segregation.singlegroup.BiasCorrectedDissim

class segregation.singlegroup.BiasCorrectedDissim(data, group_pop_var, total_pop_var, B=500, w=None, network=None, distance=None, decay=None, precompute=None, function='triangular', **kwargs)

Bias Corrected Dissimilarity Index.

Parameters:

datapandas.DataFrame or geopandas.GeoDataFrame, required

dataframe or geodataframe if spatial index holding data for location of interest

group_pop_varstr, required

name of column on dataframe holding population totals for focal group

total_pop_varstr, required

name of column on dataframe holding total overall population

Bint

The number of iterations to calculate Dissimilarity simulating randomness with multinomial distributions. Default value is 500.

wlibpysal.weights.KernelW, optional

lipysal spatial kernel weights object used to define an egohood

networkpandana.Network

pandana Network object representing the study area

distanceint

Maximum distance (in units of geodataframe CRS) to consider the extent of the egohood

decaystr

type of decay function to apply. Options include

precomputebool

Whether to precompute the pandana Network object

Attributes:

statisticfloat

BiasCorrectedDissim Index

core_dataa pandas DataFrame

A pandas DataFrame that contains the columns used to perform the estimate.

Notes

Based on Carrington, William J., and Kenneth R. Troske. “On measuring segregation in samples with small units.” Journal of Business & Economic Statistics 15.4 (1997): 402-409.

Reference: [Carrington and Troske, 1997].

__init__(data, group_pop_var, total_pop_var, B=500, w=None, network=None, distance=None, decay=None, precompute=None, function='triangular', **kwargs)

Init.

Methods

__init__(data, group_pop_var, total_pop_var)

Init.