tobler.area_weighted.area_interpolate

tobler.area_weighted.area_interpolate(source_df, target_df, extensive_variables=None, intensive_variables=None, table=None, allocate_total=True, spatial_index='auto', n_jobs=1, categorical_variables=None, categorical_frequency=True)

Area interpolation for extensive, intensive and categorical variables.

Parameters:

source_dfgeopandas.GeoDataFrame

target_dfgeopandas.GeoDataFrame

extensive_variableslist

[Optional. Default=None] Columns in dataframes for extensive variables

intensive_variableslist

[Optional. Default=None] Columns in dataframes for intensive variables

tablescipy.sparse.csr_matrix

[Optional. Default=None] Area allocation source-target correspondence table. If not provided, it will be built from source_df and target_df using tobler.area_interpolate._area_tables_binning

allocate_totalbool

[Optional. Default=True] True if total value of source area should be allocated. False if denominator is area of i. Note that the two cases would be identical when the area of the source polygon is exhausted by intersections. See Notes for more details.

spatial_indexstr

[Optional. Default=”auto”] Spatial index to use to build the allocation of area from source to target tables. It currently support the following values:

• “source”: build the spatial index on source_df

• “target”: build the spatial index on target_df

• “auto”: attempts to guess the most efficient alternative.

Currently, this option uses the largest table to build the index, and performs a bulk_query on the shorter table. This argument is ignored if n_jobs>1 (or n_jobs=-1).

n_jobsint

[Optional. Default=1] Number of processes to run in parallel to generate the area allocation. If -1, this is set to the number of CPUs available. If table is passed, this is ignored.

categorical_variableslist

[Optional. Default=None] Columns in dataframes for categorical variables

categorical_frequencyBoolean

[Optional. Default=True] If True, estimates returns the frequency of each value in a categorical variable in every polygon of target_df (proportion of area). If False, estimates contains the area in every polygon of target_df that is occupied by each value of the categorical

Returns:

estimatesgeopandas.GeoDataFrame

new geodataframe with interpolated variables as columns and target_df geometry as output geometry

Notes

The assumption is both dataframes have the same coordinate reference system. For an extensive variable, the estimate at target polygon j (default case) is:

\[ \begin{align}\begin{aligned}v_j = \sum_i v_i w_{i,j}\\w_{i,j} = a_{i,j} / \sum_k a_{i,k}\end{aligned}\end{align} \]

If the area of the source polygon is not exhausted by intersections with target polygons and there is reason to not allocate the complete value of an extensive attribute, then setting allocate_total=False will use the following weights:

$$v_j = sum_i v_i w_{i,j}$$

$$w_{i,j} = a_{i,j} / a_i$$

where a_i is the total area of source polygon i. For an intensive variable, the estimate at target polygon j is:

$$v_j = sum_i v_i w_{i,j}$$

$$w_{i,j} = a_{i,j} / sum_k a_{k,j}$$

For categorical variables, the estimate returns ratio of presence of each unique category.