Dasymetric downsampling — proportional

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.

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

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.

Value

dataframe with downsampled variables from parent_data

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)

}