tobler.dasymetric.masked_area_interpolate¶
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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, fill_nan=0.0)[source]¶ Interpolate data between two polygonal datasets using an auxiliary raster to mask out uninhabited land.
- Parameters:¶
- source_df : geopandas.GeoDataFrame¶
source data to be converted to another geometric representation.
- target_df : geopandas.GeoDataFrame¶
target geometries that will form the new representation of the input data
- raster : str¶
path to raster file that contains ancillary data
- pixel_values : list 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_variables : list¶
Columns of the input dataframe containing extensive variables to interpolate
- intensive_variables : list¶
Columns of the input dataframe containing intensive variables to interpolate
- categorical_variables : list¶
[Optional. Default=None] Columns in dataframes for categorical variables`
- allocate_total : bool¶
whether to allocate the total from the source geometries (the default is True).
- nodata : int¶
value in raster that indicates null or missing values. Default is 255
- n_jobs : int¶
[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.
- fill_nan : numeric, str, or None¶
[Optional. Default=0.0] Value to replace NaN values in the source variables. If None, NaN values are not replaced and will propagate through the interpolation. If a string is passed, it should be one of ‘mean’, ‘median’, ‘max’, or ‘min’, and NaN values will be replaced with the corresponding aggregate value from the source variable.
- Returns:¶
GeoDataFrame with geometries matching the target_df and extensive and intensive variables as the columns
- Return type:¶