proportional_reaggregate: Dasymetric downsampling

In mountainMath/tongfen: Make Data Based on Different Geographies Comparable

Dasymetric downsampling

Description

\lifecycle

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"
)

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

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
## Not run: 
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)


## End(Not run)

mountainMath/tongfen documentation built on May 5, 2023, 7:05 p.m.