Spatial Data Visualizations

Interactive and static maps of California oil spills

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# Read in the oil spill data
oil_spill <- read_sf(here("data", "oil_spill", "Oil_Spill_Incident_Tracking_%5Bds394%5D.shp")) %>% 
  clean_names()

# Read in California counties outline
ca_counties <- read_sf(here("data", "ca_counties", "CA_Counties_TIGER2016.shp")) %>% 
  clean_names()

# Check CRS (Coordinate Reference System)
# st_crs(oil_spill) # WGS 84
# st_crs(ca_counties) # WGS 84

Interactive Map

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# Set tmap viewing mode to interactive
tmap_mode("view") 

# Make an interactive map
tm_shape(ca_counties) +
  tm_borders() +
  tm_shape(oil_spill) +
  tm_dots(col = "sienna2")

Figure 1. Interactive map displaying the locations of individual California oil spill events in 2008. (Data: CA DFW Oil Spill Tracking).

Static Choropleth Map

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# Make subset of oil spill data that only includes inland oil spills
oil_spills_inland <- oil_spill %>% 
  filter(inlandmari == "Inland")  

# Spatial join
ca_oil_spills_inland <- ca_counties %>% 
  st_join(oil_spills_inland)

# Find counts of inland oil spill events by county 
inland_spill_counts <- ca_oil_spills_inland %>% 
  count(name)

# Plot a choropleth map
ggplot() +
  geom_sf(data = inland_spill_counts, 
          aes(fill = n), color = "white", size = 0.1) +
  scale_fill_gradientn(colors = c("lightgray","orange","red")) +
  theme_minimal() +
  labs(fill = "Number of \ninland oil spills",
       x = "Longitude",
       y = "Latitude", 
       title = "2008 Inland Oil Spills")

Figure 2. Choropleth map depicting the number of inland oil spill events by California county in 2008. Darker colored counties indicate a greater number of inland oil spills incidents. (Data: CA DFW Oil Spill Tracking).

Citations:

Oil spill layers: CA DFW Oil Spill Incident Tracking Database System (dataset ds394). Edition 2008. Published 2009-07-23.

California county boundaries layer: US Census Bureau’s 2016 MAF/TIGER database.