]])[1] + 0.0015) +
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geom_sf_label(data = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE),
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inherit.aes = FALSE,
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mapping = aes(label = school_focus %>% pull(name)),
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nudge_y = 0.0015,
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label.size = 0.04,
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size = 2)
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ggsave(file = paste0("figures/",
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school_focus %>% pull(name),
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" Routes - Traffic Stress_cycling.pdf"),
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title = paste0(school_focus %>% pull(name), " Cycling Routes - Traffic Stress"),
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device = pdf,
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height = 8.5,
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width = 11,
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units = "in",
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create.dir = TRUE)
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```
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## Generate map of routes with LTS (2)
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```{r mapaddresseslts, eval = runTLS, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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# generate map
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ggmap(basemap) +
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labs(title = paste0("Level of Traffic stress for biking for students at ",
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school_focus %>% pull(name)),
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subtitle = "only showing routes within the cycling boundary",
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x = NULL,
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y = NULL) +
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theme(axis.text=element_blank(),
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axis.ticks=element_blank(),
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plot.caption = element_text(color = "grey")) +
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geom_sf(data = cycle_boundary_poly,
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inherit.aes = FALSE,
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aes(color = paste0(radius, " mile cycling boundary")),
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fill = NA,
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linewidth = 1) +
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scale_color_manual(values = "blue", name = NULL) +
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new_scale_color() +
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geom_sf(data = routes_lts %>% filter(route$student_use >= 4),
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inherit.aes = FALSE,
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aes(geometry = route$geometry,
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color = route$lts,
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linewidth = route$student_use)) +
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#scale_color_gradientn(colors = bike_lts_scale$color, name = "Length of high stress travel on route from that address", limits = c(1,4)) +
|
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scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress") +
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#scale_color_distiller(palette = "YlOrRd", direction = "reverse") +
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|
scale_linewidth_continuous(range = c(0, 3)) +
|
|
annotation_raster(school_symbol,
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|
# 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()
|
|
```
|
|
|
|
# Archive
|
|
|
|
```{r archive1, eval = FALSE, echo = TRUE, results = "show", warning = TRUE, error = TRUE, message = TRUE}
|
|
# 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 <- lapply(head(addresses_near %>% arrange(number) %>% pull(number)),
|
|
# getLTSForRoute)
|
|
|
|
# system.time(routes_lts <- lapply(head(addresses_near %>% arrange(number) %>% pull(number)),
|
|
# getLTSForRoute))
|
|
|
|
```
|