segregation.batch.batch_multiscalar_singlegroup

segregation.batch.batch_multiscalar_singlegroup(gdf, distances, group_pop_var, total_pop_var, progress_bar=True, **kwargs)

Batch compute multiscalar profiles for single-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.

group_pop_varstr

The name of variable in data that contains the population size of the group of interest

total_pop_varstr

Variable in data that contains the total population count of the unit

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