API reference

Area Weighted

Area weighted approaches use the area of overlap between the source and target geometries to weight the variables being assigned to the target

area_interpolate(source_df, target_df[, …])	Area interpolation for extensive and intensive variables.
area_tables(source_df, target_df)	Construct area allocation and source-target correspondence tables.
area_tables_binning(source_df, target_df)	Construct area allocation and source-target correspondence tables using a spatial indexing approach
area_tables_raster(source_df, target_df, …)	Construct area allocation and source-target correspondence tables according to a raster ‘populated’ areas

Dasymetric

Dasymetric approaches use auxiliary data in addition to use the area of overlap between the source and target geometries to weight the variables being assigned to the target

masked_area_interpolate(source_df, target_df)

Interpolate data between two polygonal datasets using an auxiliary raster to mask out uninhabited land.

Model

Model based approaches use additional spatial data, such as a land cover raster, to estimate the relationships between population and the auxiliary data. It then uses that model to predict population levels at different scales

glm([source_df, target_df, raster, …])	Estimate interpolated values using raster data as input to a generalized linear model.
glm_pixel_adjusted([source_df, target_df, …])	Estimate interpolated values using raster data as input to a generalized linear model, then apply an adjustmnent factor based on pixel values.

Data

store_rasters()

Save raster data to the local quilt package storage.