Mapping Sinclair Broadcast's reach

Files and folders

The repo for this project is on Github, but can be easily downloaded to your desktop with the following commands:

install.packages("usethis")

usethis::use_course("https://github.com/r-journalism/sf-sinclair-map/archive/master.zip")

Getting the data

There’s a map on Sinclair’s website showing off all their stations.

Grab the json URL that’s running the map.

We’ll use the jsonlite package

#install.packages("tidyverse")
#install.packages("jsonlite")
#install.packages("stringr")

library(tidyverse)
library(stringr)
library(jsonlite)

json_url <-"http://sbgi.net/resources/assets/sbgi/MetaverseStationData.json"

## If the url above doesn't exist anymore uncomment the line below and run it
# json_url <- "MetaverseStationData.json"

stations <- fromJSON(json_url)

primary_stations <- stations %>% 
  filter(Channel=="Primary") %>% 
  mutate(
    Location=str_replace(Location, "Point \\(", ""),
    lat=str_replace(Location, " .*", ""),
    lon=str_replace(Location, ".* ", ""),
    lon=str_replace(lon, "\\)", ""))

Let’s take a look at what we’ve got:

glimpse(primary_stations)

## Observations: 192
## Variables: 28
## $ Call_Letter            <chr> "KAAS", "KAAS-LD", "KABB", "KAEF", "KAM...
## $ Logo_List              <chr> "", "", "kabb_fox.jpg", "/resources/ass...
## $ Logo_Map               <chr> "", "", "kabb_fox_map.jpg", "/resources...
## $ Web_1st_URL            <chr> "http://www.foxkansas.com", "http://www...
## $ Web_Address            <chr> "http://www.foxkansas.com", "http://www...
## $ Station                <chr> "KAAS", "KAAS-LD", "KABB", "KAEF", "KAM...
## $ Channel                <chr> "Primary", "Primary", "Primary", "Prima...
## $ Affiliation            <chr> "FOX", "FOX", "FOX", "ABC", "MyTV", "AB...
## $ DMA                    <chr> "Wichita - Hutchinson, KS", "Wichita - ...
## $ DMA_Code               <chr> "678", "678", "641", "802", "811", "820...
## $ DMA_Short              <chr> "Wichita_KS", "Wichita_KS", "San Antoni...
## $ DMA_Rank               <int> 67, 67, 31, 195, 105, 22, 58, 126, 126,...
## $ Station_Status         <chr> "O&O", "O&O", "O&O", "O&O", "JSA", "O&O...
## $ Station_Address        <chr> "316 North West Street, Wichita, KS 672...
## $ Station_City           <chr> "Wichita", "Wichita", "San Antonio", ""...
## $ Station_State          <chr> "KS", "KS", "TX", "", "NV", "OR", "AR",...
## $ Station_Zip            <int> 67203, 67203, 78229, NA, 89502, 97232, ...
## $ Station_Logo           <chr> "sbg_noimage", "sbg_noimage", "kabb_fox...
## $ Station_URL            <chr> "http://www.foxkansas.com, http://www.f...
## $ Station_Phone_Number   <chr> "316-942-2424", "316-942-2424", "210-36...
## $ Station_Fax_Number     <chr> "316-942-8927", "316-942-8927", "210-37...
## $ Actual_RF_Channel      <chr> "17", "31", "30", "22", "20", "43", "22...
## $ News_Schedule_Weekday  <chr> "", "", "0500-0900, 1100-1200, 2100-220...
## $ News_Schedule_Saturday <chr> "", "", "2100-2200", "", "", "0700-0900...
## $ News_Schedule_Sunday   <chr> "", "", "2100-2200", "", "", "0700-0900...
## $ Location               <chr> "-97.388134 37.68888)", "-97.388134 37....
## $ lat                    <chr> "-97.388134", "-97.388134", "-98.569649...
## $ lon                    <chr> "37.68888", "37.68888", "29.490196", ""...

Interesting.

Well, our ultimate goal is to map this out so readers can see the scope of influence that this company reaches.

There are two data points that we can extract from this data set:

• latitude and longitude for each station
• the DMA, or Designated Market Area, each station is assigned to

We can map out the station locations quickly using the sf package to visualize latitude and longitude of each one on top of a map of the country downloaded using the tigris package.

Note: There are performance issues when creating maps with the sf package if you’re using a Mac. To fix,download and install XQuartz. Restart and then run these commands: options(device = “X11”) and then X11.options(type = “cairo”)

Mapping station locations

# if you don't have sf or tigris installled yet, uncomment and run the lines below
# install.packages("sf")
# install.packages("tigris")

library(sf)
library(tigris)

station_latlon <- select(primary_stations, DMA_Short, Location) %>% unique() %>% 
  mutate(Location=gsub("Point \\(", "", Location)) %>% 
  mutate(Location=gsub("\\)", "", Location)) %>% 
  mutate(lon=as.numeric(gsub(" .*", "", Location))) %>% 
  mutate(lat=as.numeric(gsub(".* ", "", Location))) %>% 
  filter(!is.na(lon))



options(tigris_class = "sf")

states <- states(cb=T)

# Filter out some territories and states

states <- filter(states, !STUSPS %in% c("AK", "AS", "MP", "PR", "VI", "HI", "GU"))

# Converting the projection to Albers
states <- st_transform(states, 5070)

# Changing the projection of station_latlon so it matches the states sf dataframe map

station_latlon_projected <- station_latlon %>% 
  filter(!is.na(lon)) %>% 
  st_as_sf(coords=c("lon", "lat"), crs = "+proj=longlat") %>% 
  st_transform(crs=st_crs(states)) %>% 
  st_coordinates(geometry)

station_latlon <- cbind(station_latlon, station_latlon_projected)


# If you're using a Mac, uncomment and run the lines below
#options(device = "X11") 
#X11.options(type = "cairo")

ggplot(states) +
  geom_sf() +
  geom_point(data=station_latlon, aes(x=X, y=Y), color="blue") +
  theme_void() +
  theme(panel.grid.major=element_line(colour="transparent")) +
  labs(title="Station locations")

Alright, but let’s figure out scope.

Let’s figure out how many stations fall into a DMA based on the original JSON data we brought in.

# Fixing a bad data point

primary_stations$DMA_Code <- ifelse(primary_stations$DMA_Short=="Lincoln_NE", 722, primary_stations$DMA_Code)

## Summarizing by DMA
dma_totals <- primary_stations %>% 
  group_by(DMA, DMA_Code) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  ungroup() %>% 
  rename(dma_code=DMA_Code) %>% 
  mutate(dma_code=as.numeric(dma_code))

head(dma_totals)

## # A tibble: 6 x 3
##   DMA                                       dma_code     n
##   <chr>                                        <dbl> <int>
## 1 Eugene, OR                                     801     6
## 2 Lincoln - Hastings - Kearney, NE               722     6
## 3 Wichita - Hutchinson, KS                       678     6
## 4 Champaign - Springfield - Decatur, IL          648     5
## 5 Chico-Redding                                  868     5
## 6 Yakima - Pasco - Richland - Kennewick, WA      810     5

What’s the DMA footprint for each station?

Check around on the internet and you’ll find sources for the DMA shapefile.

Here’s one from Harvard’s Dataverse.

Download the dma_2008.zip file, unzip it, and bring it into your project directory.

We’ll read it in using the st_read() function from the sf package.

Mapping DMAs

geo <- st_read("dma_2008/DMAs.shp")

## Reading layer `DMAs' from data source `/Users/andrewtran/Projects/r-journalism/r-journalism.com/content/posts/2018-07-17-sf-sinclair-map/dma_2008/DMAs.shp' using driver `ESRI Shapefile'
## Simple feature collection with 206 features and 19 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -124.733 ymin: 24.54424 xmax: -66.94932 ymax: 49.38436
## epsg (SRID):    NA
## proj4string:    NA

# It doesn't have a CRS so we'll assign it one
st_crs(geo) <- 4326

# Converting the projection so it's Albers
geo <- st_transform(geo, 5070)

ggplot(geo) +
  geom_sf(color="red") +
  coord_sf()

Then we can join the DMA station count to it so we can visualize it.

Mapping Sinclair DMAs

# Prepping a column name to join on
geo <- geo %>% 
  mutate(dma_code=as.numeric(as.character(DMA)))


geo <- left_join(geo, dma_totals, by="dma_code") %>% 
  filter(!is.na(n)) 



ggplot() +
  geom_sf(data=states, color="red", fill=NA) +
  geom_sf(data=geo, aes(fill=n)) +
  coord_sf()

Wonderful.

We have all the components for the visualization now.

Let’s style it up so it matches the one Chris made for the story.

We’ll add the station_latlon data frame of station locations, too.

Gotta clean it up first and limit the cities displayed to avoid being cluttered.

# Filtering out locations based on map

cities <- c("Portland", "Seattle", "Butte", "Boise", "Reno", "Fresno", "Bakersfield", 
            "Reno", "Salt Lake City", "Las Vegas", "El Paso", "Austin", "San Antonio",
            "Corpus Christi", "Oklahoma City", "Wichita", "Lincoln", "Sioux City", 
            "Minneapolis", "Green Bay", "Milwaukee", "Des Moines", "Springfield", 
            "St. Louis", "Little Rock", "Flint", "Columbus", "Lexington", "Birmingham",
            "Mobile", "Macon", "Asheville", "Charleston", "Buffalo", "Johnstown", "Baltimore",
            "Norfolk", "Conway", "Savannah", "Gainesville", "West Palm Beach", "Albany",
            "Providence", "Portland")

station_latlon_filtered <- station_latlon %>% 
  mutate(DMA_Short= gsub("_.*", "", DMA_Short)) %>% 
  mutate(DMA_Short= gsub("Bozeman", "Butte", DMA_Short)) %>% 
  mutate(DMA_Short= gsub("Champaign", "Springfield", DMA_Short)) %>% 
  mutate(DMA_Short= gsub("Myrtle Beach", "Conway", DMA_Short)) %>% 
  mutate(DMA_Short= gsub("West Palm", "West Palm Beach", DMA_Short)) %>% 
  filter(DMA_Short %in% cities) %>% 
  group_by(DMA_Short) %>% 
  filter(row_number()==1)

Let’s create bins for the number of stations per DMA– we’re turning continuous data into categorical data so it’s easier to control the visualization.

Then we’re going to use a couple of special packages that will help deal with the text on the map.

• ggrepel
• shadowtext

# Need to create bins for the numbers

geo <- geo %>% 
  mutate(bin=case_when(
    n == 1 ~ "1",
    n == 2 ~ "2",
    n == 3 ~ "3",
    n == 4 ~ "4",
    n >= 5 ~ "5+"
  ))

# install.packages("ggrepel")
library(ggrepel)

# install.packages("shadowtext")
library(shadowtext)

Final map

We’re layering our different shape files:

• The state borders
• The DMA borders we combined with the counts of them from the summarized Sinclair JSON file
• The locations of a few dozen stations we also extracted from the Sinclair JSON file

And we’re adding a bunch of functions in there for styling, like scale_fill_brewer() and geom_shadowtext() and geom_text_repel() and various theme options.

ggplot() +
  geom_sf(data=states, color="gray", fill=NA, size=.3) +
  geom_sf(data=geo, aes(fill=bin), color="light gray", size=.2) +
  scale_fill_brewer(palette = "Oranges", name="Number of Sinclair-owned TV stations") +
  geom_point(data=station_latlon_filtered, aes(x=X, y=Y), 
             color="dark gray", fill="white", shape=21) +
  geom_shadowtext(data=station_latlon_filtered, aes(x=X, y=Y, label=DMA_Short), 
             color="black", bg.color="white", vjust=-1, size=2.5) +
  geom_text_repel() +
  coord_sf() +
  theme_void() +
  theme(panel.grid.major = element_line(colour = 'transparent'),
        legend.position="top", legend.direction="horizontal") +
  labs(title="Sinclair's nationwide reach",
       subtitle="This map only highlights stations owned outright by Sinclair. 
It does not include the many stations licensed to other operators but managed by Sinclair.",
       caption="Sources: U.S. Securities and Exchange Commission, Nielsen, Television Bureau of Advertising, GeoCommons")

ggsave("sinclair_ggplot.png", width=10, height=6, units="in")

Here’s how it looks saved as a PNG file.

Compare it to the original. Not bad, right?