Add a final-demand product-area stage to footprints.

Split footprint rows by the area that supplied the final-demand product, using shares from y_mat. This preserves the standard footprint totals while adding a FABIO-viewer style phase: origin product -> product/supplier area -> product -> final-demand area.

This is a compact global-view helper. It does not recompute the full origin-sector by product-sector Leontief cube. Instead, each existing footprint row is allocated over the product-area shares observed in final demand for the same target_area, target_fd, and target_item.

Usage

add_footprint_product_stage(
  footprints,
  y_mat,
  labels,
  fd_labels,
  max_product_areas = 5,
  other_area_name = "Other",
  min_share = 0
)

Arguments

footprints

Footprint table from compute_footprint() with target_area, target_item, target_fd, and value.

y_mat

Final demand matrix from build_io_model().

labels

Tibble mapping Y rows to area_code and item_cbs_code.

fd_labels

Tibble mapping Y columns to area_code and fd_col.

max_product_areas

Maximum number of supplier/product areas to keep separately for each final-demand area, item, and demand category. Smaller supplier areas are grouped into other_area_name.

other_area_name

Label for grouped supplier/product areas.

min_share

Drop split paths smaller than this percentage of the total input footprint value. Use 0 to keep all split paths.

Value

footprints with product_area, product_area_name, product_item, and product_share columns. value is replaced by the split path value.

Examples

# One footprint row of 100, split over the two areas that supply item 20 to
# the final demand of area 1 in the shares observed in `y_mat` (80 / 20).
footprints <- tibble::tibble(
  origin_area = 1L,
  origin_item = 10L,
  target_area = 1L,
  target_area_name = "Target",
  target_item = 20L,
  target_fd = "food",
  value = 100
)

add_footprint_product_stage(
  footprints = footprints,
  y_mat = Matrix::Matrix(c(80, 20), nrow = 2, sparse = TRUE),
  labels = tibble::tibble(
    area_code = c(1L, 2L),
    item_cbs_code = c(20L, 20L)
  ),
  fd_labels = tibble::tibble(area_code = 1L, fd_col = "food")
) |>
  dplyr::select(origin_item, product_area, product_share, value)
#> # A tibble: 2 × 4
#>   origin_item product_area product_share value
#>         <int>        <int>         <dbl> <dbl>
#> 1          10            1           0.8    80
#> 2          10            2           0.2    20