# 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.

## 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.