The nycgeo package contains spatial data files for various geographic
and administrative boundaries in New York City as well as tools for
working with NYC spatial data. Data is in the sf (simple
features) format and includes
boundaries for boroughs (counties), public use microdata areas (PUMAs),
community districts (CDs), neighborhood tabulation areas (NTAs), census
tracts, census blocks, city council districts, school districts, and
police precincts.
Additionally, selected demographic, social, and economic estimates from
the U.S. Census Bureau American Community Survey can be added to the
geographic boundaries in nycgeo, allowing for contextualization and
easy choropleth mapping. Finally, nycgeo makes it simple to access a
subset of spatial data in a particular geographic area, such as all
census tracts in Brooklyn and Queens.
The spatial files contained in the nycgeo package are available on
websites such as the New York City Department of City Planning’s Bytes
of the Big
Apple
and the U.S. Census Bureau TIGER/Line®
Shapefiles,
but this package aims to make accessing the spatial data more
convenient. Instead of downloading and converting shapefiles each time
you need them, nycgeo provides the files in a consistent format (sf)
with added metadata that enable joins with non-spatial data.
Other R packages share some features with nycgeo. In particular, the
wonderful tidycensus package
can access the Census Bureau’s API and download ACS estimates as well as
TIGER/Line® Shapefiles (via
tigris).
One difference between the boundaries included here and the TIGER/Line®
Shapefiles available through tigris is that these boundaries are
clipped to the shoreline, allowing for better mapping of New York City.
Additionally, nycgeo contains boundaries for geographic areas that are
not available from the Census Bureau. This includes neighborhood
tabulation areas (NTAs) and community districts (CDs).
Finally, all spatial data included in the package uses the NAD83 / New York Long Island (ftUS) State Plane projected coordinate system (EPSG 2263), which is the standard projection used by New York City government agencies.
You can install nycgeo from
GitHub with:
# install.packages("remotes")
remotes::install_github("mfherman/nycgeo")To get the most out of nycgeo, you should also install and load the
sf package when you use nycgeo.
If you haven’t attached sf, you will get this friendly reminder when
you load nycgeo:
library(nycgeo)
#> To work with the spatial data included in this package, you should also load the {sf} package with library(sf).Depending on your operating system and available libraries, sf can be
tricky to install the first time. The sf
website is a good
place to start if you’re having trouble. If you’re using macOS, this is
a good
guide
to installing the required libraries.
# install.packages("sf")
library(sf)The most basic usage of nycgeo is to get boundaries in the sf
format. Use nyc_boundaries() to get your desired geography. To make
best use of the package, you should also load the sf package when
using nycgeo. For these examples, I’ll also load tidyverse as this
will allow us to take advantage of pretty tibble printing and will
come in handy when we want to manipulate and map the spatial data later.
library(nycgeo)
library(sf)
library(tidyverse)
nyc_boundaries(geography = "tract")
#> Simple feature collection with 2166 features and 12 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: 913180.2 ymin: 120131.4 xmax: 1067382 ymax: 272798.5
#> epsg (SRID): 2263
#> proj4string: +proj=lcc +lat_1=41.03333333333333 +lat_2=40.66666666666666 +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000.0000000001 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=us-ft +no_defs
#> # A tibble: 2,166 x 13
#> geoid borough_tract_id state_fips county_fips tract_id county_name
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 3606~ 1000100 36 061 000100 New York
#> 2 3606~ 1000201 36 061 000201 New York
#> 3 3606~ 1000202 36 061 000202 New York
#> 4 3606~ 1000500 36 061 000500 New York
#> 5 3606~ 1000600 36 061 000600 New York
#> 6 3606~ 1000700 36 061 000700 New York
#> 7 3606~ 1000800 36 061 000800 New York
#> 8 3606~ 1000900 36 061 000900 New York
#> 9 3606~ 1001001 36 061 001001 New York
#> 10 3606~ 1001002 36 061 001002 New York
#> # ... with 2,156 more rows, and 7 more variables: borough_name <chr>,
#> # borough_id <chr>, nta_id <chr>, nta_name <chr>, puma_id <chr>,
#> # puma_name <chr>, geometry <MULTIPOLYGON [US_survey_foot]>If you don’t need census tracts for the entire city, you can use the
filter_by and region arguments of nyc_boundaries() to specify the
area you are interested in. For example, the following code returns only
census tracts in Brooklyn and Queens.
bk_qn_tracts <- nyc_boundaries(
geography = "tract",
filter_by = "borough",
region = c("brooklyn", "queens")
)
ggplot(bk_qn_tracts) +
geom_sf() +
theme_minimal()Note, you can select multiple regions by passing a character vector to
the region argument, but you can only choose a single geography to
filter_by. Additionally, you can only filter by a geography that is
larger than or equal to the boundaries you request. For example, it is
not possible to filter PUMAs by NTAs because NTAs are smaller than
PUMAs.
nycgeo includes selected estimates from the American Community Survey
as datasets. You can access these datasets directly or have them
appended to the spatial data. To print a tibble of ACS data, simply
call the data you want.
nta_acs_data
#> # A tibble: 189 x 27
#> nta_id pop_total_est pop_total_moe pop_white_est pop_white_moe
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 BK09 24212 891. 17734 859.
#> 2 BK17 67681 1736. 43146 1449.
#> 3 BK19 35811 1388. 24817 1139.
#> 4 BK21 31132 1268. 9804 894.
#> 5 BK23 16436 707. 15380 698.
#> 6 BK25 45031 1498. 33709 1346.
#> 7 BK26 30828 1480. 14676 961.
#> 8 BK27 32808 1293. 14483 863.
#> 9 BK28 93114 2087. 38709 1559.
#> 10 BK29 66055 1757. 29318 1293.
#> # ... with 179 more rows, and 22 more variables: pop_white_pct_est <dbl>,
#> # pop_white_pct_moe <dbl>, pop_black_est <dbl>, pop_black_moe <dbl>,
#> # pop_black_pct_est <dbl>, pop_black_pct_moe <dbl>, pop_hisp_est <dbl>,
#> # pop_hisp_moe <dbl>, pop_hisp_pct_est <dbl>, pop_hisp_pct_moe <dbl>,
#> # pop_asian_est <dbl>, pop_asian_moe <dbl>, pop_asian_pct_est <dbl>,
#> # pop_asian_pct_moe <dbl>, pop_ba_above_est <dbl>,
#> # pop_ba_above_moe <dbl>, pop_ba_above_pct_est <dbl>,
#> # pop_ba_above_pct_moe <dbl>, pop_inpov_est <dbl>, pop_inpov_moe <dbl>,
#> # pop_inpov_pct_est <dbl>, pop_inpov_pct_moe <dbl>To add census estimates to an sf object, use add_acs_data = TRUE to
an nyc_boundaries()call. For example, here we get all NTAs in
Manhattan with ACS data appended. One convenience of having the ACS data
joined to the sf object is that you can very simply make a choropleth
map. Here we do it with ggplot2, but you could use
tmap,
leaflet or any other spatial
package that works with sf objects.
mn_ntas <- nyc_boundaries(
geography = "nta",
filter_by = "borough",
region = "manhattan",
add_acs_data = TRUE
)
ggplot(mn_ntas) +
geom_sf(aes(fill = pop_ba_above_pct_est)) +
scale_fill_viridis_c(
name = "Bachelor's or above",
labels = scales::percent_format(),
option = "magma"
) +
theme_void() +
theme(panel.grid = element_line(color = "transparent")) +
labs(title = "Which neighborhoods in Manhattan are most educated?")One use case of nycgeo() is if you have non-spatial data that relates
to census tracts, NTAs, or other geographies and need to join that data
with spatial boundaries to plot or otherwise analyze. This non-spatial
data may be coded in a variety of ways and might not have names or IDs
that match your spatial data. The sf data provided in nycgeo seeks
to have a variety of geographic metadata that will match whatever labels
your non-spatial data has.
In this example, we have non-spatial data from the NYC Neighborhood Health Atlas at the NTA-level from which we would like to make a choropleth map. To do this, we import the .csv file and then join it to the spatial NTA object matching on NTA IDs. Then, we can map it as in the above example.
nta_health <- read_csv("https://raw.githubusercontent.com/mfherman/nycgeo/master/inst/extdata/nta-health.csv") %>%
select(NTA_Code, BlackCarbon)
nyc_boundaries(geography = "nta") %>%
left_join(nta_health, by = c("nta_id" = "NTA_Code")) %>%
ggplot() +
geom_sf(aes(fill = BlackCarbon)) +
scale_fill_viridis_c(name = "Black carbon (absorbance units)", option = "inferno") +
theme_void() +
theme(panel.grid = element_line(color = "transparent")) +
labs(title = "Which neighborhoods have high levels of black carbon pollution?")Point-in-polygon operations are common tasks for spatial analysis. Given a set of points we want to find out which polygon contains each point. A real-world application of this would be counting the number of schools in each community district.
We start with a (non-spatial) data frame of all schools in New York, but
with columns for latitude and longitude. Then we use those latitudes and
longitudes to convert the data frame to an sf object. From there, we can
use the nyc_point_poly() function to find which community district
(CD) each point (school) is in and then count by CD to get the total
number of schools in each
CD.
nyc_schools <- read_csv("https://raw.githubusercontent.com/mfherman/nycgeo/master/inst/extdata/nyc-schools.csv")
schools_sf <- nyc_schools %>%
st_as_sf(
coords = c("longitude", "latitude"),
crs = 4326,
stringsAsFactors = FALSE
)
nyc_point_poly(schools_sf, "cd") %>%
st_set_geometry(NULL) %>%
count(cd_name, borough_cd_id)
#> # A tibble: 60 x 3
#> cd_name borough_cd_id n
#> <chr> <chr> <int>
#> 1 Bronx Community District 1 201 69
#> 2 Bronx Community District 10 210 32
#> 3 Bronx Community District 11 211 41
#> 4 Bronx Community District 12 212 36
#> 5 Bronx Community District 2 202 29
#> 6 Bronx Community District 3 203 72
#> 7 Bronx Community District 4 204 67
#> 8 Bronx Community District 5 205 43
#> 9 Bronx Community District 6 206 55
#> 10 Bronx Community District 7 207 34
#> # ... with 50 more rows