diff --git a/cycling_route_analysis.Rmd b/cycling_route_analysis.Rmd index 523912f..0b02260 100644 --- a/cycling_route_analysis.Rmd +++ b/cycling_route_analysis.Rmd @@ -69,7 +69,7 @@ bike_lts_scale <- data.frame(code = c(1, 2, 3, 4, 9), ## 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.server = "http://127.0.0.1:5002/") options(osrm.profile = "bike") ``` @@ -125,11 +125,11 @@ square grid, the total number of points will be res*res. Increase res to obtain ```{r routes, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} routes <- list(NULL) -for(i in addresses_near$number) { +for(i in addresses_near %>% arrange(number) %>% pull(number)) { routes[[i]] <- osrmRoute( src = addresses_near %>% filter(number == i), dst = WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE)) - message(paste0("done - ", i, "of", max(addresses_near$number))) + message(paste0("done - ", i, " of ", max(addresses_near$number))) } routes <- bind_rows(routes) diff --git a/cycling_route_analysis_brouter.Rmd b/cycling_route_analysis_brouter.Rmd new file mode 100644 index 0000000..2179833 --- /dev/null +++ b/cycling_route_analysis_brouter.Rmd @@ -0,0 +1,355 @@ +--- +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() +```