tobler.dasymetric.masked_area_interpolate¶

tobler.dasymetric.masked_area_interpolate(source_df, target_df, raster, pixel_values, extensive_variables=None, intensive_variables=None, categorical_variables=None, allocate_total=True, nodata=255, n_jobs=-1, codes=None)[source]¶

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

Parameters:

source_dfgeopandas.GeoDataFrame: source data to be converted to another geometric representation.
target_dfgeopandas.GeoDataFrame: target geometries that will form the new representation of the input data
rasterstr: path to raster file that contains ancillary data
pixel_valueslist of ints: list of pixel values that should be considered part of the mask. For example if using data from NLCD Land Cover Database <https://www.mrlc.gov/data>, a common input might be [21,22,23,24], which match the “developed” land types in that dataset
extensive_variableslist: Columns of the input dataframe containing extensive variables to interpolate
intensive_variableslist: Columns of the input dataframe containing intensive variables to interpolate
categorical_variableslist: [Optional. Default=None] Columns in dataframes for categorical variables
allocate_totalbool: whether to allocate the total from the source geometries (the default is True).
nodataint: value in raster that indicates null or missing values. Default is 255
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.

Returns:

geopandas.GeoDataFrame: GeoDataFrame with geometries matching the target_df and extensive and intensive variables as the columns