tobler.area_weighted.area_tables_raster

tobler.area_weighted.area_tables_raster(source_df, target_df, raster_path, codes=[21, 22, 23, 24], force_crs_match=True)[source]

Construct area allocation and source-target correspondence tables according to a raster ‘populated’ areas

Parameters
source_dfgeopandas.GeoDataFrame

geeodataframe with geometry column of polygon type

target_dfgeopandas.GeoDataFrame

geodataframe with geometry column of polygon type

raster_pathstr

the path to the associated raster image.

codeslist

list of integer code values that should be considered as ‘populated’. Since this draw inspiration using the National Land Cover Database (NLCD), the default is 21 (Developed, Open Space), 22 (Developed, Low Intensity), 23 (Developed, Medium Intensity) and 24 (Developed, High Intensity). The description of each code can be found here: https://www.mrlc.gov/sites/default/files/metadata/landcover.html Only taken into consideration for harmonization raster based.

force_crs_matchbool (default is True)

Whether the Coordinate Reference System (CRS) of the polygon will be reprojected to the CRS of the raster file. It is recommended to let this argument as True.

Returns
tables: tuple (optional)

two 2-D numpy arrays SU: area of intersection of source geometry i with union geometry j UT: binary mapping of union geometry j to target geometry t

Notes

The assumption is both dataframes have the same coordinate reference system.

Union geometry is a geometry formed by the intersection of a source geometry and a target geometry

SU Maps source geometry to union geometry, UT maps union geometry to target geometry