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Spatially interpolate community-level data

Source: R/codec_interpolate.R
codec_interpolate.Rd

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 (for example 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(codec_read("acs_measures"),
                  cincy_neighborhood_geo())
#> # A tibble: 51 × 24
#>    geoid         year prop_poverty prop_recieved_public…¹ prop_family_househol…²
#>    <chr>        <int>        <dbl>                  <dbl>                  <dbl>
#>  1 Avondale      2024       0.511                  0.424                  0.808 
#>  2 Bond Hill     2024       0.181                  0.206                  0.587 
#>  3 CUF           2024       0.477                  0.0819                 0.407 
#>  4 California    2024       0.0228                 0.0306                 0.0665
#>  5 Camp Washin…  2024       0.282                  0.347                  0.691 
#>  6 Carthage      2024       0.0955                 0.298                  0.473 
#>  7 Clifton       2024       0.152                  0.0881                 0.221 
#>  8 College Hill  2024       0.261                  0.234                  0.463 
#>  9 Columbia Tu…  2024       0.06                   0.0531                 0.242 
#> 10 Corryville    2024       0.550                  0.0226                 0.255 
#> # ℹ 41 more rows
#> # ℹ abbreviated names: ¹​prop_recieved_public_assistance_income,
#> #   ²​prop_family_households_with_single_householder
#> # ℹ 19 more variables: 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(codec_read("property_code_enforcements"),
                  cincy_census_geo("tract", "2019"))
#> # A tibble: 1,998 × 3
#>    geoid        year n_property_code_enforcements
#>    <chr>       <int>                        <dbl>
#>  1 39061000200  2017                            2
#>  2 39061000200  2018                            6
#>  3 39061000200  2019                           10
#>  4 39061000200  2020                            0
#>  5 39061000200  2021                            8
#>  6 39061000200  2022                           19
#>  7 39061000200  2023                           30
#>  8 39061000200  2024                           11
#>  9 39061000200  2025                            0
#> 10 39061000700  2017                           65
#> # ℹ 1,988 more rows