Day 29: Overture

We have been looking at the Overture data in the CDRC and have created a data pipeline that creates a UK POI dataset. You can download this here. For a simple example of how to use the data, the following code extracts POI for the three largest coffee chains in the UK and maps these for Liverpool City Region.

First, we load the required packages and read in the CDRC Overture POI data.

library(tidyverse)
library(sf)
POI <- st_read("poi_uk.gpkg")

Reading layer `poi_uk' from data source 
  `/home/rstudio/alexsingleton.github.io/content/blog/2024-11-29-30DMC_Overture/poi_uk.gpkg' 
  using driver `GPKG'
Simple feature collection with 2521182 features and 17 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 3284.871 ymin: 8070.307 xmax: 655598.3 ymax: 1219395
Projected CRS: OSGB36 / British National Grid

Next, we need to subset the data for the different coffee chains. The categories and names are a bit messy, so to capture the POI, we need to use a combination of the store name and the two levels of category.

nero <- POI %>%
  filter(
    grepl("Caffè Nero", primary_name, ignore.case = TRUE) &
    (grepl("coffee", main_category, ignore.case = TRUE) | 
     grepl("coffee", alternate_category, ignore.case = TRUE))
  )

starbucks <- POI %>%
  filter(
    grepl("starbucks", primary_name, ignore.case = TRUE) &
    (grepl("coffee", main_category, ignore.case = TRUE) | 
     grepl("coffee", alternate_category, ignore.case = TRUE))
  )


costa <- POI %>%
  filter(
    grepl("costa", primary_name, ignore.case = TRUE) &
    (grepl("coffee", main_category, ignore.case = TRUE) | 
     grepl("coffee", alternate_category, ignore.case = TRUE))
  )

We can then read in a further set of polygons that represent the area of Liverpool City Region.

#Get City Region Boundaries and subset for Liverpool
CR_SF <- st_read("https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Combined_Authorities_May_2023_Boundaries_EN_BGC/FeatureServer/0/query?outFields=*&where=1%3D1&f=geojson") %>% filter(CAUTH24CD == "E47000004")

Reading layer `OGRGeoJSON' from data source 
  `https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Combined_Authorities_May_2023_Boundaries_EN_BGC/FeatureServer/0/query?outFields=*&where=1%3D1&f=geojson' 
  using driver `GeoJSON'
Simple feature collection with 11 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -3.229017 ymin: 51.2731 xmax: 0.514484 ymax: 55.81112
Geodetic CRS:  WGS 84

Next, we create a subset of the coffee chains that intersect with the Liverpool City Region polygon.

# Ensure all layers have the same CRS
CRS_target <- st_crs(CR_SF)  # Get the CRS of CR_SF

# Transform the points datasets to match the CRS of CR_SF
nero <- st_transform(nero, CRS_target)
costa <- st_transform(costa, CRS_target)
starbucks <- st_transform(starbucks, CRS_target)

# Subset points to only those within Liverpool City Region
nero_subset <- st_intersection(nero, CR_SF)
costa_subset <- st_intersection(costa, CR_SF)
starbucks_subset <- st_intersection(starbucks, CR_SF)

And finally, we can use these to create a map.

ggplot() +
  # Add the boundary
  geom_sf(data = CR_SF, fill = "#005f73", color = "#001219", linewidth = 0.5) +
  
  # Add coffee shops with different colors and labels
  geom_sf(data = nero_subset, aes(color = "Caffè Nero"), size = 1.2, alpha = 1) +
  geom_sf(data = costa_subset, aes(color = "Costa Coffee"), size = 1.2, alpha = 1) +
  geom_sf(data = starbucks_subset, aes(color = "Starbucks"), size = 1.2, alpha = 1) +
  
  # Add legend
  scale_color_manual(
    name = "Coffee Shops",
    values = c("Caffè Nero" = "#0096c7",
              "Costa Coffee" = "#bb3e03",
              "Starbucks" = "#6a994e")
  ) +
    theme_minimal() +


  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.title = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.spacing = unit(0, "mm"),
    plot.margin = unit(rep(0, 4), "mm"),
    plot.background = element_rect(fill = 'grey', colour = 'grey'),
    legend.text = element_text(size = 12, family = "Mono", face = "italic"),
    legend.title = element_text(size = 14, family = "Mono", face = "bold"),
    legend.position = "right"
  )