Zip code boundaries

First we download the shapefile for NYC zip codes and read it in using the sf package:

## download the file containing NYC's ZIP Code Boundaries
# dl_file <- tempfile()
# download.file(glue("https://data.cityofnewyork.us/download/", 
#                    "i8iw-xf4u/application%2Fzip"), dl_file)
# 
# ## unzip the file to the desired path
# unzip(dl_file, exdir=here(data_path, "nyc-zip-shapes", "zips"))

nyc_zips_geom <- st_read(here(data_path, "nyc-zip-shapes", "zips", 
                         "ZIP_CODE_040114.shp"), quiet = TRUE) %>% 
  select(ZIPCODE, PO_NAME, geometry)

DSNY basket inventory

I use RSocrata to read in the API resource and then use sf for the intersection between points and boundaries. Also included is some code for styling the map:

dsny_bin_url <- "https://data.cityofnewyork.us/8znf-7b2c"
bins <- RSocrata::read.socrata(dsny_bin_url) %>% 
  # 18 Baskets ('bins') have missing coords: BX (10), 
  # Q (6), BK (1), and SI (1) as of the publish date 
  filter(!is.na(stateplane_labelx)) %>%
  st_as_sf(coords = c("stateplane_labelx", "stateplane_labely"), 
           crs = st_crs(nyc_zips_geom))

# This intersection will provide us with the ZIP_CODE
# for each bin location tooltip.  
zipbins <- st_intersection(
  st_make_valid(bins), st_make_valid(nyc_zips_geom)
  ) %>%
  st_drop_geometry() %>% 
  st_as_sf(wkt = "point")

bname <- c("S" = "Standard Wire", "H" = "High-end", 
           "R" = "Black Top", "C" = "Compacting")

# DSNY Colors
bin_colorizer <- function(x) { 
  case_when( x == "S" ~ "#006749", x == "R" ~ "#1F2120", 
             x == "H" ~ "#2B8CFD", x == "C" ~ "#FBB039", 
             TRUE ~ "gray")
}

Here’s a look at the variable containing the data to be mapped, zipbins:

## Rows: 23,280
## Columns: 14
## $ basketid             <int> 41260025, 41260009, 41260059, 4126~
## $ baskettype           <chr> "S", "S", "H", "S", "H", "S", "S",~
## $ direction            <chr> "N", "NW", "SW", "SW", "NW", "SW",~
## $ fid                  <int> 11871, 11636, 20681, 20679, 20401,~
## $ location_description <chr> "N corner of ROCKAWAY BLVD and 146~
## $ ownertype            <chr> "D", "D", "D", "D", "D", "D", "D",~
## $ section              <chr> "QE126", "QE126", "QE126", "QE126"~
## $ stateplane_snappedx  <dbl> 1042085, 1042804, 1041895, 1041416~
## $ stateplane_snappedy  <dbl> 184749.0, 183407.0, 187226.9, 1847~
## $ streetname1          <chr> "ROCKAWAY BLVD", "150 ST", "SUTPHI~
## $ streetname2          <chr> "146 ST", "130 AV", "FOCH BLVD", "~
## $ ZIPCODE              <chr> "11436", "11436", "11436", "11436"~
## $ PO_NAME              <chr> "Jamaica", "Jamaica", "Jamaica", "~
## $ point                <POINT> POINT (-73.79138 40.67369), POIN~

DSNY Litter Basket Inventory Map

Standard Wire, High-end, Black Top, and Compacting

All data plotted is as of 2021-07-22. Click on points to see the zip code and other information about the baskets.

Native New Yorkers may recognize that some of the compacting baskets are clustered in high-traffic areas (e.g. Times Square). Some areas appear to have a higher density of baskets than others, too.

Live-filtering

Also great is the ability to add a query box to a map with mapboxer’s add_filter_control:

Using the query box inside the mapbox map

To achieve this I’ve made use of mapbox expressions for the filter argument. For example, changing the "H" to an "S" (capital S) in the query box in the below map immediately updates the view from “High-end” to “Standard Wire” baskets - give it a try for yourself:

basketmap %>%
  add_filter_control(
    my_layer_id,                            # Other Baskets:
    filter = list("==","baskettype", "H"),  # "R" = Black top
    pos = "top-left"                        # "C" = Solar-Powered 
  )                                         #       Compacting

While the query box doesn’t provide what I’d call an “end-user experience,” it’s a very helpful feature for quick prototyping and EDA for the technically-inclined data professional.