# Open Coffee Map V

I’ve previously looked at the problem of “what coffee shops are in walking distance in a lunchtime from the office?” and the last attempt involved persuading some OpenStreetMap data to become a Graph.

I’ve been reading Geocomputation with R, and one of the definite benefits of publishing with {bookdown} is that it stays up to date. Chapter 12 introduces a few packages that have a few different approaches to OSM data + street networks.

For this one I noticed that {dodgr} has “isochrones” - points of equal time from an origin. So I’ve taken 30 minutes walking from the office to generate a filter on coffee shops.

bbox <- tibble(x = c(-1.6200,-1.4852), y = c(53.8223,53.7769) ) %>%
as.matrix()

Leeds <- dodgr_streetnet_sc(bbox) # OSM Data grab

Leeds <- Leeds %>%
weight_streetnet(wt_profile = "foot") # Turn it into a Graph, assuming WALKING
border <- dodgr_isochrones(Leeds,
from = "1675168027", # The vertex outside QH
tlim = 30*60)  #seconds

#Transform the isochrone into a st_polygon:
border_polygon <- bind_rows(border, slice_head(border, n=1)) %>%
select(x,y) %>%
as.matrix() %>%
list() %>%
st_polygon()
tmap_mode("view")

coffee <- osmdata::opq(bbox) %>%
osmdata::add_osm_feature(key = "amenity", value = "cafe") %>%
osmdata::osmdata_sf()

filter(coffee$osm_points, st_within(coffee$osm_points, border_polygon, sparse = F)) %>%
select(name) %>%
tm_shape() + tm_dots()

Looking at the extreme points I think {dodgr} has a model of a younger and fitter pedestrian than me. There’s quite a lot of hill between the office and “waterside cafe”.

osmpoints has sometimes returned the four corners of a coffee shop instead of its center point. This seems to happen when its name is not recorded. The tea shop in Kirkgate Market is not also tagged as a cafe - I might need to go into the documentation to see if it needs an update.

I’ve a different Shiny/Golem project on the go, so plans to turn this into a Shinyapp are on hold, but with {dodgr} it got a lot easier.

If you’ve read this far, then I’m going to plug StreetComplete. Just as people like you and me ensure that Wikipedia is as good as it can be, anyone (on Android) can install a small app that asks a few questions like “does this cafe have disabled toilets?” or “what is the speed limit on this road?”. When these data are good then people can use them for planning - being able to go into town without worrying about where the facilities are, or where the local vegan eatery is.