Census block-level weights are used to spatially interpolate CoDEC data packages from the census tract-level to other Cincy geographies.
Usage
codec_interpolate(from, to, weights = c("pop", "homes", "area"))Arguments
- from
a CoDEC data package
- to
A simple features object returned by one of the
cincy_*_geo()functions (i.e.,cincy_census_geo(),cincy_neighborhood_geo(), or cincy_zcta_geo()`)- weights
which census block-level weights to use; see details
Value
a tibble with a new geographic identifier column for the to target geography (geoid)
in addition to the (interpolated) columns in from
Details
Block-level total population (pop), total number of homes (homes), or total land area (area)
from the 2020 Census can be chosen to use for the weights.
Geospatial intersection happens after transforming geographies to epsg:5072.
See codec_as_sf() for adding geography to a CoDEC data package.
Variables beginning with "n_" are interpolated using a weighted sum;
all other variables are interpolated using a weighted mean.
Examples
codec_interpolate(get_codec_dpkg("acs_measures-v0.1.0"),
cincy_neighborhood_geo())
#> # A tibble: 51 × 24
#> geoid year median_home_value prop_poverty prop_recieved_public…¹
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Avondale 2022 186638. 0.436 0.380
#> 2 Bond Hill 2022 156866. 0.194 0.154
#> 3 CUF 2022 191368. 0.462 0.0528
#> 4 California 2022 597200 0.0523 0.0274
#> 5 Camp Washington 2022 100000 0.272 0.253
#> 6 Carthage 2022 90500 0.173 0.277
#> 7 Clifton 2022 384430. 0.150 0.0704
#> 8 College Hill 2022 148486. 0.197 0.180
#> 9 Columbia Tusculum 2022 412000 0.0366 0.0498
#> 10 Corryville 2022 200113. 0.523 0.0393
#> # ℹ 41 more rows
#> # ℹ abbreviated name: ¹prop_recieved_public_assistance_income
#> # ℹ 19 more variables: prop_family_households_with_single_householder <dbl>,
#> # prop_employment_among_civilian_workforce <dbl>,
#> # prop_housing_units_occupied_by_renters <dbl>,
#> # prop_median_rent_to_income_ratio_among_renters <dbl>,
#> # prop_housing_units_vacant <dbl>, …
codec_interpolate(get_codec_dpkg("property_code_enforcements-v0.2.0"),
cincy_census_geo("tract", "2020"))
#> # A tibble: 5,876 × 6
#> geoid year month violations_per_addr n_violations n_addr
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 39061000200 1999 10.5 NaN 0 0
#> 2 39061000200 2000 6.5 NaN 0 0
#> 3 39061000200 2001 6.5 0.0105 1 95
#> 4 39061000200 2002 6.5 0.0118 9 760
#> 5 39061000200 2003 6.5 0.0274 13 475
#> 6 39061000200 2004 6.5 0.0105 1 95
#> 7 39061000200 2005 6.5 0.0105 2 190
#> 8 39061000200 2006 6.5 0.0132 5 380
#> 9 39061000200 2007 6.5 0.0105 4 380
#> 10 39061000200 2008 6.5 0.0947 45 475
#> # ℹ 5,866 more rows