tongfen (version 0.3.6)

proportional_reaggregate: Dasymetric downsampling

Description

lifecycle::badge("maturing")

Proportionally re-aggregate hierarchical data to lower-level w.r.t. values of the *base* variable Also handles cases where lower level data may be available but blinded at times by filling in data from higher level

Data at lower aggregation levels may not add up to the more accurate aggregate counts. This function distributes the aggregate level counts proportionally (by population) to the containing lower level geographic regions.

Usage

proportional_reaggregate(
  data,
  parent_data,
  geo_match,
  categories,
  base = "Population"
)

Value

dataframe with downsampled variables from parent_data

Arguments

data

The base geographic data

parent_data

Higher level geographic data

geo_match

A named string informing on what column names to match data and parent_data

categories

Vector of column names to re-aggregate

base

Column name to use for proportional weighting when re-aggregating, or named vector with column name for each category. Categries that should be re-aggregated as means should be set to NA and will only be reaggregated if the base data has NA values.

Examples

# Proportionally reaggregate visible minority data from dissemination area 2016
# census data to dissemination block geography, proportionally based on dissemination
# block population
if (FALSE) {
regions <- list(CSD="5915022")
variables <- cancensus::child_census_vectors("v_CA16_3954")

da_data <- cancensus::get_census("CA16",regions=regions,
                                 vectors=setNames(variables$vector,variables$label),
                                 level="DA")
geo_data <- cancensus::get_census("CA16",regions=regions,geo_format="sf",level="DB")

db_data <- geo_data %>% proportional_reaggregate(da_data,c("DA_UID"="GeoUID"),variables$label)

}