edited examples and readme with new figures

.gitignore

@@ -22,4 +22,4 @@ api_key
 R/route_analysis.html
 log/
 # Exclude html files generated in the top directory (from RStudio runs)
-./*.html
+*.html

README.md

@@ -18,9 +18,12 @@ This script will generate a few figures:
 ### A map of all the walking routes within the walk boundary:
 ![example routes figure](examples/example-routes.png)
 
-### A map of those routes colored by the level of traffic stress to bike
+### A map of those walking routes colored by the level of traffic stress to bike
 ![example routes-lts figure](examples/example-routes-lts.png)
 
+### A map of cycling routes colored by the level of traffic stress to bike (with a 3 mile radius)
+![example routes-lts figure](examples/example-routes-lts_cycling.png)
+
 ## Using make
 - `make osrm-data`: downloads the OpenStreetMap data for Wisconsin, and preproccesses it for use with OSRM.
 - `make osrm-container`: starts the OSRM containers (backends and frontends) for walking and biking.

cycling_route_analysis.Rmd

@@ -140,7 +140,7 @@ Notes:
 
 
 ## Combine routes with Bike LTS
-```{r routeslts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
+```{r ltscount, 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
@@ -148,11 +148,9 @@ 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))
+bike_lts <- left_join(bike_lts, as.data.frame(bike_lts_buffer %>% select(OBJECTID, student_use)), by = "OBJECTID")
 ```
-
-Notes:
-
+Notes: for each segment in bike_lts, this counts how many student's calculated routes intersect with it (within a 20 m buffer)
 
 # Make Maps
 

cycling_route_analysis_brouter.Rmd

@@ -162,7 +162,7 @@ Notes:
 
 
 ## Combine routes with Bike LTS
-```{r routeslts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
+```{r ltscount, 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
@@ -170,11 +170,23 @@ 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))
+bike_lts <- left_join(bike_lts, as.data.frame(bike_lts_buffer %>% select(OBJECTID, student_use)), by = "OBJECTID")
 ```
 
-Notes:
+Notes: for each segment in bike_lts, this counts how many student's calculated routes intersect with it (within a 20 m buffer)
 
+```{r routeslts, eval = FALSE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
+routes_lts <-list(NULL)
+for(i in addresses_near %>% arrange(number) %>% pull(number)) {
+  lts_segments <- st_intersects(routes %>% filter(student_number == i), bike_lts_buffer)
+  lts_max <- max(bike_lts_buffer %>% filter(OBJECTID.x ))
+  routes_lts[[i]] <- routes_lts_run
+  message(paste0("done - ", i, " of ", max(addresses_near$number)))
+}
+routes_lts <- bind_rows(routes_lts)
+```
+
+Notes: for each student's route, this finds which bike_lts segment it intersects with and calculates a max and an average
 
 # Make Maps
 

route_analysis.Rmd

@@ -114,7 +114,7 @@ routes <- bind_rows(routes)
 
 
 ## Combine routes with Bike LTS
-```{r routeslts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
+```{r ltscount, 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
@@ -122,8 +122,9 @@ 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))
+bike_lts <- left_join(bike_lts, as.data.frame(bike_lts_buffer %>% select(OBJECTID, student_use)), by = "OBJECTID")
 ```
+Notes: for each segment in bike_lts, this counts how many student's calculated routes intersect with it (within a 20 m buffer)
 
 # Make Maps