Not that advanced, but I wanted to play around a bit with plotting the raw data from Openstreetmap.

We’re going to Florence this fall. It’s been five years since we last visited the fair city, that has played such an important role in western history.

Openstreetmaps is, as the name implies, open.

I’m going to need some libraries

#library(OpenStreetMap)
library(osmar)
library(ggplot2)
library(broom)
library(geosphere)
library(dplyr)

osmar provides functions to interact with Openstreetmap. ggplot2 is used for the plots, broom for making some objects tidy and dplyr for manipulating data.

Getting the raw data, requires me to define a boundary box, encompassing the part of Florence I would like to work with. Looking at https://www.openstreetmap.org/export#map=13/43.7715/11.2717, I choose these coordinates:

top <- 43.7770
bottom <- 43.7642
left <- 11.2443
right <- 11.2661

After that, I can define the bounding box, tell the osmar functions at what URL we can find the relevant API (this is just the default). And then I can retrieve the data via get_osm(). I immediately save it to disc. This takes some time to download, and there is no reason to do that more than once.

box <- corner_bbox(left, bottom, right, top)
src <- osmsource_api(url = "https://api.openstreetmap.org/api/0.6/")
florence <- get_osm(box, source=src)
saveRDS(florence, "florence.rda")

Lets begin by making a quick plot:

plot(florence, xlim=c(left,right),ylim=c(bottom,top) )

Note that what we get a plot of, is, among other things, of all lines that are partly in the box. If a line extends beyond the box, we get it as well.

Looking at the data:

summary(florence$ways)

## osmar$ways object
## 6707 ways, 9689 tags, 59052 refs 
## 
## ..$attrs data.frame: 
##     id, visible, timestamp, version, changeset, user, uid 
## ..$tags data.frame: 
##     id, k, v 
## ..$refs data.frame: 
##     id, ref 
##  
## Key-Value contingency table:
##         Key         Value Freq
## 1  building           yes 4157
## 2    oneway           yes  456
## 3   highway    pedestrian  335
## 4   highway   residential  317
## 5   bicycle           yes  316
## 6       psv           yes  122
## 7   highway  unclassified  108
## 8   highway       footway  101
## 9   barrier          wall   98
## 10  surface paving_stones   87

I would like to plot the roads and buildings. For some reason there are a lot of highways, of a kind I would probably not call highways.

Anyway, lets make a list of tags. tags() finds the elements that have a key in the tag_list, way finds the lines that are represented by these elements, and find, finds the ID of the objects in “florence” matching this.
find_down() finds all the elements related to these id’s. And finally we take the subset of the large florence data-set, which have id’s matching the id’s we have in from before.

tag_list <- c("highway", "bicycle", "oneway", "building")
dat <- find(florence, way(tags(k %in% tag_list)))
dat <- find_down(florence, way(dat))
dat <- subset(florence, ids = dat)

Now, in a couple of lines, I’m gonna tidy the data. That removes the information of the type of line. As I would like to be able to color highways differently from buildings, I need to keep the information.
Saving the key-part of the tags, and the id:

types <- data.frame(dat$ways$tags$k, dat$ways$tags$id)
names(types) <- c("type", "id")

This gives me all the key-parts of all the tags. And I’m only interested in a subset of them:

types <- types %>% 
  filter(type %in% tag_list)

types$id <- as.character(types$id)

Next as_sp() converts the osmar object to a spatial object (just taking the lines):

dat <- as_sp(dat, "lines")

tidy (from the library broom), converts it to a tidy tibble

dat <- tidy(dat)

That tibble is missing the types – those are added.

new_df <- left_join(dat, types, by="id")

And now we can plot:

new_df %>% 
  ggplot(aes(x=long, y=lat, group=group)) +
  geom_path(aes(color=type)) +
  scale_color_brewer() +
    xlim(left,right) +
  ylim(bottom,top) +
  theme_void() +
theme(legend.position="none")

Nice.

Whats next? Someting like what is on this page: https://github.com/ropensci/osmplotr