Spatial Visualizations

Brianna Heggeseth

Announcements

Coming up in MSCS

• MSCS Mentoring (if interested, sign up on Google Form)
• MSCS BIPOC Affinity group meeting Feb 22 7-8:30pm in Smail Gallery
• Upcoming tech workshops
  • Latex Workshop (beginners) Feb 14 11:30-1 in OLRI 254
  • Latex Workshop (intermediate) Feb 23 11:30-1 in OLRI 254

Assessments/Feedback

• Once everyone has feedback for Assignment 2, I’ll send an automated message.
• If you completed TT2, feedback is there.
• If you haven’t tried TT1 or TT2, consider the commitments you have for the next 3 weeks.
  • You need to complete one of the next 3.
  • We are not striving for perfection (what is that anyways?)

Physical Visuals that You’ve Created

Be Inspired by your Classmates!

Quick Review

Univariate

• Quantatitive (Q) Variable: Histogram, density plot
• Categorical (C) Variable: Bar plot

Bivariate

• Q + Q: Scatterplot (each Q needs an axis)
• Q + C: Multiple densities or histograms, multiple boxplots or violin plots
• C + C: Stacked bar plot, proportional bar plot, side by side bar plot (or mosaic plots)

Multivariate

• Use facets or aesthetics to incorporate more variables to bivariate plot.
• Use heat maps or star plots to explore many variables quickly (try different ways of specifying x and y position).

Learning Goals

• Plot data points on top of a map using the ggmap() function along with ggplot2 functions
  
• Create choropleth maps using geom_map()
  
• Add points and other ggplot2 features to a map created from geom_map()
  
• Understand the basics of creating a map using leaflet, including adding points and choropleths to a base map

Template File

Download a template .Rmd of this activity. Put the file in a Assignment_04 folder within your COMP_STAT_112 folder.

• This .Rmd contains examples that we’ll work on in class and 2 multi-part coding exercises you’ll finish for Assignment 4.
• There are 4 exercises from Tuesday (no code, just writing about your insight)

Spatial Viz

• Points (Longitude, Latitude) on a Map
• Contour/Heat Maps (2D Density) on a Map
• Choropleths (color-filled polygons/regions)
• Dynamic Maps

Points on a Map: Starbucks Example

The Starbucks data, compiled by Danny Kaplan, contains information about every Starbucks in the world at the time the data were collected. It includes the Latitude and Longitude of each location. Let’s start by using familiar ggplot plotting tools.

# Starbucks locations
Starbucks <- read_csv("https://bcheggeseth.github.io/112_spring_2023/data/starbucks.csv")


ggplot(data = Starbucks) +
  geom_point(aes(x = Longitude, y = Latitude),
    alpha = 0.2,
    size = 0.2
  ) +
  theme_classic()

Map Background + Points

# Get the map information
world <- get_stamenmap(
  bbox = c(left = -180, bottom = -57, right = 179, top = 82.1),
  maptype = "terrain",
  zoom = 2
)

# Plot the points on the map
ggmap(world) + # creates the map "background"
  geom_point(
    data = Starbucks,
    aes(x = Longitude, y = Latitude),
    alpha = .3,
    size = 0.2
  ) +
  theme_map()

Map Projections (3D to 2D)

Important Context:

https://www.youtube.com/watch?v=vVX-PrBRtTY

Contour/Heat Maps

US_map2 <- get_stamenmap(
  bbox = c(left = -132, bottom = 20, right = -65, top = 55),
  maptype = "toner-lite",
  zoom = 5
)

# Contour plot
ggmap(US_map2) +
  geom_density_2d(data = Starbucks, aes(x = Longitude, y = Latitude), size = 0.3) + 
  theme_map()

# Density plot
ggmap(US_map2) +
  stat_density_2d(
    data = Starbucks,
    aes(x = Longitude, y = Latitude, fill = stat(level)),
    size = 0.1, alpha = .2, bins = 20, geom = "polygon", color = 'darkblue'
  ) +
  scale_alpha(guide = 'none') +
  scale_fill_gradient(
    low = "darkblue", high = "red",
    guide = 'none'
  ) + 
  theme_map()

Choropleths

starbucks_us_by_state <- Starbucks %>%
  filter(Country == "US") %>%
  count(`State/Province`) %>%
  mutate(state_name = str_to_lower(abbr2state(`State/Province`)))

# US states map information - coordinates used to draw borders
states_map <- map_data("state")

# map that colors state by number of Starbucks
starbucks_us_by_state %>%
  ggplot() +
  geom_map(
    map = states_map,
    aes(
      map_id = state_name,
      fill = n
    )
  ) +
  # This assures the map looks decently nice:
  expand_limits(x = states_map$long, y = states_map$lat) +
  theme_map()

Dynamic Maps with Leaflet

favorite_stp <- tibble(
  place = c(
    "Macalester College", "Groveland Recreation Center",
    "Due Focacceria", "Shadow Falls Park", "Mattocks Park",
    "Carondelet Fields", "Pizza Luce", "Cold Front Ice Cream"
  ),
  long = c(
    -93.1712321, -93.1851310,
    -93.1775469, -93.1944518, -93.171057,
    -93.1582673, -93.1524256, -93.156652
  ),
  lat = c(
    44.9378965, 44.9351034, 44.9274973,
    44.9433359, 44.9284142, 44.9251236,
    44.9468848, 44.9266768
  )
)

leaflet(data = favorite_stp) %>%
  addTiles() %>%
  addMarkers(
    lng = ~long,
    lat = ~lat,
    label = ~place
  )

Advanced Spatial Mapping

If you want to get into spatial mapping, you’ll need to learn some GIS skills and consider coordinate reference systems (CRS).

The best tool in R to do this work is the sf package, which does work well with ggplot tools.

If you’d like to learn more, see my notes for my capstone course, Correlated Data.

After Class

• Complete the exercises by next Wednesday and submit along with Multivariate exercises for Assignment 4.