Practice drawing raster maps

library(tidyverse)
library(ggmap)
library(here)

options(digits = 3)
set.seed(123)
theme_set(theme_minimal())

Run the code below in your console to download this exercise as a set of R scripts.

usethis::use_course("cis-ds/visualize-spatial-i")

New York City 311 data

New York City has an excellent data portal publishing a large volume of public records. Here we’ll look at a subset of the 311 service requests. I used RSocrata and the data portal’s API to retrieve a portion of the data set.

If you are copying-and-pasting code from this demonstration, use nyc_311 <- read_csv("https://info5940.infosci.cornell.edu/data/nyc-311.csv") to download the file from the course website.

glimpse(nyc_311)

## Rows: 352,992
## Columns: 6
## $ unique_key     <dbl> 23013481, 23013484, 23013505, 23013528, 23013557, 23013…
## $ created_date   <dttm> 2012-04-05 15:52:10, 2012-04-05 16:47:38, 2012-04-05 1…
## $ complaint_type <chr> "Food Poisoning", "Sidewalk Condition", "Sidewalk Condi…
## $ borough        <chr> "MANHATTAN", "QUEENS", "BROOKLYN", "MANHATTAN", "BROOKL…
## $ latitude       <dbl> 40.8, 40.7, 40.6, 40.7, 40.6, 40.6, 40.6, 40.8, 40.5, 4…
## $ longitude      <dbl> -74.0, -73.9, -74.0, -74.0, -74.0, -74.1, -74.1, -73.9,…

Exercise: Visualize the 311 data

Obtain map tiles using ggmap for New York City.

Click for the solution

# store bounding box coordinates
nyc_bb <- c(
  left = -74.263045,
  bottom = 40.487652,
  right = -73.675963,
  top = 40.934743
)

nyc <- get_stamenmap(
  bbox = nyc_bb,
  zoom = 11
)

# plot the raster map
ggmap(nyc)

Generate a scatterplot of complaints about sidewalk conditions.

Click for the solution

# initialize map
ggmap(nyc) +
  # add layer with scatterplot
  # use alpha to show density of points
  geom_point(
    data = filter(nyc_311, complaint_type == "Sidewalk Condition"),
    mapping = aes(
      x = longitude,
      y = latitude
    ),
    size = .25,
    alpha = .05
  )

Generate a heatmap of complaints about sidewalk conditions. Do you see any unusual patterns or clusterings?

Click for the solution

# initialize the map
ggmap(nyc) +
  # add the heatmap
  stat_density_2d(
    data = filter(nyc_311, complaint_type == "Sidewalk Condition"),
    mapping = aes(
      x = longitude,
      y = latitude,
      fill = stat(level)
    ),
    alpha = .1,
    bins = 50,
    geom = "polygon"
  )

Seems to be clustered most dense in Manhattan and Brooklyn. Makes sense because they receive significant pedestrian traffic.

Obtain map tiles for Roosevelt Island.

Click for the solution

roosevelt_bb <- c(
  left = -73.967121,
  bottom = 40.748700,
  right = -73.937080,
  top = 40.774704
)
roosevelt <- get_stamenmap(
  bbox = roosevelt_bb,
  zoom = 14
)

# plot the raster map
ggmap(roosevelt)

Generate a scatterplot of food poisoning complaints.

Click for the solution

# initialize the map
ggmap(roosevelt) +
  # add a scatterplot layer
  geom_point(
    data = filter(nyc_311, complaint_type == "Food Poisoning"),
    mapping = aes(
      x = longitude,
      y = latitude
    ),
    alpha = 0.2
  )

Session Info

sessioninfo::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.2.1 (2022-06-23)
##  os       macOS Monterey 12.3
##  system   aarch64, darwin20
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/New_York
##  date     2022-10-05
##  pandoc   2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
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Last updated on Mar 1, 2019