segregation.multigroup.SimpsonsInteraction

class segregation.multigroup.SimpsonsInteraction(data, groups, w=None, network=None, distance=None, decay=None, precompute=None, function='triangular', **kwargs)

Simpsons Concentration Index.

Parameters:

datapandas.DataFrame or geopandas.GeoDataFrame, required

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

groupslist, required

list of columns on dataframe holding population totals for each group

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

Multigroup Dissimilarity Index value

core_dataa pandas DataFrame

DataFrame that contains the columns used to perform the estimate.

Notes

Based on Equation 1 of page 37 of Reardon, Sean F., and Glenn Firebaugh. “Measures of multigroup segregation.” Sociological methodology 32.1 (2002): 33-67.

Simpson’s interaction index (I) can be simply interpreted as the probability that two individuals chosen at random and independently from the population will be found to not belong to the same group.

Higher values means lesser segregation.

Simpson’s Concentration + Simpson’s Interaction = 1

Reference: [Reardon and Firebaugh, 2002].

__init__(data, groups, w=None, network=None, distance=None, decay=None, precompute=None, function='triangular', **kwargs)

Init.

Methods

__init__(data, groups[, w, network, ...])

Init.