segregation.batch.batch_multiscalar_multigroup

segregation.batch.batch_multiscalar_multigroup(gdf, distances, groups, progress_bar=True, **kwargs)

Batch compute multiscalar profiles for multi-group indices.

Parameters:

gdfDataFrame or GeoDataFrame

DataFrame holding demographic data for study region

distanceslist

list of floats representing bandwidth distances that define a local environment.

groupslist

The variables names in data of the groups of interest of the analysis.

progress_bar: bool

Whether to show a progress bar during calculation

**kwargsdict

additional keyword arguments passed to each index (e.g. for setting a random seed in indices like ModifiedGini or ModifiedDissm)

Returns:

pandas.DataFrame

pandas Dataframe with distance as dataframe index and each segregation statistic as dataframe columns