The first query: Where are cinemas in Madrid?

To build the query, we use the pipe operator %>%, which helps to chain several functions without assigning the result to a new object. Its use is very extended especially within the tidyverse package collection. If you want to know more about its use, you can find here a tutorial.

In the first part of the query we need to indicate the place where we want to extract the information. The getbb( ) function creates a boundering box for a given place, looking for the name. The main function is opq( ) which build the final query. We add our filter criteria with the add_osm_feature( ) function. In this first query we will look for cinemas in Madrid. That’s why we use as key amenity and cinema as tag. There are several formats to obtain the resulting spatial data of the query. The osmdata_*( ) function sends the query to the server and, depending on the suffix * sf/sp/xml, returns a simple feature, spatial or XML format.

#building the query
q <- getbb("Madrid")%>%
      opq()%>%
       add_osm_feature("amenity", "cinema")

str(q) #query structure
## List of 4
##  $ bbox    : chr "40.3119774,-3.8889539,40.6437293,-3.5179163"
##  $ prefix  : chr "[out:xml][timeout:25];\n(\n"
##  $ suffix  : chr ");\n(._;>;);\nout body;"
##  $ features: chr " [\"amenity\"=\"cinema\"]"
##  - attr(*, "class")= chr [1:2] "list" "overpass_query"
cinema <- osmdata_sf(q)
cinema
## Object of class 'osmdata' with:
##                  $bbox : 40.3119774,-3.8889539,40.6437293,-3.5179163
##         $overpass_call : The call submitted to the overpass API
##                  $meta : metadata including timestamp and version numbers
##            $osm_points : 'sf' Simple Features Collection with 195 points
##             $osm_lines : NULL
##          $osm_polygons : 'sf' Simple Features Collection with 12 polygons
##        $osm_multilines : NULL
##     $osm_multipolygons : NULL

We see that the result is a list of different spatial objects. In our case, we are only interested in osm_points.

How can we visulise these points?

The advantage of sf objects is that for ggplot2 already exists a geometry function geom_sf( ). Furthermore, we can include a background map using ggmap. The get_map( ) function downloads the map for a given place. Alternatively, it can be an address, latitude/longitude or a bounding box. The maptype argument allows us to indicate the style or type of map. You can find more details in the help of the ?get_map function.

When we build a graph with ggplot we usually start with ggplot( ). In this case, we start with ggmap( ) that includes the object with our background map. Then we add with geom_sf( ) the points of the cinemas in Madrid. It is important to indicate with the argument inherit.aes = FALSE that it has to use the aesthetic mappings of the spatial object osm_points. In addition, we change the color, fill, transparency (alpha), type and size of the circles.

#our background map
mad_map <- get_map(getbb("Madrid"),maptype = "toner-background")

#final map
ggmap(mad_map)+
  geom_sf(data=cinema$osm_points,
          inherit.aes =FALSE,
          colour="#238443",
          fill="#004529",
          alpha=.5,
          size=4,
          shape=21)+
  labs(x="",y="")

Where can we find Mercadona supermarkets?

Instead of obtaining a bounding box with the function getbb( ) we can build our own box. To do this, we create a matrix of two columns, the order has to be East/West/South/North. In the query we use two features: name and shop to filter supermarkets that are of this particular brand. Depending on the area or volume of the query, it is necessary to extend the waiting time. By default, the limit is set at 25 seconds (timeout).

The map, we create in this case, consists only of the supermarket points. Therefore, we use the usual grammar by adding the geometry geom_sf( ). The theme_void( ) function removes everything except for the points. By default, the object sf in ggplot creates reticles, that can be removed using the coord_sf( ) function with the argument datum = NA.

#bounding box for the Iberian Peninsula
m <- matrix(c(-10,5,30,46),ncol=2,byrow=TRUE)
row.names(m) <- c("x","y")
names(m) <- c("min","max")

#building the query
q <- m %>% 
      opq (timeout=25*100) %>%
         add_osm_feature("name","Mercadona")%>%
         add_osm_feature("shop","supermarket")

#query
mercadona <- osmdata_sf(q)

#final map
ggplot(mercadona$osm_points)+
  geom_sf(colour="#08519c",
          fill="#08306b",
          alpha=.5,
          size=1,
          shape=21)+
  coord_sf(datum=NA)+
  theme_void()