Spatial interpolation is the
process of transforming data from one spatial resolution to another
(e.g. from census tract to ZIP code). The
cincy::interpolate
function uses census block-level weights
to interpolate data from one cincy
geography to another
cincy
geography. Variables prefixed with n_
will be interpolated extensively (weighted sum), and all other numeric
variables will be interpolated intensively (weighted mean).
Here, we interpolate fraction_poverty
from the 2018
American Community Survey from 2010 census tracts to 2010 Neighborhoods
and 2010 ZCTAs.
library(cincy)
library(dplyr)
library(sf)
library(tmap)
library(tidyr)
d_tract <- st_transform(cincy::dep_index, 3735) |>
select(census_tract_id_2010, fraction_poverty)
The cincy::interpolate
function allows the user to
choose from three sets of weights to be used for the weighted sums
and/or averages: block group population, block group
number of homes, and block group
area.
Neighborhood
d_neigh_pop <-
d_tract |>
cincy::interpolate(to = cincy::neigh_cchmc_2010, weights = "pop") |>
st_transform(3735)
ZCTA
d_zcta_pop <-
d_tract |>
cincy::interpolate(to = cincy::zcta_tigris_2010, weights = "pop") |>
st_transform(3735)
The above examples use population (pop
) weights, but
homes
or area
weights can be used by changing
the weights
argument.
Note that we lose variability when interpolating from a smaller geography to a larger geography. This is especially noticeable when interpolating from tract to ZCTA.
Also note that all three weights yield similar results in this
example. However, the result of using area
weights varies
slightly compared to homes
and population
.
This conclusion is further supported by the correlation between
interpolated fraction_poverty
values using the three sets
of weights.
Neighborhood fraction_poverty
interpolated from
tract using population
, homes
, and
area
weights
ZCTA fraction_poverty
interpolated from tract
using population
, homes
, and area
weights